Am J Physiol Cell Physiol 325: C90–C128, 2023.
First published May 8, 2023; doi:10.1152/ajpcell.00060.2023
REVIEW
The Extracellular Matrix and its Derived Effector Molecules in Aging
Strategic outline of interventions targeting extracellular matrix for promoting
healthy longevity
€ rk,3 Bradley W. English,4 Alexander Fedintsev,5 and
Ji Young Cecilia Park,1 Aaron King,2 Victor Bjo
1
Collin Y. Ewald
1
Laboratory of Extracellular Matrix Regeneration, Institute of Translational Medicine, Department of Health Sciences and
Technology, ETH Z€
urich, Schwerzenbach, Switzerland; 2Foresight Institute, San Francisco, California, United States; 3Heales
VZW, Brussels, Belgium; 4Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States;
and 5Radical Life Extension Group, Alicante, Spain
Abstract
The extracellular matrix (ECM), composed of interlinked proteins outside of cells, is an important component of the human body
that helps maintain tissue architecture and cellular homeostasis. As people age, the ECM undergoes changes that can lead to
age-related morbidity and mortality. Despite its importance, ECM aging remains understudied in the field of geroscience. In this
review, we discuss the core concepts of ECM integrity, outline the age-related challenges and subsequent pathologies and diseases, summarize diagnostic methods detecting a faulty ECM, and provide strategies targeting ECM homeostasis. To conceptualize this, we built a technology research tree to hierarchically visualize possible research sequences for studying ECM aging. This
strategic framework will hopefully facilitate the development of future research on interventions to restore ECM integrity, which
could potentially lead to the development of new drugs or therapeutic interventions promoting health during aging.
aging; biomarkers; collagen; extracellular matrix; matreotype
INTRODUCTION
To a large degree, the human body is composed of an
extracellular matrix (ECM), the “mortar between the bricks”
that maintains tissue architecture. During recent decades of
research, many advances have been made toward understanding the root causes of aging and how they give rise to the
pathologies apparent in elderly people. The Hallmarks of
Aging papers gained substantial attention in the field, dividing the currently known types of aging drivers into 12 separate
hallmarks providing a conceptual framework for scientists
studying aging (1, 2). Putatively, drugs that improve these
hallmarks would restore functionality in old age, and subsequently they have been used as a template for understanding
the origin of disease. The aging of the ECM that alters tissue
across the aging body has the potential to become a more
prominent feature of the hallmarks of aging (3). Emerging
evidence shows that ECM integrity plays a significant role
in age-related morbidity and mortality, such as contributing to cardiovascular disease and systemic fibrosis (4, 5).
The ECM’s biomechanical effects on aging span from stochastic nonenzymatic modification of collagens and other
ECM proteins, ECM protein homeostasis, to ECM-derived
signaling molecules (6). In model organisms, ECM homeostasis is required and sufficient for longevity (7). Today,
ECM aging remains relatively understudied, and a pharmaceutical pipeline to restore ECM integrity would be important for multiple age-related diseases. Currently, there are
27 clinical trials targeting eight ECM components or ECM
remodelers [connective tissue growth factor (CTGF), transforming growth factor-b (TGFb), FGF, lysyl oxidase-like 2
(LOXL2), lysyl oxidase (LOX), avb6-integrin, focal adhesion
kinase (FAK), nuclear factor kappa light chain-enhancer of
activated B cells (NF-κB)], mostly in the context of fibrosis
and cancer (8).
Given the importance of ECM in health, disease, and longevity (9), here we review the current stage of research on
ECM during aging to identify key gaps in our understanding.
To provide a framework and a vision for the aging research
field studying ECM, we leverage an approach used for strategy
games called the technology research tree (tech tree). A
tech tree is a hierarchical directed acyclic graphical representation of logical steps of technological innovations
needed to reach a goal (Fig. 1). We ordered the tech tree
based on Sydney Brenner’s prediction of the flow of science: “Progress depends on the interplay of techniques,
Correspondence: C. Y. Ewald (collin-ewald@ethz.ch).
Submitted 15 February 2023 / Revised 28 April 2023 / Accepted 28 April 2023
C90
0363-6143/23 Copyright © 2023 the American Physiological Society.
http://www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
Figure 1. Extracellular matrix (ECM) technology research
tree. The technology tree is depicted as a hierarchical map
of key sequences displayed as a flow chart. Explanations
about each node and its interconnections are referred to
in the main text. AGE, advanced glycation end product;
AMD: age-related macular degeneration; COPD, chronic
obstructive pulmonary disease; DDR, discoidin domain receptor; IPF, idiopathic pulmonary fibrosis; LOXs: lysyl oxidase family of enzymes; MMP, matrix metalloproteinase;
TGase, transglutaminase; UV, ultraviolet.
discoveries, and ideas, probably in that order of decreasing
importance” (10). The ECM tech tree starts with methods
or “diagnostic” tools to quantify changes in the ECM to
detect “age-related changes” on “core matrisome” or ECM
proteins that have physiological consequences resulting in
“pathologies” and diseases (Fig. 1). To ameliorate these
age-related pathologies, we propose “future goals” or
interventions (Fig. 1). Although this hierarchical order
makes sense to follow as a tech tree, for comprehension
and providing the required knowledge we have changed
the order in the text describing individual steps. We start
with defining the relevant ECM components, then discuss
the damage occurring to the aging ECM and its pathological consequences. Since only things that can be quantified
can be managed, we then introduce methods and diagnostics, followed by potential interventions. The individual
sections are comprehensively written so that the reader
can jump around to different sections of interest. With this
buildup of knowledge, we hope to achieve the necessary
learning objectives and lower the barrier for many interested people entering the ECM and aging research field.
Thus, to generate a new class of medicines, we intend to
provide a framework defining the specific challenges.
THE CORE CONCEPT: ECM AND
MATRISOME
Extracellular Matrix
The extracellular matrix (ECM) is a complex network of biopolymers and structural proteins that supports and surrounds
cells within tissues and organs. The ECM plays a crucial role in
tissue architecture, cell adhesion, migration, differentiation,
and homeostasis (11–13). The ECM is composed of various
macromolecules, including proteoglycans, glycosaminoglycans, elastin, and collagen, which are secreted by cells
and assembled into a dynamic and hierarchical structure
(11–14). Whereas ECMs distant from cells are more static,
accumulate age-related damage, and are not remodeled,
ECM attached to cells can be continuously remodeled and
regulated by a range of enzymes, such as matrix metalloproteinases and their inhibitors, during development,
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C91
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
tissue repair, and aging (15, 16). During the aging process,
the ECM undergoes various changes, including altered
alignment of collagen due to changes in cross-linking, varied rate of synthesis, and altered composition. These
changes can lead to stiffer ECM causing tissue dysfunction
and diseases, such as cancer, fibrosis, and atherosclerosis
(3, 6, 17, 18). The ECM can also serve as a diagnostic tool and
biomarker for various diseases, as it reflects the biochemical and mechanical changes in tissues and organs (19, 20).
For instance, the measurement of ECM components such
as collagen and elastin can be used to diagnose and monitor
fibrosis and cardiovascular diseases (19, 21, 22). Modulating
the composition and structure of the ECM can also be utilized as a therapeutic target to improve tissue function and
repair (23–25).
Cellular Sources of ECM
The cellular origin of ECM is a critical determinant of tissue-specific ECM. ECM composition and structure play a
crucial role in creating a unique microenvironment for cells
in various tissue compartments, including stem cell niches
(26, 27). The components of ECM can regulate stem cell fate
and contribute to the development of malignancies in normal cells (26, 27). Researchers have extensively studied the
composition of ECM produced by different cell types, its assembly into a functional three-dimensional (3-D) structure,
and its role in cell differentiation, tissue morphogenesis, and
physiological tissue remodeling (15, 16, 28). However, understanding of the complex effects exerted by ECM produced by
specific cells remains limited, despite the widely accepted
role of ECM in regulating these processes (27).
Mesenchymal stem cells.
Mesenchymal stem cells (MSCs) are a population of stromal
cells that possess remarkable self-renewal capacity and multipotent differentiation potential (29). They are capable of generating diverse mature cell types, including adipocytes,
osteoblasts, chondrocytes, and fibroblasts (30). These cells
are found in the embryonic mesoderm and contribute to the
development of various tissues and organs (30). MSCs play an
essential role in ECM production and remodeling by synthesizing and secreting various ECM components, such as collagen, fibronectin, and laminin. They also produce proteases,
such as matrix metalloproteinases (MMPs), that degrade ECM
components to facilitate tissue remodeling and repair (26).
The ECM is a dynamic and complex environment characterized by biophysical, mechanical, and biochemical properties
specific to each tissue and plays a crucial role in stem cell differentiation and tissue regeneration (26, 31, 32). As such,
MSCs, because of their capacity to provide ECM components
and proteases, are a vital source that significantly contributes
to tissue development, remodeling, and repair (27).
Given their potential for cell therapy and their crucial role in
maintaining in vivo homeostasis, MSCs have attracted significant scientific attention (33). However, their in vivo identity
and clinical utility remain poorly understood. This lack of
understanding is compounded by the limited knowledge of
the MSC niche and its impact on differentiation and homeostasis maintenance (33). To address this gap, researchers are beginning to explore the role of the ECM and its main regulators,
C92
including the MMPs and their counteracting inhibitors [tissue
inhibitors of metalloproteinases (TIMPs) and reversion-inducing cysteine-rich protein with Kazal motifs], in stem cell differentiation (29, 32).
Fibroblasts. Fibroblasts are mesenchymal cells present
in different tissues that produce and modify ECM components such as fibronectin and collagen (34). Cardiac fibroblasts contribute ECM to the specific structures of the
heart during fetal development and neonatal growth (35).
Although fibroblasts lack tissue-specific functional hallmarks, they play a crucial role in generating ECM, actively
migrating, producing or degrading growth factors and
cytokines, and ensuring normal heartbeat (36).
Fibroblasts have been extensively studied in vitro because
they can be easily derived from different tissues (34, 37). In
specific pathophysiological contexts, including wound healing and fibrosis, activation of fibroblasts is a requisite process for their proliferation and migration (34, 36, 37). Tissue
stress following an injury event triggers physical changes,
including alterations in the composition of the ECM that
result in tissue stiffening. Such perturbations initiate cytoskeletal remodeling, leading to alterations in cellular forces
and mechanical properties (34, 36, 37). In circumstances
where injuries persist and cannot be fully resolved, the
wound-healing response gives way to the development of fibrosis (34, 36, 37).
Fibroblast-mediated fibrosis can occur in any tissue of the
body and is a common feature of chronic inflammatory diseases (36, 38). During this process, fibroblasts transform into
myofibroblasts, which significantly alters the tissue microenvironment (37). The transforming growth factor-b pathway
plays a significant role in the transformation of fibroblasts to
myofibroblasts, involving the production of a-sma (smooth
muscle actin) and a stress fiber-like appearance (37). This
process leads to the production of ECM components like collagen type 1, migration, and proliferation (37). Matrix stiffness further activates myofibroblasts. During dermal wound
healing, myofibroblasts can either enter a quiescent state or
continue to function normally, leading the tissue down a
fibrotic path (37).
The ECM plays a critical role in tissue-specific microenvironments, with its composition and structure intricately tied
to the cellular source (26, 27). Specific cells, such as MSCs
and fibroblasts, produce and regulate the ECM, making it a
potential therapeutic and regenerative target with implications for disease development and tissue remodeling (26, 27,
34, 36, 37). ECM regulators, including MMPs and their inhibitors, have been shown to play a significant role in stem cell
differentiation, tissue regeneration, and fibrosis development (26).
The ECM comprises a complex network of proteins that
provide structural support to tissues and regulate cellular
behavior (15, 16). Among the various components of the
ECM, the matrisome, which encompasses all extracellular
proteins, is crucial for maintaining tissue integrity and function (6, 39–41).
Matrisome
The matrisome is the compendium of all extracellular
proteins that make up the ECM. Changes in matrisome
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
composition play a critical role in maintaining the structural integrity and functional properties of tissues and
organs (6, 39–41). The matrisome can be divided into the
core matrisome, which forms the actual ECM and includes
proteins such as collagens, laminins, and proteoglycans,
and the associated matrisome, which includes proteins
such as ECM remodelers or proteins that associate with the
matrix (6, 41, 42). ECM receptors, such as integrins, play a
crucial role in cell-ECM interactions and are involved in
cell adhesion, migration, and signaling (43, 44). ECM
remodelers, such as matrix metalloproteinases (MMPs)
and tissue inhibitors of metalloproteinases (TIMPs), are
enzymes that modify the ECM by degrading or synthesizing ECM components (45–47). These proteins are important for ECM remodeling during tissue development,
repair, and remodeling processes. Given these ECM functions, not surprisingly there is evidence that changes in the
expression and activity of ECM proteins and ECM remodelers may contribute to age-related changes in tissue structure and function (6, 48–50).
Collagen.
It has been postulated that collagen is the most abundant
protein in mammals, accounting for approximately onethird of their total protein content (51). However, total collagen over total protein assessments in mice showed that
collagens make up 12–17% of total protein (52). The collagen family comprises 28 distinct members, all of which
possess a signature triple-helical domain structure (53),
and has been extensively reviewed by Ricard-Blum (53)
and others. Quantitative ECM-enriched proteomics of
entire mice revealed collagen type I as the most prevalent
form of collagen (52).
Collagen is a family of fibrous proteins that can form various structures, including long, thin fibers (as in collagen
type I) and networklike structures (as in collagen type VI)
among others. Collagen imparts tensile strength and elasticity to the ECM, enabling it to withstand mechanical forces
such as those generated by the movement and contraction of
surrounding cells and tissues. Different types of collagen
have specific roles in various tissues and organs. For example, collagen type I is a major component of the ECM of connective tissues, such as skin, tendons, and ligaments, where
collagen fibers are organized into complex, regular patterns
that give these tissues their characteristic mechanical properties. Collagen type II is the main structural component
of cartilage, providing cushioning and support for joints.
Collagen type III is found in the skin, blood vessels, and internal organs, often working in conjunction with collagen
type I to provide tensile strength to these tissues. Collagen
type IV is a major component of the basement membrane, a
thin layer of connective tissue that separates epithelial cells
from underlying tissues. Other types of collagen, such as
types V, VII, and XVIII, also have important structural and
functional roles in various tissues (53–55).
In addition to its structural role, collagen also plays a key
role in the signaling and regulation of ECM functions.
Collagen can interact with various molecules, such as growth
factors, enzymes, and other ECM components, to modulate
their activity and regulate their effects on surrounding cells
(15, 16).
Collagen receptors: integrin and DDR. Collagen receptors, including integrins and discoidin domain receptors
(DDRs), are transmembrane proteins that bind to collagen, a
major component of the ECM. Collagen receptors play important roles in cell adhesion, migration, and signaling and
are involved in the development and maintenance of tissues.
INTEGRINS. Integrins are composed of a- and b-subunits,
with a total of 18 different a-subunits and 8 different b-subunits, leading to the formation of 24 distinct integrin heterodimers each with unique ligand specificities and functions
(43, 56–59). Integrins are transmembrane proteins that bind
to ECM proteins, transmitting signals across the plasma
membrane to the cytoskeleton and regulating cell adhesion,
migration, and proliferation. In addition, they can bind to intracellular signaling proteins, modulating signaling pathways (43, 56–59).
The functional importance of integrins is highlighted by
their involvement in a wide range of diseases, including cancer, inflammation, and cardiovascular disease (59, 60).
Dysregulation of integrin expression and function has been
linked to aging-related disorders such as osteoporosis, arthritis, and fibrosis (61–63). For instance, decreased expression
of the a2-subunit of integrin has been linked to age-related
bone loss (64), whereas increased expression of the av-subunit has been associated with age-related fibrosis in the lung
(65). Furthermore, the a5-subunit has been shown to be upregulated in senescent cells, and targeting this subunit has
been demonstrated to improve age-related phenotypes in
mice (66–68).
Thus, integrins are critical transmembrane proteins that
mediate various cell functions through their interactions
with ECM proteins and intracellular signaling proteins.
Their dysregulation has been implicated in the development
of age-related disorders, which requires further attention.
DISCOIDIN DOMAIN RECEPTORS. The discoidin domain
receptors (DDRs) are a family of dimeric receptor tyrosine kinases that regulate various cellular processes, including
migration, adhesion, invasion, proliferation, differentiation,
and ECM remodeling (69–71). The DDRs demonstrate a proclivity for damaged or degraded collagens and can elicit the
upregulation and activation of matrix metalloproteinases
(MMPs). These proteases are known to participate not only
in the degradation of ECM constituents but also in crucial
processes such as the proteolytic cleavage of pro-collagen I,
which is necessary for the formation of collagen fibers, and
the activation of diverse substrates via proteolysis (72–75).
Dysregulation of the DDRs has been linked to various
human diseases, such as cancer, neurodegenerative disorders, fibrosis, and inflammatory conditions (76, 77). For example, overexpression of DDR1 and DDR2 has been observed in
multiple forms of cancer, suggesting that they play a role in
the proliferation and migration of tumor cells. Collagen, a
major component of the ECM, undergoes extensive remodeling during tumor progression, and DDRs may contribute
to this process by modulating MMP activity (78, 79).
Furthermore, Hebron et al. (77) observed increased expression of DDRs in the brains of individuals who have passed
away from Alzheimer’s disease and Parkinson’s disease.
Lentiviral small hairpin RNA (shRNA) was utilized to specifically reduce the expression of DDR1 and DDR2, which
led to decreased levels of a-synuclein, tau, and b-amyloid
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C93
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
and protected against cell death in both in vivo and in vitro
settings. Additionally, inhibiting DDR1 and DDR2 appeared
to modulate the immune response in the brain and reduce
levels of Triggering Receptor Expressed in Myeloid Cells 2
(TREM-2) and microglia. Given these findings, targeting
DDR1 and DDR2 may provide a novel strategy to clear neurotoxic proteins and reduce inflammation in neurodegenerative conditions (77). Furthermore, machine learning
and subsequent in vitro assays identified DDR1 as a drug
target for ameliorating fibrosis (80).
Therefore, DDRs play a critical role in the aging process
and are involved in the development of many age-related
diseases, although the precise mechanisms are not yet fully
understood. Using mouse models with DDR deletion has
allowed us to gain more insights into their role in the progression of diseases. As a result, therapies that target DDRs,
such as RNA interference (RNAi)-based delivery, are being
developed to treat diseases with limited treatment options
(69).
Collagen remodelers: MMPs, lysyl oxidase family of
enzymes, and transglutaminase. Collagen remodelers
are enzymes that break down and rebuild collagen in the
body. These enzymes, known as collagenases and collagen
remodeling enzymes, play a key role in maintaining the
structural integrity of the ECM and in regulating the turnover of collagen. Dysregulation of collagen remodelers can
lead to a variety of health issues, including skin aging, cancer, and fibrosis (15, 81).
MATRIX METALLOPROTEINASES. Matrix metalloproteinases (MMPs) are a family of enzymes that play a key role in
the breakdown of ECM proteins, such as collagen and elastin. MMP-mediated degradation of these structural proteins
is essential for a variety of physiological processes, including
tissue repair and inflammation (15, 82, 83). MMPs are also
involved in the turnover of ECM proteins during normal tissue homeostasis, and they play a role in the activation of
growth factors and other signaling molecules (15, 82–84).
MMPs have been implicated in the age-related changes
that occur in various tissues and organs and have been
shown to have both protective and detrimental effects on
disease development (82). For example, MMP-2 and MMP-9
have protective roles, whereas membrane type 1 (MT1)-MMP,
MT3-MMP, and MT5-MMP have been implicated in disease
progression. MT5-MMP, in particular, has been detected in
amyloid plaques of Alzheimer’s patients and appears to have
a synergistic role with c-secretase in disease progression. In
mice lacking Mmp24, a reduction in the amyloid beta peptide (Ab) and amyloid precursor protein (APP) levels in the
cortex and hippocampus was observed, as well as a decrease
in glial reactivity and interleukin-1b levels. MT5-MMP has
also been categorized as an g-secretase because of its ability
to cleave APP in vitro at a site consistent with the molecular
mass of g-secretase processing products. In addition, g-secretase processing of APP has been shown to inhibit neuronal
activity in the hippocampus, further highlighting the importance of this protease in the development of Alzheimer’s disease (82).
LYSYL OXIDASE FAMILY OF ENZYMES. The lysyl oxidase
family of enzymes (LOXs) is responsible for the formation of
covalent bonds between the collagen fibers, which gives the
ECM its strength and stability. The lysyl oxidase family
C94
of enzymes includes lysyl oxidase and lysyl oxidase-like
(LOXL) 1 to 4 (85, 86).
The dysregulation of lysyl oxidase and other members of
the lysyl oxidase family has been linked to a number of
health issues. For example, overexpression of LOXL1 has
been associated with the development of fibrosis in various
tissues, including the lungs and liver (87). Dysregulation of
lysyl oxidase has also been linked to the development of
aneurysms and hypertension (88). In addition, lysyl oxidase
has been shown to play a role in cancer progression, as it has
been observed to be upregulated in several types of cancer
and has been linked to increased invasion and metastasis
(89). Lysyl oxidase and the other members of the lysyl oxidase family are regulated by various factors, including hypoxia, oxidative stress, and inflammation (90). Therefore,
there are drugs being developed that target LOXs for the
treatment of various carcinomas and fibrosis, and there is
evidence that increased levels of LOXs may contribute to
neurodegeneration and metabolic dysfunction (91). This
makes LOXs and LOX-mediated cross-links a potential biomarker for a variety of diseases, and targeting these proteins
may lead to new treatments for these conditions (92, 93).
TRANSGLUTAMINASE. The transglutaminases (TGases)
are a family of enzymes that catalyze the formation of
covalent bonds between proteins through cross-linking.
These enzymes play critical roles in various biological
processes, including blood clotting, wound healing, and
tissue remodeling (94). Among the transglutaminase family members, Transglutaminase 2 (TG2) has been shown to
form cross-links between advanced glycation end products
(AGEs) and other proteins (95–97), contributing to the development of age-related diseases such as cancer, diabetes, and atherosclerosis (98, 99).
In addition to its potential role in aging, some studies
have suggested that inhibition of TG2 may negatively affect
cell survival, highlighting the need for further investigation
in this area (100). TG2 has also been linked to the development of tissue stiffness in obesity and metabolic syndrome
(99). The concept that a TG2-related pathway may be responsible for heart diastolic dysfunction is supported by the discovery of higher TG2 expression and activity in organs from
both a rat model of obesity/metabolic syndrome and a
mouse model of aging (99). Furthermore, the inhibition of
TG2 has been shown to delay early age-induced endothelial
dysfunction and microscopic aortic abnormalities, suggesting that TG2 may be a target for the development of new
antiaging and disease-modifying therapies (101–103).
ECM glycoproteins.
Glycoproteins, which are proteins with attached carbohydrate chains, play a vital role in the ECM by connecting
structural molecules to cells and each other, forming a cohesive molecular network (104, 105). By regulating migration,
differentiation, and proliferation, these glycoproteins enable
the ECM to maintain tissue integrity and function (104, 105).
The following ECM glycoproteins were chosen based on their
potential effects on aging, life span, and longevity.
Elastins. Present in many vertebrate tissues, elastin is a
fibrous ECM glycoprotein that facilitates the maintenance of
physiological functions by upholding structural integrity
and contributing to the mechanical strength of tissues and
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
their elasticity (106, 107). With age, however, levels of elastin-degrading enzymes such as cathepsin and elastase
increase. These increases lead to the unavailability or disarray of elastin and accelerate elastic fiber fragmentations.
Such structural changes by genetic or environmental factors
have been associated with skin aging and cardiovascular diseases (108, 109).
Fibronectins. Found in almost all tissues, fibronectin, as
part of the glycoprotein family, is indispensable to ECM construction by binding to transmembrane proteins like the
integrins and other ECM components such as glycosaminoglycans, proteoglycans, collagen I and III, and heparan sulfate
proteoglycans (HSPGs) (110). Mechanical forces stretch and
unfold or relax and coil inward the cryptic binding domains
along the fibronectin protein, allowing these different ECM
proteins to bind (111). As a mediator of various ECM molecules
and also between ECM and cells, it plays a critical role in
cell differentiation, growth, adhesion, and migration (112).
Prevalent as it is, when altered fibronectin has been associated with fibrosis, Alzheimer’s disease, and various forms of
cancers (112–114). In particular, Alzheimer’s disease patients
exhibited heightened fibronectin expressions (115), and the
accumulation of fibronectin messenger RNA (mRNA) has
been attributed to in vitro cellular senescence (116). By contrast, downregulation was observed with collagen in hepatic
stellate cell senescence (117). These findings indicate fibronectin homeostasis as a critical component in health span.
Laminins. Laminin is part of the glycoprotein family
that is synthesized by various cell types and tissues,
including muscle, epithelial cells, bone marrow cells, and
neurons. Laminin is incorporated into the basal lamina,
which functions as a structural sheet of ECM that segregates muscle and epithelial cells from the connective tissues. Laminin also contains domains of cellular surface
interaction with integrins, influencing cellular behavior
such as migration, adhesion, and differentiation (118–120).
Mutations in laminins therefore often lead to pathological
conditions such as nephrotic syndrome and muscular dystrophy (121). Upregulation of laminins has been observed
in several aging tissues. Diabetic basement membranes, in
particular, exhibited a strong correlation of laminin upregulation with its loss of integrity through the age-dependent increase in thickness (122, 123).
Tenascins. The tenascin family, a highly enigmatic group
of proteins, are known to play a significant role in the intricacies of supporting the structural integrity of cells and tissues,
as well as regulating their growth and development in a
manner that is not fully understood (124). The tenascins are
subdivided into several different classes, including tenascinC, tenascin-R, and tenascin-X (124).
Tenascin-C is found in the ECM of tissues and plays a role
in the organization and development of cells and tissues
(125). In cancer, tenascin-C has been shown to play a role in
the growth, invasion, and metastasis of cancer cells, yet the
underlying mechanisms remain obscure. Tenascin-C has
also been found to promote angiogenesis, the process by
which new blood vessels form, which is essential for tumor
growth (125–129). In some cases, increased levels of tenascinC have been associated with poor prognosis in cancer
patients (130). Tenascin-R and tenascin-X, on the other
hand, are found in the central nervous system and are
involved in the development and function of the brain and
nervous system (131).
Both tenascin-C and tenascin-X have been shown to play a
role in the biological aging process (132–134). For instance, Mi
et al. (135) discovered that tenascin-C immunoreactivity manifested as substantial extracellular deposits of a diffuse nature
within the cortical gray matter, possessing diameters exceeding 100 lm. These tenascin-C plaques exhibited a complete
overlap and encirclement of cored Ab plaques (135). The
tenascin-C plaques were also found to be in close proximity to
reactive astrocytes, microglia, and dystrophic neurites comprised of phosphorylated tau, suggesting a key role for tenascin-C in the pathogenesis of Ab plaques and highlighting its
potential as a valuable biomarker and therapeutic target (135).
Meanwhile, subjects with low levels of tenascin-X may be protected from the typical onset of abnormal arterial stiffness
associated with aging and its associated impact of an anomalously swift round-trip travel time of the pressure wave (136).
Notably, Uiterwaal et al. (137) established that augmented
joint motility and skin resilience correspond with a reduction
in blood pressure and pulse pressure levels. As such, the absence of tenascin-X results in a perturbation of the standard
ECM within the blood vessel walls, and subjects with tenascin-X insufficiency may be susceptible to a greater expansion
of the internal elastic lamina, enabling larger atherosclerotic
plaques to accumulate and ultimately lead to a reduction in
the coronary lumen area (136).
Proteoglycans.
Proteoglycans contribute to the construction of ECM by
hydrating cells and tissues with their heavily glycosylated
hydrophilic properties. Experiments using knockout mice
implicate proteoglycans as the stabilizer in the collagenelastin network (138, 139). Therefore, it is not surprising to
find that connective tissue diseases attributed to the disruption in proteoglycan formation are caused by genetic
mutations that affect the synthesis, structure, or degradation of proteoglycans. These mutations result in an abnormal composition of ECM, leading to a range of symptoms,
including skeletal and cardiovascular anomalies, joint
hypermobility, and skin fragility manifested in diseases
such as Marfan, Ehlers–Danlos, Hunter, Morquio, and
Sanfilippo syndromes (11, 140–145).
Aggrecans. Aggrecan, a major proteoglycan in the ECM,
combines with hyaluronan to create massive hydrated
complexes and is present in several tissues, including the
ascending aortic wall and cartilage (146). Its core protein is
associated with glycosaminoglycan (GAG) chains, which
contribute to the formation of high-molecular-weight
aggregates (147, 148). In cartilaginous tissues, these aggregates form a densely packed, hydrated gel that provides a
significant portion of the equilibrium compressive modulus of cartilage through electrostatic repulsion forces
between negatively charged GAGs (147, 148).
Structural variations in aggrecan exist depending on age,
disease, and species, resulting in differences in GAG chain
length, sulfate ester substitution, and keratan sulfate and
chondroitin sulfate substitution (146, 149). High-resolution
atomic force microscopy (AFM) has enabled researchers to
study individual aggrecan molecules and predict molecular
interactions and macroscopic tissue behavior (149).
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C95
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
Accumulation of aggrecan in the ascending aortic wall is
asymmetric, favoring the aorto-pulmonary septum, and this
could be a reflection of regional vascular wall adaptations to hemodynamic stresses (146). Reduced production or loss of aggrecan is observed in aging, which can contribute to increased
vessel stiffness and various cardiovascular diseases (146).
In cartilaginous tissues, aggrecan plays a crucial role in
maintaining structural integrity and elasticity as the primary
load-bearing molecule in the ECM (150, 151). Its loss or
reduced production with aging can impact the mechanical
properties of cartilage, affecting its function (150, 151).
Small leucine-rich proteoglycans. Small leucine-rich
proteoglycans (SLRPs) are important regulators of the extracellular matrix (ECM) and cell signaling, consisting of a protein core with leucine-rich-repeat (LRR) motifs linked to
glycosaminoglycan (GAG) side chains (152, 153). SLRPs have
diverse compositions and can interact with various cell surface receptors, cytokines, growth factors, and other ECM
components, leading to the modulation of cellular functions
(152, 153). SLRPs can inhibit cancer progression by affecting
matrix metalloproteinases (MMPs), particularly MMP-14 activity (153). Lumican, a member of the SLRP family, has been
shown to possess antitumor activity by directly interacting
with the catalytic domain of MMP-14 and inhibiting its activity (153). Understanding the interactions between SLRPs and
MMPs, including lumican, may lead to the identification of
promising drug targets and new therapeutic strategies for
inflammatory, fibrotic, and malignant disorders (153).
Heparan sulfate proteoglycans. Heparan sulfate proteoglycans (HSPGs) are a type of glycoprotein found on the
cell surface and in the ECM, which are adapted through the
addition of glycosaminoglycan (GAG) chains. The location of
the HSPGs in the plasma membrane works as a gatekeeper of
ECM-cell signaling, and on the cell surface HSPGs serve as a
docking point to matrix metalloproteinases (MMPs) (154).
This strategic location allows HSPGs to act as a cell directory
through the ECM and promote invadopodia (155).
HSPG core proteins glypican and syndecan have been
observed in the developmental signaling mediation.
Syndecans modulate axon pathfinding activity of the SlitRobo pathway (156). In Drosophila melanogaster, glypicans
regulate embryonic development via Wingless/Integrated
(Wnt), Hedgehog (Hh), and Decapentaplegic (Dpp)/TGFb
pathways (157). In Caenorhabditis elegans, mutations in
HSPGs block longevity interventions, such as dietary restriction, reduced insulin/IGF-1 receptor signaling, and reduced
germ stem cell numbers (48, 158, 159). Additionally, HSPGs
have been shown to be involved in the clearance of amyloid
from the brain and the production of Ab (160).
AGE-RELATED CHANGES AND CHALLENGES
The ECM appears to be affected by a large number of
insults that degrade or stiffen it. However, only one of these
causes seems to be “native” to the ECM category itself; all
the others are a product of systemic issues upstream.
Calcification and Mineralization
Vascular calcification pathology is identified as abnormal
calcification of elastic arteries or muscular vessel walls. The
C96
mechanism of vascular calcification is similar to bone metabolism. Although the exact mechanism remains elusive,
integrity maintenance of vascular ECM and calcification are
correlated (161). Age-related pathologies, such as type 2 diabetes, atherosclerosis, chronic kidney disease (CKD), and
increased risk of cardiovascular events are all attributed in
part to vascular calcification (162, 163). Vascular intima diffuse calcification is associated with matrix vesicles. As part
of the ECM component, matrix vesicles are derived from cell
budding, cartilage cell, osteoblast, and odontoblast. Matrix
vesicles also part from cells to initiate organelle self-governance. These ECM components provide adequate nucleation
for calcium crystal formation, and network scaffolding for
their calcium deposit is provided by elastins in its vascular
walls (164).
Furthermore, ECM elastin fibers participate in the calcification of arterial walls. Studies using histochemistry and
electron microscopy have divulged two distinct calcifications of elastic fiber in the plaques of human atherosclerosis.
Whereas structural changes were not detected in the type I
calcification, type II showed significant structural changes in
the elastin (165). Khavandgar et al. (166) demonstrated that
in matrix Gla protein (MGP)-deficient and elastin (ELN)-haploinsufficient (MGP/; ELN þ /) murine aorta, calcification
was found to be dependent upon the presence of elastin.
This led to a significant reduction in arterial calcium deposition in the case of elastin haploinsufficiency (166).
Inflammation
Inherent to many autoimmune diseases are chronically
inflamed tissues (167). Tissue damage triggers inflammation,
wherein proteases and cytokines break down the ECM and
engage with leukocytes, thereby impacting their functionality (168–170).
Upregulation of interferon-gamma (IFNc) and tumor necrosis factor (TNF) occurs at the site of inflammation. These cytokines modify interstitial matrix synthesis and turnover, and
the cytokines also induce matrix metalloproteinase (MMP)
secretion and or activation (167). As zinc-dependent endopeptidases, MMPs site-specifically cleave ECM and other proteins
(171, 172). Hyaluronan fragmentation increases the activity of
MMP during inflammation (171, 173). MMPs increase because
of the inflammation release of fragments of tenascin and sulfated proteoglycans, ECM-derived damage-associated molecular patterns (DAMPs) (174, 175), resulting in a vicious cycle
that fragments ECM. Tissue remodeling processes facilitated
by anomalous ECM fragments are able to modify immune
cells and their immune responses in chronically inflamed tissues (174).
Ultraviolet Damage
Ultraviolet radiation (UVR) is associated with vitamin D
production in humans and may assist in cardiovascular
health through nitric oxide synthesis (176). Whereas vitamin
D halts proinflammatory programs of TH1 cells (177, 178),
chronic UVR exposure can also increase the probability of
photocarcinogenesis by incurring damage to fibroblasts and
dermis and accelerating phenotypical aging (176, 179, 180).
The quantity of available COL1A1 collagen in the ECM has
been associated with melanoma behavior modifications
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
(180, 181), and it has been shown that collagen-cleaving matrix
metalloprotein-1 (MMP1) expression is upregulated by UVRinduced fibroblast damage (182). However, the decreased deposition of collagen, specifically fibrillar collagens I and III,
basement membrane collagen IV, and the collagen-associated
proteoglycan decorin, can also induce malignant behavior
(183), suggesting that collagen has a role in carcinogenesis
beyond the scaffolding function normally associated with this
protein (180).
Even though premature skin aging has been attributed to
remodeling of the compositional architecture of the ECM
induced by prolonged UVR exposure, the exact pathway
requires further investigation (176). In particular, photodegrading downstream effects of fibronectin and fibrillin and
their induction on tissue homeostasis are less understood
(176).
Advanced Glycation End Products
Advanced glycation end products (AGEs) are nonenzymatically formed on proteins such as collagens and elastins.
Lysine and arginine residues frequently become glycated after prolonged exposure to sugars, causing age-dependent
modifications (184, 185). Matrix stiffening occurs as a result
of cross-linking of these AGEs and the steric hinders of enzymatic cleavages required for remodeling. This process is
enhanced under conditions of high glucose levels, accumulation of dicarbonyls, or low glyoxalase activity (186). As a
response, protease levels are increased, triggering yet another
cascade of further damage to the ECM (6) by causing unrestrained protease activity and collagen deposition (187).
N(6)-carboxymethyllysine (CML) is a common AGE and is
known to accumulate in human tissues in aging, diabetes,
atherosclerosis, and neurodegeneration (110, 186, 188–192),
but the buildup of AGEs and its relationship to aging and disease is still under debate (186). This lingering uncertainty
demands a global characterization of the targeted proteins
and pathways to understand the pathophysiological effects of
AGEs. Thus, to enhance our understanding, a proteomic
workflow based on selective enrichment of CML-modified
peptides coupled to mass spectrometry was developed by Di
Sanzo et al. (186) to identify and quantify specific sites of carboxymethylation on proteins, including CML and carboxymethylated protein NH2-termini. However, further development
of antibodies with higher specificity for CML-modified peptides is still needed to identify carboxymethylation sites that
occur at lower abundance (186).
Loss of Proteolysis
The breakdown of proteins (proteolysis) controls ECM assembly, modification, and endocytosis of superfluous ECM
materials, bioactive fragments, and growth factors (193, 194).
Proteolysis is also critical in ECM structure reassembly, tissue repair, pathological processes, morphogenesis, and cell
death (193, 194). Hence, any changes made by proteolysis to
the cell surface and ECM can have an immediate and irrevocable impact (194).
Maintaining a homeostatic balance of proteolysis is critical in maintaining health span as either overproduction or
deficiency can lead to disease progressions. The matrix metalloproteinases (MMPs) and the closely related A Disintegrin
and Metalloproteinases (ADAMs) possess proteolytic properties and are critical contributing factors in various physiological processes and pathologies such as cancer progression
and inflammation (195). For example, the key component of
wound healing is the degradation of provisional ECM produced by the fibrin clot caused by the wound. Injury is
repaired by the keratinocyte migration that produces urokinase plasminogen activators (uPAs) and MMPs to close the
wound through interaction with dermal fibroblast. In mice
lacking the plasminogen gene, keratinocytes are unable to
migrate and the wound does not heal, indicating the importance of proteolysis homeostasis (196).
Additionally, Kuzuya et al. (197) suggest that the formation of glycation-induced cross-links in interstitial collagen
contributes to ECM stiffness in aging and diabetes. They
demonstrated this by showing that smooth muscle cells
grown on collagen fibrils activated pro-MMP-2, which was
accompanied by increased expression of MT1-MMP and suppressed production of TIMP-2. However, when the smooth
muscle cells were grown on glycated collagen fibrils, MMP-2
activation was inhibited and expression of MT1-MMP was
reduced without affecting TIMP-2 production. This suggests
that the suppression of MT1-MMP expression may be
involved in inhibiting MMP-2 activation on glycated collagen fibrils. The inclusion of aminoguanidine, a cross-linking
inhibitor, during collagen glycation restored MMP-2 activation, indicating that cross-links may play a role in the inhibition of MMP-2 activation (197). Crosslinking reduces the
flexibility and elasticity of collagen, making it more resistant
to proteolytic degradation (197–199), leading to the accumulation of senescent ECM, which can impair tissue function
and contribute to age-related diseases (198, 199). And
Col1a1r/r mice, with their type I collagen resistance to collagenase cleavage, exhibited accelerated aging, a host of agerelated pathologies, and a dramatic acceleration of senescent
cell accumulation (200). Furthermore, Mmp24-deficient
mice exhibit reduced levels of Ab and APP and decreased
glial reactivity and IL-1b levels, the pivotal function of MT5MMP (MMP-24) in Alzheimer’s disease progression, as evidenced by its capacity to cleave APP and regulate neuronal
activity in the hippocampus (82, 201), highlighting the crucial role of proteolysis in regulating various age-related
pathologies (82, 202). Therefore, maintaining a balance of
proteolysis is crucial for maintaining the health span of
tissues and preventing age-related diseases.
CONSEQUENCES AND PATHOLOGIES
Damage to the ECM can result in a wide range of pathologies. The two major disease hubs are fibrosis and loss of
mechanotransduction. By focusing on these two areas, significant progress could be made in preventing age-related
pathologies.
Fibrosis
According to estimates, fibrotic disorders are responsible
for 45% of deaths in the United States (203). Fibrosis refers
to the stiffening of tissue that ultimately leads to organ failure. Although universally occurring throughout the body
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C97
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
with age, fibrosis is seen in accelerated forms both in fibrotic
diseases and after injury.
Many fibrotic disorders are caused by the loss of ECM homeostasis, which creates an imbalance in wound healing
and causes irreversible structural damage to biophysical
properties (25, 204). As more than a third of the developed
nations suffer from mortality due to fibrotic disorders (205),
understanding the mechanisms behind ECM maintenance is
vital in extending the health span globally.
There are currently no reliable biomarkers for diagnosing
fibrotic diseases, and the current noninvasive method for
discovering these diseases is unreliable and often leads to a
diagnosis too late, when the damage is already irreparable
(174). To improve the prognosis for individuals with fibrotic
diseases, it will be necessary to restore organ function and
reverse fibrotic scarring (174, 206).
Liver failure.
Chronic damage to the liver and ECM protein accumulation
result in liver fibrosis (207). When the symptoms of liver fibrosis become acute, it can result in cirrhosis leading to portal
hypertension and liver failure, which frequently necessitates
a liver transplant (208). In industrialized nations, liver fibrosis
is often caused by hepatitis C infection, alcohol, and fatty
liver, also known as nonalcoholic steatohepatitis (NASH)
(208).
ECM dysregulation is often attributed as a characteristic
of fibrosis, with loss of homeostasis leading to impaired
wound healing. ECM dysregulation causes alteration in the
biomechanics and architecture of the lungs (25). Even
though the liver is renowned for its regenerative abilities,
persistent irritation can lead to fibrosis (204, 209). When the
injury is unabated, wound healing fails to function effectively, and fibrosis ensues. Fibrosis causes massive ECM deposition and scar formation, which leads to hepatic stellate
cell failure (210).
Many therapeutic strategies have been emerging to inhibit
fibrosis from developing. These include anti-inflammatory
liver protection, hepatic stellate cell inhibitors, suppressor of
ECM production, and/or targeting ECM degradation. Gene
therapy is also in the pipeline to target fibrosis (211).
Heart failure.
Cardiac fibrosis is a cardiac muscle scarring event, defined
by the production of myofibroblasts through activation and
differentiation of cardiac fibroblasts and increased deposition of collagen (212). These transitions in pathology usher in
increased matrix stiffness that leads to undesirable cardiac
function (213).
The ECM network provides structural integrity, promotes
force transmission, and relays crucial signal transduction to
vascular and interstitial tissues and cardiomyocytes. Any
changes in the cardiac ECM homeostasis are therefore critically felt and may cause reduced ejection fraction, heart
arrest, and/or myocardial matrix stiffening, disturbance in
electric conductance, and possibly death (213, 214).
Because the prognosis of cardiac fibrosis is often deleterious, exploiting matricellular proteins has been proposed as a
possible therapy to modify the events of adverse remodeling.
Although studies on thrombospondin 1 (TSP-1) and secreted
protein acidic and rich in cysteine (SPARC) showed promise
C98
in protection against this detrimental remodeling (214–217),
formidable challenges lie ahead because of the overwhelming complexities of the matricellular proteins (218).
Cardiac regeneration through ECM manipulation has also
been proposed as a moon shot therapy goal, and an in vivo
study of neonatal mice implicates ECM composition impact
in myocardium regeneration activation (218). The more
attainable goal of engineering a bioartificial heart through
stem cell-induced cardiomyocyte proliferation by using cardiac matrixes as a scaffold is yet in its infancy (219, 220).
Lung failure.
The lung ECM comprises complex networks of glycoproteins, proteoglycans, glycosaminoglycans, and fibrous proteins. Besides providing architectural support, the lung ECM
conditions cell behavior through changes in the stiffness and
composition of its proteins (221–223). The pathology of lung
fibrosis is then associated with the loss of ECM homeostasis
and the breakdown of its environment, creating a positive
feedback loop to enforce fibrogenesis (224, 225). It is, therefore, self-evident that any impairment due to lung fibrosis
requires interventions to recapture ECM homeostasis.
Idiopathic pulmonary fibrosis. Idiopathic pulmonary
fibrosis (IPF) is the most common type of fibrosing interstitial pneumonia. IPF in the elderly has a prognosis of only
3–5 yr of survival (226, 227).
Key synthesizing agents of ECM are thought to be myofibroblasts in fibroblastic foci and activated fibroblasts (228),
and although the exact pathology is unknown, IPF can be
diagnosed based on disparate and cumulative subpleural fibrosis and fibrotic foci with an assembly of myofibroblasts
resisting apoptosis (228, 229). The study of active ECM synthesis and remodeling showed an increase in hyaluronan,
type I procollagen, tenascin-C, and versican expression and
decreased biglycan and decorin expression within fibroblastic foci (230). Estany et al. (230) found that tenascin-C and
versican, a chondroitin sulfate proteoglycan, are highly
expressed in the lungs with defects in wound healing ability,
leading ultimately to fibrosis.
The emergence of pirfenidone and nintedanib as two licensed therapeutics for IPF is a significant milestone in
the last decade (231). These drugs ameliorate symptoms,
improve survival, and slow down disease progression but
do not cure the disease. Pirfenidone has antifibrotic and
anti-inflammatory effects, whereas nintedanib is a tyrosine
kinase inhibitor that blocks downstream signaling. Both
drugs have common side effects, including nausea and rash
(231).
Since the advent of severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2), it has become imperative to
understand better the mechanisms behind the maintenance or reverse engineering to induce ECM protein homeostasis (232). Although the prognosis for elderly
patients with idiopathic pulmonary fibrosis is typically
unfavorable, the use of licensed therapeutics such as pirfenidone and nintedanib has displayed encouraging outcomes in impeding the disease’s advancement and
enhancing survival. Looking ahead, potential drug targets for future development may include the active
agents accountable for ECM synthesis and remodeling in
fibroblastic foci, such as tenascin-C and versican.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
Chronic obstructive pulmonary disease. The World
Health Organization has ranked chronic obstructive pulmonary disease (COPD) third in the cause of mortality and morbidity, claiming 3.23 million deaths worldwide in 2019 (233).
COPD is caused by narrowed airways, limiting airflow in
and out of the lungs. These obstructions are caused by several processes, such as inflammations, swellings, mucus
buildups, and impairment of the airway lining (234).
Constant inflammation caused by smoking and air pollution
is thought to be a major contributor to COPD pathogenesis
(233, 235).
The ECM plays a critical role in the architecture of the airway structure. In COPD patients, however, major deviations
of the ECM components are observed in all of its lung
recesses (236). It is postulated that these deviations cannot
be overcome in COPD patients and contribute to the lack of
airflow into the lungs, further causing a deleterious impact
on the patient’s health (236). Past findings of COPD have
been characterized by the reduction of elastin and proteoglycan but an increase in collagen in the alveoli (237–239).
However, we currently lack an understanding of the precise
mechanism behind how ECM influences COPD. It is hoped
that the transplantation of mesenchymal stromal/stem cells
(MSCs) may play a role by inducing the regeneration and
reassembly of damaged lungs. Developing artificial scaffolds,
instructed with cues to uphold the microenvironment, and
fine-tuning biomechanics for optimal MSC curative properties may be the future direction of COPD treatment (240).
Kidney failure.
Kidney fibrosis, identified by the superfluous pathological
aggregation and deposition of the ECM (241), is the last stage
of chronic kidney disease manifestation and affects 12% of
the global population with increased mortality (242, 243).
Kidney ECM contains glycoprotein, proteoglycan, elastin,
and collagen network and forges basal and interstitial
matrixes (244). Calcification of ECM in soft tissues, as in
blood vessels, is one of the observable traits in chronic kidney disease patients (245).
Currently, kidney fibrosis is diagnosed by invasive biopsies. However, employing the highly regulatory nature of
ECM homeostasis, it can be utilized to measure the ECM
remodeling rate, which could then be used to indicate disease progression (16, 246). Developing new protein fingerprints for identifying ECM neoepitopes created by proteases
or other posttranslational modifications could potentially
serve as a noninvasive biomarker (247, 248). Such biomarkers could be detected in bodily fluids like blood or
urine, as seen in studies related to renal interstitial fibrosis
(IF) (249), or via sweat sensors that measure metabolites
(250). Additionally, microRNAs have been suggested as
potential biomarkers for chronic kidney disease (251).
Age-related macular degeneration.
It is striking that in genome-wide association studies, many
ECM and matrisome genes (such as VEGFA, TIMP3, MMP9,
COL4A3, COL8A1, VTN, HTRA1) are associated with agerelated macular degeneration (AMD) (252). These polymorphisms might alter ECM integrity in the eye. For instance,
polymorphisms found in the promoter region of HTRA1,
a secreted serine protease, lead to its overexpression.
Transgenic mice overexpressing HTRA1 in retinal pigment
epithelial cells resulted in the degradation of fibronectin,
fibulin 5, and tropoelastin, thereby fragmenting the ECM
(Bruch’s basement membrane) (253). Neovascular agerelated macular degeneration (nAMD) is a type of age-related
macular degeneration and a major cause of blindness caused
by the growth of new blood vessels in the choroid, which can
eventually turn into a fibrous plaque or scar (254). The subretinal fibrosis in nAMD shares molecular processes similar to
fibrosis found in other organs such as the lung, liver, kidney,
heart, and skin (255).
Although there are no fibroblasts in the retina tissue, the
initiation of neovascularization can result in the recruitment
of additional inflammatory cells, the epithelial-to-mesenchymal transition (EMT) of retinal pigment epithelial cells,
or macrophage-to-myofibroblast transdifferentiation, which
serve as a direct or indirect source of myofibroblasts (256).
These cells produce ECM, proliferate, and migrate over the
basal layers to repair and regenerate damaged tissue.
However, in cases of repeated injury or chronic inflammation, fibrotic scarring persists (257).
Loss of Mechanotransduction
Mechanotransduction is a crucial process of translating
physical forces into biochemical signals. When this process
fails, it can have significant negative consequences for cellular adaptation and homeostasis (6). Impaired mechanotransduction can lead to a range of problems, including disrupted
tissue development and function, tissue degeneration, and,
in some cases, the development of diseases such as fibrosis
or cancer (258). Understanding the role of mechanotransduction in the ECM and how it can be disrupted is an important
area of research with potential implications for the treatment of a variety of conditions.
Damages to ECM can be read out by changes in forces
sensed by integrins as cell-surface ECM receptors. Integrins
transduce these physical forces either through cytoskeleton
remodeling linked to the nucleus or changing kinase cascade
signaling into the nucleus to reprogram gene expression
(258). A moderate reduction of integrin-linked kinase (ILK)
caused favorable function in increasing life span in C. elegans and D. melanogaster (259, 260). In particular, C. elegans showed an increased life span while preserving
cytoskeletal integrity (259). Further investigation through
RNAi screening found pat-4/ILK and pat-6/Parvin depletion to be responsible for its increase (259, 261). In
Drosophila, mortality and behavioral aging delay was
observed in the loss-of-function myospheroid gene mutations that encode for b1-integrin, the binding partner
of the ILK (262). Coronin-7 (Coro7), necessary for proper
cytoskeletal functions, has been associated with prolonged
life span in mice (261, 263). Also, cytoskeleton remodeling
has been known to be closely correlated to ECM remodeling, and ECM remodeling has a critical role in mechanotransduction and improved health span (6, 7, 264).
Cancer and the loss of cellular identity.
Cancer is a group of diseases characterized by uncontrolled
proliferation of abnormal cell growth. In 2020 alone, cancer
affected 18.1 million people globally (265). The ECM plays a
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C99
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
crucial role in the formation of the cancer microenvironment (266). Increased ECM deposits and cross-links have
been linked to tumorigenesis and metastasis by providing
the chemical and physical signals that are necessary for tumor development and invasion (267, 268). Moreover, tumor
cells have been found to influence the deposition of fibronectin, collagen, and tenascin-C (269, 270), and a growing
body of evidence suggests that ECM deposition promotes tumorigenesis and metastasis by helping cancer cells to proliferate and survive (271).
The loss of cellular identity is a phenomenon that occurs
when cells lose their characteristic features and become
more similar to each other (272). This can happen during the
development of cancer when cells undergo a process known
as epithelial-to-mesenchymal transition (EMT) (273). During
EMT, cells lose their epithelial characteristics and acquire
mesenchymal traits, such as increased motility and invasiveness. This allows them to break away from the primary tumor and migrate to other parts of the body, where they can
form secondary tumors or metastases (274). The ECM plays a
crucial role in the loss of cellular identity during EMT (275).
In the context of cancer, the ECM can provide the physical
and chemical cues that are necessary for EMT to occur (276).
For example, certain ECM molecules, such as fibronectin,
can promote EMT by activating signaling pathways that
drive the loss of epithelial characteristics and the acquisition
of mesenchymal traits (277). Additionally, the ECM can provide mechanical cues that can drive EMT by stretching and
deforming the cells, which can activate signaling pathways
that promote the loss of cellular identity (278). However,
more research is needed to fully understand the mechanisms
by which tumor cells produce ECM and its role in remodeling the microenvironment in order to develop targeted cancer treatments (279).
As people age, the dermis also undergoes remodeling of
the ECM and impaired cellularity (280, 281). However, it is
unclear whether these changes are due to changes in
fibroblasts. Salzer et al. (282) studied whether different
diets affected dermal fibroblasts in mice and their impact
on life span. They found that as time passed the definition
of old fibroblasts became blurred, and even when present
on the youthful dermis fibroblasts were still indistinguishable. Old fibroblasts also showed decreased expression of
genes involved in ECM formation (282). Paradoxically,
these old fibroblasts gained adipogenic traits similar to
those in the neonatal state. When placed on a caloric
restriction, these negative effects were either reversed or
reduced. By contrast, a high-fat diet exacerbated the negative effects of aging. These results suggest that loss of cell
identity may be one of the underlying mechanisms of
aging (282–284).
Stiffened ECM can also drive the pathogenic expression of
matricellular proteins by fibroblasts, leading to the conversion of muscle stem cells into fibrogenic cells. This process,
known as a fibrogenic conversion, can contribute to sarcopenia, which is the age-related loss of muscle mass and
strength. This occurs through the activation of the Yes-associated protein 1/Transcriptional coactivator with PDZ-binding motif (YAP/TAZ) signaling pathway, which is triggered
by the stiffened ECM. Overall, the ECM plays a key role in
the loss of cellular identity during EMT, which is an essential
C100
step in the development of cancer and its metastasis, as well
as in the development of sarcopenia (18).
Sarcopenia.
Aging is accompanied by a decline in skeletal muscle mass
and function, a condition known as sarcopenia, which
results from multiple cellular and molecular changes
(285–289). One of the major factors contributing to sarcopenia is the impairment of skeletal muscle regeneration
due to the decline in the function and number of muscle
stem cells (MuSCs) (285–289). The microenvironment surrounding MuSCs plays a critical role in their function and
maintenance, but little is known about this niche (290–
292). Below, we discuss recent studies that have shed light
on the role of the MuSC niche in the aging process and its
implications for muscle regeneration.
The role of the MuSC niche in aging. It has been suggested that the changes in the systemic environment that
occur during aging could be the cause of MuSC dysfunction
(293). However, it is unclear whether these changes act
directly on the stem cells or indirectly affect MuSC function
by altering the niche (294–299). Lukjanenko et al. (300)
found that the loss of fibronectin (FN) from the aged stem
cell niche affects the regenerative capacity of skeletal muscle
in mice. FN is a preferred adhesion substrate for MuSCs and
regulates the p38 and extracellular signal-regulated kinase
(ERK) MAPK aging pathways (294–299). Restoration of
attachment to FN in the aged niche can reactivate FAK signaling in MuSCs, thereby restoring the regenerative capacity
of old skeletal muscle (300).
Another study by Lukjanenko and her team investigated
the impact of aging on regulatory cells in the MuSC niche and
found that fibro-adipogenic progenitors (FAPs) indirectly
affect the myogenic potential of MuSCs (301). Using transcriptomic profiling, the study identified WISP1, a FAP-derived
matricellular signal, as a critical factor in efficient muscle
regeneration. The study also found that aged FAPs lose
WISP1, leading to MuSC dysfunction (301). Transplantation of
young FAPs or systemic treatment with WISP1 restored the
myogenic capacity of MuSCs in aged mice and rescued skeletal muscle regeneration. The study establishes that loss of
WISP1 from FAPs contributes to MuSC dysfunction in aged
skeletal muscles and that targeting this mechanism could
rejuvenate myogenesis (301).
Therapeutic targets for muscle pathological conditions. Skeletal muscle regeneration is critical for maintaining muscle mass and function, particularly in aging and
pathological conditions (291, 302, 303). Rozo et al. (291)
demonstrated that b1-integrin was a critical molecule in the
stem cell (SC) niche, supporting their homeostasis, expansion, and self-renewal during regeneration. The researchers
also revealed that b1-integrin collaborated with fibroblast
growth factor 2 (Fgf2) to activate common downstream
effectors, including the mitogen-activated protein kinase
Erk and protein kinase B (Akt) (291). The study further highlighted the altered b1-integrin activity in aged SCs, which
displayed Fgf2 insensitivity. Treatment with a monoclonal
antibody to boost b1-integrin activity restored Fgf2 sensitivity and improved muscle regeneration after experimentally
induced injury. The same treatment also enhanced regeneration and function in dystrophic muscles in mdx mice, a
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
model for Duchenne muscular dystrophy (291). Their findings suggest that b1-integrin could be a promising therapeutic target for muscle pathological conditions in which the
SC niche is compromised, such as sarcopenia and muscular
dystrophy (291).
Stem cell exhaustion.
Stem cell exhaustion is a major contributor to age-related
decline in tissue regeneration (1, 304). In vivo stem cell
induction has been shown to restore the age-related decline
in mice (305–307), highlighting the important role that tissue
stem cells play in maintaining homeostasis and tissue regeneration (304, 307, 308).
The ECM is critical in regulating stem cell pluripotency,
differentiation, tissue repair, and regeneration (26, 309).
Tenascin-C, specifically, is expressed by bone marrow
niche cells, which promote the regeneration of hematopoietic stem cells (310). However, chronic mechanical stress
can cause the ECM to stiffen, impairs mechanotransduction,
and leads to stem cell exhaustion through ECM remodeling
(26). In this process, enzymes like matrix metalloproteinases
(MMPs) can break down ECM molecules and cause the ECM
to stiffen, impairing stem cells’ ability to respond to mechanical signals and maintain their proper functions (82).
These findings suggest that stem cell rejuvenation could
potentially be enhanced for novel therapies by manipulating
the mechanisms of the ECM (308, 311).
Cell death.
Cell death is a critical process in the development of the
body and in maintaining homeostasis to prevent the onset of
disease (312). This process can be classified into two categories: nonprogrammed cell death (non-PCD), also known as
necrosis, which is the result of the irreversible cell damage
caused by extreme physical or chemical environments (313),
and programmed cell death (PCD), which can be further subclassified into lytic and nonlytic cell death (314).
Lytic cell death includes necroptosis and pyroptosis (315),
whereas nonlytic cell death, such as apoptosis, is essential
for maintaining tissue organization and ensuring the appropriate number of cells (316). The ability of the ECM to
remodel is crucial for regulating cell behavior (16), and apoptosis has been found to be involved in several diseases,
including those affecting the kidneys (317, 318). Unlike necrosis, apoptosis does not elicit an inflammatory response,
and phagocytes are responsible for removing the resulting
apoptotic bodies (312, 319). A study of rat kidneys found that
glomerular cell apoptosis was associated with glomerular
cell loss and accumulation of ECM, leading to glomerulosclerosis (320). Additionally, it was found that mesangial cells
were protected from apoptosis by the basement membrane
matrix, which induced their survival. However, when the
expression of b1-integrin was suppressed with antisense oligonucleotides, apoptosis of mesangial cells was increased
(316). These findings suggest that the regulation of mesangial
cells is dependent on the ECM surrounding them via
integrins.
Given the close relationship between mesangial cell proliferation and apoptosis, there is potential for developing novel
therapeutic strategies for renal diseases that involve manipulating apoptosis (316).
Loss of mitochondrial homeostasis.
Mitochondria are multifunctional organelles that play
essential roles in various cellular processes, including apoptosis, cell metabolism, proliferation, modulation of intracellular calcium homeostasis, and ATP production (321–
323). Because of the diverse functions of mitochondria,
any dysfunction in these organelles can have a significant
impact on human health.
There is a bidirectional relationship between the ECM and
mitochondria. Cell-ECM interactions can directly or indirectly modulate mitochondria through the actin cytoskeleton (324). This interaction is particularly relevant in the
context of metastasis, where mitochondria serve as the primary energy source for cell migration (321). In turn, mitochondrial depolarization and oxidative phosphorylation can
influence the ECM through cell-ECM adhesion and MMP-dependent ECM degradation (325). Mitochondrial homeostasis
and adaptation (mitohormesis) are interlinked with ECMintegrin cytoskeleton remodeling in human stem cells and
C. elegans important for life span extension (326–329).
Cellular senescence as a consequence of ECM damage.
Cellular senescence, characterized by alterations in the
expression of ECM components and secretion of ECM
remodeling enzymes, is a form of cell cycle arrest brought on
by various stressors (206, 330, 331). Although temporary
induction of senescence is crucial for suppressing tumors
and facilitating tissue repair, the long-term presence of senescent cells in tissues can contribute to tissue damage and
aging (206, 332).
Recently, a hypothesis has been proposed linking ECM
alterations to aging and senescent cell accumulation, suggesting that this accumulation may be due to an altered
antifibrotic program (4). In another study, the use of decellularized ECM from young cells increased the life span of
senescent human fibroblasts by 39% (333). Another piece of
evidence comes from an in vivo study: mice with a mutation
that makes type I collagen resistant to collagenase cleavage
(which mimics the effect of cross-links on the susceptibility
to collagenase-mediated ECM degradation) have accelerated
aging with weight loss, reduced adiposity, kyphosis, osteoporosis, hypertension, and dramatically accelerated accumulation of senescent cells (200).
The senescence-associated secretory phenotype (SASP)
contributes to tissue regeneration failure and a proinflammatory environment that further advances the progression
of diseases such as cancer, fibrosis, and cardiovascular disease (334). An altered ECM has been shown to cause an
imbalance of homeostasis and increase MMP activity (335).
In the context of cardiac remodeling, high levels of MMP1
have been linked to decreased collagen expression, which
results in decreased contractility and the development of
cardiomyopathy (336, 337). In idiopathic pulmonary fibrosis
(IPF), increased MMP expression and activity is a key feature
of the SASP and plays a crucial role in fibrosis (338).
Other ECM-Related Pathologies
Williams–Beuren syndrome.
Williams–Beuren syndrome (WBS), also known as Williams
syndrome, is characterized by a multisystem developmental
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C101
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
disorder that affects 1 in 7,500–18,000 people (339). Often,
WBS patients have skeletal, renal, and cardiovascular malformation, cognitive impairments, and distinctive facial features and personalities (340–342).
WBS is caused by the loss of the Williams–Beuren syndrome critical region (WBSCR) in chromosome 7. In particular, a major component of ECM, elastin (ELN) hemizygosity
is associated with arteriopathy in WBS patients (343).
Reduced elastin in those with WBS would therefore cause
the ability of elastic properties necessary for the normal
function of arteries to impaired (344–346).
Recent studies have shed light on the potential role of specific genes, such as BAZ1B, FZD9, STX1A, and WBSCR22, in
the neurodevelopment and physical growth of individuals
with WBS (347). These findings suggest that targeted gene
therapies could be developed to restore or enhance the function of these genes in WBS patients. However, further research
is needed to understand the molecular-phenotype relationship in patients with atypical deletions of WBS and to validate
the safety and efficacy of such therapies in animal models.
Arterial stiffening.
Arterial stiffness is associated with the increased probability
of major cardiovascular events by engendering pulse pressure increase and isolated systolic hypertension (348).
Loss of ECM protein homeostasis is at the root of arterial
stiffening (349). Collagen and elastin support the architecture of arterial biomechanics. These proteins constitute the
majority of arterial wall structural makeup, and deterioration in either or both of these proteins can create havoc in arterial health (350, 351). Therefore, any alteration in quantity
or quality is a determinant factor in diseases often associated
with aging, namely, diabetes, chronic renal failure, and
hypertension (350, 352).
Cardiovascular and renal drugs currently available in the
market have yet to capture the elusive mechanisms underlying the structural components of the disease. The FIDELIODKD and FIGARO-DKD studies investigated cardiovascular
and kidney outcomes in patients with type 2 diabetes and
chronic kidney disease (353). In a pooled analysis of individual patient-level data, finerenone was found to reduce the
risk of cardiovascular events and kidney failure outcomes
compared with a placebo across a broad range of CKD stages.
Notably, the reduction in risk was observed in patients with
albuminuria but with estimated glomerular filtration rate >
60 mL/min/1.73 m2. The results suggest that finerenone may
be a valuable addition to renin-angiotensin system inhibition in reducing the burden of cardiovascular and kidney
disease in at-risk patients with type 2 diabetes (353).
Cognitive impairment.
Blood vessels become stiffer with advancing age due to elastin loss and accumulation of cross-links in the ECM of a vessel wall. Pulse wave velocity (PWV), a measure of arterial
stiffness, is significantly associated with cognitive status
(354). PWV appears significantly higher in subjects with vascular dementia or Alzheimer’s disease than in those without
cognitive impairment. Moreover, PWV was higher in subjects
with mild cognitive impairment than in those without cognitive impairment. The causality of this relationship was confirmed in a mouse study in which vascular stiffness was
C102
induced by the topical application of calcium chloride on
the adventitial region of the carotid artery (355), which
resulted in an increase in the production of cerebral superoxide anion and neurodegeneration.
Integrating all findings, Iulita et al. (356) draw the hypothesis that increased arterial stiffness and the subsequent excessive pulsatility, through mechanisms involving oxidative
stress and inflammation, affect the brain microcirculation
and lead to blood-brain barrier (BBB) disruption. With compromised cerebral perfusion, the delivery of nutrients and
the clearance of toxic products is disrupted, leading to neurodegeneration and cognitive dysfunction (356).
In addition, Baidoe-Ansah et al. (357) examined the
expression levels of genes related to the neural ECM in the
brains of young, aged, and very aged mice and found that
ECM gene expression was downregulated with age, despite the accumulation of ECM proteoglycans. The most
significantly downregulated gene was carbohydrate sulfotransferase 3 (Chst3), which is responsible for modifying
proteoglycans. Baidoe-Ansah et al. (357) also found that
changes in ECM-related gene expression were associated
with a decline in cognitive performance. At the individual
level, they found a negative correlation between Chst3
expression and cognitive performance in very aged mice. A
trade-off between positive gene effects on certain cognitive
tasks and negative effects on others was also observed.
Despite an increase in CHST3 protein expression in glial
cells, there was no change in the absolute level of chondroitin 6-sulfation (C6S) modification and a decrease in C6S in
certain areas of the ECM (357).
The ECM plays a crucial role in the maintenance and regulation of the blood-brain barrier (BBB) (358). It provides a
structural scaffold for endothelial cells and astrocytes and
modulates the signaling pathways that regulate BBB permeability (358). BBB dysfunction has been linked to cognitive
decline and neurodegeneration in aging and dementia; the
underlying mechanisms are complex and involve the accumulation of harmful substances such as neurotoxins, inflammatory molecules, Ab, and neurofibrillary tangles of p-tau in
Alzheimer’s disease (359). Therefore, targeting specific molecules like RepSox (360) and secreted protein acidic and rich
in cysteine (SPARC) may hold potential for preventing or
treating BBB breakdown in neurodegenerative diseases such
as Alzheimer’s disease (359).
Osteoporosis.
Constitutional or compositional changes occur in the bone
with the progression of age. Osteoporosis, an ailment of the
bone, impacts largely the elderly female population, leaving
the bone porous and thereby elevating the risk of bone
fracture.
Osteoclasts and osteoblasts concert tightly with the bone
ECM in regulating and regenerating bone formation. In the
bone ECM, collagen constitutes the majority of its makeup,
and with the progression of time, collagen undergoes a
decline in quality due to AGEs and causes abnormalities in
bone remodeling (361). Although higher levels of AGEs were
observed in patients who had had fractures, correlation does
not necessitate causation (362).
Previous findings suggest that noncollagenous proteins
(NCPs) may consort with collagen in the structural and
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
mechanical properties of the bone matrix. It is thought that
through crystal nucleation and orientation, NCPs contribute
to bone health by functioning as a scaffold to collagen fibrils
and thereby increasing bone strength (363).
Osteoporosis has several possible treatment options, with
antiresorptive drugs like bisphosphonates being the current
mainstay (364, 365). However, their use can interfere with
bone remodeling and reduce bone flexibility (364, 365). The
Wnt signaling pathway is also a promising target for new
drug development, and studies on animal models have laid
the groundwork for new drugs that can regulate this pathway
(366, 367). Specifically, researchers are focusing on the canonical Wnt pathway and exploring ways to manipulate the
activity of glycogen synthase kinase-3 beta (GSK3b), axis inhibition protein 2 (Axin-2), and activated protein C (APC) to
promote bone formation. To overcome some of the drawbacks of newly developed agents, delivery systems using
peptides or chemicals with high affinity to bone are being
explored (366, 367).
Another promising approach is bispecific Wnt mimetics
that target both Frizzled (FZD) and lipoprotein receptorrelated protein (LRP), which have demonstrated rapid and
robust effects on bone building and correction of bone mass
deficiency (368). However, more studies are needed before
the preclinical and clinical trials of these agents. Cell/gene
therapy and nanocarrier-based therapies that interact with
the Wnt pathway are also being investigated as novel therapies for osteoporosis (369).
Osteoarthritis.
Osteoarthritis (OA) is the most prevalent degenerative joint
disease, with an estimated prevalence of >50% in individuals over the age of 65 yr (370, 371). OA is characterized by
abnormal bone remodeling and degeneration of the articular
cartilage, leading to pain and physical and financial impairments in the elderly population (372–375).
Although aging is considered a major risk factor for the development of OA, the exact mechanisms of its pathogenesis
remain poorly understood (376). Aging has been shown to disrupt the homeostasis of the ECM, resulting in loss of integrity
and deterioration of its architecture. These changes are
thought to contribute to the development of OA, with its associated increased inflammatory expression, loss of tissue regenerative capacity, and cellular senescence. Chondrocytes,
which are quiescent cells responsible for cartilage formation,
are believed to play a central role in the initiation and progression of OA (371). During the progression of the disease, chondrocytes have been observed to promote ECM malformation,
forming clusters (377). As aging progresses, chondrocytes also
affect proteoglycan synthesis and disrupt their composition
(378), leading to biomechanical modifications of the ECM and
further disrupting its homeostasis.
The current clinical trials primarily focus on addressing
regeneration/repair of cartilage and bone defects, as well as
targeting proinflammatory mediators and pain through the
antagonization of nerve growth factor (NGF) activity.
However, although these therapies target metabolic disorders and senescence/aging-related pathologies, they do not
specifically address osteoarthritis (OA). Furthermore, it is
important to note that none of these therapies has been
proven to significantly modify disease progression or
successfully prevent the need for joint replacement in the
advanced disease stage (379).
There is also evidence suggesting sex differences in the
pathogenesis of OA. Women have a higher prevalence of OA
than men, particularly after menopause (380, 381). Hormonal
changes that occur during menopause, such as reduced levels
of estrogen, may play a role in the increased susceptibility of
women to OA (382, 383). Additionally, women may have differences in their ECM composition and structure compared
with men, which may contribute to their increased susceptibility to OA (384). For example, studies have shown that
women have lower levels of collagen and proteoglycans in
their articular cartilage compared with men (385). These differences in ECM composition and structure may make
women more susceptible to the development of OA and its
associated ECM changes (386). Further research is needed to
fully understand the underlying mechanisms behind these
sex differences in the pathogenesis of OA.
Skin aging.
As we age, our skin loses its elasticity and firmness because
of a decline in the activity of fibroblasts, the cells responsible
for producing collagen and other components of the ECM.
This loss of ECM leads to systemic inflammaging, which is
characterized by increased levels of inflammatory cytokines
such as IL-1 and TNF-a. Studies have shown that even using
hydrating lotions can help to mitigate this effect by reducing
the levels of these inflammatory cytokines in elderly individuals (387).
A key driver of skin aging is the reduced activity of fibroblasts and their loss of adherence to the ECM. In younger
skin, fibroblasts are anchored to the ECM, but this
becomes increasingly disorganized with age (187). This can
lead to a decline in collagen production and a loss of skin
firmness and elasticity. To address this, dermatologists often prescribe medications such as all-trans retinoic acid
(tretinoin), which has been shown to increase collagen
production by up to 80% in the dermis, resulting in a
rejuvenated appearance of the skin (388). Treatment of
C. elegans with tretinoin promotes collagen homeostasis that
is normally lost during aging and is sufficient to increase life
span (50). Additionally, other products have been shown to
stimulate the production of ECM proteins in skin tissue. For
example, glucosamine has been shown to improve hyaluronic acid protection [Gueniche and Castiel-Higounenc
(389)], and heparan sulfate can be used to enhance glycosaminoglycan levels in the skin (390). Both glucosamine and
hyaluronic acid supplementation are sufficient to promote
collagen homeostasis and longevity in C. elegans (50, 391).
Another factor that contributes to skin aging is the increased
activity of matrix metalloproteinases (MMPs), which degrade
collagen. This is a result of fragmentation that occurs with the
fibrils and dysregulated MMP activity that exacerbates the dysfunction of fibroblasts (392). Ultraviolet (UV) damage accelerates skin aging in part because of increased MMP activity from
the damage response; the abnormal MMP activity also occurs
with intrinsic aging.
Rapamycin, a US Food and Drug Administration (FDA)approved drug targeting the mechanistic target of rapamycin
(mTOR) complex (393), has been demonstrated to have antiaging properties on the human epidermis in a clinical trial
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C103
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
(394). The trial recruited participants older than 40 yr of age
who exhibited age-related photodamage and dermal atrophy. The results of the study revealed a statistically significant decrease in the levels of the p16INK4A protein, which is
indicative of cellular senescence, and an increase in type VII
collagen, a crucial component for the basement membrane’s
structural integrity. Additionally, the study observed an
improvement in the clinical appearance of the skin, as well
as an improvement in the histological appearance of the skin
tissue. These findings suggest that rapamycin may be a
promising antiaging therapy that has been validated for efficacy in human subjects (394).
The future of treatments against skin aging may hinge on
the development of effective and stable messenger RNA
(mRNA) delivery systems. A recent study by You and colleagues (395) found that extracellular vesicles (EVs) produced from human dermal fibroblasts and encapsulating
mRNA encoding for ECM a1 type I collagen (COL1A1)
induced the formation of collagen-protein grafts and
reduced wrinkle formation in the skin of mice with photoaged skin. Additionally, the study found that intradermal
delivery of the mRNA-loaded EVs via a microneedle array
led to the prolonged and more uniform synthesis and
replacement of collagen in the animals’ skin. These findings
suggest that intradermal delivery of EV-based COL1A1
mRNA may be an effective protein-replacement therapy for
the treatment of photoaged skin (395).
DIAGNOSTICS
Matreotype: ECM Composition Changes
The matreotype is a way to analyze and understand
changes in the matrisome proteins or ECM composition that
occur with aging and disease (6). The matreotype is defined
as the molecular and structural snapshot of an ECM composition associated with or caused by a phenotype or cellular
status (6) and can be measured with techniques such as
mass spectrometry or immunohistochemistry (396–398).
The ECM is a dynamic network of molecules that surrounds
and supports cells in tissues and organs, and the matreotype
concept recognizes it as a modular, heterogeneous system
rather than a static entity. By measuring the levels of different ECM molecules and their spatial distribution, a “fingerprint” of the ECM can be generated to track changes over
time (6).
The matreotype concept has several potential advantages,
including being able to comprehensively characterize the
ECM, detecting subtle changes and applying it to a wide range
of tissues and organs (6). Moreover, recent studies have highlighted the critical role of the matrisome in the pathogenesis
of age-related diseases (399, 400). The dysregulation of the
matrisome and the consequent alterations in the biomechanical properties of the ECM contribute to the development and progression of these diseases (3). Thus, the
matreotype approach not only provides a comprehensive
characterization of the ECM during aging but also has important implications for understanding the underlying
mechanisms of age-related diseases and developing new
therapeutic strategies (6).
C104
In kidney disease, the matrisome undergoes compositional turnover and structural remodeling (400). The kidney
matrisome can be characterized by using the matreotype
concept to identify changes that occur during aging and disease (400). The matreotype can be used to identify changes
in the ECM that contribute to kidney disease progression
and identify potential therapeutic targets (400).
Research on the role of the matreotype in cancer, specifically ovarian high-grade serous carcinoma, suggests that
understanding the matreotype may help in developing more
effective cancer treatments (400, 401). Further research is
needed to fully realize the potential of the matreotype to develop reliable measurement methods.
ECM-enriched proteomics.
ECM-enriched proteomics involves the isolation and purification of ECM proteins, followed by the identification and
quantification of these proteins using mass spectrometrybased techniques (40, 396). This allows researchers to systematically analyze the ECM proteome and understand the
specific proteins that are present and their relative abundance (40, 396). Examples of different approaches to tackle
these problems are described below.
Naba et al. (40) used a combination of computational and
experimental techniques to define and characterize the ECM
proteome in normal and tumor tissues. They used in silico
(computational) methods to identify and predict the ECM
proteome and then validated their predictions with in vivo
(experimental) techniques such as mass spectrometry. The
authors found that the ECM proteome is highly dynamic and
can vary significantly between different tissues and under
different physiological conditions. They also identified several ECM proteins that are differentially expressed in normal
and tumor tissues and identified several ECM proteins that
may be potential therapeutic targets for cancer. Overall, the
study highlights the importance of the ECM in normal and
pathological processes and the potential for ECM-targeted
therapies to improve human health (40).
SWATH (Sequential Window Acquisition of All Theoretical
Fragment Ion) mass spectrometry is a mass spectrometrybased technique that may be used for the quantitative profiling of the matrisome (402, 403). In SWATH mass spectrometry, samples are first digested into peptides with proteases,
and the resulting peptides are then analyzed by mass spectrometry. During the analysis, the mass spectrometer sequentially scans through a range of m/z (mass-to-charge ratio)
values, acquiring fragment ion spectra for each m/z value.
This allows the acquisition of data from a wide range of peptides in a single experiment (404). One of the key advantages
of SWATH mass spectrometry is its ability to provide highly
sensitive and reproducible quantitation of large numbers of
proteins, making it an ideal tool for the quantitative profiling
of the matreotype (404). Additionally, the technique does not
require the use of stable isotope-labeled standards, which can
be expensive and time-consuming to produce (404, 405).
Overall, SWATH mass spectrometry is a powerful tool for the
quantitation of the matrisome and can provide valuable
insights into the composition and function of the ECM in normal and pathological conditions (404). OpenSWATH is a
highly advanced analytical pipeline designed to unravel the
complex interplay between the liver proteome, molecular
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
variations, and metabolic traits. By harnessing the power of
mass spectrometry, this pipeline generated an extensive inventory of quantified proteomic data from 662 mice to determine the connections between diet, aging, and gene
expression. A major signature of aging was the matrisome
(406).
Hiebert et al. (407) investigated the effect of nuclear factor
erythroid 2–related factor 2 (Nrf2) signaling pathway activation on cellular senescence and the reprogramming of fibroblasts. Fibroblasts are members of the connective tissue
family, known for producing extracellular matrix (ECM) proteins. The results indicated a complex interplay of factors,
which were mediated by targeted modifications of the matrisome. They also found that this process is mediated by the
targeted modification of the matreotype, specifically through
the downregulation of ECM proteins such as collagens and
the upregulation of ECM-degrading enzymes such as MMPs
(407). Their study demonstrated the potential for targeting
matrisome as a therapeutic strategy for diseases associated
with fibroblast dysfunction (407).
Tissue engineering is a field of medicine that seeks to
repair or replace damaged or diseased tissues with a variety
of approaches, including the use of scaffolds to support the
growth of new cells (408, 409). Scaffolds are three-dimensional structures that provide physical support for cells to
grow and differentiate (408, 409). Hill et al. (409) quantified
the levels of ECM proteins in a rat lung scaffold and used
this information to provide a molecular readout for tissue
engineering by using mass spectrometry to quantify the levels of ECM proteins. They found that the scaffold contained
a variety of ECM proteins, including collagens, laminins,
and proteoglycans. They also found that the levels of these
proteins varied depending on the stage of scaffold development, suggesting that the quantification of ECM proteins
may be useful as a molecular readout for tissue engineering
applications (409).
Li et al. used a combination of spatial quantitative proteomics, decellularization, laser capture microdissection, and
mass spectrometry to construct a hierarchical map of the
proteome in human skin tissue. They analyzed the specific
functions of different structures of normal skin tissue and
tissue with dermatologic disease. They then identified the
ECM glycoprotein transforming growth factor-beta (TGFb)induced protein (TGFBI) ig-h3 in the basement membrane
(BM), a thin layer at the top of the dermis, which plays a role
in the growth and function of epidermal stem cells (EpSCs)
and promotes wound healing. These findings provide
insights into the way in which ECM proteins contribute to
the function of EpSCs in the BM and may have potential clinical applications in the treatment of skin ulcers or diseases
with refractory lesions that involve epidermal cell dysfunction and impaired reepithelialization (364).
ECM-enriched proteomics is a powerful tool for studying
the ECM and its role in various biological processes, including tissue development, maintenance, and repair (40, 396,
410). It is also useful for studying the ECM in the context of
diseases, as the ECM is often altered in the context of various
diseases, including cancer and neurodegenerative diseases
(40, 411–413). Understanding these changes may provide
insights into the underlying pathological mechanisms and
identify potential therapeutic targets.
Herovici staining.
Herovici staining is a histological staining method that is
used to visualize the immature and mature collagen of tissues. It is based on the reaction of the ECM with a solution of
toluidine blue, which results in the formation of a blue dye
that can be seen under a microscope. Herovici staining can
be used to visualize collagen type I and III in various tissues,
including skin, heart, and lung, and it can provide valuable
information about the structure and composition of the
ECM (414–417).
Herovici staining can potentially be used as a biomarker
of aging, as the ECM undergoes changes in its composition
and structure with aging. These changes can affect the mechanical properties and functions of the ECM, and they can
contribute to various age-related diseases, such as diabetes
and cardiovascular disease. To use Herovici staining as a biomarker of aging, tissue samples can be obtained from a range
of ages and processed for histological staining with the
Herovici method. The stained samples can then be examined under a microscope, and the changes in the ECM can be
quantified and compared between different ages (417). This
can provide a measure of the changes in the ECM that occur
with aging, and it can be used as an indicator of the agerelated changes in the ECM. This method can provide valuable insights into the mechanisms of ECM aging and can
potentially be used to develop interventions that can slow or
reverse these changes.
ECM labeling.
ECM labeling is a technique used to visualize and quantitatively analyze the ECM in biological tissues. This can be
achieved through the use of various fluorescent dyes or antibodies that specifically bind to ECM components, such as
collagens and glycosaminoglycans (418, 419). Alterations in
the ECM composition can provide insight into the disease
state, which may be utilized as a diagnostic tool for various
pathologies, including cancer and fibrosis.
Imaging probes green fluorescent protein topaz (GFPtpz)
and mCherry-tagged collagen fusion constructs were developed by Lu and his team (420) for the live imaging of type I
collagen assembly by replacing the a2(I)-procollagen NH2terminal propeptide with GFPtpz or mCherry. These imaging
probes were transfected into cells and were used to examine
the dynamics of type I collagen assembly and its relationship
to fibronectin. The fusion proteins were found to coprecipitate with a1(I)-collagen, remaining intracellular without
ascorbate but forming a1(I) collagen-containing extracellular
fibrils in the presence of ascorbate. The study demonstrates
the utility of these imaging probes in providing new insights
into the mechanisms of extracellular collagen assembly and
its dependence on cell motion and fibronectin assembly, a
critical step in understanding the complexities of ECM formation (420).
Fischer et al. (421) employed a multifaceted approach to
investigate the intricacies of the wound-healing process in
internal organs. The ECM was labeled in different organs,
specifically the mouse peritoneum, liver, and cecum, to
reveal the transfer of preexisting matrix across organs in various injury models. The tissue of origin for the transferred
matrix was found to dictate the outcome of the healing process, whether it resulted in scarring or regeneration. The
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C105
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
transfer of matrix was found to be mediated by neutrophils,
in a manner dependent on the heat shock factor (HSF)-integrin AM/B2-kindlin3 cascade, suggesting that pharmacological inhibition of this cascade could prevent the formation of
peritoneal adhesions. These may provide a therapeutic window to mitigate scarring across a range of conditions, highlighting ECM labeling as a potential diagnostic tool in
wound healing and regenerative medicine (421).
ECM biomarkers.
Collagen fragments, specifically those derived from the
ECM, have been investigated as potential biomarkers in various pathological conditions. The degradation of ECM collagen has been shown to be indicative of matrix remodeling,
which is a hallmark of several pathological states, including
cancer and fibrosis. The following are examples of such
fragments.
Centenarians. Based on the assumption that centenarians possess a unique protein signature that is passed down to
their offspring, Sebastiani et al., using samples from the New
England Centenarian Study (NECS), aimed to characterize the
serum proteome of 77 centenarians, 82 centenarians’ offspring, and 65 age-matched control subjects for the offspring
(mean ages: 105, 80, and 79 yr) (422). Out of the top 30 differentially expressed proteins between centenarians, offspring,
and control subjects, they identified 17 ECM and matrisome
proteins (COL6A3, COL28A1, CHRDL1, CST3, SRFP1, PTN,
IGFBP2, IGFBP6, IGFALS, GDF15, FSTL3, SVEP1, SERPINF2,
SMOC1, SPON1, RSPO4, WISP2) (422), implicating a strong
association between matreotype changes and human longevity. These proteins may play a crucial role in the biomechanical properties of the ECM and cellular homeostasis, which
have been identified as drivers of aging (3). The discovery of
these proteins may pave the way for the development of novel
diagnostic tools or therapies for age-associated disorders such
as Alzheimer’s disease, which is linked to the aggregation of
Ab protein in the ECM (423). Furthermore, centenarians have
been extensively studied for genetic factors that contribute to
exceptional longevity, and the identification of genetic signatures linked to longevity could provide insights for future
research into age-related diseases (422).
Collagen marker for skin integrity. Defects in the
expression and/or structure of type VII collagen are associated with a spectrum of skin disorders, including dystrophic
epidermolysis bullosa (DEB) and recessive dystrophic epidermolysis bullosa (RDEB), characterized by skin fragility
€m
and blistering (424–427). Recent investigations by Nystro
et al. have revealed that the integrity of type VII collagen is
essential for the proper organization of laminin-332 at the
dermal-epidermal junction (DEJ), thereby facilitating the
maintenance of the mechanical link between the epidermis
and dermis, as well as for the maintenance of the laminin332/a6b4-integrin signaling axis, which guides keratinocyte
migration and reepithelialization during wound healing
(424). Furthermore, type VII collagen has been found to support dermal fibroblast migration and regulate cytokine production in the granulation tissue, thereby playing a critical
role in physiological wound healing (424). These findings
collectively highlight the importance of type VII collagen in
the maintenance of the skin’s integrity and provide valuable
insights into the development of therapeutic strategies.
C106
Collagen fragments in urine: urinary peptidomic
profile. Collagen fragments in urine have been found to be
potential biomarkers of various pathologies, including aging,
cardiovascular disease, and cancer (428–432). The presence
of these fragments can indicate the breakdown of the extracellular matrix (ECM) in the body and may be useful in early
diagnosis and monitoring of disease progression. For example, renal biopsy has long been considered the standard for
assessing renal interstitial fibrosis (IF) in chronic kidney disease (CKD). However, renal biopsy is not without its limitations, including the potential for sample error and observer
variability (433). As a result, noninvasive alternatives such
as urinary biomarkers that reflect fibrogenesis are being
explored as potential alternatives (247, 428).
Hijmans et al. (429) aimed to investigate the diagnostic
value of urinary collagen degradation products as markers
for renal fibrogenesis in a rat model of progressive kidney
disease. Proteinuria was induced in rats with Adriamycin
injection, and the rats were then treated with or without
an antifibrotic S1P-receptor modulator called FTY720. The
researchers collected urine and blood samples and performed renal biopsies at various time points. They measured collagen type I and III degradation fragments in the
samples and also looked at various inflammatory and
fibrotic markers in the kidneys. The results showed that
urinary collagen breakdown products were sensitive
markers of interstitial fibrosis, preceding histological
changes, and correlated well with fibrotic changes. The
antifibrotic treatment reduced some fibrotic markers but
did not affect collagen type III metabolism. These data
suggest that noninvasive assessment of urinary collagen
breakdown products could potentially replace invasive renal biopsy as a way to assess fibrosis (429).
Similarly, Martens et al. (431) aimed to identify and validate a urinary peptidomic profile (UPP) capable of differentiating healthy from unhealthy aging within the general
population through the examination of data collected from
the Flemish Study on Environment, Genes, and Health
Outcomes (FLEMENGHO) study. Further UPP validation
proceeded in independent patient cohorts, including individuals with diabetes, COVID-19, or CKD, by utilizing a multidimensional UPP signature, reflective of aging, generated
from the derivation dataset and validated in both timeshifted and synchronous internal validation datasets. With
correction for multiple testing and multivariable adjustment, chronological age was found to be associated with 210
sequenced peptides, primarily showing the downregulation
of collagen fragments. The UPP-age prediction model was
found to be significantly associated with various risk
markers related to cardiovascular, metabolic, and renal disease, inflammation, and medication use (431).
In conclusion, specific shifts in the urinary peptidomic
profile (UPP) have been shown to be associated with aging
and are primarily reflective of fibrosis (431, 432, 434). UPP
profiling is minimally invasive and only requires a small
urine sample, making it useful in identifying and preventing
chronic age-related diseases (431, 432, 434). Additionally,
UPP profiling can be used as an intermediary trial end point
in drug discovery, potentially shortening the duration of and
reducing the cost of clinical trials in chronic kidney disease
(431).
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
Blood collagen fragments. Blood fragment biomarkers,
such as MMP-mediated type I collagen degradation (C1M)
and carboxy-terminal cross-linked telopeptide of type 1 collagen (CTX-1), have been used to evaluate the association
between extracellular matrix (ECM) turnover and all-cause
mortality in postmenopausal women in Denmark. Dragsbæk
et al. (435) found that C1M was an independent risk factor for
all-cause mortality, whereas there was no association
between CTX-1 and all-cause mortality. This suggests that
specifically MMP-mediated tissue degradation of type I collagen is associated with mortality and the measurement of this
biomarker in blood samples can provide insight into the
state of ECM homeostasis (435). Furthermore, these biomarkers can be used to monitor the effectiveness of interventions targeting ECM homeostasis.
Similarly, the measurement of other collagen fragments,
such as serum C-reactive protein metabolite (CRPM), may be
useful in identifying an inflammatory endotype associated
with progression in osteoarthritis (OA) patients. Bay-Jensen
et al. (436) found that CRPM levels were able to separate OA
from rheumatoid arthritis (RA) with high accuracy, indicating
a strong correlation between CRPM levels and inflammatory
conditions. Additionally, a significant subset of OA patients
may have an inflammatory signature, as determined by their
CRPM levels, which can be used for better subgrouping of OA
patients for targeted drug development and delivery (436).
Furthermore, biomarkers such as the COOH-terminal end
of the telopeptide of type I collagen (CTX-1) and osteocalcin
have been identified as biomarkers of bone turnover and
have been shown to have a U-shaped association with allcause mortality in postmenopausal women. Similarly, the
formation of type VI collagen (PRO-C6), MMP-degraded type
IV collagen (C4M), formation of type III collagen (PRO-C3),
and MMP-degraded type I collagen (C1M) have been identified as biomarkers of soft tissue turnover and have been
shown to have a J-shaped or linear association with all-cause
mortality (437).
The measurement of these biomarkers in blood samples
can provide insight into the state of ECM homeostasis, which
is crucial for understanding the pathogenesis of various
chronic diseases. These biomarkers have the potential to be
useful in identifying individuals at high risk of mortality and
in monitoring the effectiveness of interventions targeting
various life-threatening pathologies (437).
ECM Posttranslation Changes
Posttranslational modifications of the ECM serve a vital
role in regulating cellular behavior and maintaining tissue
homeostasis, which may alter the physical and biological
properties of the ECM and modulate interactions with cells
and other ECM components (16).
Chemical changes.
As the ECM undergoes constant remodeling, various chemical changes occur that can affect its mechanical properties
and functions. Among these changes, posttranslational modifications, such as nonenzymatic glycation, autofluorescence, and tail tendon break time (TTBT), have been studied
as potential biomarkers of ECM aging and as targets for therapeutic interventions to mitigate age-related diseases.
Nonenzymatic glycation. Nonenzymatic glycation, also
known as the Maillard reaction, is a chemical process in
which reducing sugars or a-dicarbonyl metabolites bind to
amino or thiol groups of proteins, lipids, and nucleotides
(438). This reaction leads to the formation of advanced glycation end products (AGEs), which have been implicated in the
development of various age-related diseases, including cardiovascular disease, kidney disease, and neurodegenerative
diseases (439–441). However, it remains challenging to analyze glycation-related products, such as reactive carbonyl
species, Schiff bases, Amadori compounds, and AGEs,
because of their high heterogeneity (442).
To measure the levels of AGEs in tissues, scientists have
developed various techniques, such as fluorescence-based
assays, which can detect the accumulation of AGEs in proteins and other biomolecules. Other methods include
enzyme-linked immunosorbent assay (ELISA) and highperformance liquid chromatography (HPLC), which measure the accumulation of specific AGEs. Autophagy, an
evolutionarily conserved cellular process that maintains
cellular homeostasis through the degradation and recycling of intracellular components, has been shown to be
activated by AGEs (443, 444). This observation has led to
the postulation that targeting autophagic pathways may
represent a novel therapeutic strategy for the mitigation of
AGE-induced morbidity and mortality (445).
Autofluorescence. Autofluorescence is a phenomenon in
which cells or tissues emit fluorescence without the need for
external staining or labeling (446). This occurs because certain molecules within cells, such as proteins, nucleic acids,
and lipids, naturally fluoresce when excited by light of a specific wavelength. Autofluorescence can be used as a biomarker for various cellular processes, including aging, and it
can be measured with fluorescence microscopy or spectroscopy (447).
One possible method to use autofluorescence as an ECM
aging clock is to measure the autofluorescence of ECM molecules such as collagen and elastin. These molecules are the
major structural components of the ECM, and they play a
key role in maintaining the mechanical properties and functions of the ECM. As cells and tissues age, the ECM undergoes changes in its composition and structure, which can
affect its mechanical properties and functions (446, 448).
For example, aging can cause the ECM to stiffen, which can
impair mechanotransduction and lead to stem cell exhaustion (449). The C. elegans cuticle, which is mainly composed
of collagens, becomes autofluorescent and stiffens with
aging, and longevity interventions are able to slow or prevent
this age-related stiffening of the ECM in vivo (48, 450, 451).
To measure the autofluorescence of ECM molecules as an
aging clock, tissue samples can be obtained from a range of
ages and processed for fluorescence microscopy or spectroscopy. The samples can be excited with light of a specific
wavelength, and the emitted fluorescence can be measured
and compared between different ages. This can provide a
quantitative measure of the changes in autofluorescence
that occur with aging, and it can be used as an indicator of
the age-related changes in the ECM. This method can provide valuable insights into the mechanisms of ECM aging
and can potentially be used to develop interventions that
can slow or reverse these changes (446, 452).
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C107
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
Tail tendon break time. The tail tendon break time
(TTBT) assay is a technique that is used to measure the mechanical properties of the ECM in tissues, such as stiffness
and tensile strength (453–456), and may be used as a diagnostic tool of aging (453–456). A tail tendon of a rodent, such
as a mouse or a rat, consists mainly of collagen and is isolated and attached to a weight and then emersed in a urea
water bath. The time it takes for the tendon to rupture
because of the attached weight correlates with the crosslinking of the collagen in the tendon, which increases with age
(455).
TTBT has been used to study a wide range of conditions
that affect tendon health, including aging, diabetes, and various forms of arthritis. It has also been used to evaluate the
effectiveness of potential therapeutic interventions, such as
exercise and various drugs (453–456), and has been found to
be a reliable and valid method for measuring tendon mechanical properties in rodent models. However, it is important to note that the results of the assay may not directly
translate to humans, as the anatomy and physiology of tendons in rodents are not exactly the same as in humans.
Additionally, the assay is destructive, so it cannot be used to
test the same tendon multiple times (454, 455).
Functional changes.
Posttranslational modifications of the ECM can affect its mechanical properties and its ability to maintain its functions.
One such modification is elasticity, which is a sensitive measure of ECM aging and can potentially serve as a biomarker
for age-related diseases such as cancer. Another modification is glycosylation, which plays a key role in various physiological and pathological conditions, including T-cell
immunity and age-related diseases. Understanding the role
of these modifications in ECM aging and age-related diseases
can provide valuable insights into the development of interventions to slow or reverse these changes.
Elasticity. Elasticity is a mechanical property of materials that describes their ability to stretch and deform without
breaking. In the context of the ECM, elasticity is a crucial
property that affects the mechanical properties and functions of the ECM (26, 457, 458). As the ECM ages, it undergoes changes in its composition and structure, which can
affect its elasticity and its ability to maintain its proper functions (26, 457).
Elasticity can be used as a potential biomarker of ECM
aging, as it is a sensitive and specific measure of the changes
that occur in the ECM with aging (448, 459, 460). To measure elasticity as a diagnostic tool, tissue samples can be
obtained from a range of ages and processed for mechanical
testing. For instance, by probing the surface of cancer tissue
biopsies with indentation-type atomic force microscopy, the
stiffer tumor tissue can be detected (461). The stiffness of a
tumor can serve as a significant factor in predicting a poor
prognosis (462–464). Although tumor stiffness is not the
only measure of cancer prognosis, research has demonstrated a strong correlation between the presence of tumor
stiffness and worse outcomes in terms of cancer-specific
overall survival (OS) and recurrence-free survival (RFS), with
both univariate [P < 0.001; hazard ratio 2.821; 95% confidence interval (CI) 1.643–4.842] and multivariate (P = 0.005;
hazard ratio 2.208; 95% CI 1.272–3.833) analysis supporting
C108
this association (462–465). Furthermore, samples can be
subjected to mechanical stress, and the resulting deformations can be measured by various methods, such as laser diffraction or atomic force microscopy (460). This can provide
a quantitative measure of the changes in elasticity that occur
with aging, and it can be used as an indicator of age-related
changes in the ECM. This method can provide valuable
insights into the mechanisms of ECM aging and can potentially be used to develop interventions that can slow or
reverse these changes.
Glycosylation. Glycosylation, the process of adding sugars to proteins or lipids to form glycans, is a fundamental aspect of cellular function and plays a key role in various
physiological and pathological conditions (466–469). In particular, N-glycosylation, covalently attaches N-glycans to
proteins at asparagine (Asn) residues (470). N-glycan levels
in the blood can be measured with techniques such as mass
spectrometry or lectin-based assays, and changes in these
levels over time may provide insight into the aging process
and the risk of age-related diseases (468, 469, 471). Zaytseva
et al. (469) observed the relationship between changes in the
N-glycosylation of immunoglobulin G (IgG) and 12 different
diseases, including autoimmune, inflammatory, neurodegenerative, cardiovascular diseases, and some types of cancer,
by utilizing Mendelian randomization to examine the causal
relationship between IgG N-glycosylation and disease risks.
It was found that there is indeed a positive causal effect of
systemic lupus erythematosus (SLE) on the abundance of
certain N-glycans in the total IgG N-glycome. These findings
suggest that this particular IgG glycosylation trait could
potentially be used as a biomarker for SLE (469).
Aging is also associated with impaired T-cell immunity,
which can increase the risk of mortality from infectious
diseases such as influenza, Salmonella, and COVID-19
(471). Studies have shown that the branching of asparagine-linked glycans increases with age in females more
than males, in naive T cells more than memory T cells, and
in CD4 þ T cells more than CD8 þ T cells. Interleukin-7 (IL7) signaling increased in old female mice and triggered
increased branching, which was also increased by the ratelimiting metabolite N-acetylglucosamine. Reversing elevated branching can rejuvenate T-cell function and reduce
the severity of Salmonella infection in old female mice.
These findings suggest that IL-7 initially benefits immunity through naive T-cell maintenance but inhibits naive
T-cell function through increased branching in combination with age-dependent increases in N-acetylglucosamine
(471, 472). These studies and others highlight the importance of understanding the role of glycosylation in aging
and age-related diseases, as well as the potential for using
N-glycosylation as a biomarker and developing interventions to improve T-cell immunity in aging individuals.
Collagen crosslinking. Collagen crosslinking refers to
the covalent bonding between collagen molecules that
results in the strengthening and stabilization of the ECM.
This process is mediated by the enzymatic activity of lysyl
oxidase, which catalyzes the formation of inter- and intramolecular cross-links between lysine and hydroxylysine residues in collagen (473).
The quantification of collagen cross-links, such as hydroxylysylpyridinoline (HP) and lysylpyridinoline (LP), provides
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
valuable insights into the structural integrity of the ECM, as
well as the rate of ECM turnover, which can serve as a noninvasive diagnostic marker for osteoarthritis and as a therapeutic target for the development of disease-modifying
osteoarthritis drugs (DMOADs) (473).
Enzyme-linked immunosorbent assay (ELISA) is a widely
used assay for the quantification of collagen cross-links such
as HP and LP. ELISA is known for its high sensitivity and
specificity, making it an ideal assay for the detection of lowconcentration cross-links. However, ELISA has certain limitations, such as the need for highly purified samples, which
can be difficult to obtain, and the potential for interferences
from other components in the sample matrix (473, 474).
High-performance liquid chromatography with fluorescence detection (HPLC-FLD) is an alternative assay that has
been used for the quantification of collagen cross-links.
HPLC-FLD is known for its high resolution and sensitivity,
and it is capable of separating and quantifying multiple
cross-links in a single run. However, HPLC-FLD requires a
high level of expertise and sophisticated instrumentation
(473, 475).
Liquid chromatography-mass spectrometry (LC-MS) is a
highly sensitive and selective assay that is capable of quantifying collagen cross-links at very low concentrations. LC-MS
has the advantage of being able to detect multiple crosslinks, and it can also be used to identify unknown crosslinks. However, LC-MS is complex and also requires a high
level of expertise and sophisticated instrumentation, as well
as time-consuming and labor-intensive sample preparation
(473, 476).
Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) is an assay that is commonly used to
assess the size and integrity of collagen molecules. SDSPAGE can also be used to detect cross-links in collagen,
and it is relatively simple and inexpensive. However, SDSPAGE is less sensitive than other assays, and it can be difficult to quantitatively differentiate between different
cross-links (473, 477).
Quantification of collagen cross-links via different assays
has its own advantages and disadvantages, but they can provide valuable insight into different pathologies, such as cartilage health and the development of osteoarthritis. It is
therefore important to consider the specific needs of a study
and the resources available.
significant morbidity and mortality. Methods for detecting a
pathological ECM are discussed below.
Amorphous collagen deposition.
Pathological ECM Deposition
Amorphous collagen deposition, also known as diffuse collagenous fibrosis, is a pathological condition characterized
by the disorganized accumulation of collagen within ECM in
the absence of a defined fibrotic lesion (415, 473).
Masson’s trichrome staining. Masson’s trichrome
staining is a technique used to differentiate and analyze collagen in various types of tissue, including muscle, heart, liver,
and kidney tissue, as well as tumors in these organs (478,
479). It works by using a red acidic stain to label acidophilic
components such as collagen fibers and then treating the tissue with phosphotungstic acid. The less permeable components, including collagen fibers, will retain the red stain,
while the more permeable components will lose the red stain
and be replaced with a light green dye (418, 478). The distinctive ability of this technique to differentiate collagen fibers
has been widely utilized in numerous studies to investigate
changes in collagen fibers that occur during development,
aging, and disease, pathologies such as muscular dystrophy,
infarct, cirrhosis, and glomerular fibrosis (480–483).
Electron microscopy. Amorphous collagen deposition
appears as electron-dense aggregates or granules in the
extracellular space, often adjacent to collagen fibrils (484).
The high-resolution capabilities of electron microscopy (EM)
render it a crucial technique for investigating the molecular
mechanisms underlying the phenomenon of amorphous collagen deposition. Its capacity to identify and characterize
the aforementioned deposition positions further underscores the importance of EM in advancing our understanding of this complex process (485–490). The use of anionic
polypeptides has been studied in the context of collagen
mineralization in vitro (486). Additionally, the use of highresolution EM techniques, such as cryogenic transmission
electron microscopy (cryo-TEM), allows the identification of
the specific nature of the collagen deposits (491). This is particularly relevant in the context of biomimetic collagen mineralization and bone tissue engineering (489, 492, 493). As
such, full atomistic molecular dynamics simulation of a
high-resolution high-molecular-weight polyacrylic acid
(HPAA)-collagen structure has also been conducted to
investigate the effect of the presence of large polyelectrolyte molecules along the surface of collagen fibril on the
movement and infiltration of ions (489).
Pathological ECM deposition is a process in which excessive quantities of ECM components, such as collagen, are deposited in a tissue, causing fibrosis and malfunction. This
process is mediated by the activation and proliferation of
fibroblasts, which create and deposit ECM proteins. The
underlying causes of pathological ECM deposition are complicated and multifaceted, involving signaling pathway dysregulation and aberrant activation of inflammatory and
immune cells. Furthermore, genetic and epigenetic variables
play an important role in the regulation of pathogenic ECM
deposition. This degenerative process is a characteristic of
many chronic illnesses, including fibrotic disorders, cancer,
and cardiovascular disease, and it leads to the gradual loss of
organ function as well as the development of clinically
Second harmonic generation (SHG) imaging is a nonlinear
optical technique that utilizes the intrinsic noncentrosymmetry of certain biomolecules to generate light at twice the
frequency of the incident light (418, 494, 495). This process
is efficient at high light intensities, such as those produced
by a pulsed laser, and is selective with respect to the focal
plane in a manner analogous to two-photon fluorescence excitation (494). Collagen, a highly organized and crystalline
structural protein that is abundant in mammals, is the most
potent source of SHG in animal tissue (418, 494, 495).
Therefore, SHG imaging can be utilized as a powerful tool for
visualizing collagen distribution in various biological samples, including cirrhotic liver, normal and carious teeth, and
surgically repaired tendons (494). One notable advantage of
Second harmonic generation imaging of collagens.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C109
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
SHG imaging is that it does not result in any energy loss,
leading to the long-term stability of the generated SHG signal. Additionally, the shorter wavelength of the SHG signal
allows for easy separation from multiple fluorescent probes,
making it a valuable technique for multichannel imaging
applications (418, 494).
Van Gulick et al. (495) used polarized Raman spectroscopy
and SHG to examine age-related changes in the molecular
organization of type I collagen in rat tail tendons. The
results showed that certain bands related to proline and
hydroxyproline were sensitive to polarization and agerelated changes, while others were not. The anisotropy
degree of the Raman bands increased from adult to old collagen, indicating a higher alignment of collagen fibers
with the fascicle backbone axis in old tendons and a higher
straightness of collagen fibers (495). These findings were
confirmed with SHG, which showed a more homogeneous
spatial distribution of collagen fiber alignment in old tendons (495). These results demonstrate the usefulness of
SHG in studying age-related changes in collagen structure
and the impact of these changes on the mechanical and
physiological properties of organs (495).
Hydroxyproline. Hydroxyproline is an amino acid
derived from the posttranslational modification of collagen
via prolyl-4-hydroxylase in the endoplasmic reticulum (496,
497). The hydroxyproline at the third position of the characteristic (glycine–another amino acid–hydroxyproline) repeats
is essential for stabilizing the collagen triple helix (53).
Ehrlich’s reagent is a chemical reagent made with pdimethylaminobenzaldehyde (DMAB) mixed with concentrated hydrochloric acid. Primarily used to detect the presence of indoles such as tryptamines and serotonin (498,
499), Ehrlich’s reagent can also detect hydroxyproline in
biological matrixes such as tissue and bodily fluids, with
the intensity of the color produced directly proportional to
the amount of hydroxyproline present in the sample. The
reaction between Ehrlich’s reagent and hydroxyproline
can be quantified by measuring the absorbance of the colored product with a spectrophotometer at a specific wavelength between 540 and 570 nm (500). The absorbance can
be then used to calculate the concentration of hydroxyproline in the sample by using a standard curve, which may
serve as an accurate predictor of normal tissue or pathological fibrosis and an indicator of collagen metabolism
(52, 500).
High-performance liquid chromatography (HPLC) is an
analytical technique that utilizes a stationary phase packed
in a column through which a sample is passed and separated
based on the differential interactions of its components with
the stationary phase. It is frequently employed to quantitatively assess the concentration of hydroxyproline in biological matrixes such as tissues and bodily fluids (501). This
technique is also commonly used to evaluate collagen synthesis (502) and degradation in a variety of tissues, including
skin, bone, and cartilage (503).
Modified Sircol collagen assay. Since collagen is a
prominent metabolic indicator, accurate determination of
its content is essential for the creation of reproducible scaffolds and the investigation of tissue pathologies (504). There
are numerous ways to determine collagen concentration,
including radiolabeling, chromatography, and calorimetry,
C110
but none of them has proved optimal (505). The Sircol collagen assay (SCA) is based on the amino acid binding characteristic of Sirius Red (SR F3B; CI 35782), an anionic dye
having sulfonated acid side chain groups that react with the
side chain groups of basic amino acids (506, 507), and is frequently employed as a selective histochemical stain for collagen in normal and diseased tissue (508–510). The SCA has
almost exclusively been adopted as the quickest and most
straightforward colorimetric method for determining collagen concentration in complex protein solutions (52, 505).
Despite its prevalence, the SCA also dramatically overestimated collagen content by a factor anywhere from 3- to 24fold, particularly grievous results arising in cell culture systems and tissue hydrates because of noncollagenous protein
interference (505, 511). However, a purification technique
including a pepsin digestion and column ultrafiltration step
to accurately determine collagen content in various samples
that eliminates these interfering proteins has been added to
the current SCA approach to resolve this problem (505). The
modified Sircol collagen assay can therefore offer insight
into the aging process and the efficacy of therapies through
an accurate evaluation of collagen levels.
De novo synthesis of collagens.
De novo collagen synthesis refers to the process by which
cells produce collagen from scratch rather than simply
remodeling or repairing existing collagen fibers (512–516). De
novo collagen synthesis is important for tissue development
and repair, and it is regulated by various signaling pathways
and transcription factors (512–517).
SILAC (stable isotope labeling by amino acids in cell culture) is a mass spectrometry-based technique that uses labeled amino acids to distinguish newly synthesized proteins
from preexisting ones, providing insights into protein synthesis, turnover, and degradation processes (518). The in vivo
pulsed SILAC labeling methodology was used to quantify
new protein incorporation into various tissues in mice (519)
and C. elegans (48, 520, 521), uncovering age-related molecular changes and identifying potential pathways implicated in
disease. This approach provides dynamic protein data at a
tissue level, enabling a deeper understanding of the underlying mechanisms involved in the regulation of protein synthesis (518).
Dendra is a photoconvertible fluorescent protein that enables visualization of de novo-synthesized collagen in living
cells by being incorporated as a tag in collagen molecules,
fluorescing when newly synthesized and permitting realtime tracking of collagen fiber formation and distribution
(522, 523). A study in C. elegans revealed a thickening of the
basement membrane via the addition of newly synthesized
EMB-9 collagen to old collagens, offering insight into mechanisms of collagen synthesis and the role it plays in various
biological processes (48).
Also, Oostendorp et al. developed a novel method for
detecting and localizing newly synthesized collagen in ECM
tissues by employing the use of an antibody called GD3A12,
which specifically binds to dermatan sulfate, a component of
proteoglycans that are associated with collagen (524, 525).
Critically, this method is universal, meaning it can be utilized to detect collagen produced by cells from any species
and can be applied to any source of collagenous biomaterials
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
(525). It is designed to address the challenge of identifying
collagen that has been produced de novo by cells in the
presence of purified collagenous biomaterials, which is a
common obstacle in the evaluation of tissue-engineered
constructs and in the study of tumor dissemination (525,
526). The method has potential applications in regenerative medicine and cancer biology, including the investigation of tumor cell migration in collagen gels (525). It is
worth noting that dermatan sulfate may be associated
with other structures, such as elastin- and fibrillin-containing microfibrils, but it does not seem to be associated
with elastic fibers in the tissues studied (525, 527).
Utilization of this method can potentially be an efficient,
cost-effective, and straightforward technique for evaluating the presence and orientation of de novo-synthesized
collagen fibrils in collagen-based biomaterials (525).
Abnormalities in collagen synthesis and metabolism can
lead to various diseases and conditions, such as fibrosis,
scarring, and cancer. Understanding the mechanisms and
regulation of de novo collagen synthesis is an active and
exciting area of research in many fields, including developmental biology, tissue engineering, and medicine.
FUTURE GOALS: POTENTIAL
INTERVENTIONS
It is important to understand the dreams and goals of the
people working directly on ECM interventions. There seems
to be a mix of moon shot goals and practical small steps
being worked on to combat the age-related decline of the
ECM.
Control ECM Turnover throughout the Body—Moon
Shot
The ECM is a dynamic and essential component of the
human body, responsible for providing structural support
and regulating cellular behavior. However, the turnover of
the ECM that is not attached to cells is a relatively slow
process, and the accumulation of modified cross-links
with age can result in a decline in the overall quality and
quantity of the ECM. To mitigate this decline and preserve
the youthful integrity of the ECM, strategies aimed at
increasing the rate of ECM turnover and reducing the formation of age-related modifications may prove beneficial.
These strategies may include the modulation of specific
enzymes involved in ECM turnover as well as the implementation of lifestyle factors known to promote overall
cellular health and vitality. Ultimately, the successful
implementation of such strategies may hold significant
implications for the maintenance of tissue integrity and
the prevention of age-related pathologies.
Produce Young ECM—Moon Shot
The Yamanaka factors, also referred to as OSKM (Oct4,
Sox2, Klf4, and c-Myc), have the potential to significantly
impact our understanding of aging and the possibilities of
regenerative medicine. These transcription factors have
been shown to reprogram adult cells into a pluripotent
state, similar to that of embryonic stem cells (528). Recent
studies have demonstrated the ability of partial chemical
reprogramming to improve key drivers of aging, such as
genomic instability and epigenetic alterations (529–532).
Additionally, a study by Yang et al. (532) has shown that
aging can be driven in both directions by manipulating
the epigenome using three of the four Yamanaka factors.
Although the mechanisms by which the Yamanaka factors induce the production of ECM proteins are not yet
fully understood, it is believed that they may modulate
the expression of genes involved in ECM production,
such as collagens, fibronectin, and laminin (516, 533–
535). If this reprogramming process can be verified in a
living organism, it will be a major breakthrough in the
field of regenerative medicine and could redefine the
concept of aging.
ECM Aging Clock
The field of aging research has undergone significant
advancements in recent years, as evidenced by the accessible descriptions and measurements of aging (1, 536). These
advancements have led to the proliferation of statistical and
machine-learning models, which have been employed to
track age-related changes at the omic and phenotypic levels
(537–542).
One key application of these models is the use of the socalled “aging clock” to evaluate the effectiveness of interventions aimed at extending life span, rather than waiting for
the entire lifetime of study subjects (543, 544). Additionally,
the availability of online services for DNA methylation analysis, such as DNA methylation age and the epigenetic clock
(545) and ClockBase (546), has further facilitated the use of
aging clocks in research. Furthermore, consumer services
have also started to offer age predictions based on these
aging clocks.
However, despite these advances, there currently exists no
aging clock that directly tracks changes within the ECM.
This is particularly noteworthy given that a recent causal
investigation of DNA methylation clocks has revealed
changes in the ECM to be a major contributor to age-related
adaptations (547). To evaluate interventions targeting the
ECM, researchers must instead rely on individually tailored
markers (548) or surrogate markers, such as differential
expression of collagen genes (549) or methylomics of ECM
organization promoters (550).
Recent efforts have been made to index ECM aging,
including the use of modern computational methods. For
example, Lehallier et al. (551) used levels of ECM-associated
proteins in blood plasma to predict age with relative accuracy. McCabe et al. (448) compiled changes in skin ECM
composition during aging and photoaging using quantitative
and global proteomics, single harmonic generation, and twophoton autofluorescence imaging. Li et al. (552) used tandem
mass spectrometry alongside bioinformatic analysis to produce skin proteome profiles and matrisome features at different ages.
Given the unique influences of ECM aging on important
medical practices such as transplantation (553), the development of a standardized ECM aging clock would have a profound impact on medicine. However, until such a model is
developed numerous interesting scientific questions remain
unanswered, including which features of the ECM would be
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C111
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
most represented, whether they would be tissue specific, and
how closely they would correlate, precede, or follow other
clock changes. Additionally, it remains to be seen whether
these features could be slowed or reversed.
Glycation Crosslinking Breakers—Direct Treatment
Advanced glycation end products (AGEs) are a group of
compounds that contribute to the loss of tissue integrity due
to crosslinking with age. The most abundant cross-links in
this group are glucosepane and pentosidine (554, 555).
Although it has yet to be clinically proven, it is hypothesized
that the accumulation of these cross-links may be a causal
factor in the development of various age-related pathologies.
It is believed that their selective breakdown could potentially
improve a wide range of ECM-related aging pathologies,
including skin atrophy, arterial stiffening, and systemic fibrosis. However, other AGEs may matter more if it turns out
that disturbed cell-ECM interactions [e.g., an AGE binding to
a receptor for advanced glycation end products (RAGE)-receptor] are a major driving force behind clinical pathologies
within current life spans (556).
Despite the potential importance of glucosepane and
pentosidine as AGE cross-links, it remains to be fully elucidated whether they are major causes of aging. This is
because no experiments have been conducted in humans
to demonstrate the selective removal of these cross-links.
Glucosepane is by far the most abundant cross-link
observed in aged human tissue and is >100 times more
abundant than any other cross-link; subsequently, it is of
interest to biotech companies. The formation of glucosepane is a well-known chemical reaction, the Amadori reaction, where the amino acid lysine reacts with D-glucose,
forming an Amadori product that degrades and gets bound
to arginine, forming the final product (555, 557, 558).
Several biotech companies have been founded on the
assumption that cross-links can be broken down and that
such an approach would also restore elasticity as well as the
functionality of tissue. One such technology is a glucosepane-breaking enzyme for multiple age-related indications.
Conditions where an effect from removing glucosepane
could be anticipated to be observed would include elasticity
of blood vessels and, subsequently, hypertension, pulmonary fibrosis, and the gradual decrease in the respiratory
capacity that occurs with age. It would also be important for
skin and cartilage aging, as well as presbyopia, which is secondary to the stiffening of the eye lens that occurs almost
universally with age (559).
Specific challenges for clinical translation into humans
include finding an enzyme or small molecule that can break
down these cross-links without damaging other vital molecules in the ECM. An enzymatic approach has been pursued
so far since enzymes are better at catalyzing the breakdown
of substances. However, extracellular enzymes must also be
able to penetrate well into the ECM and have a sufficient
energy source (such as enough ATP to perform their actions)
to effectively break down glucosepane and pentosidine. The
need for further research in this area is evident, as the selective removal of AGEs such as glucosepane and pentosidine
could potentially lead to the development of novel therapeutic interventions for age-related pathologies.
C112
Small Molecules for ECM Homeostasis—Direct
Treatment
Small molecules have been found to be a potential solution for treating diseases and aging by controlling the activity of proteases and the formation of bonds within the ECM.
Proteases, such as MMPs, play a crucial role in ECM remodeling and are connected to various illnesses, including cancer
and chronic inflammation (560). The regulation of protease
activity and bond formation within the ECM through small
molecules can be achieved by either stimulating or suppressing protease activity or promoting ECM bond formation
(561). This approach has the potential to target a wide range
of diseases and aging-related conditions, including angiogenesis, tissue regeneration, and epidermal aging.
There is growing evidence that MMPs significantly influence angiogenesis beyond ECM remodeling. MMPs have
been found to release ECM-bound angiogenic factors, detach
pericytes from blood vessels, and degrade endothelial cellcell adhesions (562, 563). For example, a study on mice
infected with Mycobacterium tuberculosis (Mtb) showed that
treatment with Marimastat increased the number of blood
vessels covered by pericytes while the total blood vessel
number remained unchanged. Inhibiting MMP activity
resulted in an increase in pericyte-covered blood vessel
numbers and seemed to stabilize the integrity of the infected
lung tissue. Additionally, increased delivery and/or retention of Marimastat was observed in treated mice, leading to
improved drug efficacy (564).
Natural compounds, such as flavonoids and polyphenols,
have also been found to control ECM homeostasis and longevity through different mechanisms (3). These compounds
include melatonin, tea polyphenols, grape seed polyphenols,
honokiol (Magnolia sp.), quercetin, sulforaphane, apocynin,
aloe vera, turmeric (curcumin), silymarin (milk thistle), ginseng (Panax ginseng), algae, propolis, and others (565).
Notably, Ramachandran et al. (566) observed that both honokiol and modified citrus pectin induced dose-dependent
antioxidant activity in a synergistic manner, inhibiting NFκB and TNF-a. Together, these mechanisms work to maintain the structural integrity of the ECM, promoting tissue
regeneration and preventing epidermal aging (565).
Control Downstream Signaling
YAP1 (Yes-associated protein 1) is a transcriptional coactivator that plays a key role in the regulation of cell proliferation, differentiation, and survival, as well as responding to
age-related stiffening of the ECM (567–571). In humans, the
stiffening of the ECM with age can lead to dysregulation of
YAP1 and cellular senescence (572, 573). This can cause hindrance in the conversion of mechanical stimuli into cellular
responses and alter stem cell differentiation (574). However,
YAP1 activation has also been shown to promote tissue
regeneration, but its hyperactivation has been observed in
human cancers (575), indicating a need for careful regulation
in regenerative medicine (576).
The decline in YAP/TAZ activity during physiological
aging in stromal cells has been linked to accelerated aging,
whereas preserving YAP function has been seen to rejuvenate old cells and suppress senescence-associated inflammation (577). The GMP-AMP synthase (cGAS)-stimulator of
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
interferon genes (STING) pathway, a crucial component of
innate immunity, is regulated by YAP/TAZ (transcriptional
coactivator with PDZ-binding motif) through the preservation of nuclear envelope integrity and regulation of lamin B1
and Actin-related protein 2. Maintaining YAP/TAZ mechanosignaling and inhibiting STING could be potential strategies for promoting healthy aging, as the control of cGASSTING signaling by YAP/TAZ drives aging (577). In C. elegans, the yap-1 gene is essential for longevity and maintains
collagen homeostasis (48).
CONCLUDING REMARKS
In summary, the ECM, a labyrinthine network of interwoven proteins that exist exterior to cells, serves as a quintessential element of the human body, imbued with the
responsibility of maintaining the structural integrity and
equilibrium of tissues. However, as the sands of time inevitably flow, the ECM undergoes a series of metamorphoses that
can result in an array of age-related maladies and eventual
mortality. Despite its paramount significance, the aging of
the ECM remains a relatively uncharted territory within the
realm of geroscience. This review has delved into the fundamental concepts of ECM integrity and the intricacies of the
challenges and pathologies that arise with increasing age,
expounding upon the diagnostic techniques employed to
detect ECM dysfunction and proposing various approaches
to preserve the homeostasis of the ECM. Furthermore, the
ECM tech tree has been formulated as a means of visually
organizing and conceptualizing potential research sequences, in the hope of fostering the advancement of interventions to rejuvenate the ECM, potentially yielding novel
pharmaceuticals and therapeutics to enhance health during
the aging process.
ACKNOWLEDGMENTS
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
We thank the Ewald Lab for critical reading and comments.
16.
GRANTS
This work was supported by funding from the Swiss National
Science Foundation Funding from the Swiss National Science
Foundation SNF P3 Project 190072 to C.Y.E.
DISCLOSURES
C.Y.E. is a cofounder and shareholder of Avea Life AG and is
on the Scientific Advisory Board of Maximon AG and Galyan Bio,
Inc. A.K. was the Research Director for the Foresight Institute at
the time of writing. None of the other authors has any conflicts of
interest, financial or otherwise, to disclose.
17.
18.
19.
20.
21.
AUTHOR CONTRIBUTIONS
J.Y.C.P. and C.Y.E. conceived and designed research; A.K. prepared figures; J.Y.C.P., A.K., V.B., B.W.E., A.F., and C.Y.E. drafted
manuscript; J.Y.C.P., V.B., B.W.E., A.F., and C.Y.E. edited and revised manuscript; J.Y.C.P., A.K., V.B., B.W.E., A.F., and C.Y.E.
approved final version of manuscript.
22.
pez-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G.
Lo
The hallmarks of aging. Cell 153: 1194–1217, 2013. doi:10.1016/j.
cell.2013.05.039.
pez-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G.
Lo
Hallmarks of aging: an expanding universe. Cell 186: 243–278,
2023. doi:10.1016/j.cell.2022.11.001.
Statzer C, Park JY, Ewald CY. Extracellular matrix dynamics as an
emerging yet understudied hallmark of aging and longevity. Aging
Dis 14: 670–693, 2023. doi:10.14336/AD.2022.1116.
Fedintsev A, Moskalev A. Stochastic non-enzymatic modification of
long-lived macromolecules—a missing hallmark of aging. Ageing
Res Rev 62: 101097, 2020. doi:10.1016/j.arr.2020.101097.
€ hler M, Duca
Gorisse L, Pietrement C, Vuiblet V, Schmelzer CE, Ko
s P, Jaisson S, Gillery P. Protein carbamylation is
L, Debelle L, Forne
a hallmark of aging. Proc Natl Acad Sci USA 113: 1191–1196, 2016.
doi:10.1073/pnas.1517096113.
Ewald CY. The matrisome during aging and longevity: a systemslevel approach toward defining matreotypes promoting healthy
aging. Gerontology 66: 266–274, 2020. doi:10.1159/000504295.
Ewald CY, Landis JN, Abate JP, Murphy CT, Blackwell TK. Dauerindependent insulin/IGF-1-signalling implicates collagen remodelling
in longevity. Nature 519: 97–101, 2015. doi:10.1038/nature14021.
Lampi MC, Reinhart-King CA. Targeting extracellular matrix stiffness to attenuate disease: from molecular mechanisms to clinical
trials. Sci Transl Med 10: eaao0475, 2018. doi:10.1126/scitranslmed.
aao0475.
Vidović T, Ewald CY. Longevity-promoting pathways and transcription factors respond to and control extracellular matrix dynamics during aging and disease. Front Aging 3: 935220, 2022.
doi:10.3389/fragi.2022.935220.
Kenyon C. Sydney Brenner (1927–2019). Science 364: 638–638,
2019. doi:10.1126/science.aax8563.
€rvela
€inen H, Sainio A, Koulu M, Wight TN, Penttinen R.
Ja
Extracellular matrix molecules: potential targets in pharmacotherapy.
Pharmacol Rev 61: 198–223, 2009. doi:10.1124/pr.109.001289.
Schaefer L, Schaefer RM. Proteoglycans: from structural compounds to signaling molecules. Cell Tissue Res 339: 237–246, 2010.
doi:10.1007/s00441-009-0821-y.
Hynes RO. The extracellular matrix: not just pretty fibrils. Science
326: 1216–1219, 2009. doi:10.1126/science.1176009.
Frantz C, Stewart KM, Weaver VM. The extracellular matrix at a
glance. J Cell Sci 123: 4195–4200, 2010. doi:10.1242/jcs.023820.
Bonnans C, Chou J, Werb Z. Remodelling the extracellular matrix in
development and disease. Nat Rev Mol Cell Biol 15: 786–801, 2014.
doi:10.1038/nrm3904.
Lu P, Takai K, Weaver VM, Werb Z. Extracellular matrix degradation
and remodeling in development and disease. Cold Spring Harb
Perspect Biol 3: a005058, 2011. doi:10.1101/cshperspect.a005058.
Birch HL. Extracellular matrix and ageing. Subcell Biochem 90: 169–
190, 2018. doi:10.1007/978-981-13-2835-0_7.
Stearns-Reider KM, D’Amore A, Beezhold K, Rothrauff B, Cavalli L,
Wagner WR, Vorp DA, Tsamis A, Shinde S, Zhang C, Barchowsky
A, Rando TA, Tuan RS, Ambrosio F. Aging of the skeletal muscle
extracellular matrix drives a stem cell fibrogenic conversion. Aging
Cell 16: 518–528, 2017. doi:10.1111/acel.12578.
Brunton-O’Sullivan MM, Holley AS, Hally KE, Kristono GA, Harding
SA, Larsen PD. A combined biomarker approach for characterising
extracellular matrix profiles in acute myocardial infarction. Sci Rep 11:
12705, 2021. doi:10.1038/s41598-021-92108-z.
łtorak A, Derkacz A,
Komosinska-Vassev K, Kału_zna A, Jura-Po
Olczyk K. Circulating profile of ECM-related proteins as diagnostic
markers in inflammatory bowel diseases. J Clin Med 11: 5618, 2022.
doi:10.3390/jcm11195618.
Andriani F, Landoni E, Mensah M, Facchinetti F, Miceli R,
Tagliabue E, Giussani M, Callari M, De Cecco L, Colombo MP, Roz
L, Pastorino U, Sozzi G. Diagnostic role of circulating extracellular
matrix-related proteins in non-small cell lung cancer. BMC Cancer
18: 899, 2018. doi:10.1186/s12885-018-4772-0.
Petersen EV, Chudakova DA, Skorova EY, Anikin V, Reshetov IV,
Mynbaev OA. The extracellular matrix-derived biomarkers for diagnosis, prognosis, and personalized therapy of malignant tumors.
Front Oncol 10: 575569, 2020. doi:10.3389/fonc.2020.575569.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C113
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
23. Huang J, Zhang L, Wan D, Zhou L, Zheng S, Lin S. Extracellular matrix and its therapeutic potential for cancer treatment. Signal
Transduct Target Ther 6: 1–24, 2021. doi:10.1038/s41392-02100544-0.
24. Harisi R, Jeney A. Extracellular matrix as target for antitumor therapy. Onco Targets Ther 8: 1387–1398, 2015. doi:10.2147/OTT.
S48883.
25. Walraven M, Hinz B. Therapeutic approaches to control tissue
repair and fibrosis: extracellular matrix as a game changer. Matrix
Biol 71-72: 205–224, 2018. doi:10.1016/j.matbio.2018.02.020.
26. Gattazzo F, Urciuolo A, Bonaldo P. Extracellular matrix: a dynamic
microenvironment for stem cell niche. Biochim Biophys Acta 1840:
2506–2519, 2014. doi:10.1016/j.bbagen.2014.01.010.
27. Novoseletskaya E, Grigorieva O, Nimiritsky P, Basalova N,
Eremichev R, Milovskaya I, Kulebyakin K, Kulebyakina M,
Rodionov S, Omelyanenko N, Efimenko A. Mesenchymal stromal
cell-produced components of extracellular matrix potentiate multipotent stem cell response to differentiation stimuli. Front Cell Dev
Biol 8: 555378, 2020. doi:10.3389/fcell.2020.555378.
28. Leavitt T, Hu MS, Marshall CD, Barnes LA, Lorenz HP, Longaker
MT. Scarless wound healing: finding the right cells and signals. Cell
Tissue Res 365: 483–493, 2016. doi:10.1007/s00441-016-2424-8.
29. Assis-Ribas T, Forni MF, Winnischofer SM, Sogayar MC,
Trombetta-Lima M. Extracellular matrix dynamics during mesenchymal stem cells differentiation. Dev Biol 437: 63–74, 2018. doi:10.1016/
j.ydbio.2018.03.002.
ault BM, Phinney DG, Hare JM,
30. Pittenger MF, Discher DE, Pe
Caplan AI. Mesenchymal stem cell perspective: cell biology to clinical progress. NPJ Regen Med 4: 15, 2019. doi:10.1038/s41536-0190083-6.
31. Costa-Almeida R, Calejo I, Gomes ME. Mesenchymal stem cells
empowering tendon regenerative therapies. Int J Mol Sci 20: 3002,
2019. doi:10.3390/ijms20123002.
32. Tuan RS, Boland G, Tuli R. Adult mesenchymal stem cells and cellbased tissue engineering. Arthritis Res Ther 5: 32–45, 2003.
doi:10.1186/ar614.
33. Han Y, Yang J, Fang J, Zhou Y, Candi E, Wang J, Hua D, Shao C,
Shi Y. The secretion profile of mesenchymal stem cells and potential
applications in treating human diseases. Signal Transduct Target
Ther 7: 92, 2022. doi:10.1038/s41392-022-00932-0.
34. Plikus MV, Wang X, Sinha S, Forte E, Thompson SM, Herzog EL,
Driskell RR, Rosenthal N, Biernaskie J, Horsley V. Fibroblasts: origins, definitions, and functions in health and disease. Cell 184:
3852–3872, 2021. doi:10.1016/j.cell.2021.06.024.
35. Souders CA, Bowers SL, Baudino TA. Cardiac fibroblast: the
renaissance cell. Circ Res 105: 1164–1176, 2009. doi:10.1161/
CIRCRESAHA.109.209809.
36. Kendall RT, Feghali-Bostwick CA. Fibroblasts in fibrosis: novel roles
and mediators. Front Pharmacol 5: 123, 2014. doi:10.3389/fphar.
2014.00123.
37. D’Urso M, Kurniawan NA. Mechanical and physical regulation of
fibroblast–myofibroblast transition: from cellular mechanoresponse
to tissue pathology. Front Bioeng Biotechnol 8: 609653, 2020.
doi:10.3389/fbioe.2020.609653.
38. Wynn TA, Ramalingam TR. Mechanisms of fibrosis: therapeutic
translation for fibrotic disease. Nat Med 18: 1028–1040, 2012.
doi:10.1038/nm.2807.
39. Naba A, Clauser KR, Lamar JM, Carr SA, Hynes RO. Extracellular
matrix signatures of human mammary carcinoma identify novel metastasis promoters. eLife 3: e01308, 2014. doi:10.7554/eLife.01308.
40. Naba A, Clauser KR, Hoersch S, Liu H, Carr SA, Hynes RO. The
matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol Cell Proteomics
11: M111.014647, 2012. doi:10.1074/mcp.M111.014647.
41. Hynes RO, Naba A. Overview of the matrisome—an inventory of
extracellular matrix constituents and functions. Cold Spring Harb
Perspect Biol 4: a004903, 2012. doi:10.1101/cshperspect.a004903.
42. Sacher F, Feregrino C, Tschopp P, Ewald CY. Extracellular matrix
gene expression signatures as cell type and cell state identifiers.
Matrix Biol Plus 10: 100069, 2021. doi:10.1016/j.mbplus.2021.100069.
43. Hynes RO. Integrins: bidirectional, allosteric signaling machines.
Cell 110: 673–687, 2002. doi:10.1016/s0092-8674(02)00971-6.
C114
44. Schwartz MA. Integrins and extracellular matrix in mechanotransduction. Cold Spring Harb Perspect Biol 2: a005066, 2010.
doi:10.1101/cshperspect.a005066.
45. Bourboulia D, Stetler-Stevenson WG. Matrix metalloproteinases
(MMPs) and tissue inhibitors of metalloproteinases (TIMPs): positive
and negative regulators in tumor cell adhesion. Semin Cancer Biol
20: 161–168, 2010. doi:10.1016/j.semcancer.2010.05.002.
46. Visse R, Nagase H. Matrix metalloproteinases and tissue inhibitors
of metalloproteinases. Circ Res 92: 827–839, 2003. doi:10.1161/01.
RES.0000070112.80711.3D.
47. Brew K, Nagase H. The tissue inhibitors of metalloproteinases
(TIMPs): an ancient family with structural and functional diversity.
Biochim Biophys Acta 1803: 55–71, 2010. doi:10.1016/j.bbamcr.
2010.01.003.
48. Teuscher AC, Statzer C, Goyala A, Domenig SA, Schoen I, Hess M.
Mechanotransduction coordinates extracellular matrix protein homeostasis promoting longevity in C. elegans (Preprint). bioRxiv
2022.08.30.505802, 2022. doi:10.1101/2022.08.30.505802.
49. Lofaro FD, Cisterna B, Lacavalla MA, Boschi F, Malatesta M,
Quaglino D, Zancanaro C, Boraldi F. Age-related changes in the
matrisome of the mouse skeletal muscle. Int J Mol Sci 22: 10564,
2021. doi:10.3390/ijms221910564.
50. Statzer C, Jongsma E, Liu SX, Dakhovnik A, Wandrey F,
Mozharovskyi P, Z€
ulli F, Ewald CY. Youthful and age-related
matreotypes predict drugs promoting longevity. Aging Cell 20:
e13441, 2021.
51. Di Lullo GA, Sweeney SM, Korkko J, Ala-Kokko L, San Antonio JD.
Mapping the ligand-binding sites and disease-associated mutations
on the most abundant protein in the human, type I collagen. J Biol
Chem 277: 4223–4231, 2002. doi:10.1074/jbc.M110709200.
52. Tarnutzer K, Siva Sankar D, Dengjel J, Ewald CY. Collagen constitutes about 12% in females and 17% in males of the total protein in
mice. Sci Rep 13: 4490, 2023. doi:10.1038/s41598-023-31566-z.
53. Ricard-Blum S. The collagen family. Cold Spring Harb Perspect Biol
3: a004978, 2011. doi:10.1101/cshperspect.a004978.
54. Mouw JK, Ou G, Weaver VM. Extracellular matrix assembly: a multiscale deconstruction. Nat Rev Mol Cell Biol 15: 771–785, 2014.
doi:10.1038/nrm3902.
55. Rozario T, DeSimone DW. The extracellular matrix in development
and morphogenesis: a dynamic view. Dev Biol 341: 126–140, 2010.
doi:10.1016/j.ydbio.2009.10.026.
56. Adorno-Cruz V, Liu H. Regulation and functions of integrin a2 in cell
adhesion and disease. Genes Dis 6: 16–24, 2019. doi:10.1016/j.
gendis.2018.12.003.
57. Albelda SM, Buck CA. Integrins and other cell adhesion molecules.
FASEB J 4: 2868–2880, 1990.
58. Barczyk M, Carracedo S, Gullberg D. Integrins. Cell Tissue Res 339:
269–280, 2010. doi:10.1007/s00441-009-0834-6.
59. Park EJ, Myint PK, Ito A, Appiah MG, Darkwah S, Kawamoto E,
Shimaoka M. Integrin-ligand interactions in inflammation, cancer,
and metabolic disease: insights into the multifaceted roles of an
emerging ligand irisin. Front Cell Dev Biol 8: 588066, 2020.
doi:10.3389/fcell.2020.588066.
60. Desgrosellier JS, Cheresh DA. Integrins in cancer: biological implications and therapeutic opportunities. Nat Rev Cancer 10: 9–22,
2010. doi:10.1038/nrc2748.
61. Schnittert J, Bansal R, Storm G, Prakash J. Integrins in wound healing, fibrosis and tumor stroma: High potential targets for therapeutics and drug delivery. Adv Drug Deliv Rev 129: 37–53, 2018.
doi:10.1016/j.addr.2018.01.020.
62. Lowin T, Straub RH. Integrins and their ligands in rheumatoid arthritis. Arthritis Res Ther 13: 244, 2011. doi:10.1186/ar3464.
63. Zhao H, Kitaura H, Sands MS, Ross FP, Teitelbaum SL, Novack DV.
Critical role of beta3 integrin in experimental postmenopausal osteoporosis. J Bone Miner Res 20: 2116–2123, 2005. doi:10.1359/JBMR.
050724.
64. Stange R, Kronenberg D, Timmen M, Everding J, Hidding H, Eckes
B, Hansen U, Holtkamp M, Karst U, Pap T, Raschke MJ. Agerelated bone deterioration is diminished by disrupted collagen sensing in integrin a2b1 deficient mice. Bone 56: 48–54, 2013.
doi:10.1016/j.bone.2013.05.003.
65. Henderson NC, Arnold TD, Katamura Y, Giacomini MM, Rodriguez
JD, McCarty JH, Pellicoro A, Raschperger E, Betsholtz C, Ruminski
PG, Griggs DW, Prinsen MJ, Maher JJ, Iredale JP, Lacy-Hulbert A,
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
Adams RH, Sheppard D. Targeting of av integrin identifies a core
molecular pathway that regulates fibrosis in several organs. Nat
Med 19: 1617–1624, 2013. doi:10.1038/nm.3282.
Schaffner F, Ray AM, Dontenwill M. Integrin a5b1, the fibronectin
receptor, as a pertinent therapeutic target in solid tumors. Cancers
(Basel) 5: 27–47, 2013. doi:10.3390/cancers5010027.
van der Flier A, Badu-Nkansah K, Whittaker CA, Crowley D,
Bronson RT, Lacy-Hulbert A, Hynes RO. Endothelial a5 and av
integrins cooperate in remodeling of the vasculature during development. Development 137: 2439–2449, 2010. doi:10.1242/
dev.049551.
Borghesan M, O’Loghlen A. Integrins in senescence and aging. Cell
Cycle 16: 909–910, 2017. doi:10.1080/15384101.2017.1316573.
Leitinger B. Discoidin domain receptor functions in physiological
and pathological conditions. Int Rev Cell Mol Biol 310: 39–87, 2014.
doi:10.1016/B978-0-12-800180-6.00002-5.
Shrivastava A, Radziejewski C, Campbell E, Kovac L, McGlynn M,
Ryan TE, Davis S, Goldfarb MP, Glass DJ, Lemke G, Yancopoulos
GD. An orphan receptor tyrosine kinase family whose members
serve as nonintegrin collagen receptors. Mol Cell 1: 25–34, 1997.
doi:10.1016/s1097-2765(00)80004-0.
Vogel W, Gish GD, Alves F, Pawson T. The discoidin domain receptor tyrosine kinases are activated by collagen. Mol Cell 1: 13–23,
1997. doi:10.1016/s1097-2765(00)80003-9.
Benjamin MM, Khalil RA. Matrix metalloproteinase inhibitors as
investigative tools in the pathogenesis and management of vascular
disease. Exp Suppl 103: 209–279, 2012. doi:10.1007/978-3-03480364-9_7.
Cabral-Pacheco GA, Garza-Veloz I, Castruita-De la Rosa C,
~ a JM, Perez-Romero BA, Guerrero-Rodriguez JF,
Ramirez-Acun
Martinez-Avila N, Martinez-Fierro ML. The roles of matrix metalloproteinases and their inhibitors in human diseases. Int J Mol Sci 21:
9739, 2020. doi:10.3390/ijms21249739.
Olaso E, Labrador JP, Wang L, Ikeda K, Eng FJ, Klein R, Lovett DH,
Lin HC, Friedman SL. Discoidin domain receptor 2 regulates fibroblast proliferation and migration through the extracellular matrix in
association with transcriptional activation of matrix metalloproteinase-2. J Biol Chem 277: 3606–3613, 2002. doi:10.1074/jbc.
M107571200.
Reel B, Korkmaz CG, Arun MZ, Yildirim G, Ogut D, Kaymak A,
Micili SC, Ergur BU. The regulation of matrix metalloproteinase
expression and the role of discoidin domain receptor 1/2 signalling
in zoledronate-treated PC3 cells. J Cancer 6: 1020–1029, 2015.
doi:10.7150/jca.12733.
Borza CM, Pozzi A. Discoidin domain receptors in disease. Matrix
Biol J Biol 34: 185–192, 2014. doi:10.1016/j.matbio.2013.12.002.
Hebron M, Peyton M, Liu X, Gao X, Wang R, Lonskaya I, Moussa
CE. Discoidin domain receptor inhibition reduces neuropathology
and attenuates inflammation in neurodegeneration models. J
Neuroimmunol 311: 1–9, 2017. doi:10.1016/j.jneuroim.2017.07.009.
Ford CE, Lau SK, Zhu CQ, Andersson T, Tsao MS, Vogel WF.
Expression and mutation analysis of the discoidin domain receptors
1 and 2 in non-small cell lung carcinoma. Br J Cancer 96: 808–814,
2007. doi:10.1038/sj.bjc.6603614.
Valiathan RR, Marco M, Leitinger B, Kleer CG, Fridman R. Discoidin
domain receptor tyrosine kinases: new players in cancer progression. Cancer Metastasis Rev 31: 295–321, 2012. doi:10.1007/s10555012-9346-z.
Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy
VA, Aladinskaya AV, Terentiev VA, Polykovskiy DA, Kuznetsov
MD, Asadulaev A, Volkov Y, Zholus A, Shayakhmetov RR, Zhebrak
A, Minaeva LI, Zagribelnyy BA, Lee LH, Soll R, Madge D, Xing L,
Guo T, Aspuru-Guzik A. Deep learning enables rapid identification
of potent DDR1 kinase inhibitors. Nat Biotechnol 37: 1038–1040,
2019. doi:10.1038/s41587-019-0224-x.
Baaijens F, Bouten C, Driessen N. Modeling collagen remodeling. J
Biomech 43: 166–175, 2010. doi:10.1016/j.jbiomech.2009.09.022.
pez-Otín C. The role of maFreitas-Rodríguez S, Folgueras AR, Lo
trix metalloproteinases in aging: Tissue remodeling and beyond.
Biochim Biophys Acta Mol Cell Res 1864: 2015–2025, 2017.
doi:10.1016/j.bbamcr.2017.05.007.
Stamenkovic I. Extracellular matrix remodelling: the role of matrix
metalloproteinases. J Pathol 200: 448–464, 2003. doi:10.1002/
path.1400.
ska-Trypuć A, Matejczyk M, Rosochacki S. Matrix metallo84. Jabłon
proteinases (MMPs), the main extracellular matrix (ECM) enzymes in
collagen degradation, as a target for anticancer drugs. J Enzyme
Inhib Med Chem 31: 177–183, 2016. doi:10.3109/14756366.2016.
1161620.
85. Xiao Q, Ge G. Lysyl oxidase, extracellular matrix remodeling and
cancer metastasis. Cancer Microenviron 5: 261–273, 2012.
doi:10.1007/s12307-012-0105-z.
X, Ruiz-Trillo I, Rodriguez-Pascual F. Origin and evolu86. Grau-Bove
tion of lysyl oxidases. Sci Rep 5: 10568, 2015. doi:10.1038/
srep10568.
87. Zhao W, Yang A, Chen W, Wang P, Liu T, Cong M, Xu A, Yan X, Jia
J, You H. Inhibition of lysyl oxidase-like 1 (LOXL1) expression arrests
liver fibrosis progression in cirrhosis by reducing elastin crosslinking.
Biochim Biophys Acta Mol Basis Dis 1864: 1129–1137, 2018.
doi:10.1016/j.bbadis.2018.01.019.
88. Remus EW, O’Donnell RE, Rafferty K, Weiss D, Joseph G, Csiszar
K, Fong SF, Taylor WR. The role of lysyl oxidase family members in
the stabilization of abdominal aortic aneurysms. Am J Physiol Heart
Circ Physiol 303: H1067–H1075, 2012. doi:10.1152/ajpheart.00217.
2012.
89. Johnston KA, Lopez KM. Lysyl oxidase in cancer inhibition and metastasis. Cancer Lett 417: 174–181, 2018. doi:10.1016/j.canlet.2018.
01.006.
~ o MS, Varona
90. Martínez-Revelles S, García-Redondo AB, Avendan
~ o A, Touyz RM, MartínezS, Palao T, Orriols M, Roque FR, Fortun
González J, Salaices M, Rodríguez C, Briones AM. Lysyl oxidase
induces vascular oxidative stress and contributes to arterial stiffness
and abnormal elastin structure in hypertension: role of p38MAPK.
Antioxid Redox Signal 27: 379–397, 2017 [Erratum in Antioxid Redox
Signal 27:1520, 2017]. doi:10.1089/ars.2016.6642.
91. Gilad GM, Kagan HM, Gilad VH. Evidence for increased lysyl oxidase, the extracellular matrix-forming enzyme, in Alzheimer’s disease brain. Neurosci Lett 376: 210–214, 2005. doi:10.1016/j.
neulet.2004.11.054.
92. Yang N, Cao DF, Yin XX, Zhou HH, Mao XY. Lysyl oxidases: emerging biomarkers and therapeutic targets for various diseases. Biomed
Pharmacother 131: 110791, 2020. doi:10.1016/j.biopha.2020.110791.
93. Aronoff MR, Hiebert P, Hentzen NB, Werner S, Wennemers H.
Imaging and targeting LOX-mediated tissue remodeling with a reactive collagen peptide. Nat Chem Biol 17: 865–871, 2021. doi:10.1038/
s41589-021-00830-6.
94. Griffin M, Casadio R, Bergamini CM. Transglutaminases: nature’s
biological glues. Biochem J 368: 377–396, 2002. doi:10.1042/
BJ20021234.
95. Marinucci L, Balloni S, Fettucciari K, Bodo M, Talesa VN,
Antognelli C. Nicotine induces apoptosis in human osteoblasts via a
novel mechanism driven by H2O2 and entailing Glyoxalase 1-dependent MG-H1 accumulation leading to TG2-mediated NF-kB desensitization: implication for smokers-related osteoporosis. Free Radic Biol
Med 117: 6–17, 2018. doi:10.1016/j.freeradbiomed.2018.01.017.
96. Collighan RJ, Griffin M. Transglutaminase 2 cross-linking of matrix
proteins: biological significance and medical applications. Amino
Acids 36: 659–670, 2009. doi:10.1007/s00726-008-0190-y.
97. Rosenthal AK, Gohr CM, Mitton E, Monnier V, Burner T. Advanced
glycation end products increase transglutaminase activity in primary
porcine tenocytes. J Investig Med 57: 460–466, 2009. doi:10.2310/
JIM.0b013e3181954ac6.
98. Jones RA, Kotsakis P, Johnson TS, Chau DY, Ali S, Melino G,
Griffin M. Matrix changes induced by transglutaminase 2 lead to inhibition of angiogenesis and tumor growth. Cell Death Differ 13:
1442–1453, 2006. doi:10.1038/sj.cdd.4401816.
99. Penumatsa KC, Falcão-Pires I, Leite S, Leite-Moreira A, Bhedi CD,
Nasirova S, Ma J, Sutliff RL, Fanburg BL. Increased transglutaminase 2 expression and activity in rodent models of obesity/metabolic syndrome and aging. Front Physiol 11: 560019, 2020.
doi:10.3389/fphys.2020.560019.
100. Tatsukawa H, Hitomi K. Role of transglutaminase 2 in cell
death, survival, and fibrosis. Cells 10: 1842, 2021. doi:10.3390/
cells10071842.
101. Armstrong DM, Sikka G, Armstrong AD, Saad KR, de Freitas WR,
Berkowitz DE, Fagundes DJ, Santhanam L, Taha MO. Knockdown
of transglutaminase-2 prevents early age-induced vascular changes
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C115
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
C116
in mice. Acta Cir Bras 33: 991–999, 2018. doi:10.1590/s0102865020180110000006.
Brandes RP, Fleming I, Busse R. Endothelial aging. Cardiovasc Res
66: 286–294, 2005. doi:10.1016/j.cardiores.2004.12.027.
Toda N. Age-related changes in endothelial function and blood flow
regulation. Pharmacol Ther 133: 159–176, 2012. doi:10.1016/j.
pharmthera.2011.10.004.
€ sicke N, Sallouh M, Prior LM, Job A, Weberskirch R, Faissner A.
Bro
Extracellular matrix glycoprotein-derived synthetic peptides differentially modulate glioma and sarcoma cell migration. Cell Mol
Neurobiol 35: 741–753, 2015. doi:10.1007/s10571-015-0170-1.
Yue B. Biology of the extracellular matrix: an overview. J Glaucoma
23: S20–S23, 2014. doi:10.1097/IJG.0000000000000108.
Reichheld SE, Muiznieks LD, Lu R, Sharpe S, Keeley FW.
Sequence variants of human tropoelastin affecting assembly,
structural characteristics and functional properties of polymeric
elastin in health and disease. Matrix Biol 84: 68–80, 2019.
doi:10.1016/j.matbio.2019.06.010.
Yeo GC, Keeley FW, Weiss AS. Coacervation of tropoelastin. Adv
Colloid Interface Sci 167: 94–103, 2011. doi:10.1016/j.cis.2010.10.003.
Cocciolone AJ, Hawes JZ, Staiculescu MC, Johnson EO, Murshed
M, Wagenseil JE. Elastin, arterial mechanics, and cardiovascular disease. Am J Physiol Heart Circ Physiol 315: H189–H205, 2018.
doi:10.1152/ajpheart.00087.2018.
Panwar P, Hedtke T, Heinz A, Andrault PM, Hoehenwarter W,
€ mme D. Expression of elastolytic
Granville DJ, Schmelzer CE, Bro
cathepsins in human skin and their involvement in age-dependent
elastin degradation. Biochim Biophys Acta Gen Subj 1864: 129544,
2020. doi:10.1016/j.bbagen.2020.129544.
Singh P, Carraher C, Schwarzbauer JE. Assembly of fibronectin
extracellular matrix. Annu Rev Cell Dev Biol 26: 397–419, 2010.
doi:10.1146/annurev-cellbio-100109-104020.
Vogel V. Unraveling the mechanobiology of extracellular matrix.
Annu Rev Physiol 80: 353–387, 2018. doi:10.1146/annurev-physiol021317-121312.
Pankov R, Yamada KM. Fibronectin at a glance. J Cell Sci 115: 3861–
3863, 2002. doi:10.1242/jcs.00059.
Vakonakis I, Staunton D, Rooney LM, Campbell ID. Interdomain
association in fibronectin: insight into cryptic sites and fibrillogenesis. EMBO J 26: 2575–2583, 2007. doi:10.1038/sj.emboj.7601694.
White E, Baralle F, Muro A. New insights into form and function of fibronectin splice variants. J Pathol 216: 1–14, 2008. doi:10.1002/
path.2388.
Lepelletier FX, Mann DM, Robinson AC, Pinteaux E, Boutin H.
Early changes in extracellular matrix in Alzheimer’s disease.
Neuropathol Appl Neurobiol 43: 167–182, 2017. doi:10.1111/
nan.12295.
Kumazaki T, Robetorye RS, Robetorye SC, Smith JR. Fibronectin
expression increases during in vitro cellular senescence: correlation
with increased cell area. Exp Cell Res 195: 13–19, 1991. doi:10.1016/
0014-4827(91)90494-f.
Krizhanovsky V, Yon M, Dickins RA, Hearn S, Simon J, Miething C,
Yee H, Zender L, Lowe SW. Senescence of activated stellate cells
limits liver fibrosis. Cell 134: 657–667, 2008. doi:10.1016/j.cell.2008.
06.049.
€ssler R, Hohenester E. Laminin: the crux of basement
Sasaki T, Fa
membrane assembly. J Cell Biol 164: 959–963, 2004. doi:10.1083/
jcb.200401058.
Arriazu E, de Galarreta MR, Cubero FJ, Varela-Rey M, de Obanos
MP, Leung TM, Lopategi A, Benedicto A, Abraham-Enachescu I,
Nieto N. Extracellular matrix and liver disease. Antioxid Redox
Signal 21: 1078–1097, 2014. doi:10.1089/ars.2013.5697.
Timpl R, Rohde H, Robey PG, Rennard SI, Foidart JM, Martin GR.
Laminin—a glycoprotein from basement membranes. J Biol Chem
254: 9933–9937, 1979.
Yurchenco PD, Patton BL. Developmental and pathogenic mechanisms of basement membrane assembly. Curr Pharm Des 15: 1277–
1294, 2009. doi:10.2174/138161209787846766.
Labat-Robert J. Age-dependent remodeling of connective tissue:
role of fibronectin and laminin. Pathol Biol (Paris) 51: 563–568,
2003. doi:10.1016/j.patbio.2003.09.006.
Kern P, Regnault F, Robert L. Biochemical and ultrastructural study
of human diabetic conjunctiva. Biomedicine 24: 32–39, 1976.
124. Chiquet-Ehrismann R, Chiquet M. Tenascins: regulation and putative functions during pathological stress. J Pathol 200: 488–499,
2003. doi:10.1002/path.1415.
125. Midwood KS, Orend G. The role of tenascin-C in tissue injury and tumorigenesis. J Cell Commun Signal 3: 287–310, 2009. doi:10.1007/
s12079-009-0075-1.
126. Bhattacharyya S, Wang W, Morales-Nebreda L, Feng G, Wu M,
Zhou X, Lafyatis R, Lee J, Hinchcliff M, Feghali-Bostwick C, Lakota
K, Budinger GR, Raparia K, Tamaki Z, Varga J. Tenascin-C drives
persistence of organ fibrosis. Nat Commun 7: 11703, 2016.
doi:10.1038/ncomms11703.
127. Giblin SP, Midwood KS. Tenascin-C: form versus function. Cell Adh
Migr 9: 48–82, 2015. doi:10.4161/19336918.2014.987587.
128. Lowy CM, Oskarsson T. Tenascin C in metastasis: a view from the
invasive front. Cell Adh Migr 9: 112–124, 2015. doi:10.1080/19336918.
2015.1008331.
129. Yoshida T, Akatsuka T, Imanaka-Yoshida K. Tenascin-C and integrins in cancer. Cell Adh Migr 9: 96–104, 2015. doi:10.1080/19336918.
2015.1008332.
130. Nong Y, Wu D, Lin Y, Zhang Y, Bai L, Tang H. Tenascin-C expression is associated with poor prognosis in hepatocellular carcinoma
(HCC) patients and the inflammatory cytokine TNF-a-induced TNC
expression promotes migration in HCC cells. Am J Cancer Res 5:
782–791, 2015.
131. Jang DG, Sim HJ, Song EK, Kwon T, Park TJ. Extracellular matrixes
and neuroinflammation. BMB Rep 53: 491–499, 2020. doi:10.5483/
BMBRep.2020.53.10.156.
€ m C, Haraldsen G. Tenascins in fibrotic
132. Kasprzycka M, Hammarstro
disorders—from bench to bedside. Cell Adh Migr 9: 83–89, 2015.
doi:10.4161/19336918.2014.994901.
133. Choi YE, Song MJ, Hara M, Imanaka-Yoshida K, Lee DH, Chung
JH, Lee ST. Effects of tenascin C on the integrity of extracellular matrix and skin aging. Int J Mol Sci 21: 8693, 2020. doi:10.3390/
ijms21228693.
134. Tucker RP, Degen M. Revisiting the tenascins: exploitable as cancer
targets? Front Oncol 12: 908247, 2022. doi:10.3389/fonc.2022.
908247.
135. Mi Z, Halfter W, Abrahamson EE, Klunk WE, Mathis CA, Mufson EJ,
Ikonomovic MD. Tenascin-C is associated with cored amyloid-b plaques in Alzheimer disease and pathology burdened cognitively normal elderly. J Neuropathol Exp Neurol 75: 868–876, 2016 [Erratum
in J Neuropathol Exp Neurol 75: 1190, 2016]. doi:10.1093/jnen/
nlw062.
136. Petersen JW, Douglas JY. Tenascin-X, collagen, and Ehlers-Danlos
syndrome: Tenascin-X gene defects can protect against adverse
cardiovascular events. Med Hypotheses 81: 443–447, 2013.
doi:10.1016/j.mehy.2013.06.005.
137. Uiterwaal CS, Grobbee DE, Sakkers RJ, Helders PJ, Bank RA,
Engelbert RH. A relation between blood pressure and stiffness of
joints and skin. Epidemiology 14: 223–227, 2003. doi:10.1097/01.
EDE.0000040327.31385.9B.
138. Danielson KG, Baribault H, Holmes DF, Graham H, Kadler KE,
Iozzo RV. Targeted disruption of decorin leads to abnormal collagen
fibril morphology and skin fragility. J Cell Biol 136: 729–743, 1997.
doi:10.1083/jcb.136.3.729.
139. Maccarana M, Kalamajski S, Kongsgaard M, Magnusson SP,
€ m A. Dermatan sulfate epimerase 1-deficient
Oldberg Å, Malmstro
mice have reduced content and changed distribution of iduronic
acids in dermatan sulfate and an altered collagen structure in skin.
Mol Cell Biol 29: 5517–5528, 2009. doi:10.1128/MCB.00430-09.
140. Batzios SP, Zafeiriou DI, Papakonstantinou E. Extracellular matrix
components: an intricate network of possible biomarkers for lysosomal storage disorders? FEBS Lett 587: 1258–1267, 2013. doi:10.1016/
j.febslet.2013.02.035.
N, Vilageliu L, Grinberg D, Canals I. Sanfilippo syndrome:
141. Beneto
molecular basis, disease models and therapeutic approaches. Int J
Mol Sci 21: 7819, 2020. doi:10.3390/ijms21217819.
142. Byers PH, Murray ML. Ehlers–Danlos syndrome: a showcase of conditions that lead to understanding matrix biology. Matrix Biol 33: 10–
15, 2014. doi:10.1016/j.matbio.2013.07.005.
143. de la Fuente-Alonso A, Toral M, Alfayate A, Ruiz-Rodríguez MJ,
n-Kulichenko E, Teixido-Tura G, Martínez-Martínez S,
Bonzo
ndez-Olivares MJ, Lo
pez-Maderuelo D, González-Valde
s I,
Me
~ o-Mosquera L, De
Garcia-Izquierdo E, Mingo S, Martín CE, Muin
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
144.
145.
146.
147.
148.
149.
150.
151.
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
Backer J, Nistal JF, Forteza A, Evangelista A, Vázquez J,
Campanero MR, Redondo JM. Aortic disease in Marfan syndrome is
caused by overactivation of sGC-PRKG signaling by NO. Nat
Commun 12: 2628, 2021. doi:10.1038/s41467-021-22933-3.
Iozzo RV, Schaefer L. Proteoglycan form and function: a comprehensive nomenclature of proteoglycans. Matrix Biol J Biol 42: 11–55,
2015. doi:10.1016/j.matbio.2015.02.003.
Walimbe T, Panitch A. Proteoglycans in biomedicine: resurgence of
an underexploited class of ECM molecules. Front Pharmacol 10:
1661, 2020. doi:10.3389/fphar.2019.01661.
Koch CD, Lee CM, Apte SS. Aggrecan in cardiovascular development and disease. J Histochem Cytochem 68: 777–795, 2020.
doi:10.1369/0022155420952902.
Chandran PL, Horkay F. Aggrecan, an unusual polyelectrolyte:
review of solution behavior and physiological implications. Acta
Biomater 8: 3–12, 2012. doi:10.1016/j.actbio.2011.08.011.
Fox AJ, Bedi A, Rodeo SA. The basic science of articular cartilage.
Sports Health 1: 461–468, 2009. doi:10.1177/1941738109350438.
Ng L, Grodzinsky AJ, Patwari P, Sandy J, Plaas A, Ortiz C.
Individual cartilage aggrecan macromolecules and their constituent
glycosaminoglycans visualized via atomic force microscopy. J Struct
Biol 143: 242–257, 2003. doi:10.1016/j.jsb.2003.08.006.
Han B, Li Q, Wang C, Patel P, Adams SM, Doyran B, Nia HT,
Oftadeh R, Zhou S, Li CY, Liu XS, Lu XL, Enomoto-Iwamoto M, Qin
L, Mauck RL, Iozzo RV, Birk DE, Han L. Decorin regulates the aggrecan network integrity and biomechanical functions of cartilage
extracellular matrix. ACS Nano 13: 11320–11333, 2019. doi:10.1021/
acsnano.9b04477.
Hayes AJ, Melrose J. Aggrecan, the primary weight-bearing cartilage proteoglycan, has context-dependent, cell-directive properties
in embryonic development and neurogenesis: aggrecan glycan side
chain modifications convey interactive biodiversity. Biomolecules 10:
1244. 2020. doi:10.3390/biom10091244.
Merline R, Schaefer RM, Schaefer L. The matricellular functions of
small leucine-rich proteoglycans (SLRPs). J Cell Commun Signal, 3:
323–335, 2009. doi:10.1007/s12079-009-0066-2.
Pietraszek-Gremplewicz K, Karamanou K, Niang A, Dauchez M,
zillon S. Small leucine-rich proBelloy N, Maquart FX, Baud S, Bre
teoglycans and matrix metalloproteinase-14: key partners? Matrix
Biol 75-76: 271–285, 2019. doi:10.1016/j.matbio.2017.12.006.
Yu WH, Woessner JF. Heparan sulfate proteoglycans as extracellular docking molecules for matrilysin (matrix metalloproteinase 7).
J Biol Chem, 275: 4183–4191, 2000. doi:10.1074/jbc.275.6.4183.
Kelly T, Mueller SC, Yeh Y, Chen WT. Invadopodia promote proteolysis of a wide variety of extracellular matrix proteins. J Cell Physiol
158: 299–308, 1994. doi:10.1002/jcp.1041580212.
€cker U, Nybakken K, Perrimon N. Heparan sulphate proteoglyHa
cans: the sweet side of development. Nat Rev Mol Cell Biol 6: 530–
541, 2005. doi:10.1038/nrm1681.
Nybakken K, Perrimon N. Heparan sulfate proteoglycan modulation
of developmental signaling in Drosophila. Biochim Biophys Acta
1573: 280–291, 2002. doi:10.1016/s0304-4165(02)00395-1.
Yuan Y, Kadiyala CS, Ching TT, Hakimi P, Saha S, Xu H, Yuan C,
Mullangi V, Wang L, Fivenson E, Hanson RW, Ewing R, Hsu AL,
Miyagi M, Feng Z. Enhanced energy metabolism contributes to the
extended life span of calorie-restricted Caenorhabditis elegans.
J Biol Chem 287: 31414–31426, 2012. doi:10.1074/jbc.M112.377275.
Curran SP, Ruvkun G. Lifespan regulation by evolutionarily conserved genes essential for viability. PLoS Genet 3: e56, 2007.
doi:10.1371/journal.pgen.0030056.
Fawcett JW, Kwok JC. Proteoglycan sulphation in the function of
the mature central nervous system. Front Integr Neurosci 16:
895493, 2022. doi:10.3389/fnint.2022.895493.
Uto K, Yoshizawa S, Aoki C, Nishikawa T, Oda H. Inhibition of
extracellular matrix integrity attenuates the early phase of aortic
medial calcification in a rodent model. Atherosclerosis 319: 10–20,
2021. doi:10.1016/j.atherosclerosis.2020.12.015.
Budoff MJ, Nasir K, Katz R, Takasu J, Carr JJ, Wong ND, Allison M,
Lima JAC, Detrano R, Blumenthal RS, Kronmal R. Thoracic aortic
calcification and coronary heart disease events: the Multi-Ethnic
Study of Atherosclerosis (MESA). Atherosclerosis 215: 196–202,
2011. doi:10.1016/j.atherosclerosis.2010.11.017.
163. Ngai D, Lino M, Bendeck MP. Cell-matrix interactions and matricrine
signaling in the pathogenesis of vascular calcification. Front
Cardiovasc Med 5: 174, 2018. doi:10.3389/fcvm.2018.00174.
164. Wang K, Meng X, Guo Z. Elastin structure, synthesis, regulatory
mechanism and relationship with cardiovascular diseases. Front Cell
Dev Biol 9: 596702, 2021. doi:10.3389/fcell.2021.596702.
165. Bobryshev YV. Calcification of elastic fibers in human atherosclerotic plaque. Atherosclerosis 180: 293–303, 2005. doi:10.1016/j.
atherosclerosis.2005.01.024.
166. Khavandgar Z, Roman H, Li J, Lee S, Vali H, Brinckmann J, Davis
EC, Murshed M. Elastin haploinsufficiency impedes the progression
of arterial calcification in MGP-deficient mice. J Bone Miner Res 29:
327–337, 2014. doi:10.1002/jbmr.2039.
167. Sorokin L. The impact of the extracellular matrix on inflammation.
Nat Rev Immunol 10: 712–723, 2010. doi:10.1038/nri2852.
168. Agrawal SM, Lau L, Yong VW. MMPs in the central nervous system:
where the good guys go bad. Semin Cell Dev Biol 19: 42–51, 2008.
doi:10.1016/j.semcdb.2007.06.003.
169. Nourshargh S, Hordijk PL, Sixt M. Breaching multiple barriers: leukocyte motility through venular walls and the interstitium. Nat Rev
Mol Cell Biol 11: 366–378, 2010. doi:10.1038/nrm2889.
170. Metzemaekers M, Van Damme J, Mortier A, Proost P. Regulation
of chemokine activity—a focus on the role of dipeptidyl peptidase
IV/CD26. Front Immunol 7: 483, 2016. doi:10.3389/fimmu.2016.
00483.
171. Yong VW. Metalloproteinases: mediators of pathology and regeneration in the CNS. Nat Rev Neurosci 6: 931–944, 2005. doi:10.1038/
nrn1807.
172. Malemud CJ. Matrix metalloproteinases (MMPs) in health and disease: an overview. Front Biosci 11: 1696–1701, 2006. doi:10.2741/
1915.
173. Sugahara KN, Murai T, Nishinakamura H, Kawashima H, Saya H,
Miyasaka M. Hyaluronan oligosaccharides induce CD44 cleavage
and promote cell migration in CD44-expressing tumor cells. J Biol
Chem 278: 32259–32265, 2003. doi:10.1074/jbc.M300347200.
174. Gaudet AD, Popovich PG. Extracellular matrix regulation of inflammation in the healthy and injured spinal cord. Exp Neurol 258: 24–
34, 2014. doi:10.1016/j.expneurol.2013.11.020.
175. Brule S, Charnaux N, Sutton A, Ledoux D, Chaigneau T, Saffar L,
Gattegno L. The shedding of syndecan-4 and syndecan-1 from
HeLa cells and human primary macrophages is accelerated by
SDF-1/CXCL12 and mediated by the matrix metalloproteinase-9.
Glycobiology 16: 488–501, 2006. doi:10.1093/glycob/cwj098.
176. Watson RE, Gibbs NK, Griffiths CE, Sherratt MJ. Damage to skin
extracellular matrix induced by UV exposure. Antioxid Redox Signal
21: 1063–1077, 2014. doi:10.1089/ars.2013.5653.
177. Chauss D, Freiwald T, McGregor R, Yan B, Wang L, Nova-Lamperti
E, Kumar D, Zhang Z, Teague H, West EE, Vannella KM, RamosBenitez MJ, Bibby J, Kelly A, Malik A, Freeman AF, Schwartz DM,
Portilla D, Chertow DS, John S, Lavender P, Kemper C, Lombardi
G, Mehta NN, Cooper N, Lionakis MS, Laurence A, Kazemian M,
Afzali B. Autocrine vitamin D signaling switches off pro-inflammatory
programs of TH1 cells. Nat Immunol, 23: 62–74, 2022. doi:10.1038/
s41590-021-01080-3.
178. Minton K. Vitamin D shuts down T cell-mediated inflammation. Nat
Rev Immunol 22: 1, 2022. doi:10.1038/s41577-021-00663-3.
179. Gilchrest BA, Eller MS, Geller AC, Yaar M. The pathogenesis of melanoma induced by ultraviolet radiation. N Engl J Med 340: 1341–
1348, 1999. doi:10.1056/NEJM199904293401707.
180. Budden T, Gaudy-Marqueste C, Porter A, Kay E, Gurung S,
Earnshaw CH, Roeck K, Craig S, Traves V, Krutmann J, Muller P,
s A.
Motta L, Zanivan S, Malliri A, Furney SJ, Nagore E, Viro
Ultraviolet light-induced collagen degradation inhibits melanoma
invasion. Nat Commun 12: 2742, 2021. doi:10.1038/s41467-02122953-z.
181. Ahmadzadeh H, Webster MR, Behera R, Jimenez Valencia AM,
Wirtz D, Weeraratna AT, Shinoy VB. Modeling the two-way feedback between contractility and matrix realignment reveals a nonlinear mode of cancer cell invasion. Proc Natl Acad Sci USA 114:
E1617–E1626, 2017. doi:10.1073/pnas.1617037114.
182. Levental KR, Yu H, Kass L, Lakins JN, Egeblad M, Erler JT, Fong
SF, Csiszar K, Giaccia A, Weninger W, Yamauchi M, Gasser DL,
Weaver VM. Matrix crosslinking forces tumor progression by
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C117
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
183.
184.
185.
186.
187.
188.
189.
190.
191.
192.
193.
194.
195.
196.
197.
198.
199.
C118
enhancing integrin signaling. Cell 139: 891–906, 2009. doi:10.1016/j.
cell.2009.10.027.
Arnold SA, Rivera LB, Miller AF, Carbon JG, Dineen SP, Xie Y,
Castrillon DH, Sage EH, Puolakkainen P, Bradshaw AD, Brekken
RA. Lack of host SPARC enhances vascular function and tumor
spread in an orthotopic murine model of pancreatic carcinoma. Dis
Model Mech 3: 57–72, 2010. doi:10.1242/dmm.003228.
Schmidt AM, Hori O, Brett J, Yan SD, Wautier JL, Stern D. Cellular
receptors for advanced glycation end products. Implications for
induction of oxidant stress and cellular dysfunction in the pathogenesis of vascular lesions. Arterioscler Thromb 14: 1521–1528, 1994.
doi:10.1161/01.ATV.14.10.1521. doi:10.1161/01.atv.14.10.1521.
Vijg J, Campisi J. Puzzles, promises and a cure for ageing. Nature
454: 1065–1071, 2008. doi:10.1038/nature07216.
€ndler TL,
Di Sanzo S, Spengler K, Leheis A, Kirkpatrick JM, Ra
Baldensperger T, Dau T, Henning C, Parca L, Marx C, Wang ZQ,
Glomb MA, Ori A, Heller R. Mapping protein carboxymethylation
sites provides insights into their role in proteostasis and cell proliferation. Nat Commun 12: 6743, 2021. doi:10.1038/s41467-02126982-6.
Fisher GJ, Quan T, Purohit T, Shao Y, Cho MK, He T, Varani J,
Kang S, Voorhees JJ. Collagen fragmentation promotes oxidative
stress and elevates matrix metalloproteinase-1 in fibroblasts in aged
human skin. Am J Pathol 174: 101–114, 2009. doi:10.2353/
ajpath.2009.080599.
Ahmed N. Advanced glycation endproducts—role in pathology of diabetic complications. Diabetes Res Clin Pract 67: 3–21, 2005.
doi:10.1016/j.diabres.2004.09.004.
Gautieri A, Passini FS, Silván U, Guizar-Sicairos M, Carimati G,
Volpi P, Moretti M, Schoenhuber H, Redaelli A, Berli M, Snedeker
JG. Advanced glycation end-products: mechanics of aged collagen
from molecule to tissue. Matrix Biol 59: 95–108, 2017. doi:10.1016/j.
matbio.2016.09.001.
Glenn JV, Stitt AW. The role of advanced glycation end products in
retinal ageing and disease. Biochim Biophys Acta 1790: 1109–1116,
2009. doi:10.1016/j.bbagen.2009.04.016.
ska-Marczewska M, Głowacka E, Sobczak M, Cypryk K,
Pertyn
ski J. Glycation endproducts, soluble receptor for advanced
Wilczyn
glycation endproducts and cytokines in diabetic and non-diabetic
pregnancies. Am J Reprod Immunol 61: 175–182, 2009. doi:10.1111/
j.1600-0897.2008.00679.x.
Semba RD, Ferrucci L, Sun K, Beck J, Dalal M, Varadhan R,
Walston J, Guralnik JM, Fried LP. Advanced glycation end products
and their circulating receptors predict cardiovascular disease mortality in older community-dwelling women. Aging Clin Exp Res 21: 182–
190, 2009. doi:10.1007/BF03325227.
Werb Z. ECM and cell surface proteolysis: regulating cellular ecology. Cell 91: 439–442, 1997. doi:10.1016/s0092-8674(00)80429-8.
Solomonov I, Zehorai E, Talmi-Frank D, Wolf SG, Shainskaya A,
Zhuravlev A, Kartvelishvily E, Visse R, Levin Y, Kampf N, Jaitin DA,
David E, Amit I, Nagase H, Sagi I. Distinct biological events generated by ECM proteolysis by two homologous collagenases. Proc
Natl Acad Sci USA 113: 10884–10889, 2016. doi:10.1073/pnas.
1519676113.
Shimoda M. Extracellular vesicle-associated MMPs: a modulator of
the tissue microenvironment. Adv Clin Chem 88: 35–66, 2019.
doi:10.1016/bs.acc.2018.10.006.
Bugge TH, Kombrinck KW, Flick MJ, Daugherty CC, Danton MJ,
Degen JL. Loss of fibrinogen rescues mice from the pleiotropic
effects of plasminogen deficiency. Cell 87: 709–719, 1996.
doi:10.1016/s0092-8674(00)81390-2.
Kuzuya M, Asai T, Kanda S, Maeda K, Cheng XW, Iguchi A.
Glycation cross-links inhibit matrix metalloproteinase-2 activation in
vascular smooth muscle cells cultured on collagen lattice.
Diabetologia 44: 433–436, 2001. doi:10.1007/s001250051640.
DeGroot J, Verzijl N, Wenting-Van Wijk MJ, Bank RA, Lafeber FP,
Bijlsma JW, TeKoppele JM. Age-related decrease in susceptibility
of human articular cartilage to matrix metalloproteinase-mediated
degradation: the role of advanced glycation end products. Arthritis
Rheum 44: 2562–2571, 2001. doi:10.1002/1529-0131(200111)44:
11<2562::AID-ART437>3.0.CO;2-1.
Bourne JW, Lippell JM, Torzilli PA. Glycation cross-linking induced
mechanical-enzymatic cleavage of microscale tendon fibers. Matrix
Biol J Biol 34: 179–184, 2014. doi:10.1016/j.matbio.2013.11.005.
200. Vafaie F, Yin H, O’Neil C, Nong Z, Watson A, Arpino JM, Chu MW,
Holdsworth DW, Gros R, Pickering JG. Collagenase-resistant collagen promotes mouse aging and vascular cell senescence. Aging
Cell 13: 121–130, 2014. doi:10.1111/acel.12155.
201. Willem M, Tahirovic S, Busche MA, Ovsepian SV, Chafai M, Kootar
S, Hornburg D, Evans LD, Moore S, Daria A, Hampel H, M€
uller V,
Giudici C, Nuscher B, Wenninger-Weinzierl A, Kremmer E, Heneka
MT, Thal DR, Giedraitis V, Lannfelt L, M€
uller U, Livesey FJ,
Meissner F, Herms J, Konnerth A, Marie H, Haass C. g-Secretase
processing of APP inhibits neuronal activity in the hippocampus.
Nature 526: 443–447, 2015. doi:10.1038/nature14864.
202. Baranger K, Marchalant Y, Bonnet AE, Crouzin N, Carrete A,
Paumier JM, Py NA, Bernard A, Bauer C, Charrat E, Moschke K,
Seiki M, Vignes M, Lichtenthaler SF, Checler F, Khrestchatisky M,
Rivera S. MT5-MMP is a new pro-amyloidogenic proteinase that promotes amyloid pathology and cognitive decline in a transgenic
mouse model of Alzheimer’s disease. Cell Mol Life Sci 73: 217–236,
2016. doi:10.1007/s00018-015-1992-1.
203. Wynn TA. Fibrotic disease and the TH1/TH2 paradigm. Nat Rev
Immunol 4: 583–594, 2004. doi:10.1038/nri1412.
204. Ortiz C, Schierwagen R, Schaefer L, Klein S, Trepat X, Trebicka J.
Extracellular matrix remodeling in chronic liver disease. Curr Tissue
Microenviron Rep 2: 41–52, 2021. doi:10.1007/s43152-021-00030-3.
205. Rockey DC, Bell PD, Hill JA. Fibrosis—a common pathway to organ
injury and failure. N Engl J Med 372: 1138–1149, 2015. doi:10.1056/
NEJMra1300575.
~ oz-Espín D, Serrano M. Cellular senescence: from physiology
206. Mun
to pathology. Nat Rev Mol Cell Biol 15: 482–496, 2014. doi:10.1038/
nrm3823.
207. Friedman SL. Liver fibrosis—from bench to bedside. J Hepatol 38:
38–53, 2003. doi:10.1016/S0168-8278(02)00429-4.
208. Bataller R, Brenner DA. Liver fibrosis. J Clin Invest 115: 209–218,
2005 [Erratum in J Clin Invest 115: 1100, 2005]. doi:10.1172/JCI24282.
209. Michalopoulos GK. Liver regeneration. J Cell Physiol 213: 286–300,
2007. doi:10.1002/jcp.21172.
210. Marra F. Hepatic stellate cells and the regulation of liver inflammation. J Hepatol 31: 1106–1119, 1999. doi:10.1016/S0168-8278(99)
80327-4.
211. Tan Z, Sun H, Xue T, Gan C, Liu H, Xie Y, Yao Y, Ye T. Liver fibrosis:
therapeutic targets and advances in drug therapy. Front Cell Dev
Biol 9: 730716, 2021. doi:10.3389/fcell.2021.730176.
M, Ravelli F. Myocardial fibrosis predicts ventricu212. Disertori M, Mase
lar tachyarrhythmias. Trends Cardiovasc Med 27: 363–372, 2017.
doi:10.1016/j.tcm.2017.01.011.
213. Hinderer S, Schenke-Layland K. Cardiac fibrosis—A short review of
causes and therapeutic strategies. Adv Drug Deliv Rev 146: 77–82,
2019. doi:10.1016/j.addr.2019.05.011.
214. Frangogiannis NG. Cardiac fibrosis: cell biological mechanisms, molecular pathways and therapeutic opportunities. Mol Aspects Med
65: 70–99, 2019. doi:10.1016/j.mam.2018.07.001.
215. Schellings MW, Vanhoutte D, Swinnen M, Cleutjens JP, Debets J,
van Leeuwen RE, d’Hooge J, Van de Werf F, Carmeliet P, Pinto
YM, Sage EH, Heymans S. Absence of SPARC results in increased
cardiac rupture and dysfunction after acute myocardial infarction.
J Exp Med 206: 113–123, 2009. doi:10.1084/jem.20081244.
216. Xia Y, Dobaczewski M, Gonzalez-Quesada C, Chen W, Biernacka
A, Li N, Lee DW, Frangogiannis NG. Endogenous thrombospondin 1
protects the pressure-overloaded myocardium by modulating fibroblast phenotype and matrix metabolism. Hypertension 58: 902–911,
2011. doi:10.1161/HYPERTENSIONAHA.111.175323.
217. Frangogiannis NG, Ren G, Dewald O, Zymek P, Haudek S,
Koerting A, Winkelmann K, Michael LH, Lawler J, Entman ML.
Critical role of endogenous thrombospondin-1 in preventing expansion of healing myocardial infarcts. Circulation 111: 2935–2942,
2005. doi:10.1161/CIRCULATIONAHA.104.510354.
218. Frangogiannis NG. The extracellular matrix in ischemic and nonischemic heart failure. Circ Res 125: 117–146, 2019. doi:10.1161/
CIRCRESAHA.119.311148.
219. Taylor DA, Frazier OH, Elgalad A, Hochman-Mendez C, Sampaio
LC. Building a total bioartificial heart: harnessing nature to overcome
the current hurdles. Artif Organs 42: 970–982, 2018. doi:10.1111/
aor.13336.
220. Ott HC, Matthiesen TS, Goh SK, Black LD, Kren SM, Netoff TI,
Taylor DA. Perfusion-decellularized matrix: using nature’s platform
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
221.
222.
223.
224.
225.
226.
227.
228.
229.
230.
231.
232.
233.
234.
235.
236.
237.
238.
to engineer a bioartificial heart. Nat Med 14: 213–221, 2008.
doi:10.1038/nm1684.
Gilpin SE, Li Q, Evangelista-Leite D, Ren X, Reinhardt DP, Frey BL,
Ott HC. Fibrillin-2 and Tenascin-C bridge the age gap in lung epithelial regeneration. Biomaterials 140: 212–219, 2017. doi:10.1016/j.
biomaterials.2017.06.027.
Herrera J, Henke CA, Bitterman PB. Extracellular matrix as a driver
of progressive fibrosis. J Clin Invest 128: 45–53, 2018. doi:10.1172/
JCI93557.
Burgess JK, Mauad T, Tjin G, Karlsson JC, Westergren-Thorsson
G. The extracellular matrix – the under-recognized element in lung
disease? J Pathol 240: 397–409, 2016. doi:10.1002/path.4808.
Deng Z, Fear MW, Suk Choi Y, Wood FM, Allahham A, Mutsaers
SE, Prêle CM. The extracellular matrix and mechanotransduction in
pulmonary fibrosis. Int J Biochem Cell Biol 126: 105802, 2020.
doi:10.1016/j.biocel.2020.105802.
Hackett TL, Osei ET. Modeling extracellular matrix-cell interactions
in lung repair and chronic disease. Cells 10: 2145, 2021. doi:10.3390/
cells10082145.
King TE, Bradford WZ, Castro-Bernardini S, Fagan EA, Glaspole I,
Glassberg MK, Gorina E, Hopkins PM, Kardatzke D, Lancaster L,
Lederer DJ, Nathan SD, Pereira CA, Sahn SA, Sussman R, Swigris
JJ, Noble PW; ASCEND Study Group. A phase 3 trial of pirfenidone
in patients with idiopathic pulmonary fibrosis. N Engl J Med 370:
2083–2092, 2014 [Erratum in N Engl J Med 371: 1172, 2014].
doi:10.1056/NEJMoa1402582.
Strongman H, Kausar I, Maher TM. Incidence, prevalence, and survival of patients with idiopathic pulmonary fibrosis in the UK. Adv
Ther 35: 724–736, 2018. doi:10.1007/s12325-018-0693-1.
Kis K, Liu X, Hagood JS. Myofibroblast differentiation and survival in
fibrotic disease. Expert Rev Mol Med 13: e27, 2011. doi:10.1017/
S1462399411001967.
Jones MG, Fabre A, Schneider P, Cinetto F, Sgalla G,
Mavrogordato M, Jogai S, Alzetani A, Marshall BG, O’Reilly KM,
Warner JA, Lackie PM, Davies DE, Hansell DM, Nicholson AG,
Sinclair I, Brown KK, Richeldi L. Three-dimensional characterization
of fibroblast foci in idiopathic pulmonary fibrosis. JCI Insight 1:
e86375, 2016. doi:10.1172/jci.insight.86375.
s R, Montes A, Penín R,
Estany S, Vicens-Zygmunt V, Llatjo
Escobar I, Xaubet A, Santos S, Manresa F, Dorca J, Molina-Molina
M. Lung fibrotic tenascin-C upregulation is associated with other
extracellular matrix proteins and induced by TGFb1. BMC Pulm Med
14: 120, 2014. doi:10.1186/1471-2466-14-120.
Thong L, McElduff EJ, Henry MT. Trials and treatments: an update
on pharmacotherapy for idiopathic pulmonary fibrosis. Life (Basel)
13: 486, 2023. doi:10.3390/life13020486.
Uhler C, Shivashankar GV. Mechanogenomic coupling of lung tissue stiffness, EMT and coronavirus pathogenicity. Curr Opin Solid
State Mater Sci 25: 100874, 2021. doi:10.1016/j.cossms.2020.
100874.
World Health Organization. Chronic obstructive pulmonary disease
(COPD). 2022. https://www.who.int/news-room/fact-sheets/detail/
chronic-obstructive-pulmonary-disease-(copd) [2022 Oct 20].
Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, Calverley P,
Fukuchi Y, Jenkins C, Rodriguez-Roisin R, van Weel C, Zielinski J;
Global Initiative for Chronic Obstructive Lung Disease. Global
strategy for the diagnosis, management, and prevention of chronic
obstructive pulmonary disease. Am J Respir Crit Care Med, 176:
532–555, 2007. doi:10.1164/rccm.200703-456SO.
Finkelstein R, Fraser RS, Ghezzo H, Cosio MG. Alveolar inflammation and its relation to emphysema in smokers. Am J Respir Crit
Care Med 152: 1666–1672, 1995. doi:10.1164/ajrccm.152.5.7582312.
Annoni R, Lanças T, Yukimatsu Tanigawa R, de Medeiros
Matsushita M, de Morais Fernezlian S, Bruno A, Fernando Ferraz da
Silva L, Roughley PJ, Battaglia S, Dolhnikoff M, Hiemstra PS, Sterk
PJ, Rabe KF, Mauad T. Extracellular matrix composition in COPD. Eur
Respir J 40: 1362–1373, 2012. doi:10.1183/09031936.00192611.
Chrzanowski P, Keller S, Cerreta J, Mandl I, Turino GM. Elastin content of normal and emphysematous lung parenchyma. Am J Med
69: 351–359, 1980. doi:10.1016/0002-9343(80)90004-2.
van Straaten JF, Coers W, Noordhoek JA, Huitema S, Flipsen JT,
Kauffman HF, Timens W, Postma DS. Proteoglycan changes in the
extracellular matrix of lung tissue from patients with pulmonary emphysema. Mod Pathol 12: 697–705, 1999.
239. Cardoso WV, Sekhon HS, Hyde DM, Thurlbeck WM. Collagen and
elastin in human pulmonary emphysema. Am Rev Respir Dis 147:
975–981, 1993. doi:10.1164/ajrccm/147.4.975.
€ fdahl A, Kadefors M, So
€ derlund Z, Tykesson
240. Elowsson Rendin L, Lo
n J, Westergren-Thorsson G.
E, Rolandsson Enes S, Wige
Harnessing the ECM microenvironment to ameliorate mesenchymal
stromal cell-based therapy in chronic lung diseases. Front
Pharmacol 12: 645558, 2021. doi:10.3389/fphar.2021.645558.
241. B€
ulow RD, Boor P. Extracellular matrix in kidney fibrosis: more than
just a scaffold. J Histochem Cytochem 67: 643–661, 2019.
doi:10.1369/0022155419849388.
242. Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson
DS, Hobbs FD. Global prevalence of chronic kidney disease—a systematic review and meta-analysis. PLoS One 11: e0158765, 2016.
doi:10.1371/journal.pone.0158765.
243. Neovius M, Jacobson SH, Eriksson JK, Elinder CG, Hylander B.
Mortality in chronic kidney disease and renal replacement therapy: a
population-based cohort study. BMJ Open 4: e004251, 2014.
doi:10.1136/bmjopen-2013-004251.
244. Abdollahzadeh F, Khoshdel-Rad N, Moghadasali R. Kidney development and function: ECM cannot be ignored. Differentiation 124:
28–42, 2022. doi:10.1016/j.diff.2022.02.001.
245. Toussaint ND. Extracellular matrix calcification in chronic kidney disease. Curr Opin Nephrol Hypertens 20: 360–368, 2011. doi:10.1097/
MNH.0b013e3283479330.
246. Karsdal MA, Bay-Jensen AC, Leeming DJ, Henriksen K,
Christiansen C. Quantification of “end products” of tissue destruction in inflammation may reflect convergence of cytokine and signaling pathways—implications for modern clinical chemistry.
Biomarkers 18: 375–378, 2013. doi:10.3109/1354750X.2013.789084.
247. Genovese F, Manresa AA, Leeming DJ, Karsdal MA, Boor P. The
extracellular matrix in the kidney: a source of novel non-invasive biomarkers of kidney fibrosis? Fibrogenesis Tissue Repair 7: 4, 2014.
doi:10.1186/1755-1536-7-4.
248. Mansour SG, Puthumana J, Coca SG, Gentry M, Parikh CR.
Biomarkers for the detection of renal fibrosis and prediction of renal
outcomes: a systematic review. BMC Nephrol 18: 72, 2017.
doi:10.1186/s12882-017-0490-0.
249. Guan J, Wang M, Zhao M, Ni W, Zhang M. Discovery of fibrinogen
c-chain as a potential urinary biomarker for renal interstitial fibrosis
in IgA nephropathy. BMC Nephrol 24: 60, 2023. doi:10.1186/s12882023-03103-7.
250. Sempionatto JR, Lasalde-Ramírez JA, Mahato K, Wang J, Gao W.
Wearable chemical sensors for biomarker discovery in the omics
era. Nat Rev Chem 6: 899–915, 2022. doi:10.1038/s41570-02200439-w.
251. Li J, Ma L, Yu H, Yao Y, Xu Z, Lin W, Wang L, Wang X, Yang H.
MicroRNAs as potential biomarkers for the diagnosis of chronic kidney disease: a systematic review and meta-analysis. Front Med 8:
782561, 2022. doi:10.3389/fmed.2021.782561.
252. Fritsche LG, Igl W, Bailey JN, Grassmann F, Sengupta S, BraggGresham JL, Burdon KP, Hebbring SJ, Wen C, Gorski M, Kim IK,
Cho D, Zack D, Souied E, Scholl HP, Bala E, Lee KE, Hunter DJ,
Sardell RJ, Mitchell P, Merriam JE, Cipriani V, Hoffman JD, Schick
T, Lechanteur YT, Guymer RH, Johnson MP, Jiang Y, Stanton CM,
Buitendijk GH. A large genome-wide association study of agerelated macular degeneration highlights contributions of rare and
common variants. Nat Genet 48: 134–143, 2016. doi:10.1038/
ng.3448.
253. Vierkotten S, Muether PS, Fauser S. Overexpression of HTRA1
leads to ultrastructural changes in the elastic layer of Bruch’s membrane via cleavage of extracellular matrix components. PLoS One 6:
e22959, 2011. doi:10.1371/journal.pone.0022959.
254. Lim LS, Mitchell P, Seddon JM, Holz FG, Wong TY. Age-related
macular degeneration. Lancet 379: 1728–1738, 2012. doi:10.1016/
S0140-6736(12)60282-7.
255. Friedlander M. Fibrosis and diseases of the eye. J Clin Invest 117:
576–586, 2007. doi:10.1172/JCI31030.
256. Little K, Ma JH, Yang N, Chen M, Xu H. Myofibroblasts in macular
fibrosis secondary to neovascular age-related macular degeneration—the potential sources and molecular cues for their recruitment
and activation. eBioMedicine 38: 283–291, 2018. doi:10.1016/j.
ebiom.2018.11.029.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C119
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
257. Ishikawa K, Kannan R, Hinton DR. Molecular mechanisms of subretinal fibrosis in age-related macular degeneration. Exp Eye Res 142:
19–25, 2016. doi:10.1016/j.exer.2015.03.009.
258. Humphrey JD, Dufresne ER, Schwartz MA. Mechanotransduction
and extracellular matrix homeostasis. Nat Rev Mol Cell Biol, 15: 802,
2014. doi:10.1038/nrm3896.
259. Goddeeris MM, Cook-Wiens E, Horton WJ, Wolf H, Stoltzfus JR,
Borrusch M, Grotewiel MS. Delayed behavioural aging and altered
mortality in Drosophila beta integrin mutants. Aging Cell 2: 257–
264, 2003. doi:10.1046/j.1474-9728.2003.00060.x.
260. Kumsta C, Ching TT, Nishimura M, Davis AE, Gelino S, Catan HH,
Yu X, Chu CC, Ong B, Panowski SH, Baird N, Bodmer R, Hsu AL,
Hansen M. Integrin-linked kinase modulates longevity and thermotolerance in C. elegans through neuronal control of HSF-1. Aging
Cell 13: 419–430, 2014. doi:10.1111/acel.12189.
261. Vitiello D, Dakhovnik A, Statzer C, Ewald CY. Lifespan-associated
gene expression signatures of recombinant BXD mice implicates
Coro7 and Set in longevity. Front Genet 12: 694033, 2021.
doi:10.3389/fgene.2021.694033.
262. Hansen M, Hsu AL, Dillin A, Kenyon C. New genes tied to endocrine, metabolic, and dietary regulation of lifespan from a
Caenorhabditis elegans genomic RNAi screen. PLoS Genet 1: 119–
128, 2005. doi:10.1371/journal.pgen.0010017.
263. Statzer C, Ewald CY. The extracellular matrix phenome across
species. Matrix Biol Plus 8: 100039, 2020. doi:10.1016/j.mbplus.
2020.100039.
264. Rapisarda V, Borghesan M, Miguela V, Encheva V, Snijders AP,
Lujambio A, O’Loghlen A. Integrin beta 3 regulates cellular senescence by activating the TGF-b pathway. Cell Rep 18: 2480–2493,
2017. doi:10.1016/j.celrep.2017.02.012.
265. WCRF International. Worldwide cancer data. World Cancer
Research Fund International, 2022. https://www.wcrf.org/cancertrends/worldwide-cancer-data/ [2022 Oct 23].
266. Bissell MJ, Hines WC. Why don’t we get more cancer? A proposed
role of the microenvironment in restraining cancer progression. Nat
Med 17: 320–329, 2011. doi:10.1038/nm.2328.
267. Provenzano PP, Inman DR, Eliceiri KW, Knittel JG, Yan L, Rueden
CT, White JG, Keely PJ. Collagen density promotes mammary tumor
initiation and progression. BMC Med 6: 11, 2008. doi:10.1186/17417015-6-11.
268. Kim BG, An HJ, Kang S, Choi YP, Gao MQ, Park H, Cho NH.
Laminin-332-rich tumor microenvironment for tumor invasion in the
interface zone of breast cancer. Am J Pathol 178: 373–381, 2011.
doi:10.1016/j.ajpath.2010.11.028.
269. Kii I, Nishiyama T, Li M, Matsumoto KI, Saito M, Amizuka N, Kudo
A. Incorporation of tenascin-C into the extracellular matrix by periostin underlies an extracellular meshwork architecture. J Biol Chem
285: 2028–2039, 2010. doi:10.1074/jbc.M109.051961.
270. Xiong GF, Xu R. Function of cancer cell-derived extracellular matrix
in tumor progression. J Cancer Metastasis Treat 2: 357–364, 2016.
doi:10.20517/2394-4722.2016.08.
271. Aoudjit F, Vuori K. Integrin signaling in cancer cell survival and
chemoresistance. Chemother Res Pract 2012: e283181, 2012.
doi:10.1155/2012/283181.
272. Yuan S, Norgard RJ, Stanger BZ. Cellular plasticity in cancer.
Cancer Discov 9: 837–851, 2019. doi:10.1158/2159-8290.CD-190015.
273. Scott LE, Weinberg SH, Lemmon CA. Mechanochemical signaling
of the extracellular matrix in epithelial-mesenchymal transition. Front
Cell Dev Biol 7: 135, 2019. doi:10.3389/fcell.2019.00135.
274. Ribatti D, Tamma R, Annese T. Epithelial-mesenchymal transition in
cancer: a historical overview. Transl Oncol 13: 100773, 2020.
doi:10.1016/j.tranon.2020.100773.
275. Kim DH, Xing T, Yang Z, Dudek R, Lu Q, Chen YH. Epithelial mesenchymal transition in embryonic development, tissue repair and cancer: a comprehensive overview. J Clin Med 7: 1, 2017. doi:10.3390/
jcm7010001.
276. Walker C, Mojares E, del Río Hernández A. Role of extracellular matrix in development and cancer progression. Int J Mol Sci 19: 3028,
2018. doi:10.3390/ijms19103028.
277. Lamouille S, Xu J, Derynck R. Molecular mechanisms of epithelial–
mesenchymal transition. Nat Rev Mol Cell Biol 15: 178–196, 2014.
doi:10.1038/nrm3758.
C120
278. Miller AE, Hu P, Barker TH. Feeling Things Out: Bidirectional
Signaling of the cell-ECM interface, implications in the mechanobiology of cell spreading, migration, proliferation, and differentiation.
Adv Healthc Mater 9: e1901445, 2020. doi:10.1002/adhm.
201901445.
279. Venning FA, Wullkopf L, Erler JT. Targeting ECM disrupts cancer
progression. Front Oncol 5: 224, 2015. doi:10.3389/fonc.2015.
00224.
280. Tigges J, Krutmann J, Fritsche E, Haendeler J, Schaal H, Fischer
JW, Kalfalah F, Reinke H, Reifenberger G, St€
uhler K, Ventura N,
Gundermann S, Boukamp P, Boege F. The hallmarks of fibroblast
ageing. Mech Ageing Dev, 138: 26–44, 2014. doi:10.1016/j.
mad.2014.03.004.
281. Badi I. The curious case of dermal fibroblasts: cell identity loss may
be a mechanism underlying cardiovascular aging. Cardiovasc Res
115: e24–e25, 2019. doi:10.1093/cvr/cvz012.
282. Salzer MC, Lafzi A, Berenguer-Llergo A, Youssif C, Castellanos A,
Solanas G, Peixoto FO, Stephan-Otto Attolini C, Prats N, Aguilera
M, Martín-Caballero J, Heyn H, Benitah SA. Identity noise and adipogenic traits characterize dermal fibroblast aging. Cell 175: 1575–
1590.e22, 2018. doi:10.1016/j.cell.2018.10.012.
283. Fontana L, Partridge L. Promoting health and longevity through
diet: from model organisms to humans. Cell 161: 106–118, 2015.
doi:10.1016/j.cell.2015.02.020.
284. Sedej S. Ketone bodies to the rescue for an aging heart?
Cardiovasc Res 114: e1–e2, 2018. doi:10.1093/cvr/cvx218.
285. Blau HM, Cosgrove BD, Ho AT. The central role of muscle stem
cells in regenerative failure with aging. Nat Med 21: 854–862, 2015.
doi:10.1038/nm.3918.
286. Carlson BM, Faulkner JA. Muscle transplantation between young
and old rats: age of host determines recovery. Am J Physiol Cell
Physiol 256: C1262–C1266, 1989. doi:10.1152/ajpcell.1989.256.6.
C1262.
287. Carlson ME, Suetta C, Conboy MJ, Aagaard P, Mackey A, Kjaer
M, Conboy I. Molecular aging and rejuvenation of human muscle
stem cells. EMBO Mol Med 1: 381–391, 2009. doi:10.1002/
emmm.200900045.
288. M€
uller M, Tohtz S, Dewey M, Springer I, Perka C. Age-related
appearance of muscle trauma in primary total hip arthroplasty and
the benefit of a minimally invasive approach for patients older
than 70 years. Int Orthop 35: 165–171, 2011. doi:10.1007/s00264010-1166-6.
289. Watters JM, Clancey SM, Moulton SB, Briere KM, Zhu JM.
Impaired recovery of strength in older patients after major abdominal surgery. Ann Surg 218: 380, 1993. doi:10.1097/00000658199309000-00017.
A. An overview about the biol290. Forcina L, Miano C, Pelosi L, Musaro
ogy of skeletal muscle satellite cells. Curr Genomics 20: 24–37,
2019. doi:10.2174/1389202920666190116094736.
291. Rozo M, Li L, Fan CM. Targeting b1-integrin signaling enhances
regeneration in aged and dystrophic muscle in mice. Nat Med 22:
889–896, 2016. doi:10.1038/nm.4116.
292. Yin H, Price F, Rudnicki MA. Satellite cells and the muscle stem cell
niche. Physiol Rev 93: 23–67, 2013. doi:10.1152/physrev.00043.2011.
293. Conboy IM, Conboy MJ, Wagers AJ, Girma ER, Weissman IL,
Rando TA. Rejuvenation of aged progenitor cells by exposure to
a young systemic environment. Nature 433: 760–764, 2005.
doi:10.1038/nature03260.
294. Bernet JD, Doles JD, Hall JK, Kelly Tanaka K, Carter TA, Olwin BB.
p38 MAPK signaling underlies a cell-autonomous loss of stem cell
self-renewal in skeletal muscle of aged mice. Nat Med 20: 265–271,
2014. doi:10.1038/nm.3465.
295. Chakkalakal JV, Jones KM, Basson MA, Brack AS. The aged niche
disrupts muscle stem cell quiescence. Nature 490: 355–360, 2012.
doi:10.1038/nature11438.
296. Cosgrove BD, Gilbert PM, Porpiglia E, Mourkioti F, Lee SP, Corbel
SY, Llewellyn ME, Delp SL, Blau HM. Rejuvenation of the muscle
stem cell population restores strength to injured aged muscles. Nat
Med 20: 255–264, 2014. doi:10.1038/nm.3464.
297. Price FD, von Maltzahn J, Bentzinger CF, Dumont NA, Yin H,
Chang NC, Wilson DH, Frenette J, Rudnicki MA. Inhibition of JAKSTAT signaling stimulates adult satellite cell function. Nat Med 20:
1174–1181, 2014 [Erratum in Nat Med 20:1217, 2014]. doi:10.1038/
nm.3655.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
298. Sousa-Victor P, Gutarra S, García-Prat L, Rodriguez-Ubreva J,
Ortet L, Ruiz-Bonilla V, Jardí M, Ballestar E, González S, Serrano
~ oz-Cánoves P. Geriatric muscle stem cells
AL, Perdiguero E, Mun
switch reversible quiescence into senescence. Nature 506: 316–
321, 2014. doi:10.1038/nature13013.
299. Tierney MT, Aydogdu T, Sala D, Malecova B, Gatto S, Puri PL,
Latella L, Sacco A. STAT3 signaling controls satellite cell expansion
and skeletal muscle repair. Nat Med 20: 1182–1186, 2014.
doi:10.1038/nm.3656.
300. Lukjanenko L, Jung MJ, Hegde N, Perruisseau-Carrier C,
Migliavacca E, Rozo M, Karaz S, Jacot G, Schmidt M, Li L, Metairon
S, Raymond F, Lee U, Sizzano F, Wilson DH, Dumont NA, Palini A,
€ssler R, Steiner P, Descombes P, Rudnicki MA, Fan CM, von
Fa
Maltzahn J, Feige JN, Bentzinger CF. Loss of fibronectin from the
aged stem cell niche affects the regenerative capacity of skeletal
muscle in mice. Nat Med 22: 897–905, 2016. doi:10.1038/nm.4126.
301. Lukjanenko L, Karaz S, Stuelsatz P, Gurriaran-Rodriguez U,
Michaud J, Dammone G, et al. Aging disrupts muscle stem cell function by impairing matricellular wisp1 secretion from fibro-adipogenic
progenitors. Cell Stem Cell 24: 433–446.e7. 2019. doi:10.1016/j.
stem.2018.12.014.
302. Joanisse S, Nederveen JP, Snijders T, McKay BR, Parise G.
Skeletal muscle regeneration, repair and remodelling in aging: the
importance of muscle stem cells and vascularization. Gerontology
63: 91–100, 2017. doi:10.1159/000450922.
303. Yamakawa H, Kusumoto D, Hashimoto H, Yuasa S. Stem cell aging
in skeletal muscle regeneration and disease. Int J Mol Sci 21:1830,
2020. doi:10.3390/ijms21051830.
304. Neves J, Sousa-Victor P, Jasper H. Rejuvenating strategies for stem
cell-based therapies in aging. Cell Stem Cell 20:161–175, 2017.
doi:10.1016/j.stem.2017.01.008.
305. Rando TA, Chang HY. Aging, rejuvenation, and epigenetic reprogramming: resetting the aging clock. Cell 148: 46–57, 2012.
doi:10.1016/j.cell.2012.01.003.
306. Lavasani M, Robinson AR, Lu A, Song M, Feduska JM, Ahani B,
Tilstra JS, Feldman CH, Robbins PD, Niedernhofer LJ, Huard J.
Muscle-derived stem/progenitor cell dysfunction limits healthspan
and lifespan in a murine progeria model. Nat Commun 3: 608, 2012.
doi:10.1038/ncomms1611.
307. Ren R, Ocampo A, Liu GH, Belmonte JC. Regulation of stem cell
aging by metabolism and epigenetics. Cell Metab 26: 460–474,
2017. doi:10.1016/j.cmet.2017.07.019.
308. Cai Y, Wang S, Qu J, Belmonte JC, Liu GH. Rejuvenation of tissue
stem cells by intrinsic and extrinsic factors. Stem Cells Transl Med
11:231–238, 2022. doi:10.1093/stcltm/szab012.
309. Ge Y, Miao Y, Gur-Cohen S, Gomez N, Yang H, Nikolova M, Polak
L, Hu Y, Verma A, Elemento O, Krueger JG, Fuchs E. The aging
skin microenvironment dictates stem cell behavior. Proc Natl Acad
Sci USA 117: 5339–5350, 2020. doi:10.1073/pnas.1901720117.
310. Nakamura-Ishizu A, Okuno Y, Omatsu Y, Okabe K, Morimoto J,
Uede T, Nagasawa T, Suda T, Kubota Y. Extracellular matrix protein
tenascin-C is required in the bone marrow microenvironment primed
for hematopoietic regeneration. Blood 119: 5429–5437, 2012.
doi:10.1182/blood-2011-11-393645.
311. Watt FM, Huck WT. Role of the extracellular matrix in regulating
stem cell fate. Nat Rev Mol Cell Biol 14: 467–473, 2013. doi:10.1038/
nrm3620.
312. Yang J, Hu S, Bian Y, Yao J, Wang D, Liu X, Guo Z, Zhang S, Peng
L. Targeting cell death: pyroptosis, ferroptosis, apoptosis and necroptosis in osteoarthritis. Front Cell Dev Biol 9: 789948, 2022.
doi:10.3389/fcell.2021.789948.
313. Majno G, Joris I. Apoptosis, oncosis, and necrosis. An overview of
cell death. Am J Pathol 146: 3–15, 1995.
314. Jorgensen I, Rayamajhi M, Miao EA. Programmed cell death as a
defence against infection. Nat Rev Immunol 17: 151–164, 2017.
doi:10.1038/nri.2016.147.
315. Jorgensen I, Miao EA. Pyroptotic cell death defends against intracellular pathogens. Immunol Rev 265: 130–142, 2015. doi:10.1111/
imr.12287.
316. Makino H, Sugiyama H, Kashihara N. Apoptosis and extracellular
matrix–cell interactions in kidney disease. Kidney Int 58: S67–S75,
2000. doi:10.1046/j.1523-1755.2000.07711.x.
317. Savill J. Apoptosis and the kidney. J Am Soc Nephrol 5: 12–21, 1994.
doi:10.1681/ASN.V5112.
318. Thompson CB. Apoptosis in the pathogenesis and treatment of disease. Science 267: 1456–1462, 1995. doi:10.1126/science.7878464.
319. Fuchs Y, Steller H. Programmed cell death in animal development
and disease. Cell 147: 742–758, 2011. doi:10.1016/j.cell.2011.10.033.
320. Sugiyama H, Kashihara N, Makino H, Yamasaki Y, Ota Z.
Apoptosis in glomerular sclerosis. Kidney Int 49: 103–111, 1996.
doi:10.1038/ki.1996.14.
321. Yanes B, Rainero E. The interplay between cell-extracellular matrix
interaction and mitochondria dynamics in cancer. Cancers 14: 1433,
2022. doi:10.3390/cancers14061433.
322. Gause GF. Cancer and mitochondrial D.N.A. Br Med J 3: 413–414,
1969. doi:10.1136/bmj.3.5667.413-c.
323. Romani P, Nirchio N, Arboit M, Barbieri V, Tosi A, Michielin F,
Shibuya S, Benoist T, Wu D, Hindmarch CC, Giomo M, Urciuolo A,
Giamogante F, Roveri A, Chakravarty P, Montagner M, Calì T,
Elvassore N, Archer SL, De Coppi P, Rosato A, Martello G, Dupont
S. Mitochondrial fission links ECM mechanotransduction to metabolic redox homeostasis and metastatic chemotherapy resistance.
Nat Cell Biol 24: 168–180, 2022. doi:10.1038/s41556-022-00843-w.
324. Fletcher DA, Mullins RD. Cell mechanics and the cytoskeleton.
Nature 463: 485–492, 2010. doi:10.1038/nature08908.
325. van Waveren C, Sun Y, Cheung HS, Moraes CT. Oxidative phosphorylation dysfunction modulates expression of extracellular matrix
—remodeling genes and invasion. Carcinogenesis 27: 409–418,
2006. doi:10.1093/carcin/bgi242.
326. Ding M, King RS, Berry EC, Wang Y, Hardin J, Chisholm AD.
The cell signaling adaptor protein EPS-8 is essential for C. elegans epidermal elongation and interacts with the ankyrin repeat
protein VAB-19. PLoS One 3: e3346, 2008. doi:10.1371/journal.
pone.0003346.
327. Munkácsy E, Khan MH, Lane RK, Borror MB, Park JH, Bokov AF,
Fisher AL, Link CD, Rea SL. DLK-1, SEK-3 and PMK-3 are required
for the life extension induced by mitochondrial bioenergetic disruption in C. elegans. PLoS Genet 12: e1006133, 2016. doi:10.1371/
journal.pgen.1006133.
328. Schinzel RT, Higuchi-Sanabria R, Shalem O, Moehle EA, Webster
BM, Joe L, Bar-Ziv R, Frankino PA, Durieux J, Pender C, Kelet N,
Kumar SS, Savalia N, Chi H, Simic M, Nguyen NT, Dillin A. The hyaluronidase, TMEM2, promotes ER homeostasis and longevity independent of the UPRER. Cell 179: 1306–1318.e18, 2019. doi:10.1016/j.
cell.2019.10.018.
329. Tharp KM, Higuchi-Sanabria R, Timblin GA, Ford B, Garzon-Coral
C, Schneider C, et al. Adhesion-mediated mechanosignaling forces
mitohormesis. Cell Metab 33: 1322–1341.e13, 2021. doi:10.1016/j.
cmet.2021.04.017.
330. Burton DG, Krizhanovsky V. Physiological and pathological consequences of cellular senescence. Cell Mol Life Sci 71: 4373–4386,
2014. doi:10.1007/s00018-014-1691-3.
331. Campisi J. Aging, cellular senescence, and cancer. Annu Rev
Physiol 75: 685–705, 2013. doi:10.1146/annurev-physiol-030212183653.
332. Levi N, Papismadov N, Solomonov I, Sagi I, Krizhanovsky V. The
ECM path of senescence in aging: components and modifiers. FEBS
J 287: 2636–2646, 2020. doi:10.1111/febs.15282.
333. Choi HR, Cho KA, Kang HT, Lee JB, Kaeberlein M, Suh Y, Chung
IK, Park SC. Restoration of senescent human diploid fibroblasts by
modulation of the extracellular matrix. Aging Cell 10: 148–157, 2011.
doi:10.1111/j.1474-9726.2010.00654.x.
334. Mavrogonatou E, Pratsinis H, Papadopoulou A, Karamanos NK,
Kletsas D. Extracellular matrix alterations in senescent cells and
their significance in tissue homeostasis. Matrix Biol 75–76: 27–42,
2019. doi:10.1016/j.matbio.2017.10.004.
335. Blokland KE, Pouwels SD, Schuliga M, Knight DA, Burgess JK.
Regulation of cellular senescence by extracellular matrix during
chronic fibrotic diseases. Clin Sci (Lond) 134: 2681–2706, 2020.
doi:10.1042/CS20190893.
336. Foronjy RF, Sun J, Lemaitre V, D’armiento JM. Transgenic expression of matrix metalloproteinase-1 inhibits myocardial fibrosis and
prevents the transition to heart failure in a pressure overload mouse
model. Hypertens Res 31: 725–735, 2008. doi:10.1291/hypres.
31.725.
337. Turner NA, Porter KE. Regulation of myocardial matrix metalloproteinase expression and activity by cardiac fibroblasts. IUBMB Life
64: 143–150, 2012. doi:10.1002/iub.594.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C121
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
338. Schafer MJ, White TA, Iijima K, Haak AJ, Ligresti G, Atkinson EJ, et
al. Cellular senescence mediates fibrotic pulmonary disease. Nat
Commun 8: 14532, 2017. doi:10.1038/ncomms14532.
339. Pober BR. Williams–Beuren syndrome. N Engl J Med 362: 239–
252, 2010. doi:10.1056/NEJMra0903074.
340. Eronen M. Cardiovascular manifestations in 75 patients with
Williams syndrome. J Med Genet 39: 554–558, 2002. doi:10.1136/
jmg.39.8.554.
341. Marín-García J. Molecular determinants of congenital heart disease.
In: Post-Genomic Cardiology (2nd. ed.). Boston, MA: Academic
Press, 2014, p. 151–179.
342. Wessel A, Pankau R, Kececioglu D, Ruschewski W, B€
ursch JH.
Three decades of follow-up of aortic and pulmonary vascular lesions
in the Williams-Beuren syndrome. Am J Med Genet 52: 297–301,
1994. doi:10.1002/ajmg.1320520309.
343. Ewart AK, Morris CA, Atkinson D, Jin W, Sternes K, Spallone P,
Stock AD, Leppert M, Keating MT. Hemizygosity at the elastin locus
in a developmental disorder, Williams syndrome. Nat Genet 5: 11–16,
1993. doi:10.1038/ng0993-11.
344. Kozel BA, Danback JR, Waxler JL, Knutsen RH, de Las Fuentes L,
Reusz GS, Kis E, Bhatt AB, Pober BR. Williams syndrome predisposes to vascular stiffness modified by antihypertensive use and
copy number changes in NCF1. Hypertension 63: 74–79, 2014.
doi:10.1161/HYPERTENSIONAHA.113.02087.
345. Grant MG, Patterson VL, Grimes DT, Burdine RD. Modeling syndromic congenital heart defects in zebrafish. Curr Top Dev Biol 124:
1–40, 2017. doi:10.1016/bs.ctdb.2016.11.010.
346. Collins RT. Cardiovascular disease in Williams syndrome.
Circulation 127: 2125–2134, 2013. doi:10.1161/CIRCULATIONAHA.112.
000064.
347. Zhou J, Zheng Y, Liang G, Xu X, Liu J, Chen S, Ge T, Wen P, Zhang
Y, Liu X, Zhuang J, Wu Y, Chen J. Atypical deletion of Williams–
Beuren syndrome reveals the mechanism of neurodevelopmental
disorders. BMC Med Genomics 15: 79, 2022. doi:10.1186/s12920022-01227-7.
348. Lacolley P, Regnault V, Laurent S. Mechanisms of arterial stiffening.
Arterioscler Thromb Vasc Biol 40: 1055–1062, 2020. doi:10.1161/
ATVBAHA.119.313129.
349. Wagenseil JE, Mecham RP. Vascular extracellular matrix and arterial mechanics. Physiol Rev 89: 957–989, 2009. doi:10.1152/
physrev.00041.2008.
350. Briones AM, Arribas SM, Salaices M. Role of extracellular matrix in
vascular remodeling of hypertension. Curr Opin Nephrol Hypertens
19: 187–194, 2010. doi:10.1097/MNH.0b013e328335eec9.
351. Kelleher CM, McLean SE, Mecham RP. Vascular extracellular matrix
and aortic development. Curr Top Dev Biol 62: 153–188, 2004.
doi:10.1016/S0070-2153(04)62006-0.
352. Lacolley P, Regnault V, Segers P, Laurent S. Vascular smooth
muscle cells and arterial stiffening: relevance in development,
aging, and disease. Physiol Rev 97: 1555–1617, 2017. doi:10.1152/
physrev.00003.2017.
353. Agarwal R, Filippatos G, Pitt B, Anker SD, Rossing P, Joseph A,
Kolkhof P, Nowack C, Gebel M, Ruilope LM, Bakris GL; FIDELIODKD and FIGARO-DKD investigators. Cardiovascular and kidney
outcomes with finerenone in patients with type 2 diabetes and
chronic kidney disease: the FIDELITY pooled analysis. Eur Heart J
43: 474–484, 2022. doi:10.1093/eurheartj/ehab777.
354. Hanon O, Haulon S, Lenoir H, Seux ML, Rigaud AS, Safar M, Girerd
X, Forette F. Relationship between arterial stiffness and cognitive
function in elderly subjects with complaints of memory loss. Stroke
36: 2193–2197, 2005. doi:10.1161/01.STR.0000181771.82518.1c.
355. Sadekova N, Vallerand D, Guevara E, Lesage F, Girouard H.
Carotid calcification in mice: a new model to study the effects of arterial stiffness on the brain. J Am Heart Assoc 2: e000224, 2013.
doi:10.1161/JAHA.113.000224.
356. Iulita MF, Noriega de la Colina A, Girouard H. Arterial stiffness, cognitive impairment and dementia: confounding factor or real risk? J
Neurochem 144: 527–548, 2018. doi:10.1111/jnc.14235.
357. Baidoe-Ansah D, Sakib S, Jia S, Mirzapourdelavar H, Strackeljan L,
Fischer A, Aleshin S, Kaushik R, Dityatev A. Aging-associated
changes in cognition, expression and epigenetic regulation of chondroitin 6-sulfotransferase Chst3. Cells 11: 2033, 2022. doi:10.3390/
cells11132033.
C122
358. Baeten KM, Akassoglou K. Extracellular matrix and matrix receptors
in blood-brain barrier formation and stroke. Dev Neurobiol 71: 1018–
1039. 2011, doi:10.1002/dneu.20954.
359. Hussain B, Fang C, Chang J. Blood–brain barrier breakdown: an
emerging biomarker of cognitive impairment in normal aging and
dementia. Front Neurosci 15: 699090, 2021. doi:10.3389/
fnins.2021.688090.
360. Roudnicky F, Zhang JD, Kim BK, Pandya NJ, Lan Y, Sach-Peltason
L, et al. Inducers of the endothelial cell barrier identified through
chemogenomic screening in genome-edited hPSC-endothelial cells.
Proc Natl Acad Sci USA 117: 19854–19865, 2020. doi:10.1073/
pnas.1911532117.
361. Shiraki M, Kuroda T, Tanaka S, Saito M, Fukunaga M, Nakamura T.
Nonenzymatic collagen cross-links induced by glycoxidation (pentosidine) predicts vertebral fractures. J Bone Miner Metab 26: 93–100,
2008. doi:10.1007/s00774-007-0784-6.
362. Dyer DG, Blackledge JA, Thorpe SR, Baynes JW. Formation of pentosidine during nonenzymatic browning of proteins by glucose.
Identification of glucose and other carbohydrates as possible precursors of pentosidine in vivo. J Biol Chem 266: 11654–11660, 1991.
doi:10.1016/S0021-9258(18)99007-1.
363. Qiu SR, Wierzbicki A, Orme CA, Cody AM, Hoyer JR, Nancollas
GH, Zepeda S, De Yoreo JJ. Molecular modulation of calcium oxalate crystallization by osteopontin and citrate. Proc Natl Acad Sci
USA 101: 1811–1815, 2004. doi:10.1073/pnas.0307900100.
364. Li J, Ma J, Zhang Q, Gong H, Gao D, Wang Y, Li B, Li X, Zheng H,
Wu Z, Zhu Y, Leng L. Spatially resolved proteomic map shows that
extracellular matrix regulates epidermal growth. Nat Commun 13:
4012, 2022. doi:10.1038/s41467-022-31659-9.
365. Li SS, He SH, Xie PY, Li W, Zhang XX, Li TF, Li DF. Recent progresses in the treatment of osteoporosis. Front Pharmacol 12:
717065, 2021. doi:10.3389/fphar.2021.717065.
366. Amirhosseini M, Madsen RV, Escott KJ, Bostrom MP, Ross FP,
Fahlgren A. GSK-3b inhibition suppresses instability-induced osteolysis by a dual action on osteoblast and osteoclast differentiation. J
Cell Physiol 233: 2398–2408, 2018. doi:10.1002/jcp.26111.
367. Manandhar S, Kabekkodu SP, Pai KS. Aberrant canonical Wnt
signaling: phytochemical based modulation. Phytomedicine 76:
153243, 2020. doi:10.1016/j.phymed.2020.153243.
368. Fowler TW, Mitchell TL, Janda CY, Xie L, Tu S, Chen H, et al.
Development of selective bispecific Wnt mimetics for bone loss and
repair. Nat Commun 12: 3247, 2021. doi:10.1038/s41467-021-23374-8.
369. Li KC, Chang YH, Yeh CL, Hu YC. Healing of osteoporotic bone
defects by baculovirus-engineered bone marrow-derived MSCs
expressing microRNA sponges. Biomaterials 74: 155–166, 2016.
doi:10.1016/j.biomaterials.2015.09.046.
370. Dillon CF, Rasch EK, Gu Q, Hirsch R. Prevalence of knee osteoarthritis in the United States: arthritis data from the Third National
Health and Nutrition Examination Survey 1991-94. J Rheumatol 33:
2271–2279, 2006.
371. Rahmati M, Nalesso G, Mobasheri A, Mozafari M. Aging and osteoarthritis: central role of the extracellular matrix. Ageing Res Rev 40:
20–30, 2017. doi:10.1016/j.arr.2017.07.004.
372. Blagojevic M, Jinks C, Jeffery A, Jordan KP. Risk factors for onset
of osteoarthritis of the knee in older adults: a systematic review and
meta-analysis. Osteoarthritis Cartilage 18: 24–33, 2010. doi:10.1016/j.
joca.2009.08.010.
373. Neogi T, Zhang Y. Epidemiology of osteoarthritis. Rheum Dis Clin N
Am 39: 1–19, 2013. doi:10.1016/j.rdc.2012.10.004.
374. Sandell LJ, Aigner T. Articular cartilage and changes in arthritis. An
introduction: cell biology of osteoarthritis. Arthritis Res 3: 7, 2001.
doi:10.1186/ar148.
375. Goldring MB. Chondrogenesis, chondrocyte differentiation, and articular cartilage metabolism in health and osteoarthritis. Ther Adv
Musculoskelet Dis 4: 269–285, 2012. doi:10.1177/1759720X12448454.
376. Lotz M, Loeser RF. Effects of aging on articular cartilage homeostasis. Bone 51: 241–248, 2012. doi:10.1016/j.bone.2012.03.023.
377. Lotz MK, Otsuki S, Grogan SP, Sah R, Terkeltaub R, D’Lima D.
Cartilage cell clusters. Arthritis Rheum 62: 2206–2218, 2010.
doi:10.1002/art.27528.
378. Buckwalter JA, Woo SL, Goldberg VM, Hadley EC, Booth F,
Oegema TR, Eyre DR. Soft-tissue aging and musculoskeletal function. J Bone Joint Surg Am 75: 1533–1548, 1993. doi:10.2106/
00004623-199310000-00015.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
€ssel S, Muschter D. Recent advances in the treatment of osteo379. Gra
arthritis. F1000Res 9: F1000 Faculty Rev-325, 2020. doi:10.12688/
f1000research.22115.1.
380. Srikanth VK, Fryer JL, Zhai G, Winzenberg TM, Hosmer D, Jones
G. A meta-analysis of sex differences prevalence, incidence and severity of osteoarthritis. Osteoarthritis Cartilage 13: 769–781, 2005.
doi:10.1016/j.joca.2005.04.014.
381. Buckwalter JA, Martin JA. Osteoarthritis. Adv Drug Deliv Rev 58:
150–167, 2006. doi:10.1016/j.addr.2006.01.006.
382. Stachenfeld NS. Hormonal changes during menopause and the
impact on fluid regulation. Reprod Sci 21: 555–561, 2014. doi:10.1177/
1933719113518992.
~ eda S, Largo R, Herrero-Beaumont G.
383. Roman-Blas JA, Castan
Osteoarthritis associated with estrogen deficiency. Arthritis Res Ther
11: 241, 2009. doi:10.1186/ar2791.
384. Peshkova M, Lychagin A, Lipina M, Di Matteo B, Anzillotti G,
Ronzoni F, Kosheleva N, Shpichka A, Royuk V, Fomin V, Kalinsky
E, Timashev P, Kon E. Gender-related aspects in osteoarthritis development and progression: a review. Int J Mol Sci 23: 2767, 2022.
doi:10.3390/ijms23052767.
385. Hernandez PA, Barati Z, Hutcherson C, Wright J, Welch T,
Dhaher Y. Sexual dimorphism in the extracellular matrix of
articular cartilage. Osteoarthritis Cartilage 29: S1–S2, 2021.
doi:10.1177/19476035221121792.
386. Li C, Zheng Z. Males and females have distinct molecular events in
the articular cartilage during knee osteoarthritis. Int J Mol Sci 22:
7876, 2021. doi:10.3390/ijms22157876.
387. Ye L, Mauro TM, Dang E, Wang G, Hu LZ, Yu C, Jeong S, Feingold
K, Elias PM, Lv CZ, Man MQ. Topical applications of an emollient
reduce circulating pro-inflammatory cytokine levels in chronically
aged humans: a pilot clinical study. J Eur Acad Dermatol Venereol
33: 2197–2201, 2019. doi:10.1111/jdv.15540.
388. Griffiths CE, Finkel LJ, Ditre CM, Hamilton TA, Ellis CN, Voorhees
JJ. Topical tretinoin (retinoic acid) improves melasma. A vehicle-controlled, clinical trial. Br J Dermatol 129: 415–421, 1993. doi:10.1111/
j.1365-2133.1993.tb03169.x.
389. Gueniche A, Castiel-Higounenc I. Efficacy of glucosamine sulphate
in skin ageing: results from an ex vivo anti-ageing model and a clinical trial. Skin Pharmacol Physiol 30: 36–41, 2017. doi:10.1159/
000450832.
390. Gallo RL, Bucay VW, Shamban AT, Lima-Maribona J, Lewis AB,
Ditre CM, Mayoral FA, Gold MH. The potential role of topically
applied heparan sulfate in the treatment of photodamage. J Drugs
Dermatol 14: 669–674, 2015.
391. Weimer S, Priebs J, Kuhlow D, Groth M, Priebe S, Mansfeld J,
Merry TL, Dubuis S, Laube B, Pfeiffer AF, Schulz TJ, Guthke R,
Platzer M, Zamboni N, Zarse K, Ristow M. D-Glucosamine supplementation extends life span of nematodes and of ageing mice. Nat
Commun 5: 3563, 2014. doi:10.1038/ncomms4563.
392. Xia W, Hammerberg C, Li Y, He T, Quan T, Voorhees JJ, Fisher GJ.
Expression of catalytically active matrix metalloproteinase-1 in dermal fibroblasts induces collagen fragmentation and functional alterations that resemble aged human skin. Aging Cell 12: 661–671, 2013.
doi:10.1111/acel.12089.
393. Li J, Kim SG, Blenis J. Rapamycin: one drug, many effects. Cell
Metab 19: 373–379, 2014. doi:10.1016/j.cmet.2014.01.001.
394. Chung CL, Lawrence I, Hoffman M, Elgindi D, Nadhan K, Potnis M,
Jin A, Sershon C, Binnebose R, Lorenzini A, Sell C. Topical rapamycin reduces markers of senescence and aging in human skin: an exploratory, prospective, randomized trial. GeroScience 41: 861–869,
2019. doi:10.1007/s11357-019-00113-y.
395. You Y, Tian Y, Yang Z, Shi J, Kwak KJ, Tong Y, et al. Intradermally
delivered mRNA-encapsulating extracellular vesicles for collagenreplacement therapy. Nat Biomed Eng. 2023 Jan 12 [Epub ahead of
print]. doi:10.1038/s41551-022-00989-w.
396. Byron A, Humphries JD, Humphries MJ. Defining the extracellular
matrix using proteomics. Int J Exp Pathol 94: 75–92, 2013.
doi:10.1111/iep.12011.
397. Goddard ET, Hill RC, Barrett A, Betts C, Guo Q, Maller O, Borges
VF, Hansen KC, Schedin P. Quantitative extracellular matrix proteomics to study mammary and liver tissue microenvironments. Int J
Biochem Cell Biol 81: 223–232, 2016. doi:10.1016/j.biocel.
2016.10.014.
398. Liu JR, Modo M. Quantification of the extracellular matrix molecule
thrombospondin 1 and its pericellular association in the brain using a
semiautomated computerized approach. J Histochem Cytochem 66:
643–662, 2018. doi:10.1369/0022155418771677.
399. Statzer C, Luthria K, Sharma A, Kann MG, Ewald CY. The human
extracellular matrix diseasome reveals genotype–phenotype associations with clinical implications for age-related diseases. Biomedicines
11: 1212, 2023. doi:10.3390/biomedicines11041212.
400. Naba A. Ten years of extracellular matrix proteomics: accomplishments, challenges, and future perspectives. Mol Cell Proteomics 22:
100528, 2023. doi:10.1016/j.mcpro.2023.100528.
€ EA, Gonzalez-Molina J, Moyano-Galceran L, Jamalzadeh S,
401. Pietila
Zhang K, Lehtinen L, et al. Co-evolution of matrisome and adaptive
adhesion dynamics drives ovarian cancer chemoresistance. Nat
Commun 12: 3904, 2021. doi:10.1038/s41467-021-24009-8.
€ st H, Selevsek N, Reiter L, Bonner
402. Gillet LC, Navarro P, Tate S, Ro
R, Aebersold R. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11:
O111.016717, 2012. doi:10.1074/mcp.O111.016717.
403. Collins BC, Hunter CL, Liu Y, Schilling B, Rosenberger G, Bader SL,
et al. Multi-laboratory assessment of reproducibility, qualitative and
quantitative performance of SWATH-mass spectrometry. Nat
Commun 8: 291, 2017. doi:10.1038/s41467-017-00249-5.
404. Krasny L, Bland P, Kogata N, Wai P, Howard BA, Natrajan RC,
Huang PH. SWATH mass spectrometry as a tool for quantitative
profiling of the matrisome. J Proteomics 189: 11–22, 2018.
doi:10.1016/j.jprot.2018.02.026.
405. Tabata T, Sato T, Kuromitsu J, Oda Y. Pseudo internal standard
approach for label-free quantitative proteomics. Anal Chem 79:
8440–8445, 2007. doi:10.1021/ac701628m.
406. Williams EG, Pfister N, Roy S, Statzer C, Haverty J, Ingels J, Bohl C,
Hasan M, Cuklina
J, B€
uhlmann P, Zamboni N, Lu L, Ewald CY,
Williams RW, Aebersold R. Multiomic profiling of the liver across
diets and age in a diverse mouse population. Cell Syst 13: 43–57.e6,
2022. doi:10.1016/j.cels.2021.09.005.
407. Hiebert P, Wietecha MS, Cangkrama M, Haertel E, Mavrogonatou
E, Stumpe M, Steenbock H, Grossi S, Beer HD, Angel P,
Brinckmann J, Kletsas D, Dengjel J, Werner S. Nrf2-mediated fibroblast reprogramming drives cellular senescence by targeting the
matrisome. Dev Cell 46: 145–161.e10, 2018. doi:10.1016/j.devcel.
2018.06.012.
408. Caddeo S, Boffito M, Sartori S. Tissue engineering approaches in
the design of healthy and pathological in vitro tissue models. Front
Bioeng Biotechnol 5: 40, 2017. doi:10.3389/fbioe.2017.00040.
409. Hill RC, Calle EA, Dzieciatkowska M, Niklason LE, Hansen KC.
Quantification of extracellular matrix proteins from a rat lung scaffold
to provide a molecular readout for tissue engineering. Mol Cell
Proteomics 14: 961–973, 2015. doi:10.1074/mcp.M114.045260.
410. Lindsey ML, Weintraub ST, Lange RA. Using extracellular matrix proteomics: to understand left ventricular remodeling. Circ
Cardiovasc Genet 5: o1–o7, 2012. doi:10.1161/CIRCGENETICS.
110.957803.
411. Sonbol HS. Extracellular matrix remodeling in human disease. J
Microsc Ultrastruct 6: 123–128, 2018. doi:10.4103/JMAU.JMAU_4_18.
412. Iozzo RV, Gubbiotti MA. Extracellular matrix: the driving force of
mammalian diseases. Matrix Biol 71–72: 1–9, 2018. doi:10.1016/j.
matbio.2018.03.023.
413. Arteel GE, Naba A. The liver matrisome—looking beyond collagens.
JHEP Rep 2: 100115, 2020. doi:10.1016/j.jhepr.2020.100115.
414. Herovici C. A polychrome stain for differentiating precollagen from
collagen. Stain Technol 38: 204–205, 1963.
415. Teuscher AC, Statzer C, Pantasis S, Bordoli MR, Ewald CY.
Assessing collagen deposition during aging in mammalian tissue
and in Caenorhabditis elegans. Methods Mol Biol 1944: 169–188,
2019. doi:10.1007/978-1-4939-9095-5_13.
416. Fitzgerald AM, Kirkpatrick JJ, Foo IT, Naylor IL. Human skin histology as demonstrated by Herovici’s stain: a guide for the improvement of dermal substitutes for use with cultured keratinocytes?
Burns 22: 200–202, 1996. doi:10.1016/0305-4179(95)00119-0.
417. Badylak SF. The extracellular matrix as a scaffold for tissue reconstruction. Semin Cell Dev Biol 13: 377–383, 2002. doi:10.1016/
s1084952102000940.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C123
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
418. Leonard AK, Loughran EA, Klymenko Y, Liu Y, Kim O, Asem M,
McAbee K, Ravosa MJ, Stack MS. Methods for the visualization and
analysis of extracellular matrix protein structure and degradation.
Methods Cell Biol 143: 79–95, 2018. doi:10.1016/bs.mcb.2017.08.005.
419. Hu M, Ling Z, Ren X. Extracellular matrix dynamics: tracking in biological systems and their implications. J Biol Eng 16: 13, 2022.
doi:10.1186/s13036-022-00292-x.
420. Lu Y, Kamel-El Sayed SA, Wang K, Tiede-Lewis LM, Grillo MA,
Veno PA, Dusevich V, Phillips CL, Bonewald LF, Dallas SL. Live
imaging of type I collagen assembly dynamics in osteoblasts stably
expressing GFP and mCherry-tagged collagen constructs. J Bone
Miner Res 33: 1166–1182, 2018. doi:10.1002/jbmr.3409.
421. Fischer A, Wannemacher J, Christ S, Koopmans T, Kadri S, Zhao J,
€usl M, Krenn PW, Machens HG, Fa
€ssler
Gouda M, Ye H, M€
uck-Ha
R, Neumann PA, Hauck SM, Rinkevich Y. Neutrophils direct preexisting matrix to initiate repair in damaged tissues. Nat Immunol 23:
518–531, 2022. doi:10.1038/s41590-022-01166-6.
422. Sebastiani P, Federico A, Morris M, Gurinovich A, Tanaka T,
Chandler KB, Andersen SL, Denis G, Costello CE, Ferrucci L,
Jennings L, Glass DJ, Monti S, Perls TT. Protein signatures of centenarians and their offspring suggest centenarians age slower than
other humans. Aging Cell 20: e13290, 2021.
423. Jongsma E, Mateos JM, Ewald CY. Removal of extracellular human
amyloid beta aggregates by extracellular proteases in C. elegans
(Preprint). bioRxiv 2022.09.14.507993, 2022. doi:10.1101/2022.09.14.
507993.
€ m A, Velati D, Mittapalli VR, Fritsch A, Kern JS, Bruckner424. Nystro
Tuderman L. Collagen VII plays a dual role in wound healing. J Clin
Invest 123: 3498–3509, 2013. doi:10.1172/JCI68127.
425. Windler C, Hermsdorf U, Brinckmann J, Seeger K. A type VII collagen subdomain mutant is thermolabile and shows enhanced proteolytic degradability—implications for the pathogenesis of recessive
dystrophic epidermolysis bullosa? Biochim Biophys Acta Mol Basis
Dis 1863: 52–59, 2017. doi:10.1016/j.bbadis.2016.10.023.
426. Fine JD. Inherited epidermolysis bullosa. Orphanet J Rare Dis 5: 12,
2010. doi:10.1186/1750-1172-5-12.
427. Chung HJ, Uitto J. Type VII collagen: the anchoring fibril protein at
fault in dystrophic epidermolysis bullosa. Dermatol Clin 28: 93–105,
2010. doi:10.1016/j.det.2009.10.011.
428. Fassett RG, Venuthurupalli SK, Gobe GC, Coombes JS, Cooper
MA, Hoy WE. Biomarkers in chronic kidney disease: a review.
Kidney Int 80: 806–821, 2011. doi:10.1038/ki.2011.198.
429. Hijmans RS, Rasmussen DG, Yazdani S, Navis G, van Goor H,
Karsdal MA, Genovese F, van den Born J. Urinary collagen degradation products as early markers of progressive renal fibrosis. J
Transl Med 15: 63, 2017. doi:10.1186/s12967-017-1163-2.
430. He T, Mischak M, Clark AL, Campbell RT, Delles C, Díez J, et al.
Urinary peptides in heart failure: a link to molecular pathophysiology.
Eur J Heart Fail 23: 1875–1887, 2021. doi:10.1002/ejhf.2195.
431. Martens DS, Thijs L, Latosinska A, Trenson S, Siwy J, Zhang ZY,
Wang C, Beige J, Vlahou A, Janssens S, Mischak H, Nawrot TS,
Staessen JA; FLEMENGHO investigators. Urinary peptidomic profiles to address age-related disabilities: a prospective population
study. Lancet Healthy Longev 2: e690–e703, 2021. doi:10.1016/
S2666-7568(21)00226-9.
432. Bakun M, Senatorski G, Rubel T, Lukasik A, Zielenkiewicz P,
Dadlez M, Paczek L. Urine proteomes of healthy aging humans
reveal extracellular matrix (ECM) alterations and immune system
dysfunction. Age (Dordr) 36: 299–311, 2014. doi:10.1007/s11357-0139562-7.
433. Farris AB, Colvin RB. Renal interstitial fibrosis: mechanisms and
evaluation. Curr Opin Nephrol Hypertens 21: 289–300, 2012.
doi:10.1097/MNH.0b013e3283521cfa.
434. Nkuipou-Kenfack E, Schanstra JP, Bajwa S, Pejchinovski M, Vinel
C, Dray C, Valet P, Bascands JL, Vlahou A, Koeck T, Borries M,
Busch H, Bechtel-Walz W, Huber TB, Rudolph KL, Pich A, Mischak
H, Z€
urbig P. The use of urinary proteomics in the assessment of suitability of mouse models for ageing. PLoS One 12: e0166875, 2017.
doi:10.1371/journal.pone.0166875.
435. Dragsbæk K, Neergaard JS, Hansen HB, Byrjalsen I, Alexandersen
P, Kehlet SN, Bay-Jensen AC, Christiansen C, Karsdal MA. Matrix
metalloproteinase mediated type I collagen degradation—an independent risk factor for mortality in women. eBioMedicine 2: 723–
729, 2015. doi:10.1016/j.ebiom.2015.04.017.
C124
436. Bay-Jensen AC, Bihlet A, Byrjalsen I, Andersen JR, Riis BJ,
Christiansen C, Michaelis M, Guehring H, Ladel C, Karsdal MA.
Serum C-reactive protein metabolite (CRPM) is associated with incidence of contralateral knee osteoarthritis. Sci Rep 11: 6583, 2021.
doi:10.1038/s41598-021-86064-x.
437. Bager CL, Willumsen N, Christiansen C, Bay-Jensen AC, Nielsen
HB, Karsdal M. Bone and soft tissue turnover in relation to all-cause
mortality in postmenopausal women. J Gerontol Ser A 74: 1098–
1104, 2019. doi:10.1093/gerona/gly163.
438. Ye X, Linton JM, Schork NJ, Buck LB, Petrascheck M. A pharmacological network for lifespan extension in Caenorhabditis elegans.
Aging Cell 13: 206–215, 2014. doi:10.1111/acel.12163.
439. Ma Y, Cai J, Wang Y, Liu J, Fu S. Non-enzymatic glycation of transferrin and diabetes mellitus. Diabetes Metab Syndr Obes 14: 2539–
2548, 2021. doi:10.2147/DMSO.S304796.
440. Robert L, Labat-Robert J. Role of the Maillard reaction in aging and
age-related diseases. Studies at the cellular-molecular level. Clin
Chem Lab Med 52: 5–10, 2014. doi:10.1515/cclm-2012-0763.
441. Zhang Q, Ames JM, Smith RD, Baynes JW, Metz TO. A perspective
on the Maillard reaction and the analysis of protein glycation by
mass spectrometry: probing the pathogenesis of chronic disease. J
Proteome Res 8: 754–769, 2009. doi:10.1021/pr800858h.
442. Cho S, Duong VA, Mok JH, Joo M, Park JM, Lee H. Enrichment and
analysis of glycated proteins. Rev Anal Chem 41: 83–97, 2022.
doi:10.1515/revac-2022-0036.
443. Huang W, Ao P, Li J, Wu T, Xu L, Deng Z, Chen W, Yin C, Cheng X.
Autophagy protects advanced glycation end product-induced apoptosis and expression of MMP-3 and MMP-13 in rat chondrocytes.
Biomed Res Int 2017: 6341919, 2017. doi:10.1155/2017/6341919.
444. Takahashi A, Takabatake Y, Kimura T, Maejima I, Namba T,
Yamamoto T, Matsuda J, Minami S, Kaimori JY, Matsui I,
Matsusaka T, Niimura F, Yoshimori T, Isaka Y. Autophagy inhibits
the accumulation of advanced glycation end products by promoting
lysosomal biogenesis and function in the kidney proximal tubules.
Diabetes 66: 1359–1372, 2017. doi:10.2337/db16-0397.
445. Sruthi CR, Raghu KG. Advanced glycation end products and their
adverse effects: the role of autophagy. J Biochem Mol Toxicol 35:
e22710, 2021. doi:10.1002/jbt.22710.
446. Croce AC, Bottiroli G. Autofluorescence spectroscopy and imaging:
a tool for biomedical research and diagnosis. Eur J Histochem 58:
2461, 2014. doi:10.4081/ejh.2014.2461.
447. Kolenc OI, Quinn KP. Evaluating cell metabolism through autofluorescence imaging of NAD(P)H and FAD. Antioxid Redox Signal 30:
875–889, 2019. doi:10.1089/ars.2017.7451.
448. McCabe MC, Hill RC, Calderone K, Cui Y, Yan Y, Quan T, Fisher
GJ, Hansen KC. Alterations in extracellular matrix composition during aging and photoaging of the skin. Matrix Biol Plus 8: 100041,
2020. doi:10.1016/j.mbplus.2020.100041.
449. Boers HE, Haroon M, Le Grand F, Bakker AD, Klein-Nulend J,
Jaspers RT. Mechanosensitivity of aged muscle stem cells. J Orthop
Res 36: 632–641, 2018. doi:10.1002/jor.23797.
450. Davis BO, Anderson GL, Dusenbery DB. Total luminescence spectroscopy of fluorescence changes during aging in Caenorhabditis elegans. Biochemistry 21: 4089–4095, 1982. doi:10.1021/bi00260a027.
451. Rahimi M, Sohrabi S, Murphy CT. Novel elasticity measurement
techniques reveal that the C. elegans cuticle is required for physical
integrity with age (Preprint). bioRxiv 2021.10.04.463135, 2021.
doi:10.1101/2021.10.04.463135.
452. Wizenty J, Schumann T, Theil D, Stockmann M, Pratschke J, Tacke
F, Aigner F, Wuensch T. Recent advances and the potential for clinical use of autofluorescence detection of extra-ophthalmic tissues.
Molecules 25: 2095. 2020. doi:10.3390/molecules25092095.
453. Stammers M, Ivanova IM, Niewczas IS, Segonds-Pichon A,
Streeter M, Spiegel DA, Clark J. Age-related changes in the physical properties, cross-linking, and glycation of collagen from mouse
tail tendon. J Biol Chem 295: 10562–10571, 2020. doi:10.1074/jbc.
RA119.011031.
454. Sloane LB, Stout JT, Vandenbergh DJ, Vogler GP, Gerhard GS,
McClearn GE. Quantitative trait loci analysis of tail tendon break
time in mice of C57BL/6J and DBA/2J lineage. J Gerontol A Biol Sci
Med Sci 66: 170–178, 2011. doi:10.1093/gerona/glq169.
455. Haefke A, Ewald C. Tail tendon break time for the assessment of
aging and longitudinal healthspan in mice. Bio-101 e3833, 2020.
doi:10.21769/BioProtoc.3833.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
456. Sherman PW, Morton ML, Hoopes LM, Bochantin J, Watt JM. The
use of tail collagen strength to estimate age in Belding’s ground
squirrels. J Wildl Manag 49: 874–879, 1985. doi:10.2307/3801360.
457. Chaudhuri O, Cooper-White J, Janmey PA, Mooney DJ, Shenoy
VB. The impact of extracellular matrix viscoelasticity on cellular
behavior. Nature 584: 535–546, 2020. doi:10.1038/s41586-0202612-2.
458. Muiznieks LD, Keeley FW. Molecular assembly and mechanical
properties of the extracellular matrix: a fibrous protein perspective.
Biochim Biophys Acta Mol Basis Dis 1832: 866–875, 2013.
doi:10.1016/j.bbadis.2012.11.022.
459. Kim SH, Monticone RE, McGraw KR, Wang M. Age-associated
proinflammatory elastic fiber remodeling in large arteries. Mech
Ageing Dev 196: 111490, 2021. doi:10.1016/j.mad.2021.111490.
460. Marrese M, Guarino V, Ambrosio L. Atomic force microscopy: a
powerful tool to address scaffold design in tissue engineering. J
Funct Biomater 8: 7, 2017. doi:10.3390/jfb8010007.
461. Plodinec M, Loparic M, Monnier CA, Obermann EC, ZanettiDallenbach R, Oertle P, Hyotyla JT, Aebi U, Bentires-Alj M, Lim RY,
Schoenenberger CA. The nanomechanical signature of breast
cancer. Nat Nanotechnol 7: 757–765, 2012. doi:10.1038/nnano.
2012.167.
462. Deng B, Zhao Z, Kong W, Han C, Shen X, Zhou C. Biological role of
matrix stiffness in tumor growth and treatment. J Transl Med 20:
540, 2022. doi:10.1186/s12967-022-03768-y.
463. Ishihara S, Haga H. Matrix stiffness contributes to cancer progression by regulating transcription factors. Cancers 14: 1049, 2022.
doi:10.3390/cancers14041049.
464. Wullkopf L, West AV, Leijnse N, Cox TR, Madsen CD, Oddershede
LB, Erler JT. Cancer cells’ ability to mechanically adjust to extracellular matrix stiffness correlates with their invasive potential. Mol Biol
Cell 29: 2378–2385, 2018. doi:10.1091/mbc.E18-05-0319.
465. Ling YH, Chen JW, Wen SH, Huang CY, Li P, Lu LH, Mei J, Li SH,
Wei W, Cai MY, Guo RP. Tumor necrosis as a poor prognostic predictor on postoperative survival of patients with solitary small hepatocellular carcinoma. BMC Cancer 20: 607, 2020. doi:10.1186/
s12885-020-07097-5.
466. Reily C, Stewart TJ, Renfrow MB, Novak J. Glycosylation in health
and disease. Nat Rev Nephrol 15: 346–366, 2019. doi:10.1038/
s41581-019-0129-4.
467. Rudman N, Gornik O, Lauc G. Altered N-glycosylation profiles as
potential biomarkers and drug targets in diabetes. FEBS Lett 593:
1598–1615, 2019. doi:10.1002/1873-3468.13495.
468. Miura Y, Endo T. Glycomics and glycoproteomics focused on aging
and age-related diseases—glycans as a potential biomarker for
physiological alterations. Biochim Biophys Acta 1860: 1608–1614,
2016. doi:10.1016/j.bbagen.2016.01.013.
469. Zaytseva OO, Sharapov SZ, Perola M, Esko T, Landini A, Hayward
C, Wilson JF, Lauc G, Aulchenko YS, Klarić L, Tsepilov YA.
Investigation of the causal relationships between human IgG N-glycosylation and 12 common diseases associated with changes in the
IgG N-glycome. Hum Mol Genet 31: 1545–1559, 2022. doi:10.1093/
hmg/ddab335.
470. Stanley P, Moremen KW, Lewis NE, Taniguchi N, Aebi M. N-glycans. In: Essentials of Glycobiology (4th ed.), edited by Varki A,
Cummings RD, Esko JD. Cold Spring Harbor, NY: Cold Spring
Harbor Laboratory Press, 2022.
471. Mkhikian H, Hayama KL, Khachikyan K, Li C, Zhou RW, Pawling J,
Klaus S, Tran PQ, Ly KM, Gong AD, Saryan H, Hai JL, Grigoryan D,
Lee PL, Newton BL, Raffatellu M, Dennis JW, Demetriou M. Ageassociated impairment of T cell immunity is linked to sex-dimorphic
elevation of N-glycan branching. Nat Aging 2: 231–242, 2022.
doi:10.1038/s43587-022-00187-y.
472. Deriš H, Kifer D, Cindrić A, Petrović T, Cvetko A, Trbojević-Akma
cić
I, Kolcić I, Polašek O, Newson L, Spector T, Menni C, Lauc G.
Immunoglobulin G glycome composition in transition from premenopause to postmenopause. iScience 25: 103897, 2022. doi:10.1016/j.
isci.2022.103897.
473. Bielajew BJ, Hu JC, Athanasiou KA. Collagen: quantification, biomechanics and role of minor subtypes in cartilage. Nat Rev Mater 5:
730–747, 2020. doi:10.1038/s41578-020-0213-1.
474. Hosseini S, Vázquez-Villegas P, Rito-Palomares M, MartinezChapa SO. Advantages, disadvantages and modifications of conventional ELISA. In: Enzyme-Linked Immunosorbent Assay ELISA,
475.
476.
477.
478.
479.
480.
481.
482.
483.
484.
485.
486.
487.
488.
489.
490.
491.
492.
493.
edited by Hosseini S, Vázquez-Villegas P, Rito-Palomares M,
Martinez-Chapa SO. Singapore: Springer, 2018. p. 67–115.
Lin H, Goodin S, Strair RK, DiPaola RS, Gounder MK. Comparison
of LC-MS assay and HPLC assay of busulfan in clinical pharmacokinetics studies. Int Sch Res Notices 2012: 198683, 2012. doi:10.5402/
2012/198683.
Grebe SK, Singh RJ. LC-MS/MS in the clinical laboratory—where to
from here? Clin Biochem Rev 32: 5–31, 2011.
Kachuk C, Doucette AA. The benefits (and misfortunes) of SDS in
top-down proteomics. J Proteomics 175: 75–86, 2018. doi:10.1016/j.
jprot.2017.03.002.
Bancroft JD, Layton C. Connective and other mesenchymal tissues
with their stains. In: Bancroft’s Theory and Practice of Histological
Techniques (8th ed.), edited by Suvarna SK, Layton C, Bancroft JD.
Amsterdam: Elsevier, 2019, p. 153–175.
~ o-González I, Travieso-González CM,
Hernández-Morera P, Castan
-Corredera B, Ortega-Santana F. Quantification and statisMompeo
tical analysis methods for vessel wall components from stained
images with Masson’s trichrome. PLoS One 11: e0146954, 2016.
doi:10.1371/journal.pone.0146954.
Farris AB, Alpers CE. What is the best way to measure renal fibrosis? A pathologist’s perspective. Kidney Int Suppl 4: 9–15, 2014.
doi:10.1038/kisup.2014.3.
Gutpell KM, Hrinivich WT, Hoffman LM. Skeletal muscle fibrosis in
the mdx/utrn þ / mouse validates its suitability as a murine model
of Duchenne muscular dystrophy. PLoS One 10: e0117306, 2015.
doi:10.1371/journal.pone.0117306.
Krishna M. Role of special stains in diagnostic liver pathology. Clin
Liver Dis 2: S8–S10, 2013. doi:10.1002/cld.148.
Onoda A, Hagiwara S, Kubota N, Yanagita S, Takeda K, Umezawa
M. A novel staining method for detection of brain perivascular injuries induced by nanoparticle: periodic acid-Schiff and immunohistochemical double-staining. Front Toxicol 4: 825984, 2022.
doi:10.3389/ftox.2022.825984.
Nitiputri K, Ramasse QM, Autefage H, McGilvery CM,
Boonrungsiman S, Evans ND, Stevens MM, Porter AE.
Nanoanalytical electron microscopy reveals a sequential mineralization process involving carbonate-containing amorphous precursors.
ACS Nano 10: 6826–6835, 2016. doi:10.1021/acsnano.6b02443.
He B, Wu JP, Kirk TB, Carrino JA, Xiang C, Xu J. High-resolution
measurements of the multilayer ultra-structure of articular cartilage
and their translational potential. Arthritis Res Ther 16: 205, 2014.
doi:10.1186/ar4506.
Mahamid J, Aichmayer B, Shimoni E, Ziblat R, Li C, Siegel S, Paris
O, Fratzl P, Weiner S, Addadi L. Mapping amorphous calcium phosphate transformation into crystalline mineral from the cell to the
bone in zebrafish fin rays. Proc Natl Acad Sci U S A 107: 6316–6321,
2010. doi:10.1073/pnas.0914218107.
Nudelman F, Lausch AJ, Sommerdijk NA, Sone ED. In vitro models
of collagen biomineralization. J Struct Biol 183: 258–269, 2013.
doi:10.1016/j.jsb.2013.04.003.
Skiniotis G, Southworth DR. Single-particle cryo-electron microscopy of macromolecular complexes. Microscopy 65: 9–22, 2016.
doi:10.1093/jmicro/dfv366.
Song Q, Jiao K, Tonggu L, Wang LG, Zhang SL, Yang YD, Zhang L,
Bian JH, Hao DX, Wang CY, Ma YX, Arola DD, Breschi L, Chen JH,
Tay FR, Niu LN. Contribution of biomimetic collagen-ligand interaction to intrafibrillar mineralization. Sci Adv 5: eaav9075, 2019.
doi:10.1126/sciadv.aav9075.
Starborg T, Lu Y, Kadler KE, Holmes DF. Electron microscopy of collagen fibril structure in vitro and in vivo including three-dimensional
reconstruction. Methods Cell Biol. 88: 319–345, 2008. doi:10.1016/
S0091-679X(08)00417-2.
Newcomb CJ, Moyer TJ, Lee SS, Stupp SI. Advances in cryogenic
transmission electron microscopy for the characterization of
dynamic self-assembling nanostructures. Curr Opin Colloid Interface
Sci 17: 350–359, 2012. doi:10.1016/j.cocis.2012.09.004.
Doyle ME, Dalgarno K, Masoero E, Ferreira AM. Advances in biomimetic collagen mineralisation and future approaches to bone tissue
engineering. Biopolymers 114: e23527, 2023. doi:10.1002/bip.23527.
Wu X, Walsh K, Hoff BL, Camci-Unal G. Mineralization of biomaterials for bone tissue engineering. Bioengineering 7: 132, 2020.
doi:10.3390/bioengineering7040132.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C125
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
494. Cox G, Kable E. Second-harmonic imaging of collagen. Methods
Mol Biol 319: 15–35, 2006. doi:10.1007/978-1-59259-993-6_2.
495. Van Gulick L, Saby C, Morjani H, Beljebbar A. Age-related changes
in molecular organization of type I collagen in tendon as probed by
polarized SHG and Raman microspectroscopy. Sci Rep 9: 7280,
2019. doi:10.1038/s41598-019-43636-2.
496. Xu S, Gu M, Wu K, Li G. Unraveling the role of hydroxyproline in
maintaining the thermal stability of the collagen triple helix structure
using simulation. J Phys Chem B 123: 7754–7763, 2019. doi:10.1021/
acs.jpcb.9b05006.
497. Gabr SA, Alghadir AH, Sherif YE, Ghfar AA. Hydroxyproline as a
biomarker in liver disease. In: Biomarkers in Disease: Methods,
Discoveries and Applications, edited by Patel VB, Preedy VR.
Dordrecht, The Netherlands: Springer, 2017, p. 471–491.
498. Kesava Reddy G, Enwemeka CS. A simplified method for the analysis of hydroxyproline in biological tissues. Clin Biochem 29: 225–
229, 1996. doi:10.1016/0009-9120(96)00003-6.
499. Knowlton M, Dohan FC, Sprince H. Use of modified Ehrlich’s reagent for measurement of indolic compounds. Anal Chem 32: 666–
668, 1960. doi:10.1021/ac60162a029.
500. Cissell DD, Link JM, Hu JC, Athanasiou KA. A modified hydroxyproline assay based on hydrochloric acid in Ehrlich’s solution accurately
measures tissue collagen content. Tissue Eng Part C Methods 23:
243–250, 2017. doi:10.1089/ten.tec.2017.0018.
501. Green GD, Reagan K. Determination of hydroxyproline by high pressure liquid chromatography. Anal Biochem 201: 265–269, 1992.
doi:10.1016/0003-2697(92)90337-7.
502. Stimler NP. High-performance liquid chromatographic quantitation
of collagen biosynthesis in explant cultures. Anal Biochem 142: 103–
108, 1984. doi:10.1016/0003-2697(84)90523-2.
503. Wang HJ, Zhang Y, Kato S, Nakagawa K, Kimura F, Miyazawa T,
Wang JY. HPLC-MS/MS: a potential method to track the in vivo degradation of zein-based biomaterial. J Biomed Mater Res A 106: 606–
613, 2018. doi:10.1002/jbm.a.36252.
504. Friess W. Collagen—biomaterial for drug delivery. Eur J Pharm
Biopharm 45: 113–136, 1998. doi:10.1016/S0939-6411(98)00017-4.
505. Lareu RR, Zeugolis DI, Abu-Rub M, Pandit A, Raghunath M.
Essential modification of the Sircol Collagen Assay for the accurate
quantification of collagen content in complex protein solutions. Acta
Biomater 6: 3146–3151, 2010. doi:10.1016/j.actbio.2010.02.004.
506. Sweat F, Puchtler H, Rosenthal SI. Sirius Red F3BA as a stain for
connective tissue. Arch Pathol 78: 69–72, 1964.
507. Constantine VS, Mowry RW. Selective staining of human dermal collagen: II. The use of Picrosirius Red F3BA with polarization microscopy. J Invest Dermatol 50: 419–423, 1968. doi:10.1038/jid.1968.68.
508. Kratky RG, Ivey J, Roach MR. Collagen quantitation by video-microdensitometry in rabbit atherosclerosis. Matrix Biol 15: 141–144, 1996.
doi:10.1016/S0945-053X(96)90155-9.
509. Brotchie D, Birch M, Roberts N, Howard CV, Smith VA, Grierson I.
Localisation of connective tissue and inhibition of autofluorescence
in the human optic nerve and nerve head using a modified picrosirius red technique and confocal microscopy. J Neurosci Methods 87:
77–85, 1999. doi:10.1016/s0165-0270(98)00168-x.
510. Borges LF, Gutierrez PS, Marana HR, Taboga SR. Picrosirius-polarization staining method as an efficient histopathological tool for collagenolysis detection in vesical prolapse lesions. Micron 38: 580–
583, 2007. doi:10.1016/j.micron.2006.10.005.
511. Junqueira LC, Bignolas G, Brentani RR. Picrosirius staining plus
polarization microscopy, a specific method for collagen detection
in tissue sections. Histochem J 11: 447–455, 1979. doi:10.1007/
BF01002772.
512. Last JA, Summers P, Reiser KM. Biosynthesis of collagen crosslinks.
II. In vivo labelling and stability of lung collagen in rats. Biochim
Biophys Acta 990: 182–189, 1989.doi:10.1016/s0304-4165(89)
80032-7.
513. Last JA, Reiser KM. Biosynthesis of collagen crosslinks. III. In vivo
labeling and stability of lung collagen in rats with bleomycin-induced
pulmonary fibrosis. Am J Respir Cell Mol Biol 1: 111–117, 1989.
doi:10.1165/ajrcmb/1.2.111.
514. Onursal C, Dick E, Angelidis I, Schiller HB, Staab-Weijnitz CA.
Collagen biosynthesis, processing, and maturation in lung ageing. Front Med (Lausanne) 8: 593874, 2021. doi:10.3389/fmed.
2021.593874.
C126
515. McAnulty RJ, Laurent GJ. Collagen synthesis and degradation in
vivo. Evidence for rapid rates of collagen turnover with extensive
degradation of newly synthesized collagen in tissues of the adult rat.
Coll Relat Res 7: 93–104, 1987. doi:10.1016/s0174-173x(87)80001-8.
516. Wang F, Garza LA, Kang S, Varani J, Orringer JS, Fisher GJ,
Voorhees JJ. In vivo stimulation of de novo collagen production
caused by cross-linked hyaluronic acid dermal filler injections in
photodamaged human skin. Arch Dermatol 143: 155–163, 2007.
doi:10.1001/archderm.143.2.155.
517. Laurent GJ. Rates of collagen synthesis in lung, skin and muscle
obtained in vivo by a simplified method using [3H]proline. Biochem J
206: 535–544, 1982. doi:10.1042/bj2060535.
518. Rosmark O, Åhrman E, M€
uller C, Elowsson Rendin L, Eriksson L,
€ m A, Hallgren O, Larsson-Callerfelt AK, WestergrenMalmstro
€ m J. Quantifying extracellular matrix turnover
Thorsson G, Malmstro
in human lung scaffold cultures. Sci Rep 8: 5409, 2018. doi:10.1038/
s41598-018-23702-x.
519. Ariosa-Morejon Y, Santos A, Fischer R, Davis S, Charles P,
Thakker R, Wann AK, Vincent TL. Age-dependent changes in protein incorporation into collagen-rich tissues of mice by in vivo pulsed
SILAC labelling. eLife 10: e66635, 2021. doi:10.7554/eLife.66635.
520. Dhondt I, Petyuk VA, Cai H, Vandemeulebroucke L, Vierstraete A,
Smith RD, Depuydt G, Braeckman BP. FOXO/DAF-16 activation
slows down turnover of the majority of proteins in C. elegans. Cell
Rep 16: 3028–3040, 2016. doi:10.1016/j.celrep.2016.07.088.
521. Vukoti K, Yu X, Sheng Q, Saha S, Feng Z, Hsu AL, Miyagi M.
Monitoring newly synthesized proteins over the adult life span of
Caenorhabditis elegans. J Proteome Res 14: 1483–1494, 2015.
doi:10.1021/acs.jproteome.5b00021.
522. Gurskaya NG, Verkhusha VV, Shcheglov AS, Staroverov DB,
Chepurnykh TV, Fradkov AF, Lukyanov S, Lukyanov KA.
Engineering of a monomeric green-to-red photoactivatable fluorescent protein induced by blue light. Nat Biotechnol 24: 461–465,
2006. doi:10.1038/nbt1191.
523. Ihara S, Hagedorn EJ, Morrissey MA, Chi Q, Motegi F, Kramer JM,
Sherwood DR. Basement membrane sliding and targeted adhesion
remodels tissue boundaries during uterine–vulval attachment in
Caenorhabditis elegans. Nat Cell Biol 13: 641–651, 2011. doi:10.1038/
ncb2233.
524. Trowbridge JM, Rudisill JA, Ron D, Gallo RL. Dermatan sulfate
binds and potentiates activity of keratinocyte growth factor FGF-7. J
Biol Chem 277: 42815–42820, 2002. doi:10.1074/jbc.M204959200.
525. Oostendorp C, Uijtdewilligen PJ, Versteeg EM, Hafmans TG, van
den Bogaard EH, de Jonge PK, Pirayesh A, Von den Hoff JW,
Reichmann E, Daamen WF, van Kuppevelt TH. Visualisation of
newly synthesised collagen in vitro and in vivo. Sci Rep 6: 18780,
2016. doi:10.1038/srep18780.
526. Gilkes DM, Semenza GL, Wirtz D. Hypoxia and the extracellular matrix: drivers of tumour metastasis. Nat Rev Cancer 14: 430–439,
2014. doi:10.1038/nrc3726.
527. Zimmermann DR, Dours-Zimmermann MT, Schubert M, BrucknerTuderman L. Versican is expressed in the proliferating zone in the
epidermis and in association with the elastic network of the dermis.
J Cell Biol 124: 817–825, 1994. doi:10.1083/jcb.124.5.817.
528. Takahashi K, Yamanaka S. Induction of pluripotent stem cells from
mouse embryonic and adult fibroblast cultures by defined factors.
Cell, 126: 663–676, 2006. doi:10.1016/j.cell.2006.07.024.
529. Ocampo A, Reddy P, Martinez-Redondo P, Platero-Luengo A,
Hatanaka F, Hishida T, et al. In vivo amelioration of age-associated
hallmarks by partial reprogramming. Cell 167: 1719–1733.e12, 2016.
doi:10.1016/j.cell.2016.11.052.
530. Schoenfeldt L, Paine PT, Kamaludeen M NH, Phelps GB, Mrabti C,
Perez K, Ocampo A. Chemical reprogramming ameliorates cellular
hallmarks of aging and extends lifespan (Preprint). bioRxiv
2022.08.29.505222, 2022. doi:10.1101/2022.08.29.505222.
531. Macip CC, Hasan R, Hoznek V, Kim J, Metzger LE, Sethna S,
Davidsohn N. Gene therapy mediated partial reprogramming
extends lifespan and reverses age-related changes in aged mice
(Preprint). bioRxiv 2023.01.04.522507, 2023. doi:10.1101/2023.01.04.
522507.
532. Yang JH, Hayano M, Griffin PT, Amorim JA, Bonkowski MS,
Apostolides JK, et al. Loss of epigenetic information as a cause of
mammalian aging. Cell 186: 305–326.e27, 2023. doi:10.1016/j.
cell.2022.12.027.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
533. Song B, Kim DK, Shin J, Bae SH, Kim HY, Won B, Kim JK, Youn HD,
Kim ST, Kang SW, Jang H. OCT4 directly regulates stemness and
extracellular matrix-related genes in human germ cell tumours.
Biochem Biophys Res Commun 503: 1980–1986, 2018. doi:10.1016/j.
bbrc.2018.07.145.
534. Murgai M, Ju W, Eason M, Kline J, Beury DW, Kaczanowska S,
Miettinen MM, Kruhlak M, Lei H, Shern JF, Cherepanova OA,
Owens GK, Kaplan RN. KLF4-dependent perivascular cell plasticity
mediates pre-metastatic niche formation and metastasis. Nat Med
23: 1176–1190, 2017. doi:10.1038/nm.4400.
535. Meškytė EM, Keskas S, Ciribilli Y. MYC as a multifaceted regulator
of tumor microenvironment leading to metastasis. Int J Mol Sci 21:
7710, 2020. doi:10.3390/ijms21207710.
536. Horvath S. DNA methylation age of human tissues and cell types.
Genome Biol 14: R115, 2013. doi:10.1186/gb-2013-14-10-r115.
537. Mammalian Methylation Consortium, Lu AT, Fei Z, Haghani A,
Robeck TR, Zoller JA, et al. Universal DNA methylation age across
mammalian tissues (Preprint). bioRxiv 2021.01.18.426733, 2021.
doi:10.1101/2021.01.18.426733.
538. Galkin F, Mamoshina P, Aliper A, de Magalhães JP, Gladyshev VN,
Zhavoronkov A. Biohorology and biomarkers of aging: current
state-of-the-art, challenges and opportunities. Ageing Res Rev 60:
101050, 2020. doi:10.1016/j.arr.2020.101050.
539. Lazarus NR, Lord JM, Harridge SD. The relationships and interactions between age, exercise and physiological function. J Physiol
597: 1299–1309, 2019. doi:10.1113/JP277071.
540. Newman AB, Sanders JL, Kizer JR, Boudreau RM, Odden MC, Zeki
Al Hazzouri A, Arnold AM. Trajectories of function and biomarkers
with age: the CHS All Stars Study. Int J Epidemiol 45: 1135–1145,
2016. doi:10.1093/ije/dyw092.
541. Rutledge J, Oh H, Wyss-Coray T. Measuring biological age using
omics data. Nat Rev Genet 23: 715–727, 2022. doi:10.1038/s41576022-00511-7.
542. Schultz MB, Kane AE, Mitchell SJ, MacArthur MR, Warner E,
Vogel DS, Mitchell JR, Howlett SE, Bonkowski MS, Sinclair DA.
Age and life expectancy clocks based on machine learning analysis of mouse frailty. Nat Commun 11: 4618, 2020. doi:10.1038/
s41467-020-18446-0.
543. He X, Liu J, Liu B, Shi J. The use of DNA methylation clock in
aging research. Exp Biol Med 246: 436–446, 2021. doi:10.1177/
1535370220968802.
544. Johnson AA, English BW, Shokhirev MN, Sinclair DA, Cuellar TL.
2022. Human age reversal: Fact or fiction? Aging Cell 21: e13664.
doi:10.1111/acel.13664.
545. Horvath S. DNA methylation age calculator. DNA Methylation Age
Epigenetic Clock, 2013. https://horvath.genetics.ucla.edu/html/
dnamage/ [2023 Feb 2].
546. Ying K, Tyshkovskiy A, Trapp A, Liu H, Moqri M, Kerepesi C,
Gladyshev VN. ClockBase: a comprehensive platform for biological age profiling in human and mouse (Preprint). bioRxiv
2023.02.28.530532, 2023.
547. Ying K, Liu H, Tarkhov AE, Lu AT, Horvath S, Kutalik Z, Shen X,
Gladyshev VN. Causal Epigenetic Age Uncouples Damage and
Adaptation (Preprint). bioRxiv 2022.10.07.511382, 2022.
548. Zaseck LW, Miller RA, Brooks SV. Rapamycin attenuates age-associated changes in tibialis anterior tendon viscoelastic properties. J
Gerontol Ser A 71: 858–865, 2016. doi:10.1093/gerona/glv307.
549. Chondronasiou D, Gill D, Mosteiro L, Urdinguio RG, BerenguerLlergo A, Aguilera M, et al. Multi-omic rejuvenation of naturally
aged tissues by a single cycle of transient reprogramming. Aging
Cell 21: e13578, 2022. doi:10.1111/acel.13578.
550. Gill D, Parry A, Santos F, Okkenhaug H, Todd CD, HernandoHerraez I, Stubbs TM, Milagre I, Reik W. Multi-omic rejuvenation of
human cells by maturation phase transient reprogramming. eLife 11:
e71624, 2022. doi:10.7554/eLife.71624.
551. Lehallier B, Shokhirev MN, Wyss-Coray T, Johnson AA. Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging. Aging
Cell 19: e13256, 2020. doi:10.1111/acel.13256.
552. Li C, Chu S, Tan S, Yin X, Jiang Y, Dai X, Gong X, Fang X, Tian D.
Towards higher sensitivity of mass spectrometry: a perspective from
the mass analyzers. Front Chem 9: 813359, 2021. doi:10.3389/
fchem.2021.813359.
553. Acun A, Oganesyan R, Uygun K, Yeh H, Yarmush ML, Uygun BE.
Liver donor age affects hepatocyte function through age-dependent
changes in decellularized liver matrix. Biomaterials 270: 120689,
2021. doi:10.1016/j.biomaterials.2021.120689.
€ berg JS, Bulterijs S. Characteristics, formation, and pathophysiol554. Sjo
ogy of glucosepane: a major protein cross-link. Rejuvenation Res 12:
137–148, 2009. doi:10.1089/rej.2009.0846.
555. Monnier VM, Bautista O, Kenny D, Sell DR, Fogarty J, Dahms W,
Cleary PA, Lachin J, Genuth S. Skin collagen glycation, glycoxidation, and crosslinking are lower in subjects with long-term intensive
versus conventional therapy of type 1 diabetes: relevance of glycated collagen products versus HbA1c as markers of diabetic complications. DCCT Skin Collagen Ancillary Study Group. Diabetes
Control and Complications Trial. Diabetes 48: 870–880, 1999.
doi:10.2337/diabetes.48.4.870.
€rvela
€inen H. Extracellular matrix-cell interactions: focus
556. Sainio A, Ja
on therapeutic applications. Cell Signal 66: 109487, 2020.
doi:10.1016/j.cellsig.2019.109487.
557. Ansari NA, Dash D. Amadori glycated proteins: role in production of
autoantibodies in diabetes mellitus and effect of inhibitors on nonenzymatic glycation. Aging Dis 4: 50–56, 2013.
558. Biemel KM, Friedl DA, Lederer MO. Identification and quantification
of major Maillard cross-links in human serum albumin and lens protein: evidence for glucosepane as the dominant compound. J Biol
Chem 277: 24907–24915, 2002. doi:10.1074/jbc.M202681200.
559. Rungratanawanich W, Qu Y, Wang X, Essa MM, Song BJ.
Advanced glycation end products (AGEs) and other adducts in
aging-related diseases and alcohol-mediated tissue injury. Exp Mol
Med 53: 168–188, 2021. doi:10.1038/s12276-021-00561-7.
560. Manicone AM, McGuire JK. Matrix metalloproteinases as modulators of inflammation. Semin Cell Dev Biol 19: 34–41, 2008.
doi:10.1016/j.semcdb.2007.07.003.
561. Skrzydlewska E, Sulkowska M, Koda M, Sulkowski S. Proteolyticantiproteolytic balance and its regulation in carcinogenesis. World J
Gastroenterol 11: 1251–1266, 2005. doi:10.3748/wjg.v11.i9.1251.
562. Rundhaug JE. Matrix metalloproteinases and angiogenesis. J Cell
Mol Med 9: 267–285, 2005. doi:10.1111/j.1582-4934.2005.tb00355.x.
563. Tang Y, Nakada MT, Kesavan P, McCabe F, Millar H, Rafferty P,
Bugelski P, Yan L. Extracellular matrix metalloproteinase inducer
stimulates tumor angiogenesis by elevating vascular endothelial cell
growth factor and matrix metalloproteinases. Cancer Res 65: 3193–
3199, 2005. doi:10.1158/0008-5472.CAN-04-3605.
564. Xu Y, Wang L, Zimmerman MD, Chen KY, Huang L, Fu DJ, Kaya F,
Rakhilin N, Nazarova EV, Bu P, Dartois V, Russell DG, Shen X.
Matrix metalloproteinase inhibitors enhance the efficacy of frontline
drugs against Mycobacterium tuberculosis. PLoS Pathog 14:
e1006974, 2018. doi:10.1371/journal.ppat.1006974.
565. Dunaway S, Odin R, Zhou L, Ji L, Zhang Y, Kadekaro AL. Natural
antioxidants: multiple mechanisms to protect skin from solar radiation. Front Pharmacol 9: 392, 2018. doi:10.3389/fphar.2018.00392.
566. Ramachandran C, Wilk B, Melnick SJ, Eliaz I. Synergistic antioxidant and anti-inflammatory effects between modified citrus pectin
and honokiol. Evid Based Complement Alternat Med 2017:
8379843, 2017. doi:10.1155/2017/8379843.
567. Elosegui-Artola A, Andreu I, Beedle AE, Lezamiz A, Uroz M,
Kosmalska AJ, Oria R, Kechagia JZ, Rico-Lastres P, Le Roux AL,
Shanahan CM, Trepat X, Navajas D, Garcia-Manyes S, RocaCusachs P. Force triggers YAP nuclear entry by regulating transport
across nuclear pores. Cell 171: 1397–1410.e14, 2017. doi:10.1016/j.
cell.2017.10.008.
568. Harvey KF, Zhang X, Thomas DM. The Hippo pathway and
human cancer. Nat Rev Cancer 13: 246–257, 2013. doi:10.1038/
nrc3458.
569. Pan D. The Hippo signaling pathway in development and cancer.
Dev Cell 19: 491–505, 2010. doi:10.1016/j.devcel.2010.09.011.
570. Zanconato F, Cordenonsi M, Piccolo S. YAP/TAZ at the roots of
cancer. Cancer Cell 29: 783–803, 2016. doi:10.1016/j.ccell.2016.
05.005.
571. Zhao B, Tumaneng K, Guan KL. The Hippo pathway in organ size
control, tissue regeneration and stem cell self-renewal. Nat Cell Biol
13: 877–883, 2011. doi:10.1038/ncb2303.
572. He C, Lv X, Huang C, Hua G, Ma B, Chen X, Angeletti PC, Dong J,
Zhou J, Wang Z, Rueda BR, Davis JS, Wang C. YAP1-LATS2 feedback loop dictates senescent or malignant cell fate to maintain
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.
C127
EXTRACELLULAR MATRIX LONGEVITY TECH TREE
tissue homeostasis. EMBO Rep 20: e44948, 2019. doi:10.15252/
embr.201744948.
573. Xie Q, Chen J, Feng H, Peng S, Adams U, Bai Y, Huang L, Li J,
Huang J, Meng S, Yuan Z. YAP/TEAD–mediated transcription controls cellular senescence. Cancer Res 73: 3615–3624, 2013.
doi:10.1158/0008-5472.CAN-12-3793.
574. Pelissier FA, Garbe JC, Ananthanarayanan B, Miyano M, Lin C,
Jokela T, Kumar S, Stampfer MR, Lorens JB, LaBarge MA. Agerelated dysfunction in mechanotransduction impairs differentiation
of human mammary epithelial progenitors. Cell Rep 7: 1926–1939,
2014. doi:10.1016/j.celrep.2014.05.021.
575. Calvo F, Ege N, Grande-Garcia A, Hooper S, Jenkins RP,
Chaudhry SI, Harrington K, Williamson P, Moeendarbary E,
C128
Charras G, Sahai E. Mechanotransduction and YAP-dependent
matrix remodelling is required for the generation and maintenance of cancer-associated fibroblasts. Nat Cell Biol 15: 637–646,
2013. doi:10.1038/ncb2756.
576. Moya IM, Halder G. Hippo–YAP/TAZ signalling in organ regeneration and regenerative medicine. Nat Rev Mol Cell Biol 20: 211–226,
2019. doi:10.1038/s41580-018-0086-y.
577. Sladitschek-Martens HL, Guarnieri A, Brumana G, Zanconato F,
Battilana G, Xiccato RL, Panciera T, Forcato M, Bicciato S,
Guzzardo V, Fassan M, Ulliana L, Gandin A, Tripodo C, Foiani M,
Brusatin G, Cordenonsi M, Piccolo S. YAP/TAZ activity in stromal
cells prevents ageing by controlling cGAS–STING. Nature 607:
790–798, 2022. doi:10.1038/s41586-022-04924-6.
AJP-Cell Physiol doi:10.1152/ajpcell.00060.2023 www.ajpcell.org
Downloaded from journals.physiology.org/journal/ajpcell at Univ of California Berkeley (131.243.147.109) on December 7, 2023.