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Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies.

Moni MA, Liò P - BMC Bioinformatics (2014)

Bottom Line: Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality.Only 4 significantly dysregulated genes are common between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immune responsiveness implicated in susceptibility to infectious and inflammatory diseases.Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way.

View Article: PubMed Central - PubMed

Affiliation: Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. Mohammad.Moni@cl.cam.ac.uk.

ABSTRACT

Background: Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but the comorbidity is the key aspect to underline their different impacts. One UK doctor says "I'd rather have HIV than diabetes" as life expectancy among diabetes patients is lower than that of HIV. However, HIV has a comorbidity impact on the diabetes.

Results: We present a quantitative framework to compare and explore comorbidity between diseases. By using neighbourhood based benchmark and topological methods, we have built comorbidity relationships network based on the OMIM and our identified significant genes. Then based on the gene expression, PPI and signalling pathways data, we investigate the comorbidity association of these 2 infective pathologies with other 7 diseases (heart failure, kidney disorder, breast cancer, neurodegenerative disorders, bone diseases, Type 1 and Type 2 diabetes). Phenotypic association is measured by calculating both the Relative Risk as the quantified measures of comorbidity tendency of two disease pairs and the ϕ-correlation to measure the robustness of the comorbidity associations. The differential gene expression profiling strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response and statistically dysregulates a large number of genes, pathways and PPIs subnetworks in different pathologies such as chronic heart failure (21 genes), breast cancer (16 genes) and bone diseases (11 genes). HIV-1 induces comorbidities relationship with many other diseases, particularly strong correlation with the neurological, cancer, metabolic and immunological diseases. Similar comorbidities risk is observed from the clinical information. Moreover, SARS and HIV infections dysregulate 4 genes (ANXA3, GNS, HIST1H1C, RASA3) and 3 genes (HBA1, TFRC, GHITM) respectively that affect the ageing process. It is notable that HIV and SARS similarly dysregulated 11 genes and 3 pathways. Only 4 significantly dysregulated genes are common between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immune responsiveness implicated in susceptibility to infectious and inflammatory diseases.

Conclusions: Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way.

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Related in: MedlinePlus

Phenotypic Disease Networks (PDNs) for SARS infection. Nodes are diseases and links are correlations. Node labels identify the ICD9 codes at the 3-digit category level in (a) and 5-digit category level in (b). Only statistically significant links with relative risk RRij are shown.
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Fig8: Phenotypic Disease Networks (PDNs) for SARS infection. Nodes are diseases and links are correlations. Node labels identify the ICD9 codes at the 3-digit category level in (a) and 5-digit category level in (b). Only statistically significant links with relative risk RRij are shown.

Mentions: SARS-associated coronavirus ICD-9-CM diagnosis code is 079.82, which is under the group of "Viral and chlamydial infection in conditions classified elsewhere and of unspecified site" and ICD-9-CM diagnosis code 079. Moreover, the ICD-9-CM code 480.3 is for the pneumonia due to SARS associated coronavirus. So we have considered both ICD-9-CM codes 079.82 and 480.3 for our phenotypic SARS comorbidity study. In our 3 digit code data we have considered 079 and for 5 digit code data we have considered 480.3. Considering the relative risk RRij ≥ 10 between the disease group 079 and other disorder categories, we have constructed the PDN as shown in Figure8(a), and considering the relative risk RRij ≥ 20 between the disease group 480.3 and other disorder categories, we have constructed the PDN as shown in Figure8(b). We presented only the most significant relative risk associations (see Additional file7: Table S7 and Additional file8: Table S8).Figure 8


Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies.

Moni MA, Liò P - BMC Bioinformatics (2014)

Phenotypic Disease Networks (PDNs) for SARS infection. Nodes are diseases and links are correlations. Node labels identify the ICD9 codes at the 3-digit category level in (a) and 5-digit category level in (b). Only statistically significant links with relative risk RRij are shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4363349&req=5

Fig8: Phenotypic Disease Networks (PDNs) for SARS infection. Nodes are diseases and links are correlations. Node labels identify the ICD9 codes at the 3-digit category level in (a) and 5-digit category level in (b). Only statistically significant links with relative risk RRij are shown.
Mentions: SARS-associated coronavirus ICD-9-CM diagnosis code is 079.82, which is under the group of "Viral and chlamydial infection in conditions classified elsewhere and of unspecified site" and ICD-9-CM diagnosis code 079. Moreover, the ICD-9-CM code 480.3 is for the pneumonia due to SARS associated coronavirus. So we have considered both ICD-9-CM codes 079.82 and 480.3 for our phenotypic SARS comorbidity study. In our 3 digit code data we have considered 079 and for 5 digit code data we have considered 480.3. Considering the relative risk RRij ≥ 10 between the disease group 079 and other disorder categories, we have constructed the PDN as shown in Figure8(a), and considering the relative risk RRij ≥ 20 between the disease group 480.3 and other disorder categories, we have constructed the PDN as shown in Figure8(b). We presented only the most significant relative risk associations (see Additional file7: Table S7 and Additional file8: Table S8).Figure 8

Bottom Line: Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality.Only 4 significantly dysregulated genes are common between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immune responsiveness implicated in susceptibility to infectious and inflammatory diseases.Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way.

View Article: PubMed Central - PubMed

Affiliation: Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK. Mohammad.Moni@cl.cam.ac.uk.

ABSTRACT

Background: Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but the comorbidity is the key aspect to underline their different impacts. One UK doctor says "I'd rather have HIV than diabetes" as life expectancy among diabetes patients is lower than that of HIV. However, HIV has a comorbidity impact on the diabetes.

Results: We present a quantitative framework to compare and explore comorbidity between diseases. By using neighbourhood based benchmark and topological methods, we have built comorbidity relationships network based on the OMIM and our identified significant genes. Then based on the gene expression, PPI and signalling pathways data, we investigate the comorbidity association of these 2 infective pathologies with other 7 diseases (heart failure, kidney disorder, breast cancer, neurodegenerative disorders, bone diseases, Type 1 and Type 2 diabetes). Phenotypic association is measured by calculating both the Relative Risk as the quantified measures of comorbidity tendency of two disease pairs and the ϕ-correlation to measure the robustness of the comorbidity associations. The differential gene expression profiling strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response and statistically dysregulates a large number of genes, pathways and PPIs subnetworks in different pathologies such as chronic heart failure (21 genes), breast cancer (16 genes) and bone diseases (11 genes). HIV-1 induces comorbidities relationship with many other diseases, particularly strong correlation with the neurological, cancer, metabolic and immunological diseases. Similar comorbidities risk is observed from the clinical information. Moreover, SARS and HIV infections dysregulate 4 genes (ANXA3, GNS, HIST1H1C, RASA3) and 3 genes (HBA1, TFRC, GHITM) respectively that affect the ageing process. It is notable that HIV and SARS similarly dysregulated 11 genes and 3 pathways. Only 4 significantly dysregulated genes are common between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immune responsiveness implicated in susceptibility to infectious and inflammatory diseases.

Conclusions: Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way.

Show MeSH
Related in: MedlinePlus