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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

The gene-disease association network centred on the SARS infection is constructed based on the different categories of diseases that are connected and showed comorbidities with the SARS infection through the different genes. Red colour represents different categories of disorders and green colour represents different genes that are common with the other categories of disorders. The size of a disease node is proportional to the number of dysregulated genes shared between the infections/disorder groups. A link is placed between a disorder and a disease gene if mutations in that gene lead to the specific disorder.
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Fig1: The gene-disease association network centred on the SARS infection is constructed based on the different categories of diseases that are connected and showed comorbidities with the SARS infection through the different genes. Red colour represents different categories of disorders and green colour represents different genes that are common with the other categories of disorders. The size of a disease node is proportional to the number of dysregulated genes shared between the infections/disorder groups. A link is placed between a disorder and a disease gene if mutations in that gene lead to the specific disorder.

Mentions: In the GDN, nodes represent diseases class or genes, and two disorders are connected to each other if they share at least one gene in which mutations are associated with both diseases groups (Figures1 and2). The number of interlinked genes between SARS infection and other diseases indicates that immunological, hematological, neurological, metabolic and dermatological diseases categories are strongly associated with the SARS infection (see Figure1 and Additional file3: Table S3). Few genes are also shared between more than 2 categories of diseases i.e those disease groups are also associated through at least that genes. For an instance, the gene ATM shared among SARS infection, cancer and immunological diseases. Therefore, cancer and immunological diseases are also interrelated through the gene ATM. Among all these disease classes immunological diseases class is tightly correlated with the SARS infection due to the highest number of genes (12 genes) shared between them. On the other hand, the number of associated genes between HIV infection and other diseases indicates that neurological, metabolic, cancer and hematological diseases categories are strongly correlated with the HIV infection (see Figure2 and Additional file4: Table S4). Few HIV dysregulated genes are also shared between more than 2 categories of diseases such as the gene TGFB1 is shared among HIV infection, cancer and skeletal diseases. It is notable that 11 significant genes (4 upregulated and 7 downregulated) are similarly dysregulated in the both SARS and HIV infections.Figure 1


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

Moni MA, Liò P - BMC Bioinformatics (2014)

The gene-disease association network centred on the SARS infection is constructed based on the different categories of diseases that are connected and showed comorbidities with the SARS infection through the different genes. Red colour represents different categories of disorders and green colour represents different genes that are common with the other categories of disorders. The size of a disease node is proportional to the number of dysregulated genes shared between the infections/disorder groups. A link is placed between a disorder and a disease gene if mutations in that gene lead to the specific disorder.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: The gene-disease association network centred on the SARS infection is constructed based on the different categories of diseases that are connected and showed comorbidities with the SARS infection through the different genes. Red colour represents different categories of disorders and green colour represents different genes that are common with the other categories of disorders. The size of a disease node is proportional to the number of dysregulated genes shared between the infections/disorder groups. A link is placed between a disorder and a disease gene if mutations in that gene lead to the specific disorder.
Mentions: In the GDN, nodes represent diseases class or genes, and two disorders are connected to each other if they share at least one gene in which mutations are associated with both diseases groups (Figures1 and2). The number of interlinked genes between SARS infection and other diseases indicates that immunological, hematological, neurological, metabolic and dermatological diseases categories are strongly associated with the SARS infection (see Figure1 and Additional file3: Table S3). Few genes are also shared between more than 2 categories of diseases i.e those disease groups are also associated through at least that genes. For an instance, the gene ATM shared among SARS infection, cancer and immunological diseases. Therefore, cancer and immunological diseases are also interrelated through the gene ATM. Among all these disease classes immunological diseases class is tightly correlated with the SARS infection due to the highest number of genes (12 genes) shared between them. On the other hand, the number of associated genes between HIV infection and other diseases indicates that neurological, metabolic, cancer and hematological diseases categories are strongly correlated with the HIV infection (see Figure2 and Additional file4: Table S4). Few HIV dysregulated genes are also shared between more than 2 categories of diseases such as the gene TGFB1 is shared among HIV infection, cancer and skeletal diseases. It is notable that 11 significant genes (4 upregulated and 7 downregulated) are similarly dysregulated in the both SARS and HIV infections.Figure 1

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