Chen and Wang Journal of Clinical Bioinformatics 2011, 1:35
http://www.jclinbioinformatics.com/content/1/1/35
REVIEW
JOURNAL OF
CLINICAL BIOINFORMATICS
Open Access
Significance of bioinformatics in research of
chronic obstructive pulmonary disease
Hong Chen1 and Xiangdong Wang1,2*
Abstract
Chronic obstructive pulmonary disease (COPD) is an inflammatory disease characterized by the progressive
deterioration of pulmonary function and increasing airway obstruction, with high morality all over the world. The
advent of high-throughput omics techniques provided an opportunity to gain insights into disease pathogenesis
and process which contribute to the heterogeneity, and find target-specific and disease-specific therapies. As an
interdispline, bioinformatics supplied vital information on integrative understanding of COPD. This review focused
on application of bioinformatics in COPD study, including biomarkers searching and systems biology. We also
presented the requirements and challenges in implementing bioinformatics to COPD research and interpreted
these results as clinical physicians.
Keywords: bioinformatics, genomics, proteomics, chronic obstructive pulmonary disease, biomarkers
Introduction
Chronic obstructive pulmonary disease (COPD) is an
inflammatory disease characterized by the progressive
deterioration of pulmonary function and increasing airway obstruction [1,2]. It can be caused by inflammatory
responses triggered by noxious particles or gas, most
commonly from tobacco smoking and is accompanied
by chronic bronchitis and emphysema [3,4]. Some
patients go on to require long-term oxygen therapy or
even lung transplantation [3]. COPD was ranked as
fourth leading cause of death worldwide and is estimated to become the top third cause of mortality by
2020 [5]. According to the data in China, COPD ranks
as the fourth leading cause of death in urban areas and
third in rural areas[6]. The high mortality and morbidity
with COPD, and its chronic progressive nature, have
promoted the need to investigate the underlying
mechanisms and identify biomarkers for diagnosis, prognosis and drug target.
The understanding of COPD increased by advanced
molecular biology approaches, genetically modified animals, virally administered genes, and high-throughput
transcriptional profiling approaches. High-throughput
* Correspondence: xiangdong.wang@telia.com
1
Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University,
Shanghai, China
Full list of author information is available at the end of the article
methodologies, such as genomics and proteomics, are
commonly used. The variety of data from biology,
mainly in the form of DNA, RNA and protein sequences
is putting heavy demand in computer sciences and computational biology. Bioinformatics, including many subdisciplines, such as genomics, proteomics and system
biology, is an integration of mathematical, statistical,
and computational methods to analyze biological, biochemical, and biophysical data. Compared to wet-lab
method, bioinformatics focused on data mining via computational means. Sophisticated bioinformatics techniques are developed to analyze the vast amount of data
generated from genomics and proteomics studies, such
as gene and protein function, interactions and metabolic
and regulatory pathways. However, there is still a great
challenge to combine the computer figures with clinical
data for both bench-scientists and bedside-physicians.
In COPD studies, there are usually three ways to analyze ‘omics’ data: 1) search correlation between single
gene or protein and some clinical features in order to
find diagnostic or prognostic biomarkers; 2) integrate
clinical and wet-lab information, or omics data from different levels for database establishment and computational models. In this current review, we discussed
application of bioinformatics in COPD study. We also
presented the requirements and challenges in implementing bioinformatics to COPD research, and gave
© 2011 Chen and Wang; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Chen and Wang Journal of Clinical Bioinformatics 2011, 1:35
http://www.jclinbioinformatics.com/content/1/1/35
some suggestions on how to interprete these results as
clinical physicians.
Application of bioinformatics in biomarker
searching
The diagnosis of COPD is based on the presence of
typical symptoms of cough and shortness of breath,
together with the presence of risk factors, and is confirmed by spirometry. Therefore, searching for better
biomarkers with high specificity and sensitivity indicating the staging and severity of COPD remain as major
concerns for clinical physicians. The main value of biomarkers in COPD would be in early diagnosis and to
provide the early proof of drug efficacy during the treatment [7]. As a biomarker for COPD, it is expected to be
detected in human lung fluids or tissues, sensitive to the
progress of COPD, disease-specific to COPD and associated with the status of patients [8]. In these researches,
selected genes or proteins usually combined with several
clinical features, such as disease susceptibility, lung
function, via statistical methods, i.e. logistic regression.
Genomics
It is believed that many genetic factors increase a person’s risk of developing COPD[9]. The high mortality
and morbidity associated with COPD, and its chronic
and progressive nature, has prompted the use of molecular genetic studies in an attempt to identify susceptibility factors for the disease. The advent of highthroughput methodologies to study genetic background
variability, epigenetic regulation allowed us to explain
the individual variability in the susceptibility of human
diseases.
Single nucleotide polymorphism (SNP) was a common
method in COPD study. These analyses usually study a
group of candidate genes, and then perform statistical
test in different populations. The gene-related susceptibility can be approached by testing or unbiased study
designs [10]. By SNP microarray, many genetic factors
were found to be related with the individual risk of
developing COPD [9]. Apart from recognized deficiency
of alpha1antitrypsin [9], genomics in COPD found that
other gene alleles, such as IREB2[11], CYP2E1 and
NAT2[12], CYP1A1, CYP1A2 and CYBA[13], TNF-a
[14] were associated with COPD susceptibility. PIM3
allele of the alpha1antitrypsin gene had an association
with the pathogenesis of COPD in the Indian population
[15]. The polymorphisms in SP-A1 and SP-A2[16],
COX2 and p53 risk-alleles [17]might be genetic factors
contributing to the susceptibility to COPD. On the
other hand, COPD could influence single gene expression as well, such as cathepsin inhibitory cystatin A[18].
COPD is featured by decline in lung function in disease progression. Therefore, except susceptibility, other
Page 2 of 8
case-control genomics studies focused on the association
of several gene alleles with lung function. 105V/114V
alleles of GSTP1 and 113H/139H alleles of mEPHX and
the combination of genotypes with same alleles were
associated with imbalanced oxidative stress and lung
function in patients [19]. Polymorphisms in ADAM33
were associated with COPD and lung function decline
in long-term smokers [20] and general population [21].
The variants and their combinations of eNOS -786C,
-922G, and 4A alleles in endothelial cells contribute to
disturbed pulmonary function and oxidative stress in
COPD[22].
An interleukin13 polymorphism in the promoter
region may modulate the adverse effects of cigarette
smoking on pulmonary function in long-term cigarette
smokers [23]. These genomics findings suggested that
environmental influences were important in COPD.
Genetic polymorphisms contributed to the development
of COPD, especially to the declined lung function. The
diversity in human genes could help us to understand
the susceptibility among different ethics and different
populations. The dissection of the genetic basis of complex diseases and the development of highly individualized therapies remain lofty but achievable goals [24].
Proteomics
Proteomics is the systematic study of the many and
diverse properties of protein profiles in a parallel manner with the aim of providing detailed descriptions of
the structure, function and control of biological systems
in health and disease [25]. A major research objective is
to search for biomarkers in complex biological fluids.
The proteomic analysis highlights the avenues to investigate protein profiles of cells, biopsies and fluids,
explore protein-based mechanisms of human diseases,
define subgroups of disease, and identify novel biomarkers for diagnosis, therapy and prognosis of multiple
diseases and discover new targets for drug development.
In particular, the application of complementary
approaches, including gel- and liquid chromatography
mass spectrometry-based proteomic techniques on sputum and/or bronchoalveolar lavage may provide a better
understanding of the proteome differentially expressed
among the courses, severities and populations of COPD
[7]. We have previously reviewed the clinical studies on
COPD proteomics, highlighted the proteomic-oriented
methods applied and evaluated the diagnostic or prognostic values of potential biomarkers [8]. Those studies
mainly focused on disease classification, biomarker
detection, or identification of mechanism, while the
three components are related with each other in COPD
(Figure 1).
An important goal of proteomic studies is to understand biological roles of specific proteins and develop
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Metabolomics
Metabolomics is a global way to understanding regulation of metabolic pathways and metabolic networks of a
biologic system[36]. Metabolites trigonelline, hippurate
and formate in urinary were identified to be associated
with baseline lung function of COPD patients and considered to reflect lifestyle differences affecting overall
health [37]. Another way to analyze omics data is clustering data to form a specific pattern for different
groups by principal component analysis (PCA). Combination of PCA and metabolomics identified the metabolic fingerprint of exhaled breath condensate of COPD
patients [38].
Multiplexed ELISA
Figure 1 Overview of the utility of bioinformatics in chronic
obstructive pulmonary disease. Both genomics and proteomics
provide information on candidates. By searching in various datasets
and combining with clinical profiles, ‘omis’ studies may help to
explain questions on disease classification, biomarker detection, and
identification of mechanism.
new therapeutic targets [26]. Although there are few
proteomics studies performed in COPD patients, several
potential proteins have been regarded as biomarkers.
For example, matrix metalloproteinase (MMP)-13 and
thioredoxin-like 2 in lungs increased in patients with
COPD [27]. The serine and MMP proteinase network
was considered as an important feature in predicting
clinical worsening of airway obstruction [28]. Pulmonary
surfactant A was found to link to the pathogenesis of
COPD and could be considered as a potential COPD
biomarker [29]. Proteomic screening of sputum yields
potential biomarkers of inflammation [30]. Airway and
parenchymal phenotypes of COPD were suggested to be
associated with unique systemic serum biomarker profiles [31]. The utility of proteomic profiling would
improve the understanding of molecular mechanisms
involved in cigarette smoking-related COPD by identifying plasma proteins that correlate with declined lung
function [32]. The concentrations of neutrophil defensins 1 and 2, calgranulin A, and calgranulin B were elevated in smokers with COPD when compared to
asymptomatic smokers [33]. Other candidates, like
serum amyloid A [34], plasma retinal-binding protein,
apolipoprotein E, inter-alpha-trypsininhibitor heavy
chain H4, and glutathione peroxidase[35], were also
been detected in plasma in COPD patients by proteomics approaches.
A recent advancement in ELISA is the multiplexed
ELISA which could determine multiple proteins within
a single tissue sample. It has recently been shown to be
more sensitive than standard ELISA once optimized for
a particular cytokine [39-41] and could be a promising
diagnostic assay in lung diseases [42]. Moreover, the
measurement of multiple cytokines is required for many
diseases, particularly those like COPD that arise from a
complex process of initiation and progression of inflammation network. Simultaneous detection of multiple
cytokines will undoubtedly provide a more powerful
tool to quantifiably measure cytokines in different stages
of COPD. With help of this high-throughput measurement platform, we could integrate both biologic and
clinical data to inform predictive multiscale models ranging from the molecular to the organ levels, as shown in
Figure 2. For example, the integration of IL-9 pathway
and CCR3 pathway between biological function and
pathology demonstrated cell proliferation-related remodeling, intracellular signal-associated inflammatory
responses and over-activation of kinases-correlated
emphysema in the pathogenesis of COPD. We propose
this new way will increase our insight into disease process and have great potential to identify new biomarkers
for disease diagnosis as well as novel therapeutic targets.
Systems biology and database establishment
The difficulties encountered whilst exploring pathogenesis and searching for biomarkers may be due in part to
the complex nature of COPD, which comprises a broad
spectrum of histopathological findings and respiratory
symptoms [43]. All genetic information and molecular
knowledge need to be semantically incorporated and
associated with clinical and experimental data. System
biology in COPD (as reviewed before[44]) presented a
manifold understanding of the complexity of COPD,
therefore advanced biomedical research and drug development. This approach relies on global genome, transcriptome, proteome, and metabolome data sets
Chen and Wang Journal of Clinical Bioinformatics 2011, 1:35
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Figure 2 The integration of IL-9 pathway and CCR3 pathway between biological function and pathology demonstrated cell
proliferation-related remodeling, intracellular signal-associated inflammatory responses and over-activation of kinases-correlated
emphysema in the pathogenesis of COPD.
collected in cross-sectional patient cohorts with highthroughput measurement platforms and integrated with
biologic and clinical data to inform predictive multidimensional models ranging from the molecular to the
organ levels[45].
Comandini etc. [46] assayed a number of published
studies by creating a smoker datasets on which to perform data-mining analysis. They utilized Ingenuity Pathways Analysis, a web-based application that enables
identifying relationships, biological mechanisms, functions, and pathways of relevance associated with the
molecules under study. Their findings supported the
central role of anti-oxidant genes in smoking population
and suggested Nrf2 may be a COPD risk biomarker. A
brand new knowledge base was generated from clinical
and experimental data for COPD based on BioXM software platform [47]. This integrated database reduced
implementation time and effort for the knowledge base
compared to similar systems and provided a free, comprehensive, easy to use resource for all COPD related
clinical research.
Clinical profiles could also be considered as a form of
omics data since it provided a large quantity of patients’
information in a direct conservative way. An in-silico
research applied various explorative analysis techniques
(PCA, MCA, MDS) and unsupervised clustering
methods (KHM) to study a large dataset, acquired from
415 COPD patients, to assess the presence of hidden
structures in data corresponding to the different COPD
phenotypes observed in clinical practice. This study may
be considered as a methodological example showing
possible applications of intelligent data analysis and
visual exploratory techniques to investigate clinical
aspects of chronic pathologies where a mathematical
referring model is generally missing[48].
How to understand bioinformatics as clinical
physicians
COPD has been approached by genomic and proteomic
technologies to allow us to identify patterns of gene/
protein expression that track with clinical disease or to
identify new pathways involved in disease pathogenesis.
The results from these initial studies highlight the
potential for these omics approaches to reveal novel
insights into the pathogenesis of COPD and provide
new tools to improve diagnosis, clinical classification,
course prediction, and response to therapy. Existing
knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organized into structured
networks that are connected with clinical and experimental data. This will require collaboration among
Chen and Wang Journal of Clinical Bioinformatics 2011, 1:35
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multidisciplinary groups with expertise in the respective
technologies, bioinformatics, and clinical medicine for
the disease. More and more clinical physicians began to
realize the promise of these studies and the potential to
revolutionize the diagnosis and treatment of COPD,
while obstacles still existed between these laboratory
findings and their applications in clinical practice.
Omics results need to be interpreted by translational
medicine and systems biology. Since the vocabulary of
systems biology is different than that of molecular biology or clinical research, the biggest challenge is the shift
in thinking[49].
Instead of a single-factor approach, which is highly
effective in the lab, we need to think globally as clinical
physicians. We need to shift from an approach that tries
to explain lung pathogenesis by “one molecule, one cell
type” to approach that looks at the network of interactions between multiple molecules, pathways, and cells.
Given that all the samples were collected from human,
it would be of great significance to standardize patient
groups. Criteria of clinical informatics and medical
informatics, including age, gender, smoking history, staging, complications and clinical signs as well as examinations, should be fully considered before and after any
omics investigation. We also need to pay attention to
the relation between clinical data and laboratory findings. For this to occur, a well-done history and physical
examination would be helpful to supplement these
laboratory figures by providing multiple features of
human COPD.
Although all of these exciting technological advances
that exponentially increase the levels of knowledge
about every disease and model serve as facilitators of
integration, they do not inherently provide integrative
models of disease. Therefore, we proposed that digitalizing essential clinical profiles, such as symptoms and
signs, by questionnaires and/or scores, would provide
direct vision for physicians and shrink the distance
between lab discovery and clinical condition. The combination of epidemiologists, clinicians, geneticists and
specialists in bioinformatics, in addition to specialists in
disciplines less familiar to epidemiologists, is critical to
be prepared for new phenotypic characterizations based
on transcriptome and proteome [50]. Even though we
have spotted considerable advancement in bioinformatics, it still calls for more collaboration to fulfill its
potential (Figure 3). Interdisciplinary teams should allow
us to access omics datasets integratively and generate a
global model of COPD. It is important to have a special
attention from proteomic scientists to explore the combination between advanced proteomic biotechnology,
clinical proteomics, tissue imaging and profiling, and
organ dysfunction score systems, to improve the clinical
outcomes of these patients [51]. There is still a great
Page 5 of 8
Figure 3 Bioinformatics begin to take part in COPD
investigation. The collaboration of epidemiologists, clinicians,
geneticists and specialists in bioinformatics, in addition to specialists
in disciplines less familiar to epidemiologists, is critical for further
study. More co-operations are still needed.
need to explore the COPD-specific and/or related transcriptional factors and regulation networks generated
from omics and bioinformatics like in other diseases
[41].
Conclusions
The use of high-throughput techniques for gene and
protein expression profiling and of computerized databases has become a mainstay of biomedical research.
There is a need to perform omics studies on patients
with COPD, describing the association with the disease
in terms of specificity, severity, progress and prognosis
and monitoring the efficacy of therapies. These omics
analysis highlight the ways to investigate protein profiles
of cells, biopsies and fluids, explore protein-based
mechanisms of human diseases, identify novel biomarkers for diagnosis, therapy and prognosis of multiple
diseases and discover new targets for drug development,
as shown in Figure 3. Although the number of clinical
studies on COPD is limited, they still serve as the outstanding initiation for proteomic research in such a
complex disease. The analysis of protein profiles that
are up- or down-regulated, modified, secreted in the airways during the disease may yield vital evidences to
understand the pathogenesis and discover new therapeutic targets for the disease (Figure 4) [52]. With many
guidelines now in place and model studies on which to
design future experiments, there is reason to be optimistic that candidate protein biomarkers will be discovered
using proteomics and translated into clinical assays[53].
With better study design standardization and the implementation of novel technologies to reach the optimal
research standard, there is enough reason be optimistic
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Lungs
Page 6 of 8
Laser Capture Microdissection:
Isolation of Specific Cells
Tissue frozen section
Resources
Probe Set
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101753_s_at
101781_f_at
101876_s_at
101881_g_at
101908_s_at
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101973_at
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102156_f_at
102196_at
102207_at
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102217_at
102372_at
102585_f_at
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102767_at
102806_g_at
102906_at
102920_at
102995_s_at
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Hierarchical Clustering
Targets
Pathophysiologies
Gene
Cluster Incl AI838320:UI-M-AO0-aby-f-05-0-UI.s1 Mus musculus cDNA, 3 end /clone=UI-M-AO0-aby-f-05-0-UI /clone_end=3 /gb=AI838320 /gi=5472533 /ug=Mm.4467 /len=529
cytochrome b-245, alpha polypeptide
cytochrome P450, 2f2
eosinophil-associated ribonuclease 1
thioredoxin peroxidase, pseudogene 1
H3 histone, family 3A
TYRO protein tyrosine kinase binding protein
annexin A2
Cluster Incl J00475:Mouse germline IgH chain gene, DJC region- segment D-FL16.1 /cds=(0,1033) /gb=J00475 /gi=194417 /ug=Mm.6160 /len=1034
glycogenin 1
ribosomal protein L10A
Cluster Incl AA958903:ua19f08.r1 Mus musculus cDNA, 5 end /clone=IMAGE-1347207 /clone_end=5 /gb=AA958903 /gi=3125133 /ug=Mm.6381 /len=384
neuraminidase 1
cathepsin C
hydroxyacyl-Coenzyme A dehydrogenase, type II
protein tyrosine phosphatase, receptor type, C
ribosomal protein S3
ribosomal protein S7
protein phosphatase 1, catalytic subunit, gamma isoform
Cluster Incl U60442:Mus musculus Ig kappa light chain mRNA, partial cds /cds=(0,363) /gb=U60442 /gi=1421746 /ug=Mm.30406 /len=391
P lysozyme structural
Cluster Incl V00754:Mouse gene coding for embryonic H3 histone /cds=(0,407) /gb=V00754 /gi=51297 /ug=Mm.57118 /len=408
histocompatibility 2, T region locus 10
procollagen, type XVIII, alpha 1
CEA-related cell adhesion molecule 2
heat shock 70kD protein 5 (glucose-regulated protein, 78kD)
Cbp/p300-interacting transactivator, w ith Glu/Asp-rich carboxy-terminal domain, 2
interleukin 16
Cluster Incl AI504305:vl04g11.x1 Mus musculus cDNA, 3 end /clone=IMAGE-963236 /clone_end=3 /gb=AI504305 /gi=4402156 /ug=Mm.42485 /len=580
Cluster Incl M80423:Mus castaneus IgK chain gene, C-region, 3 end /cds=(0,322) /gb=M80423 /gi=196865 /ug=Mm.46804 /len=323
Cluster Incl U37413:Guanine nucleotide binding protein, alpha 11 /cds=(57,1136) /gb=U37413 /gi=1051237 /ug=Mm.989 /len=1342
delta-like homolog (Drosophila)
sialyltransferase 10 (alpha-2,3-sialyltransferase VI)
G protein-coupled receptor kinase 5
Cluster Incl M90766:Mouse Ig active joining chain (J chain) mRNA of the b allele, complete cds /cds=(21,500) /gb=M90766 /gi=194473 /ug=Mm.1192 /len=501
Cluster Incl AB017349:Mus musculus mRNA for immunoglobulin light chain V region, partial cds /cds=(0,353) /gb=AB017349 /gi=4519566 /ug=Mm.57251 /len=360
SFFV proviral integration 1
EST AA536815
CEA-related cell adhesion molecule 1
T-cell specific GTPase
Cluster Incl AW215585:up06c04.x1 Mus musculus cDNA, 3 end /clone=IMAGE-2651238 /clone_end=3 /gb=AW215585 /gi=6526261 /ug=Mm.1622 /len=329
granzyme A
ATP-binding cassette, sub-family B (MDR/TAP), member 2
CD48 antigen
Cluster Incl AA711773:vu58g05.r1 Mus musculus cDNA, 5 end /clone=IMAGE-1195640 /clone_end=5 /gb=AA711773 /gi=2721691 /ug=Mm.1902 /len=473
eosinophil-associated ribonuclease 3
S100 calcium binding protein A8 (calgranulin A)
Cluster Incl AW122239:UI-M-BH2.2-aov-f-02-0-UI.s1 Mus musculus cDNA, 3 end /clone=UI-M-BH2.2-aov-f-02-0-UI /clone_end=3 /gb=AW122239 /gi=6097702 /ug=Mm.2173 /len=549
Cluster Incl AW124401:UI-M-BH2.1-ape-d-04-0-UI.s1 Mus musculus cDNA, 3 end /clone=UI-M-BH2.1-ape-d-04-0-UI /clone_end=3 /gb=AW124401 /gi=6099931 /ug=Mm.19119 /len=217
S100 calcium-binding protein A9 (calgranulin B)
hemochromatosis
Cluster Incl AI852433:UI-M-BH0-ajo-g-04-0-UI.s1 Mus musculus cDNA, 3 end /clone=UI-M-BH0-ajo-g-04-0-UI /clone_end=3 /gb=AI852433 /gi=5496304 /ug=Mm.21639 /len=544
lymphocyte specific 1
Cluster Incl AA797989:ub60d04.r1 Mus musculus cDNA, 5 end /clone=IMAGE-1382119 /clone_end=5 /gb=AA797989 /gi=2860944 /ug=Mm.27253 /len=413
Cluster Incl AI844736:UI-M-AL1-ahq-c-10-0-UI.s1 Mus musculus cDNA, 3 end /clone=UI-M-AL1-ahq-c-10-0-UI /clone_end=3 /gb=AI844736 /gi=5488642 /ug=Mm.28535 /len=406
Cluster Incl AI842544:UI-M-AQ1-aea-e-05-0-UI.s1 Mus musculus cDNA, 3 end /clone=UI-M-AQ1-aea-e-05-0-UI /clone_end=3 /gb=AI842544 /gi=5476757 /ug=Mm.22337 /len=457
EST AI461938
CD52 antigen
Cluster Incl AI842065:UI-M-AN1-afg-a-10-0-UI.s1 Mus musculus cDNA, 3 end /clone=UI-M-AN1-afg-a-10-0-UI /clone_end=3 /gb=AI842065 /gi=5476278 /ug=Mm.24647 /len=444
p8 protein
Cluster Incl AI850638:UI-M-BG1-aik-e-06-0-UI.s1 Mus musculus cDNA, 3 end /clone=UI-M-BG1-aik-e-06-0-UI /clone_end=3 /gb=AI850638 /gi=5494544 /ug=Mm.19258 /len=381 /NOTE=rep
Cluster Incl AA981015:vx55c11.r1 Mus musculus cDNA, 5 end /clone=IMAGE-1279124 /clone_end=5 /gb=AA981015 /gi=3159551 /ug=Mm.22383 /len=343 /NOTE=replacement for probe
Cluster Incl AI835981:UI-M-AQ0-aah-d-09-0-UI.s2 Mus musculus cDNA, 3 end /clone=UI-M-AQ0-aah-d-09-0-UI /clone_end=3 /gb=AI835981 /gi=5470234 /ug=Mm.30099 /len=318 /NOTE=r
Cluster Incl AI848668:UI-M-AH1-agv-a-10-0-UI.s1 Mus musculus cDNA, 3 end /clone=UI-M-AH1-agv-a-10-0-UI /clone_end=3 /gb=AI848668 /gi=5492539 /ug=Mm.30119 /len=421 /NOTE=re
ribosomal protein L13
Cd63 antigen
aryl-hydrocarbon receptor
cellular repressor of E1A-stimulated genes
Selected cells
Statistical analysis
Criteria for genes
Differentially
cDNA Microarray:
expressed
Key Genes
Identification
Lists of differentially
expressed genes
Real Time Quantitative PCR:
Assess Gene Expression
Figure 4 Clinical bioinformatics can be generated from the analysis of COPD-specific pathological alterations. Patients with COPD can
be selected by clinical informatics and criteria and the specific area is selected by micro-dissection. The tissue can be used for genomic (or
proteomic) analysis and identified biomarkers are validated for the understanding of the pathogenesis.
about the future of omics research and its clinical implications [54]. Clinical bioinformatics on COPD could be
achieved from the combination of clinical informatics,
medical informatics, bioinformatics and informatics by
collaborations among clinicians, bioinformaticians, computer scientists, biologists, and mathematicians.
Author details
1
Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University,
Shanghai, China. 2Biomedical Research Center, Zhongshan Hospital, Fudan
University, Shanghai, China.
Authors’ contributions
HC mainly wrote the manuscript, as well as the revision. XDW conceived of
the review and edited and wrote part of the manuscript. All authors read
and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 3 October 2010 Accepted: 20 December 2011
Published: 20 December 2011
References
1. Celli BR, MacNee W: Standards for the diagnosis and treatment of
patients with COPD: a summary of the ATS/ERS position paper. Eur Respir
J 2004, 23(6):932-946.
2. Pauwels RA, Rabe KF: Burden and clinical features of chronic obstructive
pulmonary disease (COPD). Lancet 2004, 364(9434):613-620.
3. Hogg JC, Chu F, Utokaparch S, Woods R, Elliott WM, Buzatu L,
Cherniack RM, Rogers RM, Sciurba FC, Coxson HO, et al: The nature of
small-airway obstruction in chronic obstructive pulmonary disease. N
Engl J Med 2004, 350(26):2645-2653.
4. Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, Calverley P, Fukuchi Y,
Jenkins C, Rodriguez-Roisin R, van Weel C, et al: Global strategy for the
diagnosis, management, and prevention of chronic obstructive
pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med
2007, 176(6):532-555.
5. Lopez AD, Murray CC: The global burden of disease, 1990-2020. Nat Med
1998, 4(11):1241-1243.
6. Fang X, Wang X, Bai C: COPD in China: the burden and importance of
proper management. Chest 2011, 139(4):920-929.
7. Casado B, Iadarola P, Luisetti M, Kussmann M: Proteomics-based diagnosis
of chronic obstructive pulmonary disease: the hunt for new markers.
Expert Rev Proteomics 2008, 5(5):693-704.
8. Chen H, Wang D, Bai C, Wang X: Proteomics-based biomarkers in chronic
obstructive pulmonary disease. J Proteome Res 2010, 9(6):2798-2808.
9. Pauwels RA, Buist AS, Calverley PM, Jenkins CR, Hurd SS: Global strategy for
the diagnosis, management, and prevention of chronic obstructive
Chen and Wang Journal of Clinical Bioinformatics 2011, 1:35
http://www.jclinbioinformatics.com/content/1/1/35
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
pulmonary disease. NHLBI/WHO Global Initiative for Chronic Obstructive
Lung Disease (GOLD) Workshop summary. Am J Respir Crit Care Med 2001,
163(5):1256-1276.
Silverman EK, Spira A, Pare PD: Genetics and genomics of chronic
obstructive pulmonary disease. Proc Am Thorac Soc 2009, 6(6):539-542.
DeMeo DL, Mariani T, Bhattacharya S, Srisuma S, Lange C, Litonjua A,
Bueno R, Pillai SG, Lomas DA, Sparrow D, et al: Integration of genomic and
genetic approaches implicates IREB2 as a COPD susceptibility gene. Am
J Hum Genet 2009, 85(4):493-502.
Arif E, Vibhuti A, Alam P, Deepak D, Singh B, Athar M, Pasha MA:
Association of CYP2E1 and NAT2 gene polymorphisms with chronic
obstructive pulmonary disease. Clin Chim Acta 2007, 382(1-2):37-42.
Vibhuti A, Arif E, Mishra A, Deepak D, Singh B, Rahman I, Mohammad G,
Pasha MA: CYP1A1, CYP1A2 and CYBA gene polymorphisms associated
with oxidative stress in COPD. Clin Chim Acta 2010, 411(7-8):474-480.
Zhan P, Wang J, Wei SZ, Qian Q, Qiu LX, Yu LK, Song Y: TNF-308 gene
polymorphism is associated with COPD risk among Asians: meta-analysis
of data for 6,118 subjects. Mol Biol Rep 2011, 38(1):219-227.
Gupta J, Bhadoria DP, Lal MK, Kukreti R, Chattopadhaya D, Gupta VK,
Dabur R, Yadav V, Chhillar AK, Sharma GL: Association of the PIM3 allele of
the alpha-1-antitrypsin gene with chronic obstructive pulmonary
disease. Clin Biochem 2005, 38(5):489-491.
Saxena S, Kumar R, Madan T, Gupta V, Muralidhar K, Sarma PU: Association
of polymorphisms in pulmonary surfactant protein A1 and A2 genes
with high-altitude pulmonary edema. Chest 2005, 128(3):1611-1619.
Arif E, Vibhuti A, Deepak D, Singh B, Siddiqui MS, Pasha MA: COX2 and p53
risk-alleles coexist in COPD. Clin Chim Acta 2008, 397(1-2):48-50.
Butler MW, Fukui T, Salit J, Shaykhiev R, Mezey JG, Hackett NR, Crystal RG:
Modulation of Cystatin A Expression in Human Airway Epithelium
Related to Genotype, Smoking, COPD, and Lung Cancer. Cancer Res 2011,
71(7):2572-2581.
Vibhuti A, Arif E, Deepak D, Singh B, Qadar Pasha MA: Genetic
polymorphisms of GSTP1 and mEPHX correlate with oxidative stress
markers and lung function in COPD. Biochem Biophys Res Commun 2007,
359(1):136-142.
Sadeghnejad A, Ohar JA, Zheng SL, Sterling DA, Hawkins GA, Meyers DA,
Bleecker ER: Adam33 polymorphisms are associated with COPD and lung
function in long-term tobacco smokers. Respir Res 2009, 10:21.
van Diemen CC, Postma DS, Vonk JM, Bruinenberg M, Schouten JP,
Boezen HM: A disintegrin and metalloprotease 33 polymorphisms and
lung function decline in the general population. Am J Respir Crit Care
Med 2005, 172(3):329-333.
Arif E, Ahsan A, Vibhuti A, Rajput C, Deepak D, Athar M, Singh B, Pasha MA:
Endothelial nitric oxide synthase gene variants contribute to oxidative
stress in COPD. Biochem Biophys Res Commun 2007, 361(1):182-188.
Sadeghnejad A, Meyers DA, Bottai M, Sterling DA, Bleecker ER, Ohar JA:
IL13 promoter polymorphism 1112C/T modulates the adverse effect of
tobacco smoking on lung function. Am J Respir Crit Care Med 2007,
176(8):748-752.
Desai AA, Hysi P, Garcia JG: Integrating genomic and clinical medicine:
searching for susceptibility genes in complex lung diseases. Transl Res
2008, 151(4):181-193.
Patterson SD, Aebersold RH: Proteomics: the first decade and beyond. Nat
Genet 2003, 33(Suppl):311-323.
Marko-Varga G, Fehniger TE: Proteomics and disease–the challenges for
technology and discovery. J Proteome Res 2004, 3(2):167-178.
Lee EJ, In KH, Kim JH, Lee SY, Shin C, Shim JJ, Kang KH, Yoo SH, Kim CH,
Kim HK, et al: Proteomic analysis in lung tissue of smokers and COPD
patients. Chest 2009, 135(2):344-352.
Sepper R, Prikk K: Proteomics: is it an approach to understand the
progression of chronic lung disorders? J Proteome Res 2004, 3(2):277-281.
Ohlmeier S, Vuolanto M, Toljamo T, Vuopala K, Salmenkivi K, Myllarniemi M,
Kinnula VL: Proteomics of human lung tissue identifies surfactant protein
A as a marker of chronic obstructive pulmonary disease. J Proteome Res
2008, 7(12):5125-5132.
Gray RD, MacGregor G, Noble D, Imrie M, Dewar M, Boyd AC, Innes JA,
Porteous DJ, Greening AP: Sputum proteomics in inflammatory and
suppurative respiratory diseases. Am J Respir Crit Care Med 2008,
178(5):444-452.
Bon JM, Leader JK, Weissfeld JL, Coxson HO, Zheng B, Branch RA,
Kondragunta V, Lee JS, Zhang Y, Choi AM, et al: The influence of
Page 7 of 8
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
radiographic phenotype and smoking status on peripheral blood
biomarker patterns in chronic obstructive pulmonary disease. PLoS One
2009, 4(8):e6865.
York TP, van den Oord EJ, Langston TB, Edmiston JS, McKinney W,
Webb BT, Murrelle EL, Zedler BK, Flora JW: High-resolution mass
spectrometry proteomics for the identification of candidate plasma
protein biomarkers for chronic obstructive pulmonary disease.
Biomarkers 2010, 15(4):367-377.
Merkel D, Rist W, Seither P, Weith A, Lenter MC: Proteomic study of
human bronchoalveolar lavage fluids from smokers with chronic
obstructive pulmonary disease by combining surface-enhanced laser
desorption/ionization-mass spectrometry profiling with mass
spectrometric protein identification. Proteomics 2005, 5(11):2972-2980.
Bozinovski S, Hutchinson A, Thompson M, Macgregor L, Black J, Giannakis E,
Karlsson AS, Silvestrini R, Smallwood D, Vlahos R, et al: Serum amyloid a is
a biomarker of acute exacerbations of chronic obstructive pulmonary
disease. Am J Respir Crit Care Med 2008, 177(3):269-278.
Bandow JE, Baker JD, Berth M, Painter C, Sepulveda OJ, Clark KA, Kilty I,
VanBogelen RA: Improved image analysis workflow for 2-D gels enables
large-scale 2-D gel-based proteomics studies–COPD biomarker discovery
study. Proteomics 2008, 8(15):3030-3041.
Nicholson JK, Connelly J, Lindon JC, Holmes E: Metabonomics: a platform
for studying drug toxicity and gene function. Nat Rev Drug Discov 2002,
1(2):153-161.
McClay JL, Adkins DE, Isern NG, O’Connell TM, Wooten JB, Zedler BK,
Dasika MS, Webb BT, Webb-Robertson BJ, Pounds JG, et al: (1)H nuclear
magnetic resonance metabolomics analysis identifies novel urinary
biomarkers for lung function. J Proteome Res 2010, 9(6):3083-3090.
de Laurentiis G, Paris D, Melck D, Maniscalco M, Marsico S, Corso G,
Motta A, Sofia M: Metabonomic analysis of exhaled breath condensate in
adults by nuclear magnetic resonance spectroscopy. Eur Respir J 2008,
32(5):1175-1183.
Trune DR, Larrain BE, Hausman FA, Kempton JB, Macarthur CJ:
Simultaneous measurement of multiple ear proteins with multiplex
ELISA assays. Hear Res 2010.
Elshal MF, McCoy JP: Multiplex bead array assays: performance evaluation
and comparison of sensitivity to ELISA. Methods 2006, 38(4):317-323.
Ray CA, Bowsher RR, Smith WC, Devanarayan V, Willey MB, Brandt JT,
Dean RA: Development, validation, and implementation of a multiplex
immunoassay for the simultaneous determination of five cytokines in
human serum. J Pharm Biomed Anal 2005, 36(5):1037-1044.
Farlow EC, Patel K, Basu S, Lee BS, Kim AW, Coon JS, Faber LP, Bonomi P,
Liptay MJ, Borgia JA: Development of a multiplexed tumor-associated
autoantibody-based blood test for the detection of non-small cell lung
cancer. Clin Cancer Res 2010, 16(13):3452-3462.
Bhattacharya S, Srisuma S, Demeo DL, Shapiro SD, Bueno R, Silverman EK,
Reilly JJ, Mariani TJ: Molecular biomarkers for quantitative and discrete
COPD phenotypes. Am J Respir Cell Mol Biol 2009, 40(3):359-367.
Agusti A, Sobradillo P, Celli B: Addressing the complexity of chronic
obstructive pulmonary disease: from phenotypes and biomarkers to
scale-free networks, systems biology, and P4 medicine. Am J Respir Crit
Care Med 2011, 183(9):1129-1137.
Auffray C, Adcock IM, Chung KF, Djukanovic R, Pison C, Sterk PJ: An
integrative systems biology approach to understanding pulmonary
diseases. Chest 2010, 137(6):1410-1416.
Comandini A, Marzano V, Curradi G, Federici G, Urbani A, Saltini C: Markers
of anti-oxidant response in tobacco smoke exposed subjects: a datamining review. Pulm Pharmacol Ther 2010, 23(6):482-492.
Maier D, Kalus W, Wolff M, Kalko SG, Roca J, Marin de Mas I, Turan N,
Cascante M, Falciani F, Hernandez M, et al: Knowledge management for
systems biology a general and visually driven framework applied to
translational medicine. BMC Syst Biol 2011, 5:38.
Paoletti M, Camiciottoli G, Meoni E, Bigazzi F, Cestelli L, Pistolesi M,
Marchesi C: Explorative data analysis techniques and unsupervised
clustering methods to support clinical assessment of Chronic
Obstructive Pulmonary Disease (COPD) phenotypes. J Biomed Inform
2009, 42(6):1013-1021.
Studer SM, Kaminski N: Towards systems biology of human pulmonary
fibrosis. Proc Am Thorac Soc 2007, 4(1):85-91.
Kauffmann F: Post-genome respiratory epidemiology: a multidisciplinary
challenge. Eur Respir J 2004, 24(3):471-480.
Chen and Wang Journal of Clinical Bioinformatics 2011, 1:35
http://www.jclinbioinformatics.com/content/1/1/35
Page 8 of 8
51. Wang X, Adler KB, Chaudry IH, Ward PA: Better understanding of organ
dysfunction requires proteomic involvement. J Proteome Res 2006,
5(5):1060-1062.
52. Plymoth A, Yang Z, Lofdahl CG, Ekberg-Jansson A, Dahlback M, Fehniger TE,
Marko-Varga G, Hancock WS: Rapid proteome analysis of bronchoalveolar
lavage samples of lifelong smokers and never-smokers by micro-scale
liquid chromatography and mass spectrometry. Clin Chem 2006,
52(4):671-679.
53. Reisdorph NA, Reisdorph R, Bowler R, Broccardo C: Proteomics methods
and applications for the practicing clinician. Ann Allergy Asthma Immunol
2009, 102(6):523-529.
54. Lin JL, Bonnichsen MH, Nogeh EU, Raftery MJ, Thomas PS: Proteomics in
detection and monitoring of asthma and smoking-related lung diseases.
Expert Rev Proteomics 2010, 7(3):361-372.
doi:10.1186/2043-9113-1-35
Cite this article as: Chen and Wang: Significance of bioinformatics in
research of chronic obstructive pulmonary disease. Journal of Clinical
Bioinformatics 2011 1:35.
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