Reactome: a knowledge base of biologic pathways and processes

Brd4::Nutm1 fusion gene initiates NUT carcinoma in vivo

NUT carcinoma (NC) is an aggressive cancer with no effective treatment. About 70% of NUT carcinoma is associated with chromosome translocation events that lead to the formation of a BRD4::NUTM1 fusion gene. Because the BRD4::NUTM1 gene is unequivocally cytotoxic when ectopically expressed in cell lines, questions remain on whether the fusion gene can initiate NC. Here, we report the first genetically engineered mouse model for NUT carcinoma that recapitulates the human t(15;19) chromosome translocation in mice. We demonstrated that the mouse t(2;17) syntenic chromosome translocation, forming the Brd4::Nutm1 fusion gene, could induce aggressive carcinomas in mice. The tumors present histopathological and molecular features similar to human NC, with enrichment of undifferentiated cells. Similar to the reports of human NC incidence, Brd4::Nutm1 can induce NC from a broad range of tissues with a strong phenotypical variability. The consistent induction of poorly differentiated carcinoma demonstrated a strong reprogramming activity of BRD4::NUTM1. The new mouse model provided a critical preclinical model for NC that will lead to better understanding and therapy development for NC.

Deciphering Immune Responses to Immunization via Transcriptional Analysis: A Narrative Review of the Current Evidence towards Personalized Vaccination Strategies

The development of vaccines has drastically reduced the mortality and morbidity of several diseases. Despite the great success of vaccines, the immunological processes involved in protective immunity are not fully understood and several issues remain to be elucidated. Recently, the advent of high-throughput technologies has enabled a more in-depth investigation of the immune system as a whole and the characterization of the interactions of numerous components of immunity. In the field of vaccinology, these tools allow for the exploration of the molecular mechanisms by which vaccines can induce protective immune responses. In this review, we aim to describe current data on transcriptional responses to vaccination, focusing on similarities and differences of vaccine-induced transcriptional responses among vaccines mostly in healthy adults, but also in high-risk populations, such as the elderly and children. Moreover, the identification of potential predictive biomarkers of vaccine immunogenicity, the effect of age on transcriptional response and future perspectives for the utilization of transcriptomics in the field of vaccinology will be discussed.

OmicNavigator: open-source software for the exploration, visualization, and archival of omic studies

BACKGROUND

The results of high-throughput biology ('omic') experiments provide insight into biological mechanisms but can be challenging to explore, archive and share. The scale of these challenges continues to grow as omic research volume expands and multiple analytical technologies, bioinformatic pipelines, and visualization preferences have emerged. Multiple software applications exist that support omic study exploration and/or archival. However, an opportunity remains for open-source software that can archive and present the results of omic analyses with broad accommodation of study-specific analytical approaches and visualizations with useful exploration features.

RESULTS

We present OmicNavigator, an R package for the archival, visualization and interactive exploration of omic studies. OmicNavigator enables bioinformaticians to create web applications that interactively display their custom visualizations and analysis results linked with app-derived analytical tools, graphics, and tables. Studies created with OmicNavigator can be viewed within an interactive R session or hosted on a server for shared access.

CONCLUSIONS

OmicNavigator can be found at https://github.com/abbvie-external/OmicNavigator.

Predisposing deleterious variants in the cancer-associated human kinases in the global populations

Human kinases play essential and diverse roles in the cellular activities of maintaining homeostasis and growth. Genetic mutations in the genes encoding the kinases (or phosphotransferases) have been linked with various types of cancers. In this study, we cataloged mutations in 500 kinases genes in >65,000 individuals of global populations from the Human Genetic Diversity Project (HGDP) and ExAC databases, and assessed their potentially deleterious impact by using the in silico tools SIFT, Polyphen2, and CADD. The analysis highlighted 35 deleterious non-synonymous SNVs in the ExAC and 5 SNVs in the HGDP project. Notably, a higher number of deleterious mutations was observed in the Non-Finnish Europeans (26 SNVs), followed by the Africans (14 SNVs), East Asians (13 SNVs), and South Asians (12 SNVs). The gene set enrichment analysis highlighted NTRK1 and FGFR3 being most significantly enriched among the kinases. The gene expression analysis revealed over-expression of NTRK1 in liver cancer, whereas, FGFR3 was found over-expressed in lung, breast, and liver cancers compared to their expression in the respective normal tissues. Also, 13 potential drugs were identified that target the NTRK1 protein, whereas 6 potential drugs for the FGFR3 target were identified. Taken together, the study provides a framework for exploring the predisposing germline mutations in kinases to suggest the underlying pathogenic mechanisms in cancers. The potential drugs are also suggested for personalized cancer management.

Influenza A virus replicates productively in primary human kidney cells and induces factors and mechanisms related to regulated cell death and renal pathology observed in virus-infected patients

Introduction

Influenza A virus (IAV) infection can cause the often-lethal acute respiratory distress syndrome (ARDS) of the lung. Concomitantly, acute kidney injury (AKI) is frequently noticed during IAV infection, correlating with an increased mortality. The aim of this study was to elucidate the interaction of IAV with human kidney cells and, thereby, to assess the mechanisms underlying IAV-mediated AKI.

Methods

To investigate IAV effects on nephron cells we performed infectivity assays with human IAV, as well as with human isolates of either low or highly pathogenic avian IAV. Also, transcriptome and proteome analysis of IAV-infected primary human distal tubular kidney cells (DTC) was performed. Furthermore, the DTC transcriptome was compared to existing transcriptomic data from IAV-infected lung and trachea cells.

Results

We demonstrate productive replication of all tested IAV strains on primary and immortalized nephron cells. Comparison of our transcriptome and proteome analysis of H1N1-type IAV-infected human primary distal tubular cells (DTC) with existing data from H1N1-type IAV-infected lung and primary trachea cells revealed enrichment of specific factors responsible for regulated cell death in primary DTC, which could be targeted by specific inhibitors.

Discussion

IAV not only infects, but also productively replicates on different human nephron cells. Importantly, multi-omics analysis revealed regulated cell death as potential contributing factor for the clinically observed kidney pathology in influenza.

Enzyme Databases in the Era of Omics and Artificial Intelligence

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.

Using published pathway figures in enrichment analysis and machine learning

Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in comparison with popular pathway databases through an assessment of disease coverage and analytical applications. In addition to common pathway analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses.

Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge

Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C.

Using Published Pathway Figures in Enrichment Analysis and Machine Learning

Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. PFOCR content is extracted from published pathway figures currently emerging at a rate of 1000 new pathways each month. Here, we compare the pathway information contained in PFOCR against popular pathway databases with respect to overall and disease-specific coverage. In addition to common pathways analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses.

Genes-to-Pathways Species Conservation Analysis: Enabling the Exploration of Conservation of Biological Pathways and Processes Across Species

The last two decades have witnessed a strong momentum toward integration of cell-based and computational approaches in safety assessments. This is fueling a global regulatory paradigm shift toward reduction and replacement of the use of animals in toxicity tests while promoting the use of new approach methodologies. The understanding of conservation of molecular targets and pathways provides an opportunity to extrapolate effects across species and ultimately to determine the taxonomic applicability domain of assays and biological effects. Despite the wealth of genome-linked data available, there is a compelling need for improved accessibility, while ensuring that it reflects the underpinning biology. We present the novel pipeline Genes-to-Pathways Species Conservation Analysis (G2P-SCAN) to further support understanding on cross-species extrapolation of biological processes. This R package extracts, synthetizes, and structures the data available from different databases, that is, gene orthologs, protein families, entities, and reactions, linked to human genes and respective pathways across six relevant model species. The use of G2P-SCAN enables the overall analysis of orthology and functional families to substantiate the identification of conservation and susceptibility at the pathway level. In the present study we discuss five case studies, demonstrating the validity of the developed pipeline and its potential use as species extrapolation support. We foresee this pipeline will provide valuable biological insights and create space for the use of mechanistically based data to inform potential species susceptibility for research and safety decision purposes. Environ Toxicol Chem 2023;42:1152-1166. © 2023 UNILEVER GLOBAL IP LTD. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Adriforant is a functional antagonist of histamine receptor 4 and attenuates itch and skin inflammation in mice

BACKGROUND

Histamine has been postulated to play a role in atopic dermatitis via histamine receptor 4, mediating pruritic and inflammatory effects. The H4R antagonist adriforant (PF-3893787 or ZPL389) indicated clinical efficacy in a Ph2a study in atopic dermatitis. Preclinical investigations of adriforant had been scarce as experiments in transfectants with H4R from several species suggested partial agonism, not seen in human cells.

OBJECTIVE

During the Ph2b trial in AD, we performed experiments to understand the pharmacology of adriforant in primary murine cells and in vivo models. We assessed its effects on ERK phosphorylation and transcriptional changes in bone marrow-derived mast cells, histamine-dependent Ca²⁺ flux in neurons and histamine-induced itch response. In addition, its impact on MC903-induced skin inflammation was evaluated.

RESULTS

We show that, contrary to transfectants, adriforant is a competitive antagonist of the murine histamine receptor 4, antagonizes histamine-induced ERK phosphorylation, normalizes histamine-induced transcriptional changes in mast cells and reduces histamine-dependent Ca²⁺ flux in neurons. Administration to mice reduces acute histamine-induced itch response. In addition, adriforant ameliorates inflammation in the mouse MC903 model.

CONCLUSIONS

Our results suggest that functional inhibition of histamine receptor 4 by adriforant reduces itch and inflammation in vivo. The effects observed in mice, however, did not translate to clinical efficacy in patients as the Ph2b clinical trial with adriforant did not meet pre-specified efficacy endpoints. Given the complex pathogenesis of AD, antagonism of histamine receptor 4 alone appears insufficient to reduce disease severity in AD patients, despite the effects seen in mouse models.

Ageotypes revisited: The brain and central nervous system dysfunction as a major nutritional and lifestyle target for healthy aging

Undeniably, biological age can significantly differ between individuals of similar chronological age. Longitudinal, deep multi-omic profiling has recently enabled the identification of individuals with distinct aging phenotypes, termed 'ageotypes'. This effort has provided a plethora of data and new insights into the diverse molecular mechanisms presumed to drive aging. Translational opportunities stemming from this knowledge continue to evolve, providing an opportunity for the provision of nutritional interventions aiming to decelerate the aging process. In this framework, the contemporary ageotypes classification was revisited via in silico analyses, with the brain and nervous system being identified as the primary targets of age-related biomolecules, acting through inflammatory and metabolic pathways. Nutritional and lifestyle factors affecting these pathways in the brain and central nervous system that could help guide personalized recommendations for the attainment of healthy aging are discussed.

Cardiac Transcriptome Remodeling and Impaired Bioenergetics in Single-Ventricle Congenital Heart Disease

The mechanisms responsible for heart failure in single-ventricle congenital heart disease are unknown. Using explanted heart tissue, we showed that failing single-ventricle hearts have dysregulated metabolic pathways, impaired mitochondrial function, decreased activity of carnitine palmitoyltransferase activity, and altered functioning of the tricarboxylic acid cycle. Interestingly, nonfailing single-ventricle hearts demonstrated an intermediate metabolic phenotype suggesting that they are vulnerable to development of heart failure in the future. Mitochondrial targeted therapies and treatments aimed at normalizing energy generation could represent a novel approach to the treatment or prevention of heart failure in this vulnerable group of patients.

Transcriptional Profiling Supports the Notochordal Origin of Chordoma and Its Dependence on a TGFΒ1-TBXT Network

Chordoma is a rare malignant tumor demonstrating notochordal differentiation. It is dependent on brachyury (TBXT), a hallmark notochordal gene and transcription factor, and shares histologic features and the same anatomic location as the notochord. In this study, we perform a molecular comparison of chordoma and notochord to identify dysregulated cellular pathways. The lack of a molecular reference from appropriate control tissue limits our understanding of chordoma and its relationship to notochord. Accordingly, we conducted an unbiased comparison of chordoma, human notochord, and an atlas of normal and cancerous tissue using gene expression profiling to clarify the chordoma/notochord relationship and potentially identify novel drug targets. We found striking consistency in gene expression profiles between chordoma and notochord, supporting the hypothesis that chordoma develops from notochordal remnants. We identified a 12-gene diagnostic chordoma signature and found that the TBXT/transforming growth factor (TGF)-β/SOX6/SOX9 pathway is hyperactivated in the tumor, suggesting that pathways associated with chondrogenesis are a central driver of chordoma development. Experimental validation in chordoma cells confirms these findings and emphasizes the dependence of chordoma proliferation and survival on TGF-β. Our computational and experimental evidence provides the first molecular connection between notochord and chordoma and identifies core members of a chordoma regulatory pathway involving TBXT. This pathway provides new therapeutic targets for this unique malignant neoplasm and highlights TGF-β as a prime druggable candidate.

Differential microRNA profiles in elderly males with seborrheic dermatitis

Seborrheic dermatitis (SD) is one of the most common skin diseases characterized by inflammatory symptoms and cell proliferation, which has increased incidence in patients older than 50 years. Although the roles of microRNAs (miRNAs) have been investigated in several diseases, miRNA profiles of patients with SD remain unknown. This study aimed to identify differentially expressed miRNAs (DEMs) in lesions of elderly male patients with SD. We used a microarray-based approach to identify DEMs in lesions compared to those in non-lesions of patients with SD. Furthermore, Gene Ontology and pathway enrichment analysis were performed using bioinformatics tools to elucidate the functional significance of the target mRNAs of DEMs in lesions of patients with SD. Expression levels of two miRNAs-hsa-miR-6831-5p and hsa-miR-7107-5p-were downregulated, whereas those of six miRNAs-hsa-miR-20a-5p, hsa-miR-191-5p, hsa-miR-127-3p, hsa-miR-106b-5p, hsa-miR-342-3p, and hsa-miR-6824-5p-were upregulated. Functions of the SD-related miRNAs were predicted to be significantly associated with typical dermatological pathogenesis, such as cell proliferation, cell cycle, apoptosis, and immune regulation. In summary, SD alters the miRNA profile, and target mRNAs of the DEMs are related to immune responses and cell proliferation, which are the two main processes in SD pathogenesis.

Transcriptome analysis of intestine from alk-SMase knockout mice reveals the effect of alk-SMase

Objective

Intestinal alkaline sphingomyelinase (alk-SMase) generates ceramide and inactivates platelet-activating factor associated with digestion and inhibition of cancer. There is few study to analyze the correlated function and characterize the genes related to alk-SMase comprehensively. We characterised transcriptome landscapes of intestine tissues from alk-SMase knockout (KO) mice aiming to identify novel associated genes and research targets.

Methods

We performed the high-resolution RNA sequencing of alk-SMase KO mice and compared them to wild type (WT) mice. Differentially expressed genes (DEGs) for the training group were screened. Functional enrichment analysis of the DEGs between KO mice and WT mice was implemented using the Database for Annotation, Visualization and Integrated Discovery (DAVID). An integrated protein–protein interaction (PPI) and Kyoto Encyclopedia of Genes and Genomes (KEGG) network was chose to study the relationship of differentially expressed gene. Moreover, quantitative real-time polymerase chain reaction (qPCR) was further used to validate the accuracy of RNA-seq technology.

Results

Our RNA-seq data found 97 differentially expressed mRNAs between the WT mice and alk-SMase gene NPP7 KO mice, in which 32 were significantly up-regulated and 65 were down-regulated, including protein coding genes, non-coding RNAs. Notably, the results of gene ontology functional enrichment analysis indicated that DEGs were functionally associated with the immune response, regulation of cell proliferation and development related terms. Additionally, an integrated network analysis was shown that some modules was significantly related to alk-SMase and with accordance of previously results. We chose 6 of these genes randomly were validated the accuracy of RNA-seq technology using qPCR and 2 genes showed difference significantly (P < 0.05).

Conclusions

We investigated the potential biological significant of alk-SMase with high resolution genome-wide transcriptome of alk-SMase knockout mice. The results revealed new insight into the functional modules related to alk-SMase was involved in the intestinal related diseases.

Identification of key genes and pathways associated with diabetes of the exocrine pancreas

This study aimed to identify potential essential genes and pathways in diabetes of the exocrine pancreas (DEP) and explore possible molecular mechanisms. The array dataset GSE76895 was downloaded from the Gene Expression Omnibus database. Pancreatic tissue samples from 20 Diabetes of the exocrine pancreas and 32 nondiabetic individuals were selected for analysis. GEO2R analyzed differentially expressed genes (DEGs) in the 2 groups. Gene ontology annotation, Kyoto Encyclopedia of Genes Genomes and Reactome pathway enrichment analyses and Gene Set Enrichment Analysis were performed in this study. Protein-protein interaction (PPI) networks were constructed using Cytoscape software, and core networks were identified using MCODE plugins. A total of 62 genes, including 59 up-regulated and 3 down-regulated genes, were differentially expressed in DEP samples compared with nondiabetic patients. PPI network with 53 nodes and 138 edges was established. HLA-DRA is identified as the central gene of the PPI network and maybe a marker gene for DEP. Furthermore, up-regulated DEGs are mainly enriched in pathways related to the immune system and infection. The results of this study suggest that HLA-DRA and immune system pathways may play essential roles in DEP.

A Targeted Proteomics Approach for Screening Serum Biomarkers Observed in the Early Stage of Type I Endometrial Cancer

Endometrial cancer (EC) is the most common gynecologic malignancy, and it arises in the inner part of the uterus. Identification of serum biomarkers is essential for diagnosing the disease at an early stage. In this study, we selected 44 healthy controls and 44 type I EC at tumor stage 1, and we used the Immuno-oncology panel and the Target 96 Oncology III panel to simultaneously detect the levels of 92 cancer-related proteins in serum, using a proximity extension assay. By applying this methodology, we identified 20 proteins, associated with the outcome at binary logistic regression, with a p-value below 0.01 for the first panel and 24 proteins with a p-value below 0.02 for the second one. The final multivariate logistic regression model, combining proteins from the two panels, generated a model with a sensitivity of 97.67% and a specificity of 83.72%. These results support the use of the proposed algorithm after a validation phase.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

ErbB Signaling Pathway Genes Are Differentially Expressed in Monozygotic Twins Discordant for Sports-Related Concussion

Transcriptional changes involved in neuronal recovery after sports-related concussion (SRC) may be obscured by inter-individual variation in mRNA expression and nonspecific changes related to physical exertion. Using a co-twin study, the objective of this study was to identify important differences in mRNA expression among a single pair of monozygotic (MZ) twins discordant for concussion. A pair of MZ twins were enrolled as part of a larger study of concussion biomarkers among collegiate athletes. During the study, Twin A sustained SRC, allowing comparison of mRNA expression to the nonconcussed Twin B. Twin A clinically recovered by Day 7. mRNA expression was measured pre-injury and at 6 h and 7 days postinjury using Affymetrix HG-U133 Plus 2.0 microarray. Changes in mRNA expression from pre-injury to each postinjury time point were compared between the twins; differences >1.5-fold were considered important. Kyoto Encyclopedia of Genes and Genomes identified biologic networks associated with important transcripts. Among 38,000 analyzed genes, important changes were identified in 153 genes. The ErbB (epidermal growth factor receptor) signaling pathway was identified as the top transcriptional network from pre-injury to 7 days postinjury. Genes in this pathway with important transcriptional changes included epidermal growth factor (2.41), epiregulin (1.73), neuregulin 1 (1.54) and mechanistic target of rapamycin (1.51). In conclusion, the ErbB signaling pathway was identified as a potential regulator of clinical recovery in a MZ twin pair discordant for SRC. A co-twin study design may be a useful method for identifying important gene pathways associated with concussion recovery.

Genome-wide analysis of somatic noncoding mutation patterns in cancer

We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.

Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase

Abstract

Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway outputs were defined. We then used pathway-diagram-derived logic graphs to predict, either by inspection by biocurators or using a novel algorithm MP-BioPath, the effects of bidirectional perturbations (upregulation/activation or downregulation/inhibition) of single root inputs on the status of key outputs. These predictions were then compared to published empirical tests. In total, 4968 test cases were analyzed across 10 pathways, of which 847 were supported by published empirical findings. Out of the 847 test cases, curators’ predictions agreed with the experimental evidence in 670 and disagreed in 177 cases, resulting in ∼81% overall accuracy. MP-BioPath predictions agreed with experimental evidence for 625 and disagreed for 222 test cases, resulting in ∼75% overall accuracy. The expected accuracy of random guessing was 33%. Per-pathway accuracy did not correlate with the number of pathway edges nor the number of pathway nodes but varied across pathways, ranging from 56% (curator)/44% (MP-BioPath) for ‘Mitotic G1 phase and G1/S transition’ to 100% (curator)/94% (MP-BioPath) for ‘RAF/MAP kinase cascade’. This study highlights the potential of pathway databases such as Reactome in modeling genetic perturbations, promoting standardization of experimental pathway activity readout and supporting hypothesis-driven research by revealing relationships between pathway inputs and outputs that have not yet been directly experimentally tested.

Database URL

www.reactome.org

Application of Transcriptomics for Predicting Protein Interaction Networks, Drug Targets and Drug Candidates

Protein interaction pathways and networks are critically-required for a vast range of biological processes. Improved discovery of candidate druggable proteins within specific cell, tissue and disease contexts will aid development of new treatments. Predicting protein interaction networks from gene expression data can provide valuable insights into normal and disease biology. For example, the resulting protein networks can be used to identify potentially druggable targets and drug candidates for testing in cell and animal disease models. The advent of whole-transcriptome expression profiling techniques—that catalogue protein-coding genes expressed within cells and tissues—has enabled development of individual algorithms for particular tasks. For example,: (i) gene ontology algorithms that predict gene/protein subsets involved in related cell processes; (ii) algorithms that predict intracellular protein interaction pathways; and (iii) algorithms that correlate druggable protein targets with known drugs and/or drug candidates. This review examines approaches, advantages and disadvantages of existing gene expression, gene ontology, and protein network prediction algorithms. Using this framework, we examine current efforts to combine these algorithms into pipelines to enable identification of druggable targets, and associated known drugs, using gene expression datasets. In doing so, new opportunities are identified for development of powerful algorithm pipelines, suitable for wide use by non-bioinformaticians, that can predict protein interaction networks, druggable proteins, and related drugs from user gene expression datasets.

Identifying large scale interaction atlases using probabilistic graphs and external knowledge

Introduction:

Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability.

Methods:

We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster.

Results:

We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms.

Conclusions:

We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.

Potential biomarkers and signaling pathways associated with the pathogenesis of primary salivary gland carcinoma: a bioinformatics study

Salivary gland carcinoma (SGC) is rare cancer, constituting 6% of neoplasms in the head and neck area. The most responsible genes and pathways involved in the pathology of this disorder have not been fully understood. We aimed to identify differentially expressed genes (DEGs), the most critical hub genes, transcription factors, signaling pathways, and biological processes (BPs) associated with the pathogenesis of primary SGC. The mRNA dataset GSE153283 in the Gene Expression Omnibus database was re-analyzed for determining DEGs in cancer tissue of patients with primary SGC compared to the adjacent normal tissue (adjusted p-value < 0.001; |Log2 fold change| > 1). A protein interaction map (PIM) was built, and the main modules within the network were identified and focused on the different pathways and BP analyses. The hub genes of PIM were discovered, and their associated gene regulatory network was built to determine the master regulators involved in the pathogenesis of primary SGC. A total of 137 genes were found to be differentially expressed in primary SGC. The most significant pathways and BPs that were deregulated in the primary disease condition were associated with the cell cycle and fibroblast proliferation procedures. TP53, EGF, FN1, NOTCH1, EZH2, COL1A1, SPP1, CDKN2A, WNT5A, PDGFRB, CCNB1, and H2AFX were demonstrated to be the most critical genes linked with the primary SGC. SPIB, FOXM1, and POLR2A significantly regulate all the hub genes. This study illustrated several hub genes and their master regulators that might be appropriate targets for the therapeutic aims of primary SGC.

The reactome pathway knowledgebase 2022

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.

Multi-omics of the expression and clinical outcomes of TMPRSS2 in human various cancers: A potential therapeutic target for COVID-19

Growing evidence has shown that Transmembrane Serine Protease 2 (TMPRSS2) not only contributes to the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection, but is also closely associated with the incidence and progression of tumours. However, the correlation of coronavirus disease (COVID‐19) and cancers, and the prognostic value and molecular function of TMPRSS2 in various cancers have not been fully understood. In this study, the expression, genetic variations, correlated genes, immune infiltration and prognostic value of TMPRSS2 were analysed in many cancers using different bioinformatics platforms. The observed findings revealed that the expression of TMPRSS2 was considerably decreased in many tumour tissues. In the prognostic analysis, the expression of TMPRSS2 was considerably linked with the clinical consequences of the brain, blood, colorectal, breast, ovarian, lung and soft tissue cancer. In protein network analysis, we determined 27 proteins as protein partners of TMPRSS2, which can regulate the progression and prognosis of cancer mediated by TMPRSS2. Besides, a high level of TMPRSS2 was linked with immune cell infiltration in various cancers. Furthermore, according to the pathway analysis of differently expressed genes (DEGs) with TMPRSS2 in lung, breast, ovarian and colorectal cancer, 160 DEGs genes were found and were significantly enriched in respiratory system infection and tumour progression pathways. In conclusion, the findings of this study demonstrate that TMPRSS2 may be an effective biomarker and therapeutic target in various cancers in humans, and may also provide new directions for specific tumour patients to prevent SARS‐CoV‐2 infection during the COVID‐19 outbreak.

A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity

Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases.

An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens

Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.

A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis

BACKGROUND

There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules.

RESULT

We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10- 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS.

CONCLUSIONS

We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.

Interferon regulatory factor 7 impairs cellular metabolism in aging adipose-derived stromal cells

Dysregulated immunity and widespread metabolic dysfunctions are the most relevant hallmarks of the passing of time over the course of adult life, and their combination at midlife is strongly related to increased vulnerability to diseases; however, the causal connection between them remains largely unclear. By combining multi-omics and functional analyses of adipose-derived stromal cells established from young (1 month) and midlife (12 months) mice, we show that an increase in expression of interferon regulatory factor 7 (IRF7) during adult life drives major metabolic changes, which include impaired mitochondrial function, altered amino acid biogenesis and reduced expression of genes involved in branched-chain amino acid (BCAA) degradation. Our results draw a new paradigm of aging as the 'sterile' activation of a cell-autonomous pathway of self-defense and identify a crucial mediator of this pathway, IRF7, as driver of metabolic dysfunction with age.

An overview of graph databases and their applications in the biomedical domain

Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.

Review on Epidermal Growth Factor Receptor (EGFR) Structure, Signaling Pathways, Interactions, and Recent Updates of EGFR Inhibitors

The epidermal growth factor receptor (EGFR) belongs to the ERBB family of tyrosine kinase receptors. EGFR signaling cascade is a key regulator in cell proliferation, differentiation, division, survival, and cancer development. In this review, the EGFR structure and its mutations, signaling pathway, ligand binding and EGFR dimerization, EGF/EGFR interaction, and the progress in the development of EGFR inhibitors have been explored.

Pathway information extracted from 25 years of pathway figures

Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figures published between 1995 and 2019 and extracted 1,112,551 instances of human genes, comprising 13,464 unique NCBI genes, participating in a wide variety of biological processes. This collection represents an order of magnitude more genes than found in the text of the same papers, and thousands of genes missing from other pathway databases, thus presenting new opportunities for discovery and research.

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer

Background

Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes.

Methods

In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer.

Findings

We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients.

Interpretation

We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens.

Funding

This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.

MICA enhances sensitivity to cisplatin in patients with extensive small cell lung cancer via downregulation of ABCG2

Immunotherapy utilizing natural killer cell-activated receptor natural-killer group-2 member D ligands (NKG2DLs) has had preclinical success in the treatment of small cell lung cancer. The present study aimed to investigate the association between NKG2Ls and chemoresistance. The mRNA expression of six NKG2DLs associated with progression-free survival time (PFS) and first-line chemotherapy were assessed in the present study. Major histocompatibility complex class I polypeptide-related sequence A (MICA)-overexpressing NCI-H446 cell line was constructed, and the mRNA expression levels of 11 genes associated with chemotherapy sensitivity were determined. The results demonstrated that MICA was positively and significantly associated with PFS. Furthermore, MICA expression was 1.6 times higher in patients with prolonged PFS compared with the rapid chemoresistance group. ATP binding cassette subfamily G member 2 (ABCG2) mRNA expression was associated with chemotherapy resistance and significantly downregulated in the cell line overexpressing MICA. Moreover, following addition of nicardipine (an ABCG2 inhibitor), chemotherapeutic sensitivity increased in the MICA-overexpressing cell line. Taken together, the results of the present study suggested that MICA may enhance the chemosensitivity of patients with extensive small cell lung cancer by downregulating ABCG2.

Selective deletion of MyD88 signaling in α-SMA positive cells ameliorates experimental intestinal fibrosis via post-transcriptional regulation

Intestinal fibrosis leading to strictures remains a significant clinical problem in inflammatory bowel diseases (IBD). The role of bacterial components in activating intestinal mesenchymal cells and driving fibrogenesis is largely unexplored. Tamoxifen-inducible α-SMA promoter Cre mice crossed with floxed MyD88 mice were subjected to chronic dextran sodium sulfate colitis. MyD88 was deleted prior to or after induction of colitis. Human intestinal myofibroblasts (HIMF) were exposed to various bacterial components and assessed for fibronectin (FN) and collagen I (Col1) production. RNA sequencing was performed. Post-transcriptional regulation was assessed by polysome profiling assay. Selective deletion of MyD88 in α-SMA-positive cells prior to, but not after induction of, experimental colitis decreased the degree of intestinal fibrosis. HIMF selectively responded to flagellin with enhanced FN or Col1 protein production in a MyD88-dependent manner. RNA sequencing suggested minimal transcriptional changes induced by flagellin in HIMF. Polysome profiling revealed higher proportions of FN and Col1 mRNA in the actively translated fractions of flagellin exposed HIMF, which was mediated by eIF2 alpha and 4EBP1. In conclusion, selectivity of flagellin-induced ECM secretion in HIMF is post-transcriptionally regulated. The results may represent a novel and targetable link between the gut microbiota and intestinal fibrogenesis.

Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics

Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.

Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning

We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer’s Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer’s disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.

Study on the Antibreast Cancer Mechanism and Bioactive Components of Si-Wu-Tang by Cell Type-Specific Molecular Network

Si-Wu-Tang (SWT), a traditional Chinese herbal formula, has shown an effect on antibreast cancer. However, the mechanisms and bioactive components of SWT are still unclear. Fortunately, cell type-specific molecular network has provided an effective method. This study integrated the data of formula components, all types of biomolecules in the human body, and nonexpressed protein in breast cancer cells and constructed the breast cancer cell network and the biological network that SWT acted on the breast cancer-related targets by Entity Grammar System (EGS). Biological network showed 59 bioactive components acting on 15 breast cancer-related targets. The antibreast cancer mechanisms were summarized by enrichment analysis: regulation of cell death, response to hormone stimulation, response to organic substance, regulation of phosphorylation of amino acids, regulation of cell proliferation, regulation of signal transmission, and affection of gland development. In addition, we discovered that verbascoside played the role of antibreast cancer by inhibiting cell proliferation, but there was not a report on this effect. The results of CCK8 and western blot were consistent with the antibreast cancer effect of verbascoside based on biological network. Biological network modeling by EGS and network analysis provide an effective way for uncovering the mechanism and identifying the bioactive components of SWT.

Statin-induced myopathic changes in primary human muscle cells and reversal by a prostaglandin F2 alpha analogue

Statin-related muscle side effects are a constant healthcare problem since patient compliance is dependent on side effects. Statins reduce plasma cholesterol levels and can prevent secondary cardiovascular diseases. Although statin-induced muscle damage has been studied, preventive or curative therapies are yet to be reported. We exposed primary human muscle cell populations (n = 22) to a lipophilic (simvastatin) and a hydrophilic (rosuvastatin) statin and analyzed their expressome. Data and pathway analyses included GOrilla, Reactome and DAVID. We measured mevalonate intracellularly and analyzed eicosanoid profiles secreted by human muscle cells. Functional assays included proliferation and differentiation quantification. More than 1800 transcripts and 900 proteins were differentially expressed after exposure to statins. Simvastatin had a stronger effect on the expressome than rosuvastatin, but both statins influenced cholesterol biosynthesis, fatty acid metabolism, eicosanoid synthesis, proliferation, and differentiation of human muscle cells. Cultured human muscle cells secreted ω-3 and ω-6 derived eicosanoids and prostaglandins. The ω-6 derived metabolites were found at higher levels secreted from simvastatin-treated primary human muscle cells. Eicosanoids rescued muscle cell differentiation. Our data suggest a new aspect on the role of skeletal muscle in cholesterol metabolism. For clinical practice, the addition of omega-n fatty acids might be suitable to prevent or treat statin-myopathy.

Heterogeneous networks integration for disease–gene prioritization with node kernels

Motivation

The identification of disease–gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects’ relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems.

Results

We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease–gene associations and on a time-stamped benchmark containing 42 newly discovered associations.

Availability and implementation

Source code: https://github.com/dinhinfotech/DiGI.git.

Supplementary information

Supplementary data are available at Bioinformatics online.

Proteomic Analysis of Rat Hippocampus for Studies of Cognition and Memory Loss with Aging

This chapter describes a protocol for proteomic profiling of the rat hippocampal proteome using a combination of liquid chromatography tandem mass spectrometry (LC-MS/MS) and data analysis to determine the cellular location of the identified proteins. In the example given, many of these proteins were localized in the plasma membrane and nucleus. These could be of interest in further studies of neurological and neurodegenerative disorders linked with hippocampal dysfunction, such as aging, major depression, and Alzheimer's disease.

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.

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.

ModelBricks-modules for reproducible modeling improving model annotation and provenance

Most computational models in biology are built and intended for "single-use"; the lack of appropriate annotation creates models where the assumptions are unknown, and model elements are not uniquely identified. Simply recreating a simulation result from a publication can be daunting; expanding models to new and more complex situations is a herculean task. As a result, new models are almost always created anew, repeating literature searches for kinetic parameters, initial conditions and modeling specifics. It is akin to building a brick house starting with a pile of clay. Here we discuss a concept for building annotated, reusable models, by starting with small well-annotated modules we call ModelBricks. Curated ModelBricks, accessible through an open database, could be used to construct new models that will inherit ModelBricks annotations and thus be easier to understand and reuse. Key features of ModelBricks include reliance on a commonly used standard language (SBML), rule-based specification describing species as a collection of uniquely identifiable molecules, association with model specific numerical parameters, and more common annotations. Physical bricks can vary substantively; likewise, to be useful the structure of ModelBricks must be highly flexible-it should encapsulate mechanisms from single reactions to multiple reactions in a complex process. Ultimately, a modeler would be able to construct large models by using multiple ModelBricks, preserving annotations and provenance of model elements, resulting in a highly annotated model. We envision the library of ModelBricks to rapidly grow from community contributions. Persistent citable references will incentivize model creators to contribute new ModelBricks.

Integrated identification of key genes and pathways in Alzheimer's disease via comprehensive bioinformatical analyses

Background

Alzheimer's disease (AD) is known to be caused by multiple factors, meanwhile the pathogenic mechanism and development of AD associate closely with genetic factors. Existing understanding of the molecular mechanisms underlying AD remains incomplete.

Methods

Gene expression data (GSE48350) derived from post-modern brain was extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were derived from hippocampus and entorhinal cortex regions between AD patients and healthy controls and detected via Morpheus. Functional enrichment analyses, including Gene Ontology (GO) and pathway analyses of DEGs, were performed via Cytoscape and followed by the construction of protein-protein interaction (PPI) network. Hub proteins were screened using the criteria: nodes degree≥10 (for hippocampus tissues) and ≥ 8 (for entorhinal cortex tissues). Molecular Complex Detection (MCODE) was used to filtrate the important clusters. University of California Santa Cruz (UCSC) and the database of RNA-binding protein specificities (RBPDB) were employed to identify the RNA-binding proteins of the long non-coding RNA (lncRNA).

Results

251 & 74 genes were identified as DEGs, which consisted of 56 & 16 up-regulated genes and 195 & 58 down-regulated genes in hippocampus and entorhinal cortex, respectively. Biological analyses demonstrated that the biological processes and pathways related to memory, transmembrane transport, synaptic transmission, neuron survival, drug metabolism, ion homeostasis and signal transduction were enriched in these genes. 11 genes were identified as hub genes in hippocampus and entorhinal cortex, and 3 hub genes were identified as the novel candidates involved in the pathology of AD. Furthermore, 3 lncRNAs were filtrated, whose binding proteins were closely associated with AD.

Conclusions

Through GO enrichment analyses, pathway analyses and PPI analyses, we showed a comprehensive interpretation of the pathogenesis of AD at a systematic biology level, and 3 novel candidate genes and 3 lncRNAs were identified as novel and potential candidates participating in the pathology of AD. The results of this study could supply integrated insights for understanding the pathogenic mechanism underlying AD and potential novel therapeutic targets.