GlucoKinaseDB: A comprehensive, curated resource of glucokinase modulators for clinical and molecular research.

Glucokinase (GK), an isoform of hexokinase expressed predominantly in liver, pancreas and hypothalamus is crucial to blood glucose management. It is a critical component of the glucose-sensing mechanism of the pancreatic islet cells and glycogen regulation in hepatocytes. GK modulators such as allosteric GKAs (glucokinase activators) and GK-GKRP (glucokinase regulatory protein) disruptors have found potential applications as safer antihyperglycemics. Recent studies have also demonstrated the potential of GK modulators as antiparasitic agents. Researchers targeting GK often undertake the time-consuming task of independently collecting and compiling modulator information due to the lack of any dedicated single-platform resource. Towards this, in the present study we demonstrate the design and development of GlucoKinaseDB (GKDB), a comprehensive, curated, online resource of GK modulators. GKDB contains experimentally derived structural and bioactivity information of 1723 modulators along with their detailed molecular descriptors. The web-interface is user-friendly with features such as in-browser visualization, advanced search queries, cross-links to other databases and original reference etc. The bioactivity and descriptor data can be downloaded in bulk (for entire database) or for individual modulators. The 3D structures are also downloadable in multiple formats. GKDB employs a PHP-based web design with Bootstrap styling and a MySQL database backend. GKDB can be utilized for clinical and molecular research via development of pharmacophore hypotheses, QSAR/QSPR models, predictive machine learning models etc. GKDB is freely accessible online at https://glucokinasedb.in.

PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics.

Functional genomics studies have helped researchers annotate differentially expressed gene lists, extract gene expression signatures, and identify biological pathways from omics profiling experiments conducted on biological samples. The current geneset, network, and pathway analysis (GNPA) web servers, e.g., DAVID, EnrichR, WebGestaltR, or PAGER, do not allow automated integrative functional genomic downstream analysis. In this study, we developed a new web-based interactive application, "PAGER Web APP", which supports online R scripting of integrative GNPA. In a case study of melanoma drug resistance, we showed that the new PAGER Web APP enabled us to discover highly relevant pathways and network modules, leading to novel biological insights. We also compared PAGER Web APP's pathway analysis results retrieved among PAGER, EnrichR, and WebGestaltR to show its advantages in integrative GNPA. The interactive online web APP is publicly accessible from the link, https://aimed-lab.shinyapps.io/PAGERwebapp/.

PluriMetNet: A dynamic electronic model decrypting the metabolic variations in human embryonic stem cells (hESCs) at fluctuating oxygen concentrations.

Stem cells are an excellent resource in translational medicine however much is known only in terms of transcriptional and epigenetic regulation of human embryonic stem cells (hESCs). Metabolic regulation of hESCs is still unexplored in many ways, particularly the role of energy metabolism, which is intrinsic to the maintenance of cell viability, however, is very little explored in the past years. Also, there exists no hESC specific core metabolic model of pluripotency as per our knowledge. Through our work, we establish such a metabolic model of hESC using combinatorial in-silico approach of genome scale model reduction and literature curation. Further, through perturbations taking oxygen as a parameter we propose that under lower levels of oxygen concentration there is a significant dynamic change in the energy metabolism of the hESC. We further investigated energy subsystem pathways and their respective reactions in order to locate the direction of energy production along with the dynamic of nutrient metabolites like glucose and glutamine. The output shows a steep increment/decrement at a certain oxygen range. These sharp increments/decrements under hypoxic conditions are termed here as a critical range for hESC metabolic pathway. The data also resonates with the previous experimental studies on hESC energy metabolism confirming the robustness of our model. The model helps to extract range for different pathways in the energy subsystem, making us a little closer in understanding the metabolism of hESC. We also demonstrated the possible range of pathway changes in hESC's energy metabolism that can serve as the crucial preliminary data for further prospective studies. The model also offers a promise in the prediction of the flux behaviour of various metabolites in hESC.Communicated by Ramaswamy H. Sarma.

Corrigendum: Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples.

[This corrects the article DOI: 10.3389/fdata.2021.725276.].

Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples.

Unsupervised learning techniques, such as clustering and embedding, have been increasingly popular to cluster biomedical samples from high-dimensional biomedical data. Extracting clinical data or sample meta-data shared in common among biomedical samples of a given biological condition remains a major challenge. Here, we describe a powerful analytical method called Statistical Enrichment Analysis of Samples (SEAS) for interpreting clustered or embedded sample data from omics studies. The method derives its power by focusing on sample sets, i.e., groups of biological samples that were constructed for various purposes, e.g., manual curation of samples sharing specific characteristics or automated clusters generated by embedding sample omic profiles from multi-dimensional omics space. The samples in the sample set share common clinical measurements, which we refer to as "clinotypes," such as age group, gender, treatment status, or survival days. We demonstrate how SEAS yields insights into biological data sets using glioblastoma (GBM) samples. Notably, when analyzing the combined The Cancer Genome Atlas (TCGA)-patient-derived xenograft (PDX) data, SEAS allows approximating the different clinical outcomes of radiotherapy-treated PDX samples, which has not been solved by other tools. The result shows that SEAS may support the clinical decision. The SEAS tool is publicly available as a freely available software package at https://aimed-lab.shinyapps.io/SEAS/.

VIRdb 2.0: Interactive analysis of comorbidity conditions associated with vitiligo pathogenesis using co-expression network-based approach.

Vitiligo is a disease of mysterious origins in the context of its occurrence and pathogenesis. The autoinflammatory theory is perhaps the most widely accepted theory that discusses the occurrence of Vitiligo. The theory elaborates the clinical association of vitiligo with autoimmune disorders such as Psoriasis, Multiple Sclerosis and Rheumatoid Arthritis and Diabetes. In the present work, we discuss the comprehensive set of differentially co-expressed genes involved in the crosstalk events between Vitiligo and associated autoimmune disorders (Psoriasis, Multiple Sclerosis and Rheumatoid Arthritis). We progress our previous tool, Vitiligo Information Resource (VIRdb), and incorporate into it a compendium of Vitiligo-related multi-omics datasets and present it as VIRdb 2.0. It is available as a web-resource consisting of statistically sound and manually curated information. VIRdb 2.0 is an integrative database as its datasets are connected to KEGG, STRING, GeneCards, SwissProt, NPASS. Through the present study, we communicate the major updates and expansions in the VIRdb and deliver the new version as VIRdb 2.0. VIRdb 2.0 offers the maximum user interactivity along with ease of navigation. We envision that VIRdb 2.0 will be pertinent for the researchers and clinicians engaged in drug development for vitiligo.