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

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.

Figure 1

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.

Figure 2

The gene-disease association network centred on the HIV infection is constructed based on the different categories of diseases that are connected and showed comorbidities with the HIV-1 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.

Figure 3

Network of the eight diseases or infections (chronic heart failure, kidney disorders, breast cancer, parkinson, osteoporosis, HIV/SARS infection, type 1 and type 2 diabetes) that are associated and showed co-morbidities with the (a) SARS infection and (b) HIV infection through the shared genes and common pathways. There are some highly up and down regulated genes that are common between SARS/HIV infection and the other 8 diseases or infections. Up and down arrows are indicated the common highly up and down dysregulated genes between SARS/HIV infection and the corresponding infection or disease.

Figure 4

Protein–protein interaction network of the eight diseases and infections (chronic heart failure, kidney disorders, breast cancer, parkinson, osteoporosis, HIV infection, type 1 and type 2 diabetes) that are associated and showed comorbidities with the SARS infection through the sharing protein subnetwork.

Figure 5

Protein–protein interaction network of the eight diseases and infection (chronic heart failure, kidney disorders, breast cancer, parkinson, osteoporosis, SARS infection, type 1 and type 2 diabetes) that are associated and showed comorbidities with the HIV infection through the sharing protein subnetwork.

Figure 6

Network of the comorbidities risk probability among 9 diseases and infections (SARS infection, Osteoporosis, Parkinson, Type 1 Diabetes, Type 2 Diabetes, Heart failure, Kidney disorders, Breast cancer and HIV-1 infection). Comorbidities probability distances among infections and diseases are presented through the edges and variances are represented by the size of the nodes.

Figure 7

Phenotypic Disease Networks (PDNs). Nodes are diseases and links are correlations. Node colour identifies the diseases based on the ICD9 category. Only statistically significant links with R R _ij > = 20 and ϕ > = 0.06 are shown.

Figure 8

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 R R _ij are shown.

Figure 9

Phenotypic Disease Networks (PDNs) for HIV-1 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 R R _ij are shown.

Figure 10

Correlation between the number of shared genes and phenotypic relative risk for the disease comorbidities.

Figure 11

Four genes (ANXA3, HIST1H1C, RASA3 and GNS) that are linked between SARS infection and ageing. For the causes of these genes, ageing process of the SARS infected patients increase faster. Up arrows indicate the highly up dysregulated genes.

Figure 12

Three genes (HBA1, TFRC and GHITM) that are linked between HIV-1 infection and ageing. For the causes of these genes, ageing process of the HIV infected patients increase faster. Up and down arrows indicate the highly up and down dysregulated genes.

Figure 13

Venn diagram of the highly over and under expressed genes for the SARS-CoV infection in lung and PBMC cells and MERS-CoV infection in the lung cells vs. corresponding to their Mock.

Figure 14

Log fold changes of the expression level (y axis) of the MERS-CoV infected genes (x axis) corresponding to Mock in different time points.

Figure 15

Log fold changes of the expression level (y axis) of the SARS-Cov infected genes (x axis) corresponding to Mock in different time points.

Figure 16

Log fold changes of the expression level (y axis) of the SARS infected NFKBIA and EGR1 genes (x axis) corresponding to Mock.

Figure 17

Progressive temporal activation of pathways. A schematic view of networks becoming disease comorbidities increased for the perturbation of the pathways dysregulation advances with time. The red circles indicate increased levels of dysregulated gene expression relative to control and the red linked indicate dysregulated pathways that have been increased from infection as compared with normal control. The green indicated transcripts that are the same in control and infection condition. The four panels represent the network with time intervals of the infection progression. With time the inflammation confidence level is increased which is indicated by confidence interval.