Application of artificial intelligence and machine learning in drug repurposing.

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology.

SEMA3B inhibits TGFβ-induced extracellular matrix protein production and its reduced levels are associated with a decline in lung function in IPF.

Idiopathic pulmonary fibrosis (IPF) is marked by the activation of fibroblasts, leading to excessive production and deposition of extracellular matrix (ECM) within the lung parenchyma. Despite the pivotal role of ECM overexpression in IPF, potential negative regulators of ECM production in fibroblasts have yet to be identified. Semaphorin class 3B (SEMA3B), a secreted protein highly expressed in lung tissues, has established roles in axonal guidance and tumor suppression.

Transcriptome analysis in acute gastrointestinal graft- host disease reveals a unique signature in children and shared biology with pediatric inflammatory bowel disease.

We performed transcriptomic analyses on freshly frozen (n=21) and paraffin-embedded (n=35) gastrointestinal (GI) biopsies from children with and without acute acute GI graft-versus-host disease (GvHD) to study differential gene expressions. We identified 164 significant genes, 141 upregulated and 23 downregulated, in acute GvHD from freshy frozen biopsies. CHI3L1 was the top differentially expressed gene in acute GvHD, involved in macrophage recruitment and bacterial adhesion.

: A multi-view gaussian gene embedding learner for analyzing transcriptomic networks.

Analyzing gene co-expression networks can help in the discovery of biological processes and regulatory mechanisms underlying normal or perturbed states. Unlike standard differential analysis, network-based approaches consider the interactions between the genes involved leading to biologically relevant results. Applying such network-based methods to jointly analyze multiple transcriptomic networks representing independent disease cohorts or studies could lead to the identification of more robust gene modules or gene regulatory networks.

Consensus Gene Co-Expression Network Analysis Identifies Novel Genes Associated with Severity of Fibrotic Lung Disease.

Idiopathic pulmonary fibrosis (IPF) is a severe fibrotic lung disease characterized by irreversible scarring of the lung parenchyma leading to dyspnea, progressive decline in lung function, and respiratory failure. We analyzed lung transcriptomic data from independent IPF cohorts using weighted gene co-expression network analysis (WGCNA) to identify gene modules based on their preservation status in these cohorts. The consensus gene modules were characterized by leveraging existing clinical and molecular data such as lung function, biological processes, pathways, and lung cell types.

Computational workflow for functional characterization of COVID-19 through secondary data analysis.

Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals.

Inhibition of PDIA3 in club cells attenuates osteopontin production and lung fibrosis.

Background: The role of club cells in the pathology of idiopathic pulmonary fibrosis (IPF) is not well understood. Protein disulfide isomerase A3 (PDIA3), an endoplasmic reticulum-based redox chaperone required for the functions of various fibrosis-related proteins; however, the mechanisms of action of PDIA3 in pulmonary fibrosis are not fully elucidated.

Objectives: To examine the role of club cells and PDIA3 in the pathology of pulmonary fibrosis and the therapeutic potential of inhibition of PDIA3 in lung fibrosis.

Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19.

Standard transcriptomic analyses alone have limited power in capturing the molecular mechanisms driving disease pathophysiology and outcomes. To overcome this, unsupervised network analyses are used to identify clusters of genes that can be associated with distinct molecular mechanisms and outcomes for a disease. In this study, we developed an integrated network analysis framework that integrates transcriptional signatures from multiple model systems with protein-protein interaction data to find gene modules.

Mucosal Genomics Implicate Lymphocyte Activation and Lipid Metabolism in Refractory Environmental Enteric Dysfunction.

Background & Aims: Environmental enteric dysfunction (EED) limits the Sustainable Development Goals of improved childhood growth and survival. We applied mucosal genomics to advance our understanding of EED.

Methods: The Study of Environmental Enteropathy and Malnutrition (SEEM) followed 416 children from birth to 24 months in a rural district in Pakistan.

Pan-transcriptome-based candidate therapeutic discovery for idiopathic pulmonary fibrosis.

Background: There are two US Food and Drug Administration (FDA)-approved drugs, pirfenidone and nintedanib, for treatment of patients with idiopathic pulmonary fibrosis (IPF). However, neither of these drugs provide a cure. In addition, both are associated with several drug-related adverse events.

Inhibition of Aurora Kinase B attenuates fibroblast activation and pulmonary fibrosis.

Fibroblast activation including proliferation, survival, and ECM production is central to initiation and maintenance of fibrotic lesions in idiopathic pulmonary fibrosis (IPF). However, druggable molecules that target fibroblast activation remain limited. In this study, we show that multiple pro-fibrotic growth factors, including TGFα, CTGF, and IGF1, increase aurora kinase B (AURKB) expression and activity in fibroblasts.

Correction to: Comprehensive functional annotation of susceptibility variants associated with asthma.

In the original article published, the "p" value in the Fig. 5 legend is incorrectly presented as *p < 0.50.

MI-MAAP: marker informativeness for multi-ancestry admixed populations.

Background: Admixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual's genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations.

Comprehensive functional annotation of susceptibility variants associated with asthma.

Genome-wide association studies (GWAS) have identified hundreds of primarily non-coding disease-susceptibility variants that further need functional interpretation to prioritize and discriminate the disease-relevant variants. We present a comprehensive genome-wide non-coding variant prioritization scheme followed by validation using Pyrosequencing and TaqMan assays in asthma. We implemented a composite Functional Annotation Score (cFAS) to investigate over 32,000 variants consisting of 1525 GWAS-lead asthma-susceptibility variants and their LD proxies (r ≥ 0.

LEI: A Novel Allele Frequency-Based Feature Selection Method for Multi-ancestry Admixed Populations.

Next-generation sequencing technologies now make it possible to sequence and genotype hundreds of thousands of genetic markers across the human genome. Selection of informative markers for the comprehensive characterization of individual genomic makeup using a high dimensional genomics dataset has become a common practice in evolutionary biology and human genetics. Although several feature selection approaches exist to determine the ancestry proportion in two-way admixed populations including African Americans, there are limited statistical tools developed for the feature selection approaches in three-way admixed populations (including Latino populations).

PAMAM: Power analysis in multiancestry admixture mapping.

Admixed populations arise when two or more previously isolated populations interbreed. Admixture mapping (AM) methods are used for tracing the ancestral origin of disease-susceptibility genetic loci in the admixed population such as African American and Latinos. AM is different from genome-wide association studies in that ancestry rather than genotypes are tracked in the association process.

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

Motivation: Advances in high-throughput sequencing technologies have made it possible to generate multiple omics data at an unprecedented rate and scale. The accumulation of these omics data far outpaces the rate at which biologists can mine and generate new hypothesis to test experimentally. There is an urgent need to develop a myriad of powerful tools to efficiently and effectively search and filter these resources to address specific post-GWAS functional genomics questions.