Transcriptomic evaluation of skin tape-strips in children with allergic asthma uncovers epidermal barrier dysfunction and asthma-associated biomarkers abnormalities.

INTRODUCTION: Tape-strips, a minimally invasive method validated for the evaluation of several skin diseases, may help identify asthma-specific biomarkers in the skin of children with allergic asthma. METHODS: Skin tape-strips were obtained and analyzed with RNA-Seq from children with moderate allergic asthma (MAA) (n = 11, mean age 7.00; SD = 1.67), severe allergic asthma (SAA) (n = 9, mean age 9.11; SD = 2.37), and healthy controls (HCs) (n = 12, mean age 7.36; SD = 2.03). Differentially expressed genes (DEGs) were identified by fold change ≥2 with a false discovery rate <0.05. Transcriptomic biomarkers were analyzed for their accuracy in distinguishing asthma from HCs, their relationships with asthma-related outcomes (exacerbation rate, lung function-FEV1, IOS-R5-20, and lung inflammation-FeNO), and their links to skin (barrier and immune response) and lung (remodeling, metabolism, aging) pathogenetic pathways. RESULTS: RNA-Seq captured 1113 in MAA and 2117 DEGs in SAA. Epidermal transcriptomic biomarkers for terminal differentiation (FLG/filaggrin), cell adhesion (CDH19, JAM2), lipid biosynthesis/metabolism (ACOT2, LOXL2) were significantly downregulated. Gene set variation analysis revealed enrichment of Th1/IFNγ pathways (p < .01). MAA and SAA shared downregulation of G-protein-coupled receptor (OR4A16, TAS1R3), upregulation of TGF-ß/ErbB signaling-related (ACVR1B, EGFR, ID1/2), and upregulation of mitochondrial-related (HIGD2A, VDAC3, NDUFB9) genes. Skin transcriptomic biomarkers correlated with the annualized exacerbation rate and with lung function parameters. A two-gene classifier (TSSC4-FAM212B) was able to differentiate asthma from HCs with 100% accuracy. CONCLUSION: Tape-strips detected epithelial barrier and asthma-associated signatures in normal-appearing skin from children with allergic asthma and may serve as an alternative to invasive approaches for evaluating asthma endotypes.

Toxicology knowledge graph for structural birth defects.

BACKGROUND: Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes. METHODS: To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules. RESULTS: Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes. CONCLUSIONS: ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.

While birth defects are common, for most birth defects there are no known causes. During pregnancy, developing babies are exposed to drugs, cosmetics, food, and environmental pollutants that may cause birth defects. However, exactly how these environmental factors are involved in producing birth defects is difficult to discern. Also, birth defects can be a consequence of the genes inherited from the parents. We combined general data about human genes and drugs with specific data previously implicating genes and drugs in inducing birth defects to create a knowledge graph representation that connects genes, drugs, and birth defects. This knowledge graph can be used to explore new links that may explain why birth defects occur, particularly those that result from a combination of inherited and environmental influences.

ChEA3: transcription factor enrichment analysis by orthogonal omics integration.

Identifying the transcription factors (TFs) responsible for observed changes in gene expression is an important step in understanding gene regulatory networks. ChIP-X Enrichment Analysis 3 (ChEA3) is a transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate a composite rank that improves the prediction of the correct upstream TF compared to ranks produced by individual libraries. We compare ChEA3 with existing TF prediction tools and show that ChEA3 performs better. By integrating the ChEA3 libraries, we illuminate general transcription factor properties such as whether the TF behaves as an activator or a repressor. The ChEA3 web-server is available from https://amp.pharm.mssm.edu/ChEA3.