1
Towards a Systems Biology Approach to
Osteoporosis
C.Holmes

Abstract— Osteoporosis is a multi-factorial, increasingly
prevalent disease that is characterized by abnormal bone
remodelling, thus resulting in decreased bone mass, and increased
fracture risk. Maintenance of normal adult skeletal mass involves
an intricate balance between bone formation and bone resorption,
involving the spatially and temporally coordinated interaction of
three cell types: osteoblasts, osteoclasts, and osteocytes. The
production, function, lifespan, and interaction of these cells, upon
which bone remodelling is dependent, is in turn regulated by a
variety of autocrine and paracrine growth factors. These
molecular factors act and interact via complex signal transduction
pathways. The cellular and molecular complexity of bone
turnover suggests that a systems biology approach would be
beneficial to furthering understanding of the process. Although
quantitative models to date have been highly simplistic, systems
biology promises to be of great value to the future of osteoporosis
treatment.
Index Terms—bone remodelling, osteoblasts, osteoclasts,
osteoporosis, systems biology
I. INTRODUCTION
Osteoporosis (OP) is a multi-factorial, age-related metabolic
bone disease characterized by a reduction in bone mass, bone
tissue microarchitectural deterioration, and increased fracture
risk. Mainly affecting older men and women, OP, and its
social and economic impacts, are becoming increasingly
prevalent as the population of the Western World ages. In the
U.S. alone, over 1.5 million fractures annually can be
attributed to OP at an estimated cost of $17 billion in 2001; a
cost that is on the rise. [1]
Mainly consisting of hydroxyapatite crystals and various
extracellular matrix proteins (such as collagen I, osteocalcin,
osteonectin, osteopontin, and bone salioprotein), bone is a
dynamic tissue created and maintained by three cell types:
osteoblasts, which form bone; osteoclasts, which resorb bone;
and osteocytes, whose exact function remains unclear, but are
thought to play a role in skeletal maintenance. All three cell
types interact to maintain healthy skeletal bone mass, striking a
delicate balance between bone formation and resorption.
Disruption of this equilibrium is what ultimately leads to
osteoporosis; a condition where the amount of bone lost due to
aging exceeds that which is laid down during skeletal growth
and remodelling. [2]
Clinically, the most common forms of primary bone loss
are type I, or postmenopausal, and type II, or age-related
osteoporosis respectively. Type I OP is mainly observed in
postmenopausal women, where estrogen loss disrupts local
regulatory control of cytokines in the bone marrow. [2-3]
Post-menopausal OP is characterized by increased bone
turnover, with an associated increase in both osteoblast and
osteoclast numbers.[4] Type II OP, on the other hand, is
observed in both men and women and is characterized by a
decrease in osteoblast and osteoclast numbers. Although bone
turnover is not increased, age-related OP leads to greater
morbidity and mortality than type I OP. [2-4] Secondary
causes
of
osteoporosis
include
hypercortisolism,
hyperthyroidism, hyperparathyroidism, alcohol abuse,
immobilization, and glucocorticoid therapy. [2-4]
Clearly, osteoporosis is a complex disorder with various
underlying cellular and metabolic pathologies, all of which
lead to the uncoupling of normal bone formation and
resorption. More detailed knowledge of the cellular functions
and interactions involved in bone remodelling, and the
molecular mechanisms that regulate them, is thus necessary to
better understand the factors contributing to OP. The
complexity of the bone remodelling process, on both a cellular
and molecular level, suggests that a systems biology approach
would be beneficial to further our understanding, and attempts
in treating, osteoporosis.
II. BONE REMODELLING
Bone remodelling is a highly regulated process involving
the synthesis and mineralization of bone matrix by osteoblasts,
and bone resorption by osteoclasts. Depending upon various
factors, including local mechanical loading demands, bone
turnover rates demonstrate considerable regional variation
throughout the skeleton. For example, the alveolar ridge bone
in the jaw has the fastest turnover rate, while ear ossicles
remodel very slowly. [4] Overall, the adult skeleton is almost
completely regenerated every 10 years via the spatially and
temporally co-ordinated interaction of osteoblasts, osteoclasts,
and osteocytes. [4-5]
Bone resorption and formation are closely linked within
temporary anatomical structures known as basic multicellular
units (BMUs), which consist of osteoclasts, osteoblasts, a
blood supply, and associated connective tissue. [5] Bone
remodelling begins at a quiescent skeletal surface with the
proliferation of new blood vessels, which bring recruited
resorbing osteoclasts. Once at the site, the osteoclasts retract
the inactive flat cells covering the skeletal surface to expose
2
the mineralized surface beneath for resorption. [5] As the
BMU advances along the site, osteoclast precursors are
recruited to the resorption front to maintain the youngest and
presumably most active osteoclasts for forward progression,
while older osteoclasts complete lateral bone excavation and
ultimately undergo apoptosis. [4-5] After bone resorption,
mononuclear phagocytes smooth out jagged erosion bays, and
old bone becomes coated with a thin layer of cement-like
substance (consisting of a collagen- and mineral- poor matrix
rich in glycosoaminoglycans, glycoproteins, and acid
phosphates) to which new osteoblasts attach. It is generally
believed that these new bone-forming osteoblasts are recruited
to the site via growth factors released during bone resorption,
as they assemble only where osteoclasts have recently been
active. [4-5]
Osteocytes, the most abundant cell type found in bone, are
also thought to be involved in BMU formation. Buried within
the bone matrix, osteocytes are believed to communicate with
surface osteoblasts and osteoclasts, as well as each other, via
cell processes and gap junctions extending though fluid-filled
channels in an extensive network. This network probably
detects bone in need of repair and transmits signals to
progenitor cells in the bone marrow to stimulate differentiation
and initiate remodelling and BMU formation. [4-5]
Adult skeletal bone mass is thus maintained via complex
interactions between osteoblasts, osteoclasts, osteocytes and
extenuating factors such as hormonal fluctuations,
inflammatory cytokines, and mechanical stimuli. [4-6] This
intricate balance between bone formation and resorption
involves regulation of osteoblast and osteoclast production
from their precursors, as well as cellular function, interactions,
and lifespan. [4-7]
III. OSTEOBLASTS (OBS) AND OSTEOCLASTS (OCS):
MOLECULAR REGULATION OF CELLULAR DIFFERENTIATION
AND INTERACTIONS
Although residing in the same bone and bone marrow
microenvironments, osteoblasts and osteoclasts arise from
distinct progenitor cell lineages. [7] From their beginnings as
precursor and stem cell, to their differentiation into fully
functional elements of a BMU, osteoblasts and osteoclasts
interact with one another and their environment via a variety of
factors. These autocrine and paracrine factors act and interact
via complex signal transduction pathways which regulate
cellular commitment, lifespan and function, and on a larger
scale, bone mass, remodelling, and disease. [5-7]
Osteoblasts differentiate from mesenchymal stem cells
(MSCs) in the bone marrow stroma and periosteum, and from
committed downstream osteoprogenitors in stromal- and bonederived cell populations. [7-8] This differentiation is an
intricate process involving the interactions of a number of
genes, such as Runx2, as well as locally and systematically
produced growth factors, such as BMPs. [5-6]
Runx2 is known as the “master” gene of osteoblast
differentiation and chondrocyte hypertrophy. It encodes a
transcription factor by the same name that induces osteoblast
differentiation via activation of OB-specific genes to produce a
characteristic molecular repertoire, which includes alkaline
phosphatase, osteopontin, osteocalcin, type I procollagen, and
bone salioprotein. [4-6] Runx2 function is regulated by a
number of factors, including the “twist” genes which encode
proteins which inhibit Runx2 DNA-binding. [6, 9]
Bone morphogenetic proteins (BMPs), members of the
TGF-β family of proteins, are also key regulators of osteoblast
and chondrocyte differentiation, and are believed to interact
with Runx2 during skeletal development. [6] BMP signalling
occurs via binding to two types of transmembrane
serine/threonine kinase receptors (types I and II respectively).
The BMP signalling cascade is quite complex, and is regulated
at multiple steps (see Fig. 1). At the receptor level, type II
receptors possess higher binding affinities than their type I
counterparts, while type I receptors can be bound by inhibitor
proteins, such as BAMPI. Various secreted proteins, such as
noggin, chordin, follistatin, and DAN family proteins, act as
antagonists and bind to BMPs themselves, thus preventing
their binding to receptors of both types. After BMP-binding,
type I receptors phosphorylate SMAD transcription factor
proteins, which then translocate to the nucleus where they bind
to target gene regulatory regions, thus controlling their
transcription. [4, 6]
BMP
Antagonists
Type I
Type II
Smad
Phosphorylation
Nuclear translocation
DNA-binding,
transcriptional regulation
Fig. 1: A schematic representation of the BMP signal transduction cascade.
Signalling is initiated upon BMP binding to type I and II transmembrane
receptors, which are serine/threonine kinases. The type I receptors
phosphorylate Smad transcription factors which go on to form a complex
which moves into the nucleus. There it regulates the transcription of the
target genes. This signalling pathway is regulated by a number of factors at
multiple steps (see text).
Osteoclasts, meanwhile, are derived from haematopoietic
cells of the monocyte/macrophage lineage. Osteoclastogenesis
is influenced by many diverse factors including inflammatory
cytokines (i.e. TNFά and IL-1), growth factors (e.g. TGF-β
and IFN-γ), and osteoblasts (via MSCF and RANKL). [5-7]
The inflammatory cytokines TNFά and IL-1 have
demonstrated
stimulatory
effects
upon
osteoclast
differentiation and function respectively. [6-7] Released upon
osteoclastic bone resorption, TGF-β and BMPs enhance
osteoclast differentiation in haematopoietic cells stimulated
with RANKL and MSCF. [6]
3
Osteoblast-osteoclast cell-cell interaction is extremely
important in osteoclastogenesis. Osteoblasts play essential
roles in inducing osteoclast formation, producing macrophagecolony stimulating factor (MCSF) and membrane-bound
receptor activator of nuclear factor κB ligand (RANKL), both
of which act, via their associated receptors expressed by
osteoclast progenitors, to promote osteoclast differentiation
(see Fig 2.). [4-6] RANKL is a membrane-associated protein
expressed by osteoblast and stromal cells. This expression can
be up-regulated by osteotropic hormones and factors, such as
IL-11, PTH, PGE2, and 1,25(OH) 2D3, or induced by
independent signals mediated by vitamin D receptors, cAMP,
gp130, and intracellular calcium. [6] Osteoclast progenitors
recognize RANKL via cell surface expression of RANK, the
transmembrane signalling receptor for RANKL. RANKLRANK binding, which can be antagonized by a soluble decoy
receptor for RANKL known as OPG, goes on to induce
activation of the transcription factor NF-KB which appears to
play a role in the regulation of osteoclast differentiation. [4-6]
mass. [12] This model, although extremely simplified, has
helped to shed light upon the value of systems biology as a
tool for understanding bone remodelling.
Komarova et al. used a system of ordinary differential
equations to model the interactions between osteoblast and
osteoclast populations, via their dependence upon autocrine
and paracrine growth factors, as well as related changes in
bone mass (see Fig 3). Initial model parameters were
estimated
from
experimental
data
derived
from
histomorphometric analysis of bone sections. [12]
The model predicted two different stable modes of dynamic
behaviour resembling targeted bone remodelling, a single
cycle due to external stimuli, and random bone remodelling,
oscillations representing a series of internally initiated cycles.
Alterations to the lumped autocrine and paracrine factor
constants revealed that the osteoclast autocrine factor (g11)
demonstrated the ability to switch the system between the two
modes; an effect that the authors speculated could biologically
be mediated by TGF-β. A third mode of behaviour, similar to
the pattern of bone remodelling observed in Paget’s disease,
was also predicted by the model. [12]
dx1
g
  1 x1 11 x 2g 21   1 x1
dt
(1, 2)
dx 2
g12 g 22
  2 x1 x 2   2 x 2
dt
Osteotropic Factors
Fig. 2: A schematic representation of osteoclast differentiation and function
as influenced by osteoblasts. Osteotropic factors such as 1,25 (OH)2D3, PTH,
PGE2 and IL-11 stimulate the expression of RANKL as a membrane
associated factor in osteoblasts. Osteoclast progenitors recognize RANKL
expressed by osteoblasts through cell-to-cell interaction, which leads to
RANK-RANKL binding, and differentiate into osteoclasts. M-CSF produced
by osteoblasts is another essential factor for osteoclast differentiation. OPG, a
soluble decoy receptor, can also bind to RANKL, acting as an antagonist. [T.
Katagiri, N. Takahashi, 2002]
IV. SYSTEMS BIOLOGY: MATHEMATICAL MODELING OF BONE
TURNOVER
The complex interactions between osteoblasts, osteoclasts,
and growth factors, as well as the signalling pathways involved
in bone remodelling lend themselves well to a systems biology
approach. The goal of systems biology is to use quantitative
means, such as mathematical and computer models, to both
describe and predict the behaviour of complex biological
systems under a variety of circumstances. Although a number
of mathematical models depicting the biomechanical
properties of bone have been presented, few models to date
have quantitatively described bone remodelling on a cellular
level.[10-11] Komarova et al. recently published one such
study, mathematically modeling autocrine and paracrine
interactions among osteoblasts and osteoclasts, thus allowing
for the study of cell population dynamics and changes in bone
where x1= number of osteoclasts, x2= number of osteoblasts,
αi=activity of cell production, βi=activities of cell removal (i.e.
death constant), gij = lumped parameter representing net
effectiveness of osteoclast(i=1) or osteoblast(i=2) derived
autocrine (i=j) or paracrine(i  j) factors, such as TGFβ, and
RANKL.
dz
 k1 y1  k 2 y 2 (3)
dt
where:
 xi  xi if xi  xi 
yi  
 (4)
if xi  xi 
 0
and yi = the number of osteoblasts or osteoclasts above steady
state, xi, (i.e. the number of cells actively resorbing or forming
bone), z = total bone mass, ki=normalized activity of bone
resorption or formation.
Fig. 3: Komarova et al.’s ordinary differential equation model of osteoblast,
osteoclast, and bone mass dynamics.
Although revealing highly complex non-linear
behaviours, Komarova et al.’s model was extremely simplified
and thus made a number of limiting assumptions that do not
reflect reality. Firstly, the autocrine and paracrine factors were
assumed to regulate only the rate of osteoblast and osteoclast
production, while the removal rates and cellular activities were
only modeled to be proportional to the current cell numbers.
The authors chose to simplify their model further by ignoring
4
the fact that many factors, such as RANKL, can also promote
osteoblast and osteoclast survival. In developing their model
only two cell types were considered, and a power law
approximation was made lumping all autocrine and paracrine
factor effects into four constants. In reality the parameters
describing the effectiveness of autocrine and paracrine
regulation are complex and involve multiple factors. [12]
Despite its limitations, the mathematical model of bone
mass dynamics presented by Komarova et al. represents an
important first step towards a systems biology understanding
of bone remodelling and osteoporosis. The model predicted
different modes of behaviour that resemble bone remodelling
patterns that can be observed in vivo, including the pathology
of Paget’s disease. The model also goes on to suggest that the
system is most sensitive to osteoclast autocrine regulation, by
such factors and TGF-β. More intricate models now need to
be developed taking into account the role of osteocytes, as
well as the complex nature and varying effectiveness of
different individual growth factors.
V. SUMMARY
As our experiments become more refined and our
knowledge of the cellular and molecular interactions that
regulate osteoblast, osteoclast, and osteocyte behaviour
grows, more complex quantitative models of bone
remodelling need to be developed. The roles of individual
growth factors in regulating cellular behaviour, and the
signal transduction pathways through which they act, such
as the BMP and RANK-RANKL systems, should be
quantitatively modeled. Furthermore, the interactions of
these factors and pathways as they influence cellular
function and interactions must also be described. Ultimately,
interconnection of these models to incorporate the
complexity of the molecular and cellular interactions
involved in bone remodelling would be ideal. Although
such an undertaking presents an immense challenge, the
value of a systems biology approach in devising and
evaluating treatments for osteoporosis make it well worth
the effort.
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