Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies
© Moni and Liò; licensee BioMed Central. 2014
Received: 30 December 2013
Accepted: 19 September 2014
Published: 24 October 2014
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.
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.
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.
The term "comorbidity" refers to the coexistence of multiple diseases or disorders in relation to a primary disease or disorder in an individual . A comorbidity relationship between two diseases exists whenever they appear simultaneously in a patient more than chance alone . It represents the co–occurrence of diseases or presence of different medical conditions one after another in the same patient [1, 3]. Some diseases or infections can coexist in an individual by coincidence, and there is no pathological association among them. However, in most of the cases, multiple diseases (acute or chronic events) occur together in a patient because of the associations among them. These comorbidity associations can be due to direct or indirect causal relationships and the shared risk factors among diseases . For an instance, a type of genetic abnormality linked to cancer is more common in patient of type 2 diabetes than other people . Examples of comorbidity studies are many, often referring to chronic obstructive pulmonary disease (COPD) [6, 7], obesity , mental disorders , immune-related diseases , cancer  etc.
Comorbidity can be attributed to the disease connections on the molecular level, such as dysregulated genes, PPIs (protein–protein interactions), and metabolic pathways as potential causes of comorbidity [1, 3, 12, 13]. From a genetic perspective, a pair of diseases is connected because they have both been associated with the same dysregulated genes [14, 15], whereas from a proteomics perspective phenotypically similar diseases are related via biological modules such as PPIs or molecular pathways [16, 17].
Population-based disease association is important in conjunction with molecular and genetic data to uncover the molecular origins of diseases and disease comorbidities. Patient medical records contain important clarification regarding the co-occurrences of diseases affecting the same patient . During the last few years, several researchers have been conducted the disease comorbidity analysis to understand the origins of many diseases [1, 12, 18]. Goh, Cusick, Valle, Childs, Vidal, Barabasi et al. and Feldman, Rzhetsky, Vitkup et al. built networks of gene-disease associations by connecting diseases that have been associated with the same genes [14, 15], whereas Lee, Park, Kay, Christakis, Oltvai and Barabási et al. constructed a network in which two diseases are linked if metabolic reactions are associated between them . Disease association studies from proteomic point of view have been studied by Rual, Venkatesan, Hao, Hirozane-Kishikawa, Dricot, Li, Berriz, Gibbons, Dreze, Ayivi-Guedehoussou et al. and Stelzl, Worm, Lalowski, Haenig, Brembeck, Goehler, Stroedicke, Zenkner, Schoenherr, Koeppen et al. [19, 20]. Rzhetsky, Wajngurt, Park and Zheng et al. inferred the comorbidity links between 161 disorders from the disease history of 1.5 million patients . However, all of these efforts have focused on the role of a single molecular or phenotypic measure to capture disease–disease relationships. In our work we have used disease–gene associations, PPIs, molecular pathways and clinical information to obtain statistically significant associations and comorbidity risks among diseases.
Inflammation is a hallmark of many serious human infectious diseases associated to a wide variety of infections, such as HIV-1 . UK doctor Max Pemberton 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. Also the flu can cause complications, including bacterial pneumonia, or the worsening of chronic health problems. Asthma is the most common comorbidity in patients hospitalized for swine influenza (H1N1) infection . Dengue can cause myocardial impairment, arrhythmias and, occasionally, fulminant myocarditis . Chronic medical conditions, such as heart disease, lung disease, diabetes, renal disease, rheumatologic disease, dementia, and stroke are risk factors for influenza complications . Common chronic infections such as periodontitis or infection with Helicobacter pylori may also increase stroke risk . Moreover, the severity of pneumonia in patients coinfected with influenza virus and bacteria is significantly higher than in those infected with bacteria alone. The incidence of flu is higher in children and younger adults than in older individuals, but influenza-associated morbidity and mortality increase with age, especially for individuals with underlying medical conditions such as chronic cardiovascular diseases . During the ageing process the immune system becomes compromised and it causes an increasing inflammation . In particular, chronic inflammation (inflammageing) and metabolic function are strongly affected by the ageing process . The ageing of populations is leading to an unprecedented increase different diseases like cancer and fatalities. It is reported that 80% of the elderly population has three or more chronic conditions .
On the other hand, respiratory viruses are an emerging threat to global health security and have led to worldwide epidemics with substantial morbidity and mortality . Coronaviruses (CoVs) cause respiratory and enteric diseases in human and other animals that induce fatal respiratory, gastrointestinal and neurological disease. Severe acute respiratory syndrome (SARS) is an epidemic human disease, is caused by a coronavirus (CoV), called SARS-associated coronavirus (SARS-CoV) . SARS patients may present with a spectrum of disease severity ranging from flu-like symptoms and viral pneumonia to acute respiratory distress syndrome and death . Most of the deaths were attributed to complications related to sepsis, ARDS and multiorgan failure, which occurred commonly in the elderly for comorbidities . Age and comorbidity (e.g. diabetes mellitus, heart disease) were consistently found to be significant independent predictors of various adverse outcomes in SARS . Children with SARS have better prognosis than adults . Advanced age and comorbidities were significantly associated with increased risk of SARS-CoV related death, due to acute respiratory distress syndrome . Mild degree of anaemia is common in the SARS infected patients and patients who have recovered from SARS show symptoms of psychological trauma . Another novel coronavirus MERS-CoV, which is a new threat for public health, has similar clinical characteristics to SARS-CoV, but the comorbidity is the key aspect to underline their different impacts [36, 37]. MERS-CoV causes respiratory infections of varying severity and sometimes fatal infections in humans including kidney failure and severe acute pneumonia . Despite sharing some clinical similarities with SARS (eg, fever, cough, incubation period), there are also some important differences such as the rapid progression to respiratory failure, which we have studied on comorbidities point of view.
Infection with the human immunodeficiency virus-1 (HIV) and the resulting acquired immune deficiency syndrome (AIDS) affects cellular immune regulation . HIV infection severely impacts on the immune system causing phenotypic changes in peripheral cells and dysregulates the innate immune system . Significant number of HIV-1 infected patients exhibits osteopenia and osteoporosis, leading to higher incidence to develop weak and fragile bones during the course of disease . HIV has also been associated with an increased risk of developing both diabetes and cardiovascular disease . Infection with HIV weakens the immune system and reduces the body’s ability to fight infections that may lead to cancer [43, 44]. People infected with human immunodeficiency virus (HIV) have a higher risk of some types of cancer (Kaposi sarcoma, non-Hodgkin lymphoma, cervical cancer, anal, liver, lung cancer, and Hodgkin lymphoma) than uninfected people . Many people infected with HIV are also infected with other viruses that cause certain cancers [46, 47]. HIV infection even when controlled by highly active antiretroviral therapy (HAART) is being linked to chronic inflammation . People with HIV-1 infection appear to have a markedly higher rate of chronic kidney disease than the general public . It is because some of the risk factors associated with HIV-1 acquisition are the same as those that lead to kidney disease because of the virus itself and some therapies (e.g. HAART therapy). Antiretroviral therapy for HIV may increase the risk of developing metabolic syndrome (abdominal obesity, hyperglycaemia, dyslipidaemia and hypertension) and thus predispose to type 2 diabetes and cardiovascular disease. Many of the biologic factors thought to be causally associated with inflammation in HIV disease are also thought to be causally associated with the inflammation of ageing .
Infections (acute and chronic conditions) are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. Comorbidities related to flu have been recently investigated . Comorbidities for tuberculosis have also been studied recently [52, 53]. To understand the overall mechanism we have studied the comorbidity associations of SARS and HIV infections. Both HIV and SARS are emerging infectious diseases in the modern world; each of these diseases has caused global societal and economic impact related to unexpected illnesses and deaths . SRAS is a significant public health threat and HIV is a long term chronic infection. Since these two infections are associated with high mortality rates and there are no clinically approved antiviral treatments or vaccines available for either of these infections, we have selected these two infections for our study. Centred on the SARS and HIV-1 infections we have investigated highly heterogeneous disease comorbidity networks using the disease–gene associations, PPI subnetwork, molecular pathways and clinical information.
Results and discussion
We have presented a systematic and quantitative approach to discover human disease comorbidities using different sources of available mRNA expression, protein-protein interactions, signalling pathways, disease–gene associations, disease–disease associations and disease–drug associations data. It has been shown that SARS coronavirus infects and replicates in a wide variety of host cells in susceptible animals and human beings [55, 56]. To understand the host response to this pathogen, we analysed the gene expression patterns of SARS infected patients, compared to normal subjects using oligo-nucleotide microarrays from the NCBI GEO (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1739) . We analysed the microarray gene expression data of over 8,700 genes from the PBMCs of 10 SARS patients, and compared with healthy control samples. We found that 274 genes (p < 0.01, > 1.5 fold change) were differentially expressed as compared to healthy controls in which 120 genes were significantly up regulated and 154 genes were significantly down regulated (see Additional file 1: Table S1).
On the other hand, monocytes are the key immune responsive cells whose function is adversely impacted by HIV-1. HIV-1 infection radically alters the monocyte phenotype, which is reflected in an HIV-1 induced gene expression analysis. Monocyte gene expression microarray data were collected for control and HIV patients from the NCBI GEO (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18464) . To find out the significant dysregulated genes during the HIV-1 infection, we have performed global gene expression analysis. We found that 186 genes (p < 0.01, > 1.5 fold change) were differentially expressed as compared to healthy controls in which 71 genes were up regulated and 115 genes were down regulated (see Additional file 2: Table S2).
Considering the significantly dysregulated genes of SARS (274 genes) and HIV-1 (186 genes) infections, and gene-disease associations information, we have constructed two gene-disease associations networks (GDN), which are used to explore the shared genetic associations and disease comorbidity. Starting from the bipartite graph we generated biologically relevant network projections and constructed multi-relational gene-disease network in which nodes are diseases or genes, and edges indicate association between gene and disease. This bipartite graph consists of two disjoint sets of nodes, where one set corresponds to all known genetic disorders and the other set corresponds to all of our identified significant genes for SARS and HIV-1 infections. The list of disorders, disease genes, and associations between them were obtained from the Online Mendelian Inheritance in Man (OMIM) , a compendium of human disease genes and phenotypes (see details in the Methods section). We classified each disorder into one of 21 disorder categories based on the physiological system affected as introduced in Goh, Cusick, Valle, Childs, Vidal, Barabasi et al. .
On the other hand, similar diseases share common genes and could be treated by the same drugs , which may allow us to make predictions for new uses of existing drugs. For an instance, the anti-diabetic drug metformin plays a major protective effect against cancer development and increases significantly higher survival rate of the cancer patients . The finding is that the earlier the metformin regimen was initiated, the greater the preventive benefit for the cancer patient. There is an evidence that the antiviral medication, ribavirin, does not work in case of SARS infection . To this end, we used Connectivity Map (Cmap), which is a database of more than 1,400 drug transcriptional signatures in several cell lines . This database allows to identify of molecules that induce similar or opposite transcriptional changes relative to the query signature, based on their connectivity enrichment scores. As a query signature we used our 274 highly dysregulated genes for the SARS infection. We generated the connectivity score value ranges between +1 and -1, where a highly positive score indicates that the drug induces changes similar to those induced by viral infection, while a highly negative score indicates that the drug reverses the expression of the SARS signature. Based on the connectivity score we have selected most potential positive and negative regulators of SARS viral response (see details in the Additional file 15: Table S13). Potential negative regulators indicate that drugs reverse the SARS signature gene expression. Among the negative potential regulator, the drug molecule tetracycline, zalcitabine, gibberellic acid, prestwick-642 and sulfaquinoxaline are more potential for the MCF7 cell line and vorinostat for the HL60 cell line. Based on the data demonstrate the efficacy of different drug against SARS virus can be predicted effective drug treatment for the emergent viruses. Furthermore, immunomodulatory drugs that reduce the excessive host inflammatory response to respiratory viruses have therapeutic benefit to reduce the SARS infection as well as disease comorbidities.
We presented and analysed multi-relational disease comorbidity relationships of SARS and HIV-1 infections with other diseases or infections based on the associations of genetics, proteomics, molecular signalling pathways and phenotypic disorders. The combination of molecular biology, genetics and clinical medicine has greatly facilitated understanding of how different diseases relate to each other. Based on the combined genetics, PPIs, pathways and clinical data, our disease networks can disclose potentially novel disease relationships that have not been captured by previous individual studies. The underlying hypothesis behind this line of research is that once we catalogue all disease-related genes, PPI complex and signalling pathways, if we do not consider environmental changes, we will be able to predict the susceptibility of each individual to future diseases using various molecular biomarkers and it will help us to enter an era of predictive medicine. Our results indicate that such a combination of molecular and population-level data could help to build novel hypotheses about disease mechanisms. Furthermore, if two or more diseases have associated comorbidity, the occurrence of one of them in a patient may increase the likelihood of developing the other diseases.
We have also studied the differences between MERS-CoV and SARS-CoV in the host response. This enables rapid assessment of viral properties and the ability to anticipate possible differences in human clinical responses to MERS-CoV and SARS-CoV and their impact on comorbidities with respect to the general comorbidities conditions. We used this information to predict potential effective drugs against SARS-CoV, a method that could be more generally used to identify candidate therapeutics in future disease outbreaks. These investigation approach may also help to generate hypotheses and make rapid advancements in characterising the new viruses.
We also found that patients’ response of SARS appears to be mainly an innate inflammatory response using NFKBIA, rather than any specific immune response against a viral infection such as HIV. However, HIV infection and highly active antiretroviral therapy (HAART) also increase the immune reconstitution inflammatory syndrome (IRIS) and inflammation through the NF- κB pathways . Moreover we have studied before about the impact of HIV infection on bone diseases and infection (e. g. osteoporosis and osteomyelitis). We observed that genes (e.g. RANKL) and pathways (e.g. NF- κB) that are dysregulated by HIV infection also impact on the bone remodelling and bone related diseases. It is also recognised that inflammation plays a role in cancer aetiology, and various studies have found that inflammation may causes IRIS, obesity and tumour-promoting effects . Moreover, inflammation is an important concomitant cause of many major age-associated pathologies such as cancer, neurodegeneration and diabetes . Our study provides important evidence to associate diseases with the ageing process at the system level and helps to understand more about the comorbidities of the complex diseases. The ageing process itself is accompanied by a chronic low-grade inflammation, which is termed "inflammageing". The combination of metabolic-driven and age-driven inflammatory pathways plays a pivotal role in disease progression. This observation suggests that inflammageing and meta-inflammation can share stimuli and pathogenic mechanisms for comorbidities.
We suppose that what is happening for the comorbidities we investigate is similar to what found for prions [71, 72]. Similar to most infectious agents, prion causes inflammatory responses by activating innate immunity through glial cells in the brain.
Disease genes play a central role in the human interactomes. Overlapping component genes serve as bridges across the relatively independent functional modules or pathways. So perturbation in one pathway, such as the NF- κB signalling pathway, could be propagated throughout the other relevant pathways. We found SARS and HIV-1 infections share 11 significantly dysregulated genes as well as molecular pathways. Both SARS and HIV-1 viruses may infect and find an already existing comorbidity or generate a new comorbidity through the perturbation of the infected pathways. Furthermore, it may provide us an opportunity to investigate the role of other genes from the same pathway in the disease space. Therefore, pathways could be used to represent the underlying biology of diseases and make prediction of disease comorbidities. In most of the cases, the correlativeness among genes, pathways and diseases are many-to-many, e.g. a disease is associated to many different genes and pathways; and a pathway is associated to many different diseases. This study suggests that a single pathway can be involved in many diseases whereas a disease may have dysregulation in many biological processes. Hence, if a drug is already available to treat a disease through modulating the activity of a pathway, then it could potentially be used to treat other diseases that are strongly linked with the same pathway. On the other hand, when a disease shows dysregulation in multiple pathways, a pathway-guided combined drug may be employed in the treatment. Moreover, the protein subnetwork–based approach to diseases may aid in drug discovery, in fact it can potentially be used to treat other diseases that are linked to the same protein complex. Thus, our findings not only potentially help us to understand how different diseases are related based on their underlying molecular mechanisms but also provide insights into the design of novel, protein complex-guided therapeutic interventions for diseases.
Personalised medicine: guidelines for predicting comorbidities
Extending the concept of subclassifying patient cohorts to the single patient level refers to as personalised medicine. During the last few years, acceptance level of the personalised medicine is sharply increased as it has been apparent that standard treatment approaches are rarely efficient across the entire patient population. Advances in high-throughput molecular assay technologies in the fields of genomics, proteomics and other "omics" is increasing the diagnostic and therapeutic strategies for personalised treatment. As a result, declining per-sample cost has given rise to numerous public repositories of biomolecular data. In particular, the availability of these data sets for many different diseases presents a ripe opportunity to use data-driven approaches to advance our current knowledge of disease relationships in a systematic way. The identified disease patterns can then be further investigated with regards to their diagnostic utility or help in predicting novel therapeutic targets. Medicine will focus on each individual patient. It will become intrinsically proactive and will increasingly focus on wellness rather than disease. Proactive and personalised medicine will bring fundamental changes to healthcare, taking carefully targeted preventative or therapeutic action at the earliest indications of risk or disease comorbidities.
We are entering into the genomic era of medicine, where a patient’s genetic/genomic data is becoming important for clinical decision making, including disease risk assessment, disease diagnosis and subtyping, drug therapy and dose selection, risk assessment for adverse drug reaction, and family planning . Today multi-scale and complex biomedical data are gathered and analysed to uncover combinations of predictive disease profiles. Our genome, as well as multiple proteomes, multiple transcriptomes, multiple metabolomes, and other personalised data sets obtained at different points in our lives, will be readily available at affordable prices for each individual. In the near future, clinicians will have to consider genetic/genomic implications to patient care throughout their clinical workflow, including electronic prescribing of medications. Therefore, for the implementation of the personalised medicine system, a model could be developed that will take individual genetic data. Dysregulated biomarker genes will be identified from this genetic data and disease will be identified from the gene–disease association database. Based on the information of the existing disease, the model will predict disease comorbidities using the disease–disease associations database. This will provide us to detect many diseases at the earliest detectable phase, even weeks, months, or maybe years before the symptoms appear and it will afford crucial insights into optimizing of our wellness. Thus, personalised medicine will give fundamental new insights into disease mechanisms, and hence will open new opportunities for diagnosis, therapy and prevention from the disease comorbidities.
In this study, we have considered all available categories of omics and phenotypic data to quantify the SARS and HIV-1 infections centred comorbidity associations. We have shown that the phenotype disease network (PDN) has a heterogeneous structure where some diseases are highly connected while others are hardly connected at all. Our findings showed that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. Detecting comorbidity in a large population is of clinical interest due to the fact that it may reveal new information useful for cause of diseases as well as for new treatment strategies. This study demonstrates the value of an integrated approach in revealing disease relationships and new opportunities for therapeutic applications. So we can say that this kind of approach will be helpful for making evidence-based recommendations about disease comorbidities. Moreover, considering environmental factors (such as physiological stress, diet), ethnic group and gender discriminations are important factors in the comorbidity analysis. Our network approach could be extended as a comorbidity map by integrating diet, exercise and other factors as in .
The gene-disease associations data used in this study were collected from the Online Mendelian Inheritance in Man (OMIM) database (http://www.ncbi.nlm.nih.gov/omim/). This OMIM database is the best-curated repository of all known disease genes and their associated disorders [75, 76]. Genotype-phenotype relationships, as summarised in the OMIM database, contained more than 5000 human disease-genes associations involving 1500 diseases and 3000 disease associated genes. Each entry of the OMIM is composed of four fields, the name of the disorder, the associated gene symbols, its corresponding OMIM id, and the chromosomal location. We selected the entries with the "(3)" tag, for which there is strong evidence that at least one mutation is cause of the disorder. OMIM initially focused on monogenic disorders but in recent years has expanded to include complex traits and the associated genetic mutations that confer susceptibility to these common disorders . Subsequently we classified each disorder into 21 primary disorder classes based on the physiological system affected as introduced in Goh, Cusick, Valle, Childs, Vidal and Barabasi et al. . Disorders having distinct multiple clinical features are assigned to the "multiple" class. This classification scheme reflects the phenotypic similarities among diseases in the same class and has been successfully used in the recent studies of systematic disease analysis .
The gene expression data used in this study was obtained from the NCBI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) . We have considered 10 different data sets for our analysis (accession numbers are GSE1739, GSE45042, GSE17400, GSE9006, GSE9128, GSE15072, GSE7158, GSE8977, GSE18464 and GSE7621) [32, 55, 57, 64, 78–82]. These data sets contain data from the patients of different age and sex. After several rounds of filtering, normalization and statistical analysis, we had microarrays representing SARS, MERS, HIV-1 infections and 7 other human diseases (heart failure, kidney disorders, breast cancer, parkinson, osteoporosis, type 1 and type 2 diabetes).
The protein-protein interaction (PPI) data for human was obtained from the Human Protein Reference Database (HPRD) . HPRD contains the maximum number of PPI data among all publicly available literature-derived databases for human PPI . We have used the reactome knowledge base of human biological pathways database for our pathways association analysis . For the cross compare analysis between the SARS and HIV infections, and ageing process we have download ageing data from the human ageing genomic resources (http://genomics.senescence.info/download.html) [62, 63]. They have collected human ageing genes after an extensive review of the literature. These genes are commonly dysregulated during the ageing process.
To test the validity of the proposed disease associations, we examined the disease co-occurrence information at the population level. We obtained statistically significant pairwise comorbidity associations reconstructed from over 32 million medical records in the US Medicare claims database recorded in the ICD-9-CM format (http://www.icd9data.com), which are frequently used for epidemiological and demographic studies and collected from . We used MedPAR records from 1990 to 1993, where the dates and reasons for all hospitalizations were reported in ICD-9-CM format and it contains the diagnoses of 13039018 elderly patients. Each record consists of the date of visit, a primary diagnosis and up to 9 secondary diagnosis. All diagnoses are specified by ICD9 codes of up to 5 digits. The first three digits specify the main disease category while the last two are used to give additional information about the disease. In total, the ICD-9-CM classification consists of 657 different categories at the 3 digit level and 16,459 categories at the 5 digit level .
To determine whether some existing drug compounds can reverse the SARS infection signature, we used the publicly available Connectivity Map (Cmap) database . Cmap provides associations among genes, chemicals and disease or infection conditions. It is a collection of genome-wide transcriptional data from cultured human cells treated with 1,400 different compounds.
The method of global gene expression analysis using oligonucleotide microarrays has proven to be a sensitive method to develop and refine the molecular determinants of human disorders . Using this technology, we compared the gene expression profiles of SARS, HIV and other diseases. To avoid the problems of comparing microarray data of different platforms and experimental systems, we normalized the gene expression data in each microarray sample (disease state or control) using the Z-score transformation (Z ij ) for each disease gene expression matrix using , where SD is the standard deviation, g ij represents the expression value of gene i in sample j. This transformation allows for the direct comparison of gene expression values across various microarray samples and diseases. To combined more than one data series or experiments for a given disease, we employed a linear regression approach to obtain a combined t-test statistic between two conditions. Data were l o g2-transformed and we calculated expression level for each gene using a linear regression model : Y i = β0 + β1X i , where Y i is the gene expression value and X i is a disease state (disease or control). The coefficients β0 and β1 are the parameters of this model and were estimated by least squares. The t-test statistic, when estimating the value of β1, is the same as the standard t-test statistic between disease and control states.
Time series microarray gene expression data analysis was divide into two steps: pre-processing and identification of statistically significant points by t-test, ANOVA and regression analysis to find differently expressed gene profiles in different time points. In the first step, we pre-processed the experimental data using different statistical methods and finally followed by post less normalization, recommended by the Golden Spike Project . In the second step, we have used a most suitable method "maSigPro" (microarray Significant Profiles) to identify differentially expressed genes in time-course microarray experiments, which is a two step regression method successfully applied on more than one groups of time-series [85, 86]. This two steps regression strategy is used to find genes with significant temporal expression changes and significant differences between experimental groups. This procedure first adjusts this global model by the least-squared technique to identify differentially expressed genes and selects significant genes applying false discovery rate control procedures. Then stepwise regression is applied as a variable selection strategy to study differences between experimental groups and statistically significant profiles. After finding differentially gene expression profiles among the group of experiments, the next step is to cluster them according to their profile similarities. The hierarchical clustering and the median gene expression profiles of clusters are performed according to the "maSigPro" package in R .
Student’s unpaired T-test was performed to identify genes that were differentially expressed in patients over normal samples and significant genes were selected. A threshold of at least 1.5 fold change and a p value for the t-tests of less than 0.01 were chosen. In addition, a two-way ANOVA with Bonferroni’s post-hoc test was used to establish statistical significance between groups (< 0.01). Pathways and functional categories were considered as over-represented when Fisher’s exact test p value was < 0.01. For presenting the signalling and interaction pathways of the different significant genes, we used cytoscape for data integration and network visualization [87, 88] and reactome functional interaction (FI) cytoscape plugin for knowledge base of human biological pathways and network processes .
where E is the set of all edges. The number of shared pathways and protein subnetwork that links between diseases i and j are calculated using the equation 1 and the link prediction score is measured using the equation 2.
where N is the total number of patients in the population, P i is the incidences/prevalences of disease i, P j is the incidence of disease j and C ij is the number of patients that have been diagnosed with both diseases i and j. For R R ij > = 1 comorbidity is larger than expected by chance and for R R ij < 1 comorbidity is smaller than expected by chance. To calculate the significance of the relative risk R R ij , we used the Katz, Baptista, Azen and Pike et al. method to estimate confidence intervals . According to their estimation, the 99% confidence interval for the R R ij between two diseases i and j is calculated by: Lower bounds of the confidence interval (LB) = R R ij ∗ exp(-2.56 ∗ σ ij ) and Upper bounds of the confidence interval (UB) = R R ij ∗ exp(2.56 ∗ σ ij ), where σ ij is given by: . Disease pairs within the 99% confidence interval are only considered if the LB value is larger than 1 when R R ij is larger than 1, or if the UB value is smaller than 1 when R R ij is smaller than 1.
where C ij is the number of patients affected by both diseases, N is the total number of patients in the studied population, and P i and P j are the morbidity or incidence of the i th and j th diseases respectively. The ϕ-correlation is the Pearson’s correlation for the variables which only take 0 or 1 values . For ϕ ij > 0 comorbidity is larger than expected by chance and for ϕ ij < 0 comorbidity is smaller than expected by chance. We can determine the significance of ϕ ≠ 0 by performing a t-test. This consists of calculating t according to the formula: , where n is the number of observations used to calculate ϕ.
for all possible disease pairs i and j, for the cases that one index disease (I) is present (k = true) or absent (k = false). Then for each pair of diseases, we say that i and j are mediated by that index disease if the is significantly different (higher or lower) from (p = 0.01).
where C ij is the observed co-occurrence and P i and P j are the morbidity or prevalence of the i th and j th diseases respectively.
where w ij ≥ 0 is a weight for the measurement between a pair of points (i,j), d ij (X) is the Euclidean distance between i and j, and δ ij is the ideal distance between the points (their separation) in the m-dimensional data space. Note that w ij is used to specify a degree of confidence in the similarity between points (e.g. 0 can be specified if there is no information for a particular pair). A configuration X which minimizes σ(X) gives a plot in which points that are close together correspond to points that are also close together in the original m-dimensional data space. Programming scripts are freely available at http://www.cl.cam.ac.uk/~mam211/comoR/.
We thank FP7–Health–F5–2012 for providing financial support, under grant agreement n 305280 (MIMOmics).
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