Network medicine analysis of COPD multimorbidities
© Grosdidier et al.; licensee BioMed Central Ltd. 2014
Received: 22 May 2014
Accepted: 12 August 2014
Published: 24 September 2014
Patients with chronic obstructive pulmonary disease (COPD) often suffer concomitant disorders that worsen significantly their health status and vital prognosis. The pathogenic mechanisms underlying COPD multimorbidities are not completely understood, thus the exploration of potential molecular and biological linkages between COPD and their associated diseases is of great interest.
We developed a novel, unbiased, integrative network medicine approach for the analysis of the diseasome, interactome, the biological pathways and tobacco smoke exposome, which has been applied to the study of 16 prevalent COPD multimorbidities identified by clinical experts.
Our analyses indicate that all COPD multimorbidities studied here are related at the molecular and biological level, sharing genes, proteins and biological pathways. By inspecting the connections of COPD with their associated diseases in more detail, we identified known biological pathways involved in COPD, such as inflammation, endothelial dysfunction or apoptosis, serving as a proof of concept of the methodology. More interestingly, we found previously overlooked biological pathways that might contribute to explain COPD multimorbidities, such as hemostasis in COPD multimorbidities other than cardiovascular disorders, and cell cycle pathway in the association of COPD with depression. Moreover, we also observed similarities between COPD multimorbidities at the pathway level, suggesting common biological mechanisms for different COPD multimorbidities. Finally, chemicals contained in the tobacco smoke target an average of 69% of the identified proteins participating in COPD multimorbidities.
The network medicine approach presented here allowed the identification of plausible molecular links between COPD and comorbid diseases, and showed that many of them are targets of the tobacco exposome, proposing new areas of research for understanding the molecular underpinning of COPD multimorbidities.
KeywordsDiseasome Systems biology Network medicine Comorbidity Multimorbidity COPD Tobacco chemicals
Multimorbidities, including cardiovascular diseases (CVD), skeletal muscle weakness, osteoporosis, metabolic syndrome, depression and lung cancer, among others, are highly prevalent in patients with chronic obstructive pulmonary disease (COPD) - and contribute to worsen their health-status and vital prognosis ,. The pathogenic mechanisms linking COPD and its concomitant diseases are incompletely understood , but shared risk factors (tobacco smoking, physical inactivity, ageing) and COPD-related specific mechanisms (systemic inflammation, tissue hypoxia, abnormal protein metabolism) may potentially contribute. It is also possible that COPD and its multimorbidities share genes, proteins and pathways that explain their tendency to co-occur together in a particular patient -. A systematic investigation of this "shared component hypothesis" can provide insights to improve the prevention, early diagnosis, prognosis and/or treatment of COPD multimorbidities ,. The new discipline of network medicine offers a platform to systematically explore the molecular complexity of a given disease with the potential to identify new molecular relationships among apparently distinct clinical manifestations ,.
In this study, we sought to test the shared component hypothesis in COPD. Under this hypothesis, the multimorbidities of COPD most frequently seen in the clinic would be related between them and with COPD at the molecular level by common genes, proteins and biological pathways. To this end, we used a network medicine approach that included: (1) data mining of the diseasome (or disease network), the interactome (as defined by a protein-protein interaction (PPI) network) to enrich this diseasome and the tobacco smoking exposome (representing the exposure to tobacco smoke chemicals, the main risk factor for COPD); and, (2) a functional analysis to identify the biological pathways potentially involved in COPD multimorbidity. Our findings indicate that the most prevalent COPD multimorbidities are likely to be related at the molecular level, and highlight some previously overlooked pathways that might contribute to explain their co-occurrence. Remarkably, the results here presented propose new hypothesis for explaining the molecular underpinning of COPD multimorbidities.
The diseases analyzed in this study and their corresponding number of UMLS Concept Unique Identifiers (CUIs) and associated genes in DisGeNET
Respiratory Tract Diseases
COPD associated diseases
Nutritional and Metabolic Diseases
Respiratory Tract Diseases
Hemic and Lymphatic Diseases
Ischemic heart disease
Pathological Conditions, Signs and Symptoms
Behavior and Behavior Mechanisms
Building the COPD diseasome
where proteins dis1 and proteins dis2 are the proteins associated with disease 1 and 2, respectively, proteins dis1→dis2 are those proteins associated with disease 1 that interact with those associated with disease 2 (and vice versa (proteins dis2→dis1 )), ∩ is the intersection operator and ∪ is the union operator between two sets of elements (proteins dis1 and proteins dis2 ). The sets resulting in both numerator and denominator are written within vertical bars to indicate their cardinality (number of element).
Functional analysis of multimorbidity proteins
To identify the most significant biological functions of the shared proteins between two diseases, a functional enrichment analysis with biological pathways from Reactome was performed . The Reactome database contains information on genes, proteins and their participation in biological pathways. In our analysis, we used the R package ReactomePA, which uses the hypergeometric function to test the significance of annotations . Significant annotations were those with q-values ≤ 0.05 (the q-value corresponds to the false discovery rate (FDR), an adjusted equivalent to the standard statistical p-value incorporated in ReactomePA).
where multimorbidity 1 and multimorbidity 2 represent two pairs of COPD multimorbidities (for instance lung cancer-COPD and diabetes-COPD) whereas pathways of multimorbidity 1 and pathways of multimorbidity 2 represent the biological pathways in which the proteins associated with the pairs multimorbidity 1 and multimorbidity 2 participate respectively. The Jaccard Coefficient is a measure of the degree of similarity between two COPD multimorbidities (for instance lung cancer-COPD and diabetes-COPD) at the level of biological pathways. Results were visualized as heat-maps using Gitools .
Exploration of the tobacco exposome
Using the Comparative Toxicogenomics Database (CTD), a database gathering information about gene/protein and chemical interactions, we investigated if the genes and proteins shared by COPD multimorbidities were potential biological targets for chemical compounds present in the tobacco smoke ,.
COPD is linked through genes and proteins to its comorbid diseases
The proposed network medicine workflow not only allows a global analysis of the genes and proteins supporting a particular multimorbidity (see next sections), but it also provides detailed information on specific genes and their association to the multimorbid diseases under study. For example, the gene ADRB2, is associated to several multimorbidities: COPD with cardiovascular diseases (atrial fibrillation, heart failure, ischemic heart disease and stroke), and with diabetes, lung cancer and obesity. Interestingly, the protein encoded by this gene has been shown to participate in smooth muscle relaxation in bronchi resulting in a facilitated respiration ; in blood vessels, it dilates coronary and skeletal muscle arteries  and it also contributes to insulin secretion from pancreas . ADRB2 has been the focus of pharmacogenomics studies in COPD: several polymorphisms of this gene have been shown to influence patient response to bronchodilators treatment . A recent study showed a significant correlation between the R16G polymorphism with COPD severity in term of FEV1 (forced expiratory volume in 1 second) in a Greek population . Interestingly, the R16G–Q27E haplotype has been shown to be associated with glucose tolerance and insulin sensitivity in obese postmenopausal women . Thus, this polymorphism might influence both bronchodilator response and glucose tolerance in specific patient populations, constituting a potential link to explain the association between COPD and diabetes.
Another study in a Danish population showed that the ADRB2 T164I polymorphism is associated with a reduced lung function and an increased risk of COPD in the general population . This polymorphism is also associated with increased blood pressure, higher frequency of hypertension and increased risk of ischemic heart disease amongst women in the general population . Thus, the ADRB2 T164I polymorphism could explain the association between COPD and cardiovascular diseases.
Further analysis using haplotype frequencies indicated that haplotypes G16-Q27-I164 and (non-G16-Q27)-T164, as compared to the reference haplotype G16-Q27-T164, were significantly associated with a decreased risk of myocardial infarction . In summary, the ADRB2 gene represents an interesting candidate to explain the multimorbidity of COPD with diabetes, obesity and ischemic heart disease.
Exploring the function of shared genes and proteins in the COPD diseasome
Overlap of identified biological pathways in the COPD diseasome
Exploration of the tobacco exposome
According to the CTD , the 106 tobacco smoke chemicals present in the tobacco smoke , target an average of 69% of the proteins shared by COPD and the multimorbidities shown in Figure 4 (see complete data set on Additional file 8: Table S2 in the online data supplement). For instance, 468 out of the 759 (61.7%) proteins shared by COPD and lung cancer are known targets of at least one compound present in tobacco smoke. This proportion was even higher (72%) in the case of stroke with COPD. This is not surprisingly since tobacco smoke is a main risk factor of COPD, CVD and lung cancer. Yet, even in multimorbidities that, at first glance, might seem unrelated to smoking, such as metabolic syndrome, diabetes or anemia, we also found a high percentage of shared proteins that are established targets of tobacco smoke compounds in the CTD, which provides potential candidates to investigate the role of tobacco smoke compounds in COPD multimorbidities.
The network medicine analysis of COPD multimorbidities presented here shows that 16 prevalent COPD multimorbidities often share genes, proteins and biological pathways with COPD, and that many of these proteins are targets for tobacco smoke chemicals. Overall, these observations support the shared component hypothesis of the COPD diseasome, and allow the identification of specific molecular links (genes, proteins, biological pathways) between COPD and its comorbid diseases.
The vast amount of information available today in different areas of science makes it necessary to properly mine and connect it in a network of information that allows the generation of new knowledge. This strategy has the potential to bring out new hypotheses that are not self-evident when using more traditional scientific approaches. In the present study, we performed an integrative mining approach developed to exploit information from different databases (HIPPIE, Pathway Commons, Reactome, CTD, DisGeNET) and experts in COPD multimorbidities (AF, RF, JR, BC, AA, JG) and bioinformatics (SG, JP, FS, LIF), who participated in the analysis phases. Future studies will have to explore experimentally the new hypotheses generated in this analysis.
COPD is a paradigmatic gene-environment disease since tobacco smoking, and other environmental factors, are well established risk factors, but not all exposed subjects develop the disease, suggesting a role for the genetic background of the exposed individual . The fact that COPD clusters in families further supports this latter aspect . Multimorbidities frequently occur in COPD patients and have a very significant impact in the natural history of the disease . Yet, the precise pathogenic mechanism(s) underlying this COPD diseasome are unclear. To our knowledge, this is the first study to use a comprehensive and integrative bioinformatic approach to investigate the so-called shared component hypothesis- as a pathogenic mechanism of COPD multimorbidities.
Our findings suggest that the most prevalent COPD multimorbidities are related at the molecular level, and highlight some previously overlooked pathways that might contribute to explain their co-occurrence. In this context, that COPD shares certain molecular mechanisms with cardiovascular diseases or lung cancer is probably not surprising. By contrast, that it also shares these mechanisms with depression or diabetes is more intriguing. Likewise, it is of note that proteins involved in the “Nitric Oxide Metabolism” pathway were associated with multimorbidities involving the cardiovascular system, but also with metabolic diseases, as well as with osteoporosis, pulmonary hypertension, lung cancer and depression (Additional file 6: Figure S5 in the online data supplement).
Some of the shared biological pathways identified, including those related to the immune response, apoptosis, cytokine signaling or endothelial function, are well described in the field, albeit they serve as a proof of concept for the methodology employed. Yet, our analysis has also identified others pathways not previously related to COPD multimorbidities, such as hemostasis in multimorbidities other than cardiovascular disorders, or alterations in cell cycle in the COPD-depression association. Of note, it has been previously reported that increases in platelet activation and aggregation augment the risk of patients with depression to suffer CVD . It is also worth noting that abnormalities in adult neurogenesis has been implicated in the etiology of depression , thus indicating that a disruption of cell cycle pathways in neural cells might underlie the observed association of COPD with depression. These new and unexpected findings require now to be tested experimentally but, all in all, our observations suggest that the multimorbidities in COPD should probably be understood as a network of clinical manifestations (i.e., diseasome) that expresses a complex interaction between a number of environmental, biological and genetic (and epigenetic) governing factors ,.
Tobacco smoke is the main risk factor of both COPD and some of its associated diseases . According to CTD , chemicals contained in the tobacco smoke target an average of 69% of the proteins studied (Figure 3). This is not surprising for diseases like CVD or lung cancer, where tobacco smoking is a well-established risk factor, but it may open new avenues for research in other comorbid diseases.
Strengths and limitations
The novelty, as well as the unbiased and integrative nature of our scientific approach, is a clear strength of our analysis. However, some limitations are worth noting. The most relevant one is that our analysis depends on the scientific information that is available and recorded in public databases, such as Medline, Reactome and other repositories of biological data. Further, due to limitations in database development and curation, many of such databases are likely to be incomplete . Hence, the results of our analysis should be mostly regarded as hypothesis generators requiring experimental validation.
Our network medicine analysis identified plausible molecular connections in the COPD diseasome (through genes, proteins and biological pathways), and showed that many of them are targets of the tobacco exposome, proposing new areas of research for understanding the molecular underpinning of COPD multimorbidities.
Conceived and designed the study: LIF, AA, FS, JG. Performed the experiments: SG, JP. Analyzed the data: SG, RF, AF, LIF, AA, BC, JR. Wrote the paper: SG, LIF, FS, AA, JG. All authors read and approved the final manuscript.
Chronic obstructive pulmonary disease
Comparative toxicogenomics database
Concept unique identifier
Molecular comorbidity index
Unified medical language system
This work was supported by the Instituto de Salud Carlos III FEDER (CP10/00524 and PI13/00082), COMBIOMED and SAF2011-26908 grants. The Research Programme on Biomedical Informatics (GRIB) is a node of the Spanish National Institute of Bioinformatics (INB).
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