An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach

Inflammatory bowel disease (IBD) is an illness with increasing prevalence, particularly in emerging countries, which can have a substantial impact on the quality of life of the patient. The illness is rather heterogeneous with different evolution among patients. A machine learning approach is followed in this paper to identify potential genes that are related to IBD. This is done by following a Monte Carlo simulation approach. In total, 23 different machine learning techniques were tested (in addition to a base level obtained using artificial neural networks). The best model identified 74 genes selected by the algorithm as being potentially involved in IBD. IBD seems to be a polygenic illness, in which environmental factors might play an important role. Following a machine learning approach, it was possible to obtain a classification accuracy of 84.2% differentiating between patients with IBD and control cases in a large cohort of 2490 total cases. The sensitivity and specificity of the model were 82.6% and 84.4%, respectively. It was also possible to distinguish between the two main types of IBD: (1) Crohn's disease and (2) ulcerative colitis.

Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in Metisa plana Hormone Pathways

Metisa plana Walker (Lepidoptera: Psychidae) is a major oil palm pest species distributed across Southeast Asia. M. plana outbreaks are regarded as serious ongoing threats to the oil palm industry due to their ability to significantly reduce fruit yield and subsequent productivity. Currently, conventional pesticide overuses may harm non-target organisms and severely pollute the environment. This study aims to identify key regulatory genes involved in hormone pathways during the third instar larvae stage of M. plana gene co-expression network analysis. A weighted gene co-expression network analysis (WGCNA) was conducted on the M. plana transcriptomes to construct a gene co-expression network. The transcriptome datasets were obtained from different development stages of M. plana, i.e., egg, third instar larvae, pupa, and adult. The network was clustered using the DPClusO algorithm and validated using Fisher's exact test and receiver operating characteristic (ROC) analysis. The clustering analysis was performed on the network and 20 potential regulatory genes (such as MTA1-like, Nub, Grn, and Usp) were identified from ten top-most significant clusters. Pathway enrichment analysis was performed to identify hormone signalling pathways and these pathways were identified, i.e., hormone-mediated signalling, steroid hormone-mediated signalling, and intracellular steroid hormone receptor signalling as well as six regulatory genes Hnf4, Hr4, MED14, Usp, Tai, and Trr. These key regulatory genes have a potential as important targets in future upstream applications and validation studies in the development of biorational pesticides against M. plana and the RNA interference (RNAi) gene silencing method.

Translational research into frailty from bench to bedside: Salivary biomarkers for inflammaging

Frailty is a complex physiological syndrome associated with adverse ageing and decreased physiological reserves. Frailty leads to cognitive and physical disability and is a significant cause of morbidity, mortality and economic costs. The underlying cause of frailty is multifaceted, including immunosenescence and inflammaging, changes in microbiota and metabolic dysfunction. Currently, salivary biomarkers are used as early predictors for some clinical diseases, contributing to the effective prevention and treatment of diseases, including frailty. Sample collection for salivary analysis is non-invasive and simple, which are paramount factors for testing in the vulnerable frail population. The aim of this review is to describe the current knowledge on the association between frailty and the inflammatory process and discuss methods to identify putative biomarkers in salivary fluids to predict this syndrome. This study describes the relationship between i.-inflammatory process and frailty; ii.-infectious, chronic, skeletal, metabolic and cognitive diseases with inflammation and frailty; iii.-inflammatory biomarkers and salivary fluids. There is a limited number of previous studies focusing on the analysis of inflammatory salivary biomarkers and frailty syndrome; hence, the study of salivary fluids as a source for biomarkers is an open area of research with the potential to address the increasing demands for frailty-associated biomarkers.

Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins

Functional modules can be predicted using genome-wide protein-protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Background

Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset.

Methods

We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher’s exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher’s exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.

Results

The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients' data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

A system biology approach based on metabolic biomarkers and protein-protein interactions for identifying pathways underlying schizophrenia and bipolar disorder

Mental disorders (MDs), including schizophrenia (SCZ) and bipolar disorder (BD), have attracted special attention from scientists due to their high prevalence and significantly debilitating clinical features. The diagnosis of MDs is still essentially based on clinical interviews, and intensive efforts to introduce biochemical based diagnostic methods have faced several difficulties for implementation in clinics, due to the complexity and still limited knowledge in MDs. In this context, aiming for improving the knowledge in etiology and pathophysiology, many authors have reported several alterations in metabolites in MDs and other brain diseases. After potentially fishing all metabolite biomarkers reported up to now for SCZ and BD, we investigated here the proteins related to these metabolites in order to construct a protein-protein interaction (PPI) network associated with these diseases. We determined the statistically significant clusters in this PPI network and, based on these clusters, we identified 28 significant pathways for SCZ and BDs that essentially compose three groups representing three major systems, namely stress response, energy and neuron systems. By characterizing new pathways with potential to innovate the diagnosis and treatment of psychiatric diseases, the present data may also contribute to the proposal of new intervention for the treatment of still unmet aspects in MDs.

A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine

Motivation

Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on ‘multiple network analysis’ in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases.

Results

By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered.

Availability

The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55].

Supplementary Information

A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.

Bile Acids Impair Vaccine Response in Children With Biliary Atresia

Background

Vaccination is the best way to protect children under 5 years from death or disability. Children with biliary atresia (BA), which is the most common pediatric cholestatic end-stage liver disease (PELD), are more vulnerable to infectious diseases. However, the vaccination coverage and factors modulating vaccine responses in children with BA are largely unknown.

Methods

In this study, 288 children (median age: 7 months) diagnosed with BA before liver transplantation were enrolled for the evaluation of vaccination status and the factors affecting the immune response to the hepatitis B (HBV) vaccine. Moreover, 49 BA children (median age: 4 months) were enrolled for flow cytometric analysis of CD4⁺ T cells and CD19⁺ B cell subsets and correlations with serum bile acid levels.

Results

Generally, these children had very low routine vaccination rates for the meningococcal serogroup AC (Men AC) (41.2%), measles-mumps-rubella (MMR) (31.3%), poliomyelitis (Polio) (25.3%), hepatitis A (HAV) (25.0%), Japanese encephalitis (JE) (15.0%), diphtheria-tetanus-pertussis (DTP) (14.2%), meningococcal serogroup A (Men A) (13.5%) and varicella (VAR) (10.8%) vaccines, but not for the HBV (96.2%) and bacillus Calmette-Guérin (BCG) (84.7%) vaccines. Remarkably, 19.8% (57/288) of the patients had HBV infection. Out of 220 patients vaccinated for HBV, 113 (51.4%), 85 (38.6%) and 22 (10%) had one, two or three doses of the HBV vaccine, respectively. Furthermore, logistic regression analysis revealed that the bile acid level was an independent factor associated with poor HBV vaccine response (p = 0.03; OR = 0.394; 95% CI = 0.170-0.969). Immunophenotyping showed that bile acids were only negatively correlated with the CD19⁺CD27⁺IgG⁺ post-class-switched memory B cell ratio (p = 0.01).

Conclusion

This study reveals the overall vaccination rates of routine vaccines in Chinese BA children are very low and the poor HBV vaccine responses are associated with bile acids, possibly via the inhibition of CD19⁺CD27⁺IgG⁺ post-class-switched memory B cell response.

Clinical Trial Registration

http://www.chictr.org.cn, identifier ChiCTR1800019165.

BiClusO: A Novel Biclustering Approach and Its Application to Species-VOC Relational Data

In this paper, we propose a novel biclustering approach called BiClusO. Biclustering can be applied to various types of bipartite data such as gene-condition or gene-disease relations. For example, we applied BiClusO to bipartite relations between species and volatile organic compounds (VOCs). VOCs, which are emitted by different species, have huge environmental and ecological impacts. The biosynthesis of VOCs depends on different metabolic pathways which can be used to categorize the species. A previous study related to the KNApSAcK VOC database classified microorganisms based on their VOC profiles, which confirmed the consistency between VOC-based and pathogenicity-based classifications. However, due to limited data, classification of all species in terms of VOC profiles was not performed. In this study, we enriched our database with additional data collected from different online sources and journals. Then, by applying BiClusO to species-VOC relational data, we determined that VOC-based classification is consistent with taxonomy-based classification of the species. We also assessed the diversity of VOC pathways across different kingdoms of species.

Development of a susceptibility gene based novel predictive model for the diagnosis of ulcerative colitis using random forest and artificial neural network

Ulcerative colitis is a type of inflammatory bowel disease characterized by chronic and recurrent nonspecific inflammation of the intestinal tract. To find susceptibility genes and develop a novel predictive model of ulcerative colitis, two sets of cases and a control group containing the ulcerative colitis gene expression profile (training set GSE109142 and validation set GSE92415) were downloaded and used to identify differentially expressed genes. A total of 781 upregulated and 127 downregulated differentially expressed genes were identified in GSE109142. The random forest algorithm was introduced to determine 1 downregulated and 29 upregulated differentially expressed genes contributing highest to ulcerative colitis occurrence. Expression data of these 30 genes were transformed into gene expression scores, and an artificial neural network model was developed to calculate differentially expressed genes weights to ulcerative colitis. We established a universal molecular prognostic score (mPS) based on the expression data of the 30 genes and verified the mPS system with GSE92415. Prediction results agreed with that of an independent data set (ROC-AUC=0.9506/PR-AUC=0.9747). Our research creates a reliable predictive model for the diagnosis of ulcerative colitis, and provides an alternative marker panel for further research in disease early screening

Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools

Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (TPX2) was the most promising independent prognostic biomarker in stage I EC. TPX2 expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, TPX2 mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1–2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of TPX2.

Discovery of inflammatory bowel disease-associated miRNAs using a novel bipartite clustering approach

BACKGROUND

Multidimensional data mining from an integrated environment of different data sources is frequently performed in computational system biology. The molecular mechanism from the analysis of a complex network of gene-miRNA can aid to diagnosis and treatment of associated diseases.

METHODS

In this work, we mainly focus on finding inflammatory bowel disease (IBD) associated microRNAs (miRNAs) by biclustering the miRNA-target interactions aided by known IBD risk genes and their associated miRNAs collected from several sources. We rank different miRNAs by attributing to the dataset size and connectivity of IBD associated genes in the miRNA regulatory modules from biclusters. We search the association of some top-ranking miRNAs to IBD related diseases. We also search the network of discovered miRNAs to different diseases and evaluate the similarity of those diseases to IBD.

RESULTS

According to different literature, our results show the significance of top-ranking miRNA to IBD or related diseases. The ratio analysis supports our ranking method where the top 20 miRNA has approximately tenfold attachment to IBD genes. From disease-associated miRNA network analysis we found that 71% of different diseases attached to those miRNAs show more than 0.75 similarity scores to IBD.

CONCLUSION

We successfully identify some miRNAs related to IBD where the scoring formula and disease-associated network analysis show the significance of our method. This method can be a promising approach for isolating miRNAs for similar types of diseases.

Graph cluster approach in identifying novel proteins and significant pathways involved in polycystic ovary syndrome

RESEARCH QUESTION

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach?

METHODS

Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways.

RESULTS

The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity.

CONCLUSIONS

These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.

Association between matrix Gla protein and ulcerative colitis according to DNA microarray data

Background

Matrix Gla protein (MGP) is a secreted protein contributed to the immunomodulatory functions of mesenchymal stromal cells. Microarray profiling found a significantly higher expression level of the extracellular matrix gene MGP in patients with ulcerative colitis (UC). However, little is known about the role of MGP in UC and its upstream signaling regulation. This study aimed to identify the expression of MGP in UC and its upstream regulator mechanism.

Methods

Colonic mucosa biopsies were obtained from patients with UC and healthy controls. DNA microarray profiling was used to explore underlying genes correlating with UC development. Mice were fed with water containing different concentrations of dextran sodium sulfate (DSS) to induce an experimental colitis model. Colonic tissues were collected and evaluated using immunohistochemistry, immunoblot, real-time polymerase chain reaction, and chromatin immunoprecipitation assay. Bioinformatics analysis was performed to identify candidate MGP gene-promoter sequence and transcription-initiation sites. Luciferase-reporter gene assay was conducted to examine the potential transcription factor of MGP gene expression.

Results

The expression of MGP was significantly increased in colonic tissues from UC patients and DSS-induced colitis models, and was positively correlated with disease severity. Bioinformatics analysis showed a conserved binding site for Egr-1 in the upstream region of human MGP gene. The significantly higher level of Egr-1 gene expression was found in UC patients than in healthy controls. The activity of luciferase was significantly enhanced in the Egr-1 expression plasmid co-transfected group than in the control group and was further inhibited when co-transfected with the Egr-1 binding-site mutated MGP promoter.

Conclusions

Up-regulated expression of MGP was found in UC patients and DSS-induced colitis. The expression of MGP can be regulated by Egr-1.