PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful.

Pathway Data Integration Portal (PathDIP) is an integrated pathway database that was developed to increase functional gene annotation coverage and reduce bias in pathway enrichment analysis. PathDIP 5 provides multiple improvements to enable more interpretable analysis: users can perform enrichment analysis using all sources, separate sources or by combining specific pathway subsets; they can select the types of sources to use or the types of pathways for the analysis, reducing the number of resulting generic pathways or pathways not related to users' research question; users can use API. All pathways have been mapped to seven representative types. The results of pathway enrichment can be summarized through knowledge-based pathway consolidation. All curated pathways were mapped to 53 pathway ontology-based categories. In addition to genes, pathDIP 5 now includes metabolites. We updated existing databases, included two new sources, PathBank and MetabolicAtlas, and removed outdated databases. We enable users to analyse their results using Drugst.One, where a drug-gene network is created using only the user's genes in a specific pathway. Interpreting the results of any analysis is now improved by multiple charts on all the results pages. PathDIP 5 is freely available at https://ophid.utoronto.ca/pathDIP.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

MirDIP 5.2: tissue context annotation and novel microRNA curation.

MirDIP is a well-established database that aggregates microRNA-gene human interactions from multiple databases to increase coverage, reduce bias, and improve usability by providing an integrated score proportional to the probability of the interaction occurring. In version 5.2, we removed eight outdated resources, added a new resource (miRNATIP), and ran five prediction algorithms for miRBase and mirGeneDB. In total, mirDIP 5.2 includes 46 364 047 predictions for 27 936 genes and 2734 microRNAs, making it the first database to provide interactions using data from mirGeneDB. Moreover, we curated and integrated 32 497 novel microRNAs from 14 publications to accelerate the use of these novel data. In this release, we also extend the content and functionality of mirDIP by associating contexts with microRNAs, genes, and microRNA-gene interactions. We collected and processed microRNA and gene expression data from 20 resources and acquired information on 330 tissue and disease contexts for 2657 microRNAs, 27 576 genes and 123 651 910 gene-microRNA-tissue interactions. Finally, we improved the usability of mirDIP by enabling the user to search the database using precursor IDs, and we integrated miRAnno, a network-based tool for identifying pathways linked to specific microRNAs. We also provide a mirDIP API to facilitate access to its integrated predictions. Updated mirDIP is available at https://ophid.utoronto.ca/mirDIP.

miRAnno-network-based functional microRNA annotation.

MOTIVATION: Functional annotation is a common part of microRNA (miRNA)-related research, typically carried as pathway enrichment analysis of the selected miRNA targets. Here, we propose miRAnno, a fast and easy-to-use web application for miRNA annotation. RESULTS: miRAnno uses comprehensive molecular interaction network and random walks with restart to measure the association between miRNAs and individual pathways. Independent validation shows that miRAnno achieves higher signal-to-noise ratio compared to the standard enrichment analysis. AVAILABILITY AND IMPLEMENTATION: miRAnno is freely available at https://ophid.utoronto.ca/miRAnno/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species.

PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein-protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.

mirDIP 4.1-integrative database of human microRNA target predictions.

MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.

pathDIP: an annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis.

Molecular pathway data are essential in current computational and systems biology research. While there are many primary and integrated pathway databases, several challenges remain, including low proteome coverage (57%), low overlap across different databases, unavailability of direct information about underlying physical connectivity of pathway members, and high fraction of protein-coding genes without any pathway annotations, i.e. 'pathway orphans'. In order to address all these challenges, we developed pathDIP, which integrates data from 20 source pathway databases, 'core pathways', with physical protein-protein interactions to predict biologically relevant protein-pathway associations, referred to as 'extended pathways'. Cross-validation determined 71% recovery rate of our predictions. Data integration and predictions increase coverage of pathway annotations for protein-coding genes to 86%, and provide novel annotations for 5732 pathway orphans. PathDIP (http://ophid.utoronto.ca/pathdip) annotates 17 070 protein-coding genes with 4678 pathways, and provides multiple query, analysis and output options.

Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Osteoarthritis Data Integration Portal (OsteoDIP): A web-based gene and non-coding RNA expression database.

Objective: OsteoDIP aims to collect and provide, in a simple searchable format, curated high throughput RNA expression data related to osteoarthritis. Design: Datasets are collected annually by searching "osteoarthritis gene expression profile" in PubMed. Only publications containing patient data and a list of differentially expressed genes are considered. From 2020, the search has expanded to include non-coding RNAs. Moreover, a search in GEO for "osteoarthritis" datasets has been performed using 'Homo sapiens' and 'Expression profiling by array' filters. Annotations for genes linked to osteoarthritis have been downloaded from external databases. Results: Out of 1204 curated papers, 63 have been included in OsteoDIP, while GEO curation led to the collection of 28 datasets. Literature data provides a snapshot of osteoarthritis research derived from 1924 human samples, while GEO datasets provide expression for additional 1012 patients. Similar to osteoarthritis literature, OsteoDIP data has been created mostly from studies focused on knee, and the tissue most frequently investigated is cartilage. GEO data sets were fully integrated with associated clinical data. We showcase examples and use cases applicable for translational research in osteoarthritis. Conclusions: OsteoDIP is publicly available at http://ophid.utoronto.ca/OsteoDIP. The website is easy to navigate and all the data is available for download. Data consolidation allows researchers to perform comparisons across studies and to combine data from different datasets. Our examples show how OsteoDIP can integrate with and improve osteoarthritis researchers' pipelines.

Estrogen Receptor 1 Inhibition of Wnt/ß-Catenin Signaling Contributes to Sex Differences in Hepatocarcinogenesis.

BACKGROUND: Hepatocellular Carcinoma (HCC) is a sexually dimorphic cancer, with female sex being independently protective against HCC incidence and progression. The aim of our study was to understand the mechanism of estrogen receptor signaling in driving sex differences in hepatocarcinogenesis. METHODS: We integrated 1,268 HCC patient sample profiles from publicly available gene expression data to identify the most differentially expressed genes (DEGs). We mapped DEGs into a physical protein interaction network and performed network topology analysis to identify the most important proteins. Experimental validation was performed in vitro on HCC cell lines, in and in vivo, using HCC mouse model. RESULTS: We showed that the most central protein, ESR1, is HCC prognostic, as increased ESR1 expression was protective for overall survival, with HR=0.45 (95%CI 0.32-0.64, p=4.4E-06), and was more pronounced in women. Transfection of HCC cell lines with ESR1 and exposure to estradiol affected expression of genes involved in the Wnt/ß-catenin signaling pathway. ER-α (protein product of ESR1) agonist treatment in a mouse model of HCC resulted in significantly longer survival and decreased tumor burden (p<0.0001), with inhibition of Wnt/ß-Catenin signaling. In vitro experiments confirmed colocalization of ß-catenin with ER-α, leading to inhibition of ß-catenin-mediated transcription of target genes c-Myc and Cyclin D1. CONCLUSION: Combined, the centrality of ESR1 and its inhibition of the Wnt/ß-catenin signaling axis provide a biological rationale for protection against HCC incidence and progression in women.

Retraction: Circulating plant miRNAs can regulate human gene expression in vitro.

This corrects the article DOI: 10.1038/srep32773.

Circulating plant miRNAs can regulate human gene expression in vitro.

While Brassica oleracea vegetables have been linked to cancer prevention, the exact mechanism remains unknown. Regulation of gene expression by cross-species microRNAs has been previously reported; however, its link to cancer suppression remains unexplored. In this study we address both issues. We confirm plant microRNAs in human blood in a large nutrigenomics study cohort and in a randomized dose-controlled trial, finding a significant positive correlation between the daily amount of broccoli consumed and the amount of microRNA in the blood. We also demonstrate that Brassica microRNAs regulate expression of human genes and proteins in vitro, and that microRNAs cooperate with other Brassica-specific compounds in a possible cancer-preventive mechanism. Combined, we provide strong evidence and a possible multimodal mechanism for broccoli in cancer prevention.