SEMA3B inhibits TGFß-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. However, the role of SEMA3B in ECM production by fibroblasts in the pathogenesis of IPF remains unexplored. Here, we show the downregulation of SEMA3B and its cognate binding receptor, neuropilin 1 (NRP1) in IPF lungs compared with healthy controls. Notably, the reduced expression of SEMA3B and NRP1 is associated with a decline in lung function in IPF. The downregulation of SEMA3B and NRP1 transcripts was validated in the lung tissues of patients with IPF, and two alternative mouse models of pulmonary fibrosis. In addition, we show that TGFß functions as a negative regulator of SEMA3B and NRP1 expression in lung fibroblasts. Furthermore, we demonstrate the anti-fibrotic effects of SEMA3B against TGFß-induced ECM production in IPF lung fibroblasts. Overall, our findings uncovered a novel role of SEMA3B in the pathogenesis of pulmonary fibrosis and provided novel insights into modulating the SEMA3B-NRP1 axis to attenuate pulmonary fibrosis.

Transcriptome analysis in acute gastrointestinal graft-versus 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. Mitochondrial genes were among the top downregulated genes. Immune deconvolution identified a macrophage cellular signature. Weighted gene co-expression network analysis showed enrichment of genes in the ERK1/2 cascade. Transcriptome data from 206 ulcerative colitis (UC) patients were included to uncover genes and pathways shared between GvHD and UC. Comparison with the UC transcriptome showed both shared and distinct pathways. Both UC and GvHD transcriptomes shared an innate antimicrobial signature and FCγ1RA/CD64 was upregulated in both acute GvHD (log-fold increase 1.7, P=0.001) and UC. Upregulation of the ERK1/2 cascade pathway was specific to GvHD. We performed additional experiments to confirm transcriptomics. Firstly, we examined phosphorylation of ERK (pERK) by immunohistochemistry on GI biopsies (acute GvHD n=10, no GvHD n=10). pERK staining was increased in acute GvHD biopsies compared to biopsies without acute GvHD (P=0.001). Secondly, plasma CD64, measured by enzyme-linked immunsorbant assay (n=85) was elevated in acute GI GvHD (P<0.001) compared with those without and was elevated in GVHD compared with inflammatory bowel disease (n=47) (P<0.001), confirming the upregulated expression seen in the transcriptome.

gene2gauss: 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. We present gene2gauss, a novel feature learning framework that is capable of embedding genes as multivariate gaussian distributions by taking into account their long-range interaction neighborhoods across multiple transcriptomic studies. Using multiple gene co-expression networks from idiopathic pulmonary fibrosis, we demonstrate that these multi-dimensional gaussian features are suitable for identifying regulons of known transcription factors (TF). Using standard TF-target libraries, we demonstrate that the features from our method are highly relevant in comparison with other feature learning approaches on transcriptomic data.

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. From a total of 32 consensus gene modules identified, two modules were found to be significantly correlated with the disease, lung function, and preserved in other IPF datasets. The upregulated gene module was enriched for extracellular matrix, collagen metabolic process, and BMP signaling while the downregulated module consisted of genes associated with tube morphogenesis, blood vessel development, and cell migration. Using a combination of connectivity-based and trait-based significance measures, we identified and prioritized 103 "hub" genes (including 25 secretory candidate biomarkers) by their similarity to known IPF genetic markers. Our validation studies demonstrate the dysregulated expression of CRABP2, a retinol-binding protein, in multiple lung cells of IPF, and its correlation with the decline in lung function.

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. METHODS: Role of PDIA3 and aberrant club cells in lung fibrosis was studied by analyses of human transcriptome dataset from Lung Genomics Research Consortium, other public resources, the specific deletion or inhibition of PDIA3 in club cells and blocking SPP1 downstream of PDIA3 in mice. RESULTS: PDIA3 and club cell secretory protein (SCGB1A1) signatures are upregulated in IPF compared with control patients. PDIA3 or SCGB1A1 increases also correlate with a decrease in lung function in patients with IPF. The bleomycin (BLM) model of lung fibrosis showed increases in PDIA3 in SCGB1A1 cells in the lung parenchyma. Ablation of Pdia3, specifically in SCGB1A1 cells, decreases parenchymal SCGB1A1 cells along with fibrosis in mice. The administration of a PDI inhibitor LOC14 reversed the BLM-induced parenchymal SCGB1A1 cells and fibrosis in mice. Evaluation of PDIA3 partners revealed that SPP1 is a major interactor in fibrosis. Blocking SPP1 attenuated the development of lung fibrosis in mice. CONCLUSIONS: Our study reveals a new relationship with distally localised club cells, PDIA3 and SPP1 in lung fibrosis and inhibition of PDIA3 or SPP1 attenuates lung fibrosis.

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. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases. For complete details on the use and execution of this protocol, please refer to Ghandikota et al. (2021).

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. Through a meta-analysis of different enriched features from these gene modules, we extract communities of highly interconnected features. These clusters of higher-order features, working as a multifeatured machine, enable collective assessment of their contribution for disease or phenotype characterization. We show the utility of this workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identify several pathways and biological processes that could enable understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19.

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. Biomarkers were measured at 9 months and tested for association with growth at 24 months. The duodenal methylome and transcriptome were determined in 52 undernourished SEEM participants and 42 North American controls and patients with celiac disease. RESULTS: After accounting for growth at study entry, circulating insulin-like growth factor-1 (IGF-1) and ferritin predicted linear growth, whereas leptin correlated with future weight gain. The EED transcriptome exhibited suppression of antioxidant, detoxification, and lipid metabolism genes, and induction of anti-microbial response, interferon, and lymphocyte activation genes. Relative to celiac disease, suppression of antioxidant and detoxification genes and induction of antimicrobial response genes were EED-specific. At the epigenetic level, EED showed hyper-methylation of epithelial metabolism and barrier function genes, and hypo-methylation of immune response and cell proliferation genes. Duodenal coexpression modules showed association between lymphocyte proliferation and epithelial metabolic genes and histologic severity, fecal energy loss, and wasting (weight-for-length/height Z < -2.0). Leptin was associated with expression of epithelial carbohydrate metabolism and stem cell renewal genes. Immune response genes were attenuated by giardia colonization. CONCLUSIONS: Children with reduced circulating IGF-1 are more likely to experience stunting. Leptin and a gene signature for lymphocyte activation and dysregulated lipid metabolism are implicated in wasting, suggesting new approaches for EED refractory to nutritional intervention. ClinicalTrials.gov, Number: NCT03588013. (https://clinicaltrials.gov/ct2/show/NCT03588013).

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. Hence, the pursuit for newer IPF therapeutics continues. Recent studies show that joint analysis of systems-biology-level information with drug-disease connectivity are effective in discovery of biologically relevant candidate therapeutics. METHODS: Publicly available gene expression signatures from patients with IPF were used to query a large-scale perturbagen signature library to discover compounds that can potentially reverse dysregulated gene expression in IPF. Two methods were used to calculate IPF-compound connectivity: gene expression-based connectivity and feature-based connectivity. Identified compounds were further prioritized if their shared mechanism(s) of action were IPF-related. RESULTS: We found 77 compounds as potential candidate therapeutics for IPF. Of these, 39 compounds are either FDA-approved for other diseases or are currently in phase II/III clinical trials suggesting their repurposing potential for IPF. Among these compounds are multiple receptor kinase inhibitors (e.g. nintedanib, currently approved for IPF, and sunitinib), aurora kinase inhibitor (barasertib), epidermal growth factor receptor inhibitors (erlotinib, gefitinib), calcium channel blocker (verapamil), phosphodiesterase inhibitors (roflumilast, sildenafil), PPAR agonists (pioglitazone), histone deacetylase inhibitors (entinostat), and opioid receptor antagonists (nalbuphine). As a proof of concept, we performed in vitro validations with verapamil using lung fibroblasts from IPF and show its potential benefits in pulmonary fibrosis. CONCLUSIONS: As about half of the candidates discovered in this study are either FDA-approved or are currently in clinical trials for other diseases, rapid translation of these compounds as potential IPF therapeutics is possible. Further, the integrative connectivity analysis framework in this study can be adapted in early phase drug discovery for other common and rare diseases with transcriptomic profiles.The reviews of this paper are available via the supplemental material section.

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. Mechanistically, we demonstrate that Wilms tumor 1 (WT1) is a key transcription factor that mediates TGFα-driven AURKB upregulation in fibroblasts. Importantly, we found that inhibition of AURKB expression or activity is sufficient to attenuate fibroblast activation. We show that fibrosis induced by TGFα is highly dependent on AURKB expression and treating TGFα mice with barasertib, an AURKB inhibitor, reverses fibroblast activation, and pulmonary fibrosis. Barasertib similarly attenuated fibrosis in the bleomycin model of pulmonary fibrosis. Together, our preclinical studies provide important proof-of-concept that demonstrate barasertib as a possible intervention therapy for IPF.

Mucosal Inflammatory and Wound Healing Gene Programs Reveal Targets for Stricturing Behavior in Pediatric Crohn's Disease.

BACKGROUND AND AIMS: Ileal strictures are the major indication for resective surgery in Crohn's disease (CD). We aimed to define ileal gene programs present at diagnosis linked with future stricturing behavior during five year follow-up, and to identify potential small molecules to reverse these gene signatures. METHODS: Antimicrobial serologies and pre-treatment ileal gene expression were assessed in a representative subset of 249 CD patients within the RISK multicenter pediatric CD inception cohort study, including 113 that are unique to this report. These data were used to define genes associated with stricturing behavior and for model testing to predict stricturing behavior. A bioinformatics approach to define small molecules which may reverse the stricturing gene signature was applied. RESULTS: 19 of the 249 patients developed isolated B2 stricturing behavior during follow-up, while 218 remained B1 inflammatory. Using deeper RNA sequencing than in our prior report, we have now defined an inflammatory gene signature including an oncostatin M co-expression signature, tightly associated with extra-cellular matrix (ECM) gene expression in those who developed stricturing complications. We further computationally prioritize small molecules targeting macrophage and fibroblast activation and angiogenesis which may reverse the stricturing gene signature. A model containing ASCA and CBir1 serologies and a refined eight ECM gene set was significantly associated with stricturing development by year five after diagnosis (AUC (95th CI) = 0.82 (0.7-0.94)). CONCLUSION: An ileal gene program for macrophage and fibroblast activation is linked to stricturing complications in treatment naïve pediatric CD, and may inform novel small molecule therapeutic approaches.

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. The correct p value is *p < 0.050.

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. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection. RESULTS: To address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data. CONCLUSIONS: MI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at https://research.cchmc.org/mershalab/MI-MAAP/login/.

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 (r2 ≥ 0.80). Functional annotation pipeline in cFAS revealed 274 variants with significant score at 1% false discovery rate. This study implicates a novel locus 4p16 (SLC26A1) with eQTL variant (rs11936407) and known loci in 17q12-21 and 5q22 which encode ORM1-like protein 3 (ORMDL3, rs406527, and rs12936231) and thymic stromal lymphopoietin (TSLP, rs3806932 and rs10073816) epithelial gene, respectively. Follow-up validation analysis through pyrosequencing of CpG sites in and nearby rs4065275 and rs11936407 showed genotype-dependent hypomethylation on asthma cases compared with healthy controls. Prioritized variants are enriched for asthma-specific histone modification associated with active chromatin (H3K4me1 and H3K27ac) in T cells, B cells, lung, and immune-related interferon gamma signaling pathways. Our findings, together with those from prior studies, suggest that SNPs can affect asthma by regulating enhancer activity, and our comprehensive bioinformatics and functional analysis could lead to biological insights into asthma pathogenesis.Graphic abstract.

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). Herein, we present a new likelihood-based feature selection method called Lancaster Estimator of Independence (LEI) that utilizes allele frequency information to prioritize the most informative features useful to determine ancestry proportion from multiple ancestral populations in admixed individuals. The ability of LEI to leverage summary-level statistics from allele frequency data, thereby avoiding the many restrictions (and big data issues) that can accompany access to individual-level genotype data, is appealing to minimize the computation and time-consuming ancestry inference in an admixed population. We compared our allele-frequency based approach with genotype-based approach in estimating admixed proportions in three-way admixed population scenarios. Our results showed ancestry estimates using the top-ranked features from LEI were comparable with the estimates using features from genotype-based methods in three-way admixed population. We provide an easy-to-use R code to assist researchers in using the LEI tool to develop allele frequency-based informative features to conduct admixture mapping studies from mixed samples of multiple ancestry origin.

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. The power and sample size of AM primarily depend on proportion of admixture and differences in the risk allele frequencies among the ancestral populations. Ensuring sufficient power to detect the effect of ancestry on disease susceptibility is critical for interpretability and reliability of studies using AM approach. However, there is no power and sample size analysis tool existing for AM studies in admixed population. In this study, we developed power analysis of multiancestry AM (PAMAM) to estimate power and sample size for two-way and three-way population admixtures. PAMAM is the first web-based bioinformatics tool developed to calculate power and sample size in admixed population under a variety of genetic and disease phenotype models. It is a valuable resource for investigators to design a cost-efficient study and develop grant application to pursue AM studies. PAMAM is built on JavaScript back-end with HTML front-end. It is accessible through any modern web browser such as Firefox, Internet Explorer, and Google Chrome regardless of operating system. It is a user-friendly tool containing links for support information including user manual and examples, and freely available at https://research.cchmc.org/mershalab/PAMAM/login.html.

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. However, to date, these resources are scattered across several databases and often lack a unified portal for data annotation and analytics. In addition, existing tools to analyze and visualize these databases are highly fragmented, resulting researchers to access multiple applications and manual interventions for each gene or variant in an ad hoc fashion until all the questions are answered. Results: In this study, we present GENEASE, a web-based one-stop bioinformatics tool designed to not only query and explore multi-omics and phenotype databases (e.g. GTEx, ClinVar, dbGaP, GWAS Catalog, ENCODE, Roadmap Epigenomics, KEGG, Reactome, Gene and Phenotype Ontology) in a single web interface but also to perform seamless post genome-wide association downstream functional and overlap analysis for non-coding regulatory variants. GENEASE accesses over 50 different databases in public domain including model organism-specific databases to facilitate gene/variant and disease exploration, enrichment and overlap analysis in real time. It is a user-friendly tool with point-and-click interface containing links for support information including user manual and examples. Availability and implementation: GENEASE can be accessed freely at http://research.cchmc.org/mershalab/GENEASE/login.html. Supplementary information: Supplementary data are available at Bioinformatics online.