Methods
Disease Vocabulary
Clinical experts selected 16 prevalent diseases frequently associated with COPD ( Table 1 ). Their Concept Unique Identifiers (CUI) were obtained from the Unified Medical Language System (UMLS) Metathesaurus (Figure 1 panel A). After manual curation, the disease vocabulary used in the analysis included a mean of 59 CUIs (range 4–332) for each disease (and 14 CUIs for COPD, 33 for emphysema and 16 for chronic bronchitis (Additional file 1: Table S1 http://respiratory-research.com/content/15/1/111/additional)).
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Figure 1.
Schematic representation of the methodology applied in this study.PanelA: Clinical experts selected prevalent multimorbidities in COPD, and manually curated lists of Concept Unique Identifiers (CUI) obtained from the Unified Medical Language System (UMLS) Metathesaurus to extract relevant synonyms for each disease. PanelB: DisGeNet was used to obtain gene-disease associations and every protein encoded by the selected gene was mapped into a protein-protein network created from HIPPIE and Pathway Commons databases. PanelC: Gene-disease associations and protein-protein interaction information were combined to build the diseasome. The Molecular Comorbidity Index estimates the strength of association between 2 diseases based on shared genes and proteins. The genes and proteins targeted by cigarette smoke components were obtained from CTD. PanelD: A functional enrichment analysis was performed to identify the most likely pathways that could lead to the development of a specific multimorbidity and PanelE: pathway similarity between multimorbidities was assessed calculating the Jaccard Coefficient.
Building the COPD Diseasome
The "diseasome" is a network representation of the associations between diseases, where two of them are linked if they share alterations in genes or proteins, or if the altered proteins are connected in the interactome (9) (Figure 2). DisGeNET (version 2.0), a database that integrates information on human diseases and their associated genes from various public databases and from the biomedical literature, was used to collect information on proteins altered in COPD and the multimorbidities studied here using our disease vocabulary (Figure 1 panel B). The human interactome was obtained by combining PPI information from the Pathway Commons database (http://www.pathwaycommons.org/), gathering biological pathway information collected from public pathway databases and HIPPIE (http://cbdm.mdc-berlin.de/tools/hippie/information.php), a human PPI database that integrates multiple experimental PPI datasets (Figure 1 panel B).
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Figure 2.
Building the diseasome. PanelA: four different diseases (D1, D2, D3 and D4) can share relevant genes (GA, GB, GC) (left graph). Using this information, a diseasome can be built where D1 shares GA with D2, and D3 shares GC with D4 (right). PanelB: The genetic information identified above can be complemented with the interactome to build the diseasome, where the protein encoded by GB (PB) interacts with that encoded by GC (PC) (left graph). For further explanations, see text.
When building the diseasome based on proteins in common between two diseases, it could be argued that it is more likely to find shared proteins among diseases that have been characterized more extensively. In order to minimize this bias when estimating the strength of the association between two given diseases (dis1 and dis2) in the diseasome (Figure 1 panel C), we calculated the Molecular Comorbidity Index (MCI) as defined by:
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where proteinsdis1 and proteinsdis2 are the proteins associated with disease 1 and 2, respectively, proteinsdis1→dis2 are those proteins associated with disease 1 that interact with those associated with disease 2 (and vice versa (proteinsdis2→dis1)), ∩ is the intersection operator and ∪ is the union operator between two sets of elements (proteinsdis1 and proteinsdis2). 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).
To evaluate the number of biological pathways shared by COPD multimorbidities, we used the Jaccard coefficient, which is defined as:
?dctmLink chronic_id='0901c79180822798' object_id='0901c79180822798' edit_widget_type=graphic??dctmEditor selectedObject='0901c79180822798'?
where multimorbidity1 and multimorbidity2 represent two pairs of COPD multimorbidities (for instance lung cancer-COPD and diabetes-COPD) whereas pathways of multimorbidity1 and pathways of multimorbidity2 represent the biological pathways in which the proteins associated with the pairs multimorbidity1 and multimorbidity2 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.