Pathway-based analysis tools for complex diseases: a review

Bioinformatics and Systems Biology Approaches to Identify the Synergistic Effects of Alcohol Use Disorder on the Progression of Neurological Diseases

Clinical investigations showed that individuals with Alcohol Use Disorder (AUD) have worse Neurological Disease (ND) development, pointing to possible pathogenic relationships between AUD and NDs. It remains difficult to identify risk factors that are predisposing between AUD and NDs. In order to fix these issues, we created the bioinformatics pipeline and network-based approaches for employing unbiased methods to discover genes abnormally stated in both AUD and NDs and to pinpoint some of the common molecular pathways that might underlie AUD and ND interaction. We found 100 differentially expressed genes (DEGs) in both the AUD and ND patient's tissue samples. The most important Gene Ontology (GO) terms and metabolic pathways, including positive control of cytotoxicity caused by T cells, proinflammatory responses, antigen processing and presentation, and platelet-triggered interactions with vascular and circulating cell pathways were then extracted using the overlapped DEGs. Protein-protein interaction analysis was used to identify hub proteins, including CCL2, IL1B, TH, MYCN, HLA-DRB1, SLC17A7, and HNF4A, in the pathways that have been reported as playing a function in these disorders. We determined several TFs (HNF4A, C4A, HLA-B, SNCA, HLA-DMB, SLC17A7, HLA-DRB1, HLA-C, HLA-A, and HLA-DPB1) and potential miRNAs (hsa-mir-34a-5p, hsa-mir-34c-5p, hsa-mir-449a, hsa-mir-155-5p, and hsa-mir-1-3p) were crucial for regulating the expression of AUD and ND which could serve as prospective targets for treatment. Our methodologies discovered unique putative biomarkers that point to the interaction between AUD and various neurological disorders, as well as pathways that could one day be the focus of therapeutic intervention.

StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling

The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.

Multi-omics and pathway analyses of genome-wide associations implicate regulation and immunity in verbal declarative memory performance

BACKGROUND

Uncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia.

METHODS

We performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. To identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes.

RESULTS

The single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues.

CONCLUSIONS

VDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals.

New Insights into Clinical Management for Sickle Cell Disease: Uncovering the Significant Pathways Affected by the Involvement of Sickle Cell Disease

One of the severe monogenic conditions with the highest prevalence in the globe is sickle cell disease. Although the significance of chronic anemia, hemolysis, and vasculopathy has been established, hemoglobin polymerization, which results in erythrocyte stiffness and Vaso-occlusion, is important to the pathophysiology of this disease. Clinical management is elementary, and there is scant reliable data for many treatments. The onset of cerebrovascular illness and cognitive impairment are two of the major issues associated with sickle cell disease in children, and it is only now that researchers are beginning to understand how blood transfusions and hydroxycarbamide can prevent these complications. When Vaso occlusion and inflammation occur repeatedly, the majority of organs are gradually damaged, including the brain, kidneys, lungs, bones, and cardiovascular system. This damage worsens with age. In our study, we focused on the specific pathways which are affected by the involvement of effected genes. Firstly, we retrieved the gene datasets from the publically available data source website DisGNET. Using literature-based genes, we identified 290 highly regulated genes that are directly associated with sickle cell disease. We subsequently performed a gene expression analysis and extracted a gene set using GEO2R analysis, which was then used to prune 290 differentially expressed genes (DEGs). After pruning we got 60 highly expressed genes. After identification of DEGs, we used these genes for pathway analysis. For the pathway analysis, we used Reactome software and we found that these DEGs are directly associated with 7 different pathways, which are alpha beta signaling pathways, 15 antiviral mechanism, Oligoadenylate synthetase (OAS) antiviral response, interleukin 1 signaling pathways, interleukin 4 and 13, interleukin 10 signaling pathway, and aspirin ADME pathway. After pathway analysis, we can exactly relate how sickle cell disease alters the gene expression and how these genes affect the different pathways. Additionally, we performed gene ontology of 60 genes and identified the gene biological process, cellular component, and molecular functions as we mentioned in our results. With the help of our study data, there is a chance for pre-identification of sickle cell disease person. Our gene result was used as a biomarker of sickle cell disease. In this paper, our result is the primary approach for sickle cell disease; with the help of this paper any researcher can get their primary data and use that for further research.

Evaluation of Density-Based Spatial Clustering for Identifying Genomic Loci Associated with Ischemic Stroke in Genome-Wide Data

The genetic architecture of ischemic stroke (IS), which is one of the leading causes of death worldwide, is complex and underexplored. The traditional approach for associative gene mapping is genome-wide association studies (GWASs), testing individual single-nucleotide polymorphisms (SNPs) across the genomes of case and control groups. The purpose of this research is to develop an alternative approach in which groups of SNPs are examined rather than individual ones. We proposed, validated and applied to real data a new workflow consisting of three key stages: grouping SNPs in clusters, inferring the haplotypes in the clusters and testing haplotypes for the association with phenotype. To group SNPs, we applied the clustering algorithms DBSCAN and HDBSCAN to linkage disequilibrium (LD) matrices, representing pairwise r2 values between all genotyped SNPs. These clustering algorithms have never before been applied to genotype data as part of the workflow of associative studies. In total, 883,908 SNPs and insertion/deletion polymorphisms from people of European ancestry (4929 cases and 652 controls) were processed. The subsequent testing for frequencies of haplotypes restored in the clusters of SNPs revealed dozens of genes associated with IS and suggested the complex role that protocadherin molecules play in IS. The developed workflow was validated with the use of a simulated dataset of similar ancestry and the same sample sizes. The results of classic GWASs are also provided and discussed. The considered clustering algorithms can be applied to genotypic data to identify the genomic loci associated with different qualitative traits, using the workflow presented in this research.

The pathogenetic influence of smoking on SARS-CoV-2 infection: Integrative transcriptome and regulomics analysis of lung epithelial cells

Corona virus disease (COVID-19) has been emerged as pandemic infectious disease. The recent epidemiological data suggest that the smokers are more vulnerable to infection with COVID-19; however, the influence of smoking (SMK) on the COVID-19 infected patients and the mortality is not known yet. In this study, we aimed to discern the influence of SMK on COVID-19 infected patients utilizing the transcriptomics data of COVID-19 infected lung epithelial cells and transcriptomics data smoking matched with controls from lung epithelial cells. The bioinformatics based analysis revealed the molecular insights into the level of transcriptional changes and pathways which are important to identify the impact of smoking on COVID-19 infection and prevalence. We compared differentially expressed genes (DEGs) between COVID-19 and SMK and 59 DEGs were identified as consistently dysregulated at transcriptomics levels. The correlation network analyses were constructed for these common genes using WGCNA R package to see the relationship among these genes. Integration of DEGs with network analysis (protein-protein interaction) showed the presence of 9 hub proteins as key so called "candidate hub proteins" overlapped between COVID-19 patients and SMK. The Gene Ontology and pathways analysis demonstrated the enrichment of inflammatory pathway such as IL-17 signaling pathway, Interleukin-6 signaling, TNF signaling pathway and MAPK1/MAPK3 signaling pathways that might be the therapeutic targets in COVID-19 for smoking persons. The identified genes, pathways, hubs genes, and their regulators might be considered for establishment of key genes and drug targets for SMK and COVID-19.

Marker-free characterization of full-length transcriptomes of single live circulating tumor cells

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cell (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion.

Bioinformatics approach to analyse COVID-19 biomarkers accountable for generation of intracranial aneurysm in COVID-19 patients

COVID-19 became a health emergency on January 30, 2020. SARS-CoV-2 is the causative agent of the coronavirus disease known as COVID-19 and can develop cardiometabolic and neurological disorders. Intracranial aneurysm (IA) is considered the most significant reason for hemorrhagic stroke,and it accounts for approximately 85% of all subarachnoid hemorrhages (SAH). Retinoid signaling abnormalities may explain COVID-19's pathogenesis with inhibition of AEH2, from which COVID-19 infection may enhance aneurysm formation and rupture due to abrupt blood pressure changes, endothelial cell injury, and systemic inflammation. The objective of this study was to investigate the potential biomarkers, differentially expressed genes (DEGs), and metabolic pathways associated with both COVID-19 and intracranial aneurysm (IA) using simulation databases like DIsGeNET. The purpose was to confirm prior findings and gain a comprehensive understanding of the underlying mechanisms that contribute to the development of these conditions. We combined the regulated genes to describe intracranial aneurysm formation in COVID-19. To determine DEGs in COVID-19 and IA patient tissues, we compared gene expression transcriptomic datasets from healthy and diseased individuals. There were 41 differentially expressed genes (DEGs) shared by both the COVID-19 and IA datasets (27 up-regulated genes and 14 down-regulated genes). Using protein-protein interaction analysis, we were able to identify hub proteins (C3, NCR1, IL10RA, OXTR, RSAD2, CD38, IL10RB, MX1, IL10, GFAP, IFIT3, XAF1, USP18, OASL, IFI6, EPSTI1, CMPK2, and ISG15), which were not described as key proteins for both COVID-19 and IA before. We also used Gene Ontology analysis (6 significant ontologies were validated), Pathway analysis (the top 20 were validated), TF-Gene interaction analysis, Gene miRNA analysis, and Drug-Protein interaction analysis methods to comprehend the extensive connection between COVID-19 and IA. In Drug-Protein interaction analysis, we have gotten the following three drugs: LLL-3348, CRx139, and AV41 against IL10 which was both common for COVID-19 and IA disease. Our study with different cabalistic methods has showed the interaction between the proteins and pathways with drug analysis which may direct further treatment development for certain diseases.

PathIN: an integrated tool for the visualization of pathway interaction networks

PathIN is a web-service that provides an easy and flexible way for rapidly creating pathway-based networks at several functional biological levels: genes, compounds and reactions. The tool is supported by a database repository of reference pathway networks across a large set of species, developed through the freely available information included in the KEGG, Reactome and Wiki Pathways database repositories. PathIN provides networks by means of five diverse methodologies: (a) direct connections between pathways of interest, (b) direct connections as well as the first neighbours of the given pathways, (c) direct connections, the first neighbours and the connections in between them, and (d) two additional methodologies for creating complementary pathway-to-pathway networks that involve additional (missing) pathways that interfere in-between pathways of interest. PathIN is expected to be used as a simple yet informative reference tool for understanding networks of molecular mechanisms related to specific diseases.

A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci

Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.

Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein–protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Proteomic analysis of the IPF mesenchymal progenitor cell nuclear proteome identifies abnormalities in key nodal proteins that underlie their fibrogenic phenotype

IPF is a progressive fibrotic lung disease whose pathogenesis remains incompletely understood. We have previously discovered pathologic mesenchymal progenitor cells (MPCs) in the lungs of IPF patients. IPF MPCs display a distinct transcriptome and create sustained interstitial fibrosis in immune deficient mice. However, the precise pathologic alterations responsible for this fibrotic phenotype remain to be uncovered. Quantitative mass spectrometry and interactomics is a powerful tool that can define protein alterations in specific subcellular compartments that can be implemented to understand disease pathogenesis. We employed quantitative mass spectrometry and interactomics to define protein alterations in the nuclear compartment of IPF MPCs compared to control MPCs. We identified increased nuclear levels of PARP1, CDK1, and BACH1. Interactomics implicated PARP1, CDK1, and BACH1 as key hub proteins in the DNA damage/repair, differentiation, and apoptosis signaling pathways respectively. Loss of function and inhibitor studies demonstrated important roles for PARP1 in DNA damage/repair, CDK1 in regulating IPF MPC stemness and self-renewal, and BACH1 in regulating IPF MPC viability. Our quantitative mass spectrometry studies combined with interactomic analysis uncovered key roles for nuclear PARP1, CDK1, and BACH1 in regulating IPF MPC fibrogenicity.

Bioinformatic Prioritization and Functional Annotation of GWAS-Based Candidate Genes for Primary Open-Angle Glaucoma

Background: Primary open-angle glaucoma (POAG) is the most prevalent glaucoma subtype, but its exact etiology is still unknown. In this study, we aimed to prioritize the most likely ‘causal’ genes and identify functional characteristics and underlying biological pathways of POAG candidate genes. Methods: We used the results of a large POAG genome-wide association analysis study from GERA and UK Biobank cohorts. First, we performed systematic gene-prioritization analyses based on: (i) nearest genes; (ii) nonsynonymous single-nucleotide polymorphisms; (iii) co-regulation analysis; (iv) transcriptome-wide association studies; and (v) epigenomic data. Next, we performed functional enrichment analyses to find overrepresented functional pathways and tissues. Results: We identified 142 prioritized genes, of which 64 were novel for POAG. BICC1, AFAP1, and ABCA1 were the most highly prioritized genes based on four or more lines of evidence. The most significant pathways were related to extracellular matrix turnover, transforming growth factor-β, blood vessel development, and retinoic acid receptor signaling. Ocular tissues such as sclera and trabecular meshwork showed enrichment in prioritized gene expression (>1.5 fold). We found pleiotropy of POAG with intraocular pressure and optic-disc parameters, as well as genetic correlation with hypertension and diabetes-related eye disease. Conclusions: Our findings contribute to a better understanding of the molecular mechanisms underlying glaucoma pathogenesis and have prioritized many novel candidate genes for functional follow-up studies.

Genetic variants in choline metabolism pathway are associated with the risk of bladder cancer in the Chinese population

Choline metabolism alteration is considered as a metabolic hallmark in cancer, reflecting the complex interactions between carcinogenic signaling pathways and cancer metabolism, but little is known about whether genetic variants in the metabolism pathway contribute to the susceptibility of bladder cancer. Herein, a case-control study comprising 580 patients and 1,101 controls was carried out to analyze the association of bladder cancer with genetic variants on candidate genes involved in the choline metabolism pathway using unconditional logistic regression. Gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were applied for differential gene expression analysis. Cox regression was also applied to estimate the role of candidate genes on bladder cancer prognosis. Our results demonstrated that C allele of rs6810830 in ENPP6 was a significant protective allele of bladder cancer, compared to the T allele [Odds ratio (OR) = 0.74, 95% confidence interval (CI) = 0.64-0.86, P = 7.14 × 10^-5 in additive model]. Besides, we also found that the expression of ENPP6 remarkably decreased in bladder tumors compared with normal tissues. Moreover, high expression of ENPP6 was associated with worse overall survival (OS) in bladder cancer patients [hazard ratio (HR) with their 95% CI 1.39 (1.02-1.90), P = 0.039]. In conclusion, our results suggested that SNP rs6810830 (T > C) in ENPP6 might be a potential susceptibility loci for bladder cancer, and these findings provided novel insights into the underlying mechanism of choline metabolism in cancers.

Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection

In high-throughput genetics studies, an important aim is to identify gene–environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.

Silica-coated magnetic-nanoparticle-induced cytotoxicity is reduced in microglia by glutathione and citrate identified using integrated omics

BACKGROUND

Nanoparticles have been utilized in brain research and therapeutics, including imaging, diagnosis, and drug delivery, owing to their versatile properties compared to bulk materials. However, exposure to nanoparticles leads to their accumulation in the brain, but drug development to counteract this nanotoxicity remains challenging. To date, concerns have risen about the potential toxicity to the brain associated with nanoparticles exposure via penetration of the brain blood barrier to address this issue.

METHODS

Here the effect of silica-coated-magnetic nanoparticles containing the rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)] were assessed on microglia through toxicological investigation, including biological analysis and integration of transcriptomics, proteomics, and metabolomics. MNPs@SiO2(RITC)-induced biological changes, such as morphology, generation of reactive oxygen species, intracellular accumulation of MNPs@SiO2(RITC) using transmission electron microscopy, and glucose uptake efficiency, were analyzed in BV2 murine microglial cells. Each omics data was collected via RNA-sequencing-based transcriptome analysis, liquid chromatography-tandem mass spectrometry-based proteome analysis, and gas chromatography- tandem mass spectrometry-based metabolome analysis. The three omics datasets were integrated and generated as a single network using a machine learning algorithm. Nineteen compounds were screened and predicted their effects on nanotoxicity within the triple-omics network.

RESULTS

Intracellular reactive oxygen species production, an inflammatory response, and morphological activation of cells were greater, but glucose uptake was lower in MNPs@SiO2(RITC)-treated BV2 microglia and primary rat microglia in a dose-dependent manner. Expression of 121 genes (from 41,214 identified genes), and levels of 45 proteins (from 5918 identified proteins) and 17 metabolites (from 47 identified metabolites) related to the above phenomena changed in MNPs@SiO2(RITC)-treated microglia. A combination of glutathione and citrate attenuated nanotoxicity induced by MNPs@SiO2(RITC) and ten other nanoparticles in vitro and in the murine brain, protecting mostly the hippocampus and thalamus.

CONCLUSIONS

Combination of glutathione and citrate can be one of the candidates for nanotoxicity alleviating drug against MNPs@SiO2(RITC) induced detrimental effect, including elevation of intracellular reactive oxygen species level, activation of microglia, and reduction in glucose uptake efficiency. In addition, our findings indicate that an integrated triple omics approach provides useful and sensitive toxicological assessment for nanoparticles and screening of drug for nanotoxicity.

Short-term transcriptomic response to plasma membrane injury

Plasma membrane repair mechanisms are activated within seconds post-injury to promote rapid membrane resealing in eukaryotic cells and prevent cell death. However, less is known about the regeneration phase that follows and how cells respond to injury in the short-term. Here, we provide a genome-wide study into the mRNA expression profile of MCF-7 breast cancer cells exposed to injury by digitonin, a mild non-ionic detergent that permeabilizes the plasma membrane. We focused on the early transcriptional signature and found a time-dependent increase in the number of differentially expressed (> twofold, P < 0.05) genes (34, 114 and 236 genes at 20-, 40- and 60-min post-injury, respectively). Pathway analysis highlighted a robust and gradual three-part transcriptional response: (1) prompt activation of immediate-early response genes, (2) activation of specific MAPK cascades and (3) induction of inflammatory and immune pathways. Therefore, plasma membrane injury triggers a rapid and strong stress and immunogenic response. Our meta-analysis suggests that this is a conserved transcriptome response to plasma membrane injury across different cell and injury types. Taken together, our study shows that injury has profound effects on the transcriptome of wounded cells in the regeneration phase (subsequent to membrane resealing), which is likely to influence cellular status and has been previously overlooked.

Targeting Common Signaling Pathways for the Treatment of Stroke and Alzheimer's: a Comprehensive Review

Neurodegenerative diseases such as stroke and Alzheimer's disease (AD) are two inter-related disorders that affect the neurons in the brain and central nervous system. Alzheimer's is a disease by undefined origin and causes. Stroke and its most common type, ischemic stroke (IS), occurs due to the blockade of cerebral blood vessels. As an important feature, both of disorders are associated with irreversible damages to the brain and nervous system. In this regard, finding common signaling pathways and the same molecular origin between these two diseases may be a promising way for their solution. On the basis of literature appraisal, the most common signaling cascades implicated in the pathogenesis of AD and stroke including notch, autophagy, inflammatory, and insulin signaling pathways were reviewed. Furthermore, current therapeutic strategies including natural and synthetic pharmaceuticals aiming modulation of respective signaling factors were scrutinized to ameliorate neural deficits in AD and stroke. Taken together, digging deeper in the common connections and signal targeting can be greatly helpful in understanding and unified treating of these disorders.

netgsa: Fast computation and interactive visualization for topology-based pathway enrichment analysis

Existing software tools for topology-based pathway enrichment analysis are either computationally inefficient, have undesirable statistical power, or require expert knowledge to leverage the methods' capabilities. To address these limitations, we have overhauled NetGSA, an existing topology-based method, to provide a computationally-efficient user-friendly tool that offers interactive visualization. Pathway enrichment analysis for thousands of genes can be performed in minutes on a personal computer without sacrificing statistical power. The new software also removes the need for expert knowledge by directly curating gene-gene interaction information from multiple external databases. Lastly, by utilizing the capabilities of Cytoscape, the new software also offers interactive and intuitive network visualization.

Pathway-based analysis of anthocyanin diversity in diploid potato

Anthocyanin biosynthesis is one of the most studied pathways in plants due to the important ecological role played by these compounds and the potential health benefits of anthocyanin consumption. Given the interest in identifying new genetic factors underlying anthocyanin content we studied a diverse collection of diploid potatoes by combining a genome-wide association study and pathway-based analyses. By using an expanded SNP dataset, we identified candidate genes that had not been associated with anthocyanin variation in potatoes, namely a Myb transcription factor, a Leucoanthocyanidin dioxygenase gene and a vacuolar membrane protein. Importantly, a genomic region in chromosome 10 harbored the SNPs with strongest associations with anthocyanin content in GWAS. Some of these SNPs were associated with multiple anthocyanin compounds and therefore could underline the existence of pleiotropic genes or anthocyanin biosynthetic clusters. We identified multiple anthocyanin homologs in this genomic region, including four transcription factors and five enzymes that could be governing anthocyanin variation. For instance, a SNP linked to the phenylalanine ammonia-lyase gene, encoding the first enzyme in the phenylpropanoid biosynthetic pathway, was associated with all of the five anthocyanins measured. Finally, we combined a pathway analysis and GWAS of other agronomic traits to identify pathways related to anthocyanin biosynthesis in potatoes. We found that methionine metabolism and the production of sugars and hydroxycinnamic acids are genetically correlated to anthocyanin biosynthesis. The results contribute to the understanding of anthocyanins regulation in potatoes and can be used in future breeding programs focused on nutraceutical food.

Integrating Multi–Omics Data for Gene-Environment Interactions

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

Proteomics for Target Identification in Psychiatric and Neurodegenerative Disorders

Psychiatric and neurodegenerative disorders such as schizophrenia (SCZ), Parkinson's disease (PD), and Alzheimer's disease (AD) continue to grow around the world with a high impact on health, social, and economic outcomes for the patient and society. Despite efforts, the etiology and pathophysiology of these disorders remain unclear. Omics technologies have contributed to the understanding of the molecular mechanisms that underlie these complex disorders and have suggested novel potential targets for treatment and diagnostics. Here, we have highlighted the unique and common pathways shared between SCZ, PD, and AD and highlight the main proteomic findings over the last 5 years using in vitro models, postmortem brain samples, and cerebrospinal fluid (CSF) or blood of patients. These studies have identified possible therapeutic targets and disease biomarkers. Further studies including target validation, the use of large sample sizes, and the integration of omics findings with bioinformatics tools are required to provide a better comprehension of pharmacological targets.

Gene-Environment Interaction: A Variable Selection Perspective

Gene-environment interactions have important implications for elucidating the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and the presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in the majority of published reviews on genetic interaction studies. In this article, we first survey existing studies on both gene-environment and gene-gene interactions. Then, after a brief introduction to the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms, respectively, under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies, have also been provided.

Identification of Prognostic Genes and Pathways in Lung Adenocarcinoma Using a Bayesian Approach

Lung cancer is the leading cause of cancer-associated mortality in the United States and the world. Adenocarcinoma, the most common subtype of lung cancer, is generally diagnosed at the late stage with poor prognosis. In the past, extensive effort has been devoted to elucidating lung cancer pathogenesis and pinpointing genes associated with survival outcomes. As the progression of lung cancer is a complex process that involves coordinated actions of functionally associated genes from cancer-related pathways, there is a growing interest in simultaneous identification of both prognostic pathways and important genes within those pathways. In this study, we analyse The Cancer Genome Atlas lung adenocarcinoma data using a Bayesian approach incorporating the pathway information as well as the interconnections among genes. The top 11 pathways have been found to play significant roles in lung adenocarcinoma prognosis, including pathways in mitogen-activated protein kinase signalling, cytokine-cytokine receptor interaction, and ubiquitin-mediated proteolysis. We have also located key gene signatures such as RELB, MAP4K1, and UBE2C. These results indicate that the Bayesian approach may facilitate discovery of important genes and pathways that are tightly associated with the survival of patients with lung adenocarcinoma.

TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as-is to ethnically mixed-cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example, MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

A Systems Bioinformatics Approach to Interconnect Biological Pathways

Signal transduction tasks as well as other complex biological processes involve many different changes in groups of genes, proteins, and metabolites linked together in chains or networks called pathways or networks of pathways. In a classical functional analysis, the biomolecules found to play a role in the biological status under investigation are members of a group of pathways that are not necessarily interconnected. However, interconnectivity is a critical factor for functionality. Thus, it is necessary to be able to construct "connected functional stories" to understand better the complex biological processes. PathwayConnector is a recently introduced web-tool that facilitates the construction of complementary pathway-to-pathway networks, bringing to our attention missing pathways that are crucial links towards the understanding of the molecular mechanisms related to complex diseases. Current version of the web-tool draws from an expanded pathway reference network and provides information deriving from 19 different organisms and 2 different pathway repositories: the KEGG and the REACTOME. Novel genes, proteins, and pathways derived from any experimental/computational method either in large-scale (omics) or even in smaller scale (specific laboratory experiments) can potentially be projected and analyzed through PathwayConnector. This chapter describes in details the pipeline and methodologies used for the latest updated version of PathwayConnector, providing an easy way for rapidly relating human or other organism's pathways together. Recent studies have shown that pathway networks and subnetworks, generated by PathwayConnector, are an integral part towards the individualization of disease, leading to a more precise and personalized management of the treatment.

Genome-wide pathway-based quantitative multiple phenotypes analysis

For complex diseases, genome-wide pathway association studies have become increasingly promising. Currently, however, pathway-based association analysis mainly focus on a single phenotype, which may insufficient to describe the complex diseases and physiological processes. This work proposes a combination model to evaluate the association between a pathway and multiple phenotypes and to reduce the run time based on asymptotic results. For a single phenotype, we propose a semi-supervised maximum kernel-based U-statistics (mSKU) method to assess the pathway-based association analysis. For multiple phenotypes, we propose the fisher combination function with dependent phenotypes (FC) to transform the p-values between the pathway and each marginal phenotype individually to achieve pathway-based multiple phenotypes analysis. With real data from the Alzheimer Disease Neuroimaging Initiative (ADNI) study and Human Liver Cohort (HLC) study, the FC-mSKU method allows us to specify which pathways are specific to a single phenotype or contribute to common genetic constructions of multiple phenotypes. If we only focus on single-phenotype tests, we may miss some findings for etiology studies. Through extensive simulation studies, the FC-mSKU method demonstrates its advantages compared with its counterparts.

Explaining the Genetic Causality for Complex Phenotype via Deep Association Kernel Learning

The genetic effect explains the causality from genetic mutations to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a deep association kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects where existing approaches fail. When applied to four real-world GWAS datasets including cancers and schizophrenia, our DAK discovered potential casual pathways, including the association between dilated cardiomyopathy pathway and schizophrenia.

Identification of therapeutic targets from genetic association studies using hierarchical component analysis

Background

Mapping disease-associated genetic variants to complex disease pathophysiology is a major challenge in translating findings from genome-wide association studies into novel therapeutic opportunities. The difficulty lies in our limited understanding of how phenotypic traits arise from non-coding genetic variants in highly organized biological systems with heterogeneous gene expression across cells and tissues.

Results

We present a novel strategy, called GWAS component analysis, for transferring disease associations from single-nucleotide polymorphisms to co-expression modules by stacking models trained using reference genome and tissue-specific gene expression data. Application of this method to genome-wide association studies of blood cell counts confirmed that it could detect gene sets enriched in expected cell types. In addition, coupling of our method with Bayesian networks enables GWAS components to be used to discover drug targets.

Conclusions

We tested genome-wide associations of four disease phenotypes, including age-related macular degeneration, Crohn’s disease, ulcerative colitis and rheumatoid arthritis, and demonstrated the proposed method could select more functional genes than S-PrediXcan, the previous single-step model for predicting gene-level associations from SNP-level associations.

An improved gene synthesis method with asymmetric directions of oligonucleotides designed using a simulation program

Artificial gene synthesis based on oligonucleotide augmentation is known as overlap extension PCR which generates a variety of intermediate synthetic products. The orientation and concentration of oligomers can be adjusted to reduce the synthesis of intermediates and optimize the full-length process of DNA synthesis, using a simulation program for serial oligomer extension. The efficiency of the serial oligomer extension process is predicted to be greatest when oligomers are in a 'forward-reverse-reverse-reverse' direction. Oligomers with such designed directions demonstrated generation of the desired product in the shortest time (number of cycles) by repeated annealing and elongation. This method, named Asymmetric Extension supported by a Simulator for Oligonucleotide Extension (AESOE), has shown efficiency and effectiveness with potentials for future improvements and optimal usage in DNA synthesis.

A personalised approach for identifying disease-relevant pathways in heterogeneous diseases

Numerous time-course gene expression datasets have been generated for studying the biological dynamics that drive disease progression; and nearly as many methods have been proposed to analyse them. However, barely any method exists that can appropriately model time-course data while accounting for heterogeneity that entails many complex diseases. Most methods manage to fulfil either one of those qualities, but not both. The lack of appropriate methods hinders our capability of understanding the disease process and pursuing preventive treatments. We present a method that models time-course data in a personalised manner using Gaussian processes in order to identify differentially expressed genes (DEGs); and combines the DEG lists on a pathway-level using a permutation-based empirical hypothesis testing in order to overcome gene-level variability and inconsistencies prevalent to datasets from heterogenous diseases. Our method can be applied to study the time-course dynamics, as well as specific time-windows of heterogeneous diseases. We apply our personalised approach on three longitudinal type 1 diabetes (T1D) datasets, where the first two are used to determine perturbations taking place during early prognosis of the disease, as well as in time-windows before autoantibody positivity and T1D diagnosis; and the third is used to assess the generalisability of our method. By comparing to non-personalised methods, we demonstrate that our approach is biologically motivated and can reveal more insights into progression of heterogeneous diseases. With its robust capabilities of identifying disease-relevant pathways, our approach could be useful for predicting events in the progression of heterogeneous diseases and even for biomarker identification.

DNA Methylation Associated with Mitochondrial Dysfunction in a South African Autism Spectrum Disorder Cohort

Autism spectrum disorder (ASD) is characterized by phenotypic heterogeneity and a complex genetic architecture which includes distinctive epigenetic patterns. We report differential DNA methylation patterns associated with ASD in South African children. An exploratory whole‐epigenome methylation screen using the Illumina 450 K MethylationArray identified differentially methylated CpG sites between ASD and controls that mapped to 898 genes (P ≤ 0.05) which were enriched for nine canonical pathways converging on mitochondrial metabolism and protein ubiquitination. Targeted Next Generation Bisulfite Sequencing of 27 genes confirmed differential methylation between ASD and control in our cohort. DNA pyrosequencing of two of these genes, the mitochondrial enzyme Propionyl‐CoA Carboxylase subunit Beta (PCCB) and Protocadherin Alpha 12 (PCDHA12), revealed a wide range of methylation levels (9–49% and 0–54%, respectively) in both ASD and controls. Three CpG loci were differentially methylated in PCCB (P ≤ 0.05), while PCDHA12, previously linked to ASD, had two significantly different CpG sites (P ≤ 0.001) between ASD and control. Differentially methylated CpGs were hypomethylated in ASD. Metabolomic analysis of urinary organic acids revealed that three metabolites, 3‐hydroxy‐3‐methylglutaric acid (P = 0.008), 3‐methyglutaconic acid (P = 0.018), and ethylmalonic acid (P = 0.043) were significantly elevated in individuals with ASD. These metabolites are directly linked to mitochondrial respiratory chain disorders, with a putative link to PCCB, consistent with impaired mitochondrial function. Our data support an association between DNA methylation and mitochondrial dysfunction in the etiology of ASD. Autism Res 2020, 13: 1079‐1093. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.

Lay Summary

Epigenetic changes are chemical modifications of DNA which can change gene function. DNA methylation, a type of epigenetic modification, is linked to autism. We examined DNA methylation in South African children with autism and identified mitochondrial genes associated with autism. Mitochondria are power‐suppliers in cells and mitochondrial genes are essential to metabolism and energy production, which are important for brain cells during development. Our findings suggest that some individuals with ASD also have mitochondrial dysfunction.

Brief Report: A Gene Enrichment Approach Applied to Sleep and Autism

Sleep disorders (SD) are common in autism spectrum disorder (ASD), yet relatively little is known about the potential genetic mechanisms involved in SD and ASD comorbidity. The current study begins to fill this gap with a gene enrichment study that (1) identifies risk genes that contribute to both SD and ASD which implicate circadian entrainment, melatonin synthesis, and several genetic syndromes. An over-representation analysis identified several enriched pathways that suggest dopamine and serotonin synapses as potential shared SD and ASD mechanisms. This overlapping gene set and the highlighted biological pathways may serve as a preliminary stepping-stone for new genetic investigations of SD and ASD comorbidity.

Pathway mapping of leukocyte transcriptome in influenza patients reveals distinct pathogenic mechanisms associated with progression to severe infection

Background

Influenza infections produce a spectrum of disease severity, ranging from a mild respiratory illness to respiratory failure and death. The host-response pathways associated with the progression to severe influenza disease are not well understood.

Methods

To gain insight into the disease mechanisms associated with progression to severe infection, we analyzed the leukocyte transcriptome in severe and moderate influenza patients and healthy control subjects. Pathway analysis on differentially expressed genes was performed using a topology-based pathway analysis tool that takes into account the interaction between multiple cellular pathways. The pathway profiles between moderate and severe influenza were then compared to delineate the biological mechanisms underpinning the progression from moderate to severe influenza.

Results

107 patients (44 severe and 63 moderate influenza patients) and 52 healthy control subjects were included in the study. Severe influenza was associated with upregulation in several neutrophil-related pathways, including pathways involved in neutrophil differentiation, migration, degranulation and neutrophil extracellular trap (NET) formation. The degree of upregulation in neutrophil-related pathways were significantly higher in severely infected patients compared to moderately infected patients. Severe influenza was also associated with downregulation in immune response pathways, including pathways involved in antigen presentation such as CD4+ T-cell co-stimulation, CD8+ T cell and Natural Killer (NK) cells effector functions. Apoptosis pathways were also downregulated in severe influenza patients compare to moderate and healthy controls.

Conclusions

These findings showed that there are changes in gene expression profile that may highlight distinct pathogenic mechanisms associated with progression from moderate to severe influenza infection.

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression

Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment.

A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases

Neurological diseases (NDs) are progressive disorders, the progression of which can be significantly affected by a range of common diseases that present as comorbidities. Clinical studies, including epidemiological and neuropathological analyses, indicate that patients with type 2 diabetes (T2D) have worse progression of NDs, suggesting pathogenic links between NDs and T2D. However, finding causal or predisposing factors that link T2D and NDs remains challenging. To address these problems, we developed a high-throughput network-based quantitative pipeline using agnostic approaches to identify genes expressed abnormally in both T2D and NDs, to identify some of the shared molecular pathways that may underpin T2D and ND interaction. We employed gene expression transcriptomic datasets from control and disease-affected individuals and identified differentially expressed genes (DEGs) in tissues of patients with T2D and ND when compared to unaffected control individuals. One hundred and ninety seven DEGs (99 up-regulated and 98 down-regulated in affected individuals) that were common to both the T2D and the ND datasets were identified. Functional annotation of these identified DEGs revealed the involvement of significant cell signaling associated molecular pathways. The overlapping DEGs (i.e., seen in both T2D and ND datasets) were then used to extract the most significant GO terms. We performed validation of these results with gold benchmark databases and literature searching, which identified which genes and pathways had been previously linked to NDs or T2D and which are novel. Hub proteins in the pathways were identified (including DNM2, DNM1, MYH14, PACSIN2, TFRC, PDE4D, ENTPD1, PLK4, CDC20B, and CDC14A) using protein-protein interaction analysis which have not previously been described as playing a role in these diseases. To reveal the transcriptional and post-transcriptional regulators of the DEGs we used transcription factor (TF) interactions analysis and DEG-microRNAs (miRNAs) interaction analysis, respectively. We thus identified the following TFs as important in driving expression of our T2D/ND common genes: FOXC1, GATA2, FOXL1, YY1, E2F1, NFIC, NFYA, USF2, HINFP, MEF2A, SRF, NFKB1, USF2, HINFP, MEF2A, SRF, NFKB1, PDE4D, CREB1, SP1, HOXA5, SREBF1, TFAP2A, STAT3, POU2F2, TP53, PPARG, and JUN. MicroRNAs that affect expression of these genes include mir-335-5p, mir-16-5p, mir-93-5p, mir-17-5p, mir-124-3p. Thus, our transcriptomic data analysis identifies novel potential links between NDs and T2D pathologies that may underlie comorbidity interactions, links that may include potential targets for therapeutic intervention. In sum, our neighborhood-based benchmarking and multilayer network topology methods identified novel putative biomarkers that indicate how type 2 diabetes (T2D) and these neurological diseases interact and pathways that, in the future, may be targeted for treatment.

De novo variants in exomes of congenital heart disease patients identify risk genes and pathways

Background

Congenital heart disease (CHD) affects ~ 1% of live births and is the most common birth defect. Although the genetic contribution to the CHD has been long suspected, it has only been well established recently. De novo variants are estimated to contribute to approximately 8% of sporadic CHD.

Methods

CHD is genetically heterogeneous, making pathway enrichment analysis an effective approach to explore and statistically validate CHD-associated genes. In this study, we performed novel gene and pathway enrichment analyses of high-impact de novo variants in the recently published whole-exome sequencing (WES) data generated from a cohort of CHD 2645 parent-offspring trios to identify new CHD-causing candidate genes and mutations. We performed rigorous variant- and gene-level filtrations to identify potentially damaging variants, followed by enrichment analyses and gene prioritization.

Results

Our analyses revealed 23 novel genes that are likely to cause CHD, including HSP90AA1, ROCK2, IQGAP1, and CHD4, and sharing biological functions, pathways, molecular interactions, and properties with known CHD-causing genes.

Conclusions

Ultimately, these findings suggest novel genes that are likely to be contributing to CHD pathogenesis.

PathwayConnector: finding complementary pathways to enhance functional analysis

SUMMARY

PathwayConnector is a web-tool that facilitates the construction of complementary pathway-to-pathway networks and subnetworks of them, based on a reference pathway network derived from the rich information available either in KEGG or Reactome database for pathway mapping. Specifically, for a given set of pathways, PathwayConnector (i) finds all the direct connections between them, (ii) adds a minimum set of complementary pathways required to achieve connectivity between the pathways, leading to informative fully connected networks and (ii) provides a series of clustering methods for the further grouping of pathways in to sub-clusters. The proposed web-tool is a simple yet informative tool towards identifying connected groups of pathways that are significantly related to specific diseases.

AVAILABILITY AND IMPLEMENTATION

http://bioinformatics.cing.ac.cy/PathwayConnector.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An omics perspective on drug target discovery platforms

The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

Genomic and Genetic Approaches to Deciphering Acute Respiratory Distress Syndrome Risk and Mortality

Significance: Acute respiratory distress syndrome (ARDS) is a severe, highly heterogeneous critical illness with staggering mortality that is influenced by environmental factors, such as mechanical ventilation, and genetic factors. Significant unmet needs in ARDS are addressing the paucity of validated predictive biomarkers for ARDS risk and susceptibility that hamper the conduct of successful clinical trials in ARDS and the complete absence of novel disease-modifying therapeutic strategies. Recent Advances: The current ARDS definition relies on clinical characteristics that fail to capture the diversity of disease pathology, severity, and mortality risk. We undertook a comprehensive survey of the available ARDS literature to identify genes and genetic variants (candidate gene and limited genome-wide association study approaches) implicated in susceptibility to developing ARDS in hopes of uncovering novel biomarkers for ARDS risk and mortality and potentially novel therapeutic targets in ARDS. We further attempted to address the well-known health disparities that exist in susceptibility to and mortality from ARDS. Critical Issues: Bioinformatic analyses identified 201 ARDS candidate genes with pathway analysis indicating a strong predominance in key evolutionarily conserved inflammatory pathways, including reactive oxygen species, innate immunity-related inflammation, and endothelial vascular signaling pathways. Future Directions: Future studies employing a system biology approach that combines clinical characteristics, genomics, transcriptomics, and proteomics may allow for a better definition of biologically relevant pathways and genotype-phenotype connections and result in improved strategies for the sub-phenotyping of diverse ARDS patients via molecular signatures. These efforts should facilitate the potential for successful clinical trials in ARDS and yield a better fundamental understanding of ARDS pathobiology.

A comparative study of topology-based pathway enrichment analysis methods

BACKGROUND

Pathway enrichment extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples.

RESULTS

The findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment.

CONCLUSION

The analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

GENEASE: real time bioinformatics tool for multi-omics and disease ontology exploration, analysis and visualization

Motivation

Advances in high-throughput sequencing technologies have made it possible to generate multiple omics data at an unprecedented rate and scale. The accumulation of these omics data far outpaces the rate at which biologists can mine and generate new hypothesis to test experimentally. There is an urgent need to develop a myriad of powerful tools to efficiently and effectively search and filter these resources to address specific post-GWAS functional genomics questions. However, to date, these resources are scattered across several databases and often lack a unified portal for data annotation and analytics. In addition, existing tools to analyze and visualize these databases are highly fragmented, resulting researchers to access multiple applications and manual interventions for each gene or variant in an ad hoc fashion until all the questions are answered.

Results

In this study, we present GENEASE, a web-based one-stop bioinformatics tool designed to not only query and explore multi-omics and phenotype databases (e.g. GTEx, ClinVar, dbGaP, GWAS Catalog, ENCODE, Roadmap Epigenomics, KEGG, Reactome, Gene and Phenotype Ontology) in a single web interface but also to perform seamless post genome-wide association downstream functional and overlap analysis for non-coding regulatory variants. GENEASE accesses over 50 different databases in public domain including model organism-specific databases to facilitate gene/variant and disease exploration, enrichment and overlap analysis in real time. It is a user-friendly tool with point-and-click interface containing links for support information including user manual and examples.

Availability and implementation

GENEASE can be accessed freely at http://research.cchmc.org/mershalab/GENEASE/login.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

A comparison of mechanistic signaling pathway activity analysis methods

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

Big data in IBD: a look into the future

Big data methodologies, made possible with the increasing generation and availability of digital data and enhanced analytical capabilities, have produced new insights to improve outcomes in many disciplines. Application of big data in the health-care sector is in its early stages, although the potential for leveraging underutilized data to gain a better understanding of disease and improve quality of care is enormous. Owing to the intrinsic characteristics of inflammatory bowel disease (IBD) and the management dilemmas that it imposes, the implementation of big data research strategies not only can complement current research efforts but also could represent the only way to disentangle the complexity of the disease. In this Review, we explore important potential applications of big data in IBD research, including predictive models of disease course and response to therapy, characterization of disease heterogeneity, drug safety and development, precision medicine and cost-effectiveness of care. We also discuss the strengths and limitations of potential data sources that big data analytics could draw from in the field of IBD, including electronic health records, clinical trial data, e-health applications and genomic, transcriptomic, proteomic, metabolomic and microbiomic data.

Genetic effects of welding fumes on the development of respiratory system diseases

BACKGROUND

The welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WF exposure may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood.

METHODS

We developed a quantitative framework to identify gene expression effects of WF exposure that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (diseasome) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology.

RESULTS

We observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEG analysis, using disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC.

CONCLUSIONS

Our network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.