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Type 2 diabetes mellitus (T2DM) is a metabolic disease and comorbidity associated with several conditions, including cardiac dysfunction leading to heart failure with preserved ejection fraction (HFpEF), in turn resulting in T2DM-induced cardiomyopathy (T2DM-CM). However, the molecular mechanisms underlying the development of T2DM-CM are poorly understood. It is hypothesized that molecular alterations in myopathic genes induced by diabetes promote the development of HFpEF, whereas cardiac myosin inhibitors can rescue the resultant T2DM-mediated cardiomyopathy. To test this hypothesis, a Leptin receptor-deficient db/db homozygous (Lepr db/db) mouse model was used to define the pathogenesis of T2DM-CM. Echocardiographic studies at 4 and 6 months revealed that Lepr db/db hearts started developing cardiac dysfunction by four months, and left ventricular hypertrophy with diastolic dysfunction was evident at 6 months. RNA-seq data analysis, followed by functional enrichment, revealed the differential regulation of genes related to cardiac dysfunction in Lepr db/db heart tissues. Strikingly, the level of cardiac myosin binding protein-C phosphorylation was significantly increased in Lepr db/db mouse hearts. Finally, using isolated skinned papillary muscles and freshly isolated cardiomyocytes, CAMZYOS® (mavacamten, MYK-461), a prescription heart medicine used for symptomatic obstructive hypertrophic cardiomyopathy treatment, was tested for its ability to rescue T2DM-CM. Compared with controls, MYK-461 significantly reduced force generation in papillary muscle fibers and cardiomyocyte contractility in the db/db group. This line of evidence shows that 1) T2DM-CM is associated with hyperphosphorylation of cardiac myosin binding protein-C and 2) MYK-461 significantly lessened disease progression in vitro, suggesting its promise as a treatment for HFpEF.

Introduction: There is significant unexplained variability in behavioral and executive functioning after pediatric traumatic brain injury (TBI). Prior research indicates that there are likely genetic contributions; however, current research is limited. The purpose of this study is to use a systems biology informed approach to characterize the genomic signature related to behavioral and executive functioning ∼12 months after moderate through severe TBI in children.Methods: Participants were from two prospective cohorts of children with severe TBI (Cohort #1) and moderate-severe TBI and an orthopedic injury (OI) group (Cohort #2). Participants included 196 children (n = 72 and n = 124 total from each respective cohort), ranging in age between 0–17 years at the time of injury. In total, 86 children had severe TBI, 49 had moderate TBI, and 61 had an OI. Global behavioral functioning assessed via the Child Behavior Checklist and executive function assessed via the Behavioral Rating Inventory of Executive Function at ∼ 12 months post injury served as outcomes. To test for a genomic signature, we compared the number of nominally significant (p < 0.05) polymorphisms associated with the outcomes in our systems biology identified genes to a set 10,000 permutations using control genes (e.g., not implicated by systems biology). We used the ToppFun application from Toppgene Suite to identify enriched biologic processes likely to be associated with behavioral and executive function outcomes.Results: At 12 months post injury, injury type (TBI vs OI) by polymorphism interaction was significantly enriched in systems biology selected genes for behavioral and executive function outcomes, suggesting these genes form a genomic signature. Effect sizes of the associations from our genes of interest ranged from .2–.5 for the top 5% of variants. Systems biology analysis of the variants associated with the top 5% effect sizes indicated enrichment in several specific biologic processes and systems.Discussion: Findings indicate that a genomic signature may explain heterogeneity of behavioral and executive outcomes after moderate and severe TBI. This work provides the foundation for constructing genomic signatures and integrating systems biology and genetic information into future recovery, prognostic, and treatment algorithms.

Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease of unknown etiology. Currently, pirfenidone and nintedanib are the only FDA-approved drugs for the treatment of IPF and are now the standard of care. This is a significant step in slowing down the progression of the disease. However, the drugs are unable to stop or reverse established fibrosis. Several retrospective clinical studies indicate that proton pump inhibitors (PPIs; FDA-approved to treat gastroesophageal reflux) are associated with favorable outcomes in patients with IPF, and emerging preclinical studies report that PPIs possess antifibrotic activity. In this study, we evaluated the antifibrotic efficacy of the PPI esomeprazole when combined with pirfenidone in vitro and in vivo. In cell culture studies of IPF lung fibroblasts, we assessed the effect of the combination on several fibrosis-related biological processes including TGFβ-induced cell proliferation, cell migration, cell contraction, and collagen production. In an in vivo study, we used mouse model of TGFβ-induced lung fibrosis to evaluate the antifibrotic efficacy of esomeprazole/pirfenidone combination. We also performed computational studies to understand the molecular mechanisms by which esomeprazole and/or pirfenidone regulate lung fibrosis. We found that esomeprazole significantly enhanced the anti-proliferative effect of pirfenidone and favorably modulated TGFβ-induced cell migration and contraction of collagen gels. We also found that the combination significantly suppressed collagen production in response to TGFβ in comparison to pirfenidone monotherapy. In addition, our animal study demonstrated that the combination therapy effectively inhibited the differentiation of lung fibroblasts into alpha smooth muscle actin (αSMA)-expressing myofibroblasts to attenuate the progression of lung fibrosis. Finally, our bioinformatics study of cells treated with esomeprazole or pirfenidone revealed that the drugs target several extracellular matrix (ECM) related pathways with esomeprazole preferentially targeting collagen family members while pirfenidone targets the keratins. In conclusion, our cell biological, computational, and in vivo studies show that the PPI esomeprazole enhances the antifibrotic efficacy of pirfenidone through complementary molecular mechanisms. This data supports the initiation of prospective clinical studies aimed at repurposing PPIs for the treatment of IPF and other fibrotic lung diseases where pirfenidone is prescribed.

Ulcerative colitis (UC), Crohn’s disease (CD), and celiac disease are prevalent intestinal inflammatory disorders with nonsatisfactory therapeutic interventions. Analyzing patient data-driven cohorts can highlight disease pathways and new targets for interventions. Long noncoding RNAs (lncRNAs) are attractive candidates, since they are readily targetable by RNA therapeutics, show relative cell-specific expression, and play key cellular functions. Uniformly analyzing gut mucosal transcriptomics from 696 subjects, we have highlighted lncRNA expression along the gastrointestinal (GI) tract, demonstrating that, in control samples, lncRNAs have a more location-specific expression in comparison with protein-coding genes. We defined dysregulation of lncRNAs in treatment-naive UC, CD, and celiac diseases using independent test and validation cohorts. Using the Predicting Response to Standardized Pediatric Colitis Therapy (PROTECT) inception UC cohort, we defined and prioritized lncRNA linked with UC severity and prospective outcomes, and we highlighted lncRNAs linked with gut microbes previously implicated in mucosal homeostasis. HNF1A-AS1 lncRNA was reduced in all 3 conditions and was further reduced in more severe UC form. Similarly, the reduction of HNF1A-AS1 ortholog in mice gut epithelia showed higher sensitivity to dextran sodium sulfate–induced colitis, which was coupled with alteration in the gut microbial community. These analyses highlight prioritized dysregulated lncRNAs that can guide future preclinical studies for testing them as potential targets.

Genes associated with risk for brain disease exhibit characteristic expression patterns that reflect both anatomical and cell type relationships. Brain-wide transcriptomic patterns of disease risk genes provide a molecular-based signature, based on differential co-expression, that is often unique to that disease. Brain diseases can be compared and aggregated based on the similarity of their signatures which often associates diseases from diverse phenotypic classes. Analysis of 40 common human brain diseases identifies 5 major transcriptional patterns, representing tumor-related, neurodegenerative, psychiatric and substance abuse, and 2 mixed groups of diseases affecting basal ganglia and hypothalamus. Further, for diseases with enriched expression in cortex, single-nucleus data in the middle temporal gyrus (MTG) exhibits a cell type expression gradient separating neurodegenerative, psychiatric, and substance abuse diseases, with unique excitatory cell type expression differentiating psychiatric diseases. Through mapping of homologous cell types between mouse and human, most disease risk genes are found to act in common cell types, while having species-specific expression in those types and preserving similar phenotypic classification within species. These results describe structural and cellular transcriptomic relationships of disease risk genes in the adult brain and provide a molecular-based strategy for classifying and comparing diseases, potentially identifying novel disease relationships. Analysis of the transcription patterns of risk genes for human brain disease reveals characteristic expression signatures across brain anatomy; these can be used to compare and aggregate diseases, providing associations that often differ from conventional phenotypic classification.

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

Port Wine Birthmarks (PWBs) are a congenital vascular malformation on the skin, occurring in 1–3 per 1000 live births. We have recently generated PWB-derived induced pluripotent stem cells (iPSCs) as clinically relevant disease models. The metabolites associated with the pathological phenotypes of PWB-derived iPSCs are unknown, and so we aim to explore them in this study. Metabolites were separated by ultra-performance liquid chromatography and screened with electrospray ionization mass spectrometry. Orthogonal partial least-squares discriminant, multivariate, and univariate analyses were used to identify differential metabolites (DMs). KEGG analysis was used to determine the enrichment of metabolic pathways. A total of 339 metabolites was identified. There were 22 DMs, among which nine were downregulated—including sphingosine—and 13 were upregulated, including glutathione in PWB iPSCs, as compared to controls. Pathway enrichment analysis confirmed the upregulation of glutathione and the downregulation of sphingolipid metabolism in PWB-derived iPSCs as compared to normal ones. We next examined the expression patterns of the key molecules associated with glutathione metabolism in PWB lesions. We found that hypoxia-inducible factor 1α (HIF1α), glutathione S-transferase Pi 1 (GSTP1), γ-glutamyl transferase 7 (GGT7), and glutamate cysteine ligase modulatory subunit (GCLM) were upregulated in PWB vasculatures as compared to blood vessels in normal skin. Other significantly affected metabolic pathways in PWB iPSCs included pentose and glucuronate interconversions; amino sugar and nucleotide sugars; alanine, aspartate, and glutamate; arginine, purine, D-glutamine, and D-glutamate; arachidonic acid, glyoxylate, and dicarboxylate; nitrogen, aminoacyl-tRNA biosynthesis, pyrimidine, galactose, ascorbate, and aldarate; and starch and sucrose. Our data demonstrated that there were perturbations in sphingolipid and cellular redox homeostasis in PWB vasculatures, which could facilitate cell survival and pathological progression. Our data implied that the upregulation of glutathione could contribute to laser-resistant phenotypes in some PWB vasculatures.

Abstract Introduction Idiopathic pulmonary fibrosis (IPF) is an orphan disease characterized by progressive loss of lung function resulting in shortness of breath and often death within 3–4 years of diagnosis. Repetitive lung injury in susceptible individuals is believed to promote chronic oxidative stress, inflammation, and uncontrolled collagen deposition. Several preclinical and retrospective clinical studies in IPF have reported beneficial outcomes associated with the use of proton pump inhibitors (PPIs) such as esomeprazole. Accordingly, we sought to investigate molecular mechanism(s) by which PPIs favorably regulate the disease process. Methods We stimulated oxidative stress, pro-inflammatory and profibrotic phenotypes in primary human lung epithelial cells and fibroblasts upon treatment with bleomycin or transforming growth factor β (TGFβ) and assessed the effect of a prototype PPI, esomeprazole, in regulating these processes. Results Our study shows that esomeprazole controls pro-inflammatory and profibrotic molecules through nuclear translocation of the transcription factor nuclear factor-like 2 (Nrf2) and induction of the cytoprotective molecule heme oxygenase 1 (HO1). Genetic deletion of Nrf2 or pharmacological inhibition of HO1 impaired esomeprazole-mediated regulation of proinflammatory and profibrotic molecules. Additional studies indicate that activation of Mitogen Activated Protein Kinase (MAPK) pathway is involved in the process. Our experimental data was corroborated by bioinformatics studies of an NIH chemical library which hosts gene expression profiles of IPF lung fibroblasts treated with over 20,000 compounds including esomeprazole. Intriguingly, we found 45 genes that are upregulated in IPF but downregulated by esomeprazole. Pathway analysis showed that these genes are enriched for profibrotic processes. Unbiased high throughput RNA-seq study supported antifibrotic effect of esomeprazole and revealed several novel targets. Conclusions Taken together, PPIs may play antifibrotic role in IPF through direct regulation of the MAPK/Nrf2/HO1 pathway to favorably influence the disease process in IPF.

Myosin binding protein-C (MyBP-C) is a sarcomeric protein which regulates the force of contraction in striated muscles. Mutations in the MYBPC family of genes, including slow skeletal (MYBPC1), fast skeletal (MYBPC2) and cardiac (MYBPC3), can result in cardiac and skeletal myopathies. Nonetheless, their evolutionary pattern, pathogenicity and impact on MyBP-C protein structure remain to be elucidated. Therefore, the present study aimed to systematically assess the evolutionarily conserved and epigenetic patterns of MYBPC family mutations. Leveraging a machine learning (ML) approach, the Genome Aggregation Database (gnomAD) provided variants in MYBPC1, MYBPC2, and MYBPC3 genes. This was followed by an analysis with Ensembl’s variant effect predictor (VEP), resulting in the identification of 8,618, 3,871, and 3,071 variants in MYBPC1, MYBPC2, and MYBPC3, respectively. Missense variants comprised 61%–66% of total variants in which the third nucleotide positions in the codons were highly altered. Arginine was the most mutated amino acid, important because most disease-causing mutations in MyBP-C proteins are arginine in origin. Domains C5 and C6 of MyBP-C were found to be hotspots for most mutations in the MyBP-C family of proteins. A high percentage of truncated mutations in cMyBP-C cause cardiomyopathies. Arginine and glutamate were the top hits in fMyBP-C and cMyBP-C, respectively, and tryptophan and tyrosine were the most common among the three paralogs changing to premature stop codons and causing protein truncations at the carboxyl terminus. A heterogeneous epigenetic pattern was identified among the three MYBP-C paralogs. Overall, it was shown that databases using computational approaches can facilitate diagnosis and drug discovery to treat muscle disorders caused by MYBPC mutations.

Background & Aims: We used patient-derived organoids (PDOs) to study the epithelial-specific transcriptional and secretome signatures of the ileum during Crohn’s disease (CD) with varying phenotypes to screen for disease profiles and potential druggable targets. Methods: RNA sequencing was performed on isolated intestinal crypts and 3-week-old PDOs derived from ileal biopsies of CD patients (n = 8 B1, inflammatory; n = 8 B2, stricturing disease) and non-inflammatory bowel disease (IBD) controls (n = 13). Differentially expressed (DE) genes were identified by comparing CD vs control, B1 vs B2, and inflamed vs non-inflamed. DE genes were used for computational screening to find candidate small molecules that could potentially reverse B1and B2 gene signatures. The secretome of a second cohort (n = 6 non-IBD controls, n = 7 CD, 5 non-inflamed, 2 inflamed) was tested by Luminex using cultured organoid conditioned medium. Results: We found 90% similarity in both the identity and abundance of protein coding genes between PDOs and intestinal crypts (15,554 transcripts of 19,900 genes). DE analysis identified 814 genes among disease group (CD vs non-IBD control), 470 genes different between the CD phenotypes, and 5 false discovery rate correction significant genes between inflamed and non-inflamed CD. The PDOs showed both similarity and diversity in the levels and types of soluble cytokines and growth factors they released. Perturbagen analysis revealed potential candidate compounds to reverse B2 disease phenotype to B1 in PDOs. Conclusions: PDOs are similar at the transcriptome level with the in vivo epithelium and retain disease-specific gene expression for which we have identified secretome products, druggable targets, and corresponding pharmacologic agents. Targeting the epithelium could reverse a stricturing phenotype and improve outcomes.

Summary: 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).

Idiopathic pulmonary fibrosis (IPF) is a fatal fibrotic lung disease associated with unremitting fibroblast activation including fibroblast-to-myofibroblast transformation (FMT), migration, resistance to apoptotic clearance, and excessive deposition of extracellular matrix (ECM) proteins in the distal lung parenchyma. Aberrant activation of lung-developmental pathways is associated with severe fibrotic lung disease; however, the mechanisms through which these pathways activate fibroblasts in IPF remain unclear. Sry-box transcription factor 9 (Sox9) is a member of the high-mobility group box family of DNA-binding transcription factors that are selectively expressed by epithelial cell progenitors to modulate branching morphogenesis during lung development. We demonstrate that Sox9 is upregulated via MAPK/PI3K-dependent signaling and by the transcription factor Wilms’ tumor 1 in distal lung-resident fibroblasts in IPF. Mechanistically, using fibroblast activation assays, we demonstrate that Sox9 functions as a positive regulator of FMT, migration, survival, and ECM production. Importantly, our in vivo studies demonstrate that fibroblast-specific deletion of Sox9 is sufficient to attenuate collagen deposition and improve lung function during TGF-α–induced pulmonary fibrosis. Using a mouse model of bleomycin-induced pulmonary fibrosis, we show that myofibroblast-specific Sox9 overexpression augments fibroblast activation and pulmonary fibrosis. Thus, Sox9 functions as a profibrotic transcription factor in activating fibroblasts, illustrating the potential utility of targeting Sox9 in IPF treatment.

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.

Idiopathic Pulmonary Fibrosis (IPF) is a severe fibrotic lung disease characterized by excessive collagen deposition and progressive decline in lung function. Th2 T cell-derived cytokines including IL-4 and IL-13 have been shown to contribute to inflammation and fibrotic remodeling in multiple tissues. Interleukin-31 (IL-31) is a newly identified cytokine that is predominantly produced by CD4 Th2 T cells, but its signaling receptor IL-31RA is primarily expressed by non-hematopoietic cells. However, the potential role of the IL-31-IL31RA axis in pulmonary inflammation and fibrosis has remained largely unknown. To determine the role of IL-31RA deficiency in pulmonary fibrosis, wildtype, and IL-31RA knockout mice were treated with bleomycin and measured changes in collagen deposition and lung function. Notably, the loss of IL-31 signaling attenuated collagen deposition and lung function decline during bleomycin-induced pulmonary fibrosis. The total lung transcriptome analysis showed a significant reduction in fibrosis-associated gene transcripts including extracellular matrix and epithelial cell-associated gene networks. Furthermore, the lungs of human IPF showed an elevated expression of IL-31 when compared to healthy subjects. In support, the percentage of IL-31 producing CD4+ T cells was greater in the lungs and PBMCs from IPF patients compared to healthy controls. Our findings suggest a pathogenic role for IL-31/IL-31RA signaling during bleomycin-induced pulmonary fibrosis. Thus, therapeutic targeting the IL-31-IL-31RA axis may prevent collagen deposition, improve lung function, and have therapeutic potential in pulmonary fibrosis.

ObjectivesIncorporation of genetic factors in psychosocial/perioperative models for predicting chronic postsurgical pain (CPSP) is key for personalization of analgesia. However, single variant associations with CPSP have small effect sizes, making polygenic risk assessment important. Unfortunately, pediatric CPSP studies are not sufficiently powered for unbiased genome wide association (GWAS). We previously leveraged systems biology to identify candidate genes associated with CPSP. The goal of this study was to use systems biology prioritized gene enrichment to generate polygenic risk scores (PRS) for improved prediction of CPSP in a prospectively enrolled clinical cohort.MethodsIn a prospectively recruited cohort of 171 adolescents (14.5 ± 1.8 years, 75.4% female) undergoing spine fusion, we collected data about anesthesia/surgical factors, childhood anxiety sensitivity (CASI), acute pain/opioid use, pain outcomes 6–12 months post-surgery and blood (for DNA extraction/genotyping). We previously prioritized candidate genes using computational approaches based on similarity for functional annotations with a literature-derived “training set.” In this study, we tested ranked deciles of 1336 prioritized genes for increased representation of variants associated with CPSP, compared to 10,000 randomly selected control sets. Penalized regression (LASSO) was used to select final variants from enriched variant sets for calculation of PRS. PRS incorporated regression models were compared with previously published non-genetic models for predictive accuracy.ResultsIncidence of CPSP in the prospective cohort was 40.4%. 33,104 case and 252,590 control variants were included for association analyses. The smallest gene set enriched for CPSP had 80/1010 variants associated with CPSP (p < 0.05), significantly higher than in 10,000 randomly selected control sets (p = 0.0004). LASSO selected 20 variants for calculating weighted PRS. Model adjusted for covariates including PRS had AUROC of 0.96 (95% CI: 0.92–0.99) for CPSP prediction, compared to 0.70 (95% CI: 0.59–0.82) for non-genetic model (p < 0.001). Odds ratios and positive regression coefficients for the final model were internally validated using bootstrapping: PRS [OR 1.98 (95% CI: 1.21–3.22); β 0.68 (95% CI: 0.19–0.74)] and CASI [OR 1.33 (95% CI: 1.03–1.72); β 0.29 (0.03–0.38)].DiscussionSystems biology guided PRS improved predictive accuracy of CPSP risk in a pediatric cohort. They have potential to serve as biomarkers to guide risk stratification and tailored prevention. Findings highlight systems biology approaches for deriving PRS for phenotypes in cohorts less amenable to large scale GWAS.

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 .

Abstract Background Idiopathic Pulmonary Fibrosis (IPF) is a fatal fibrotic lung disease occurring predominantly in middle-aged and older adults. The traditional diagnostic classification of IPF is based on clinical, radiological, and histopathological features. However, the considerable heterogeneity in IPF presentation suggests that differences in gene expression profiles can help to characterize and distinguish disease severity. Methods We used data-driven unsupervised clustering analysis, combined with a knowledge-based approach to identify and characterize IPF subgroups. Results Using transcriptional profiles on lung tissue from 131 patients with IPF/UIP and 12 non-diseased controls, we identified six subgroups of IPF that generally correlated with the disease severity and lung function decline. Network-informed clustering identified the most severe subgroup of IPF that was enriched with genes regulating inflammatory processes, blood pressure and branching morphogenesis of the lung. The differentially expressed genes in six subgroups of IPF compared to healthy control include transcripts of extracellular matrix, epithelial-mesenchymal cell cross-talk, calcium ion homeostasis, and oxygen transport. Further, we compiled differentially expressed gene signatures to identify unique gene clusters that can segregate IPF from normal, and severe from mild IPF. Additional validations of these signatures were carried out in three independent cohorts of IPF/UIP. Finally, using knowledge-based approaches, we identified several novel candidate genes which may also serve as potential biomarkers of IPF. Conclusions Discovery of unique and redundant gene signatures for subgroups in IPF can be greatly facilitated through unsupervised clustering. Findings derived from such gene signatures may provide insights into pathogenesis of IPF and facilitate the development of clinically useful biomarkers.

Schizophrenia and rheumatoid arthritis: Exploring the link Researchers in the USA identify variations in eight genes that potentially confer differential risk for schizophrenia and rheumatoid arthritis. Previous studies have shown that patients with schizophrenia (and their relatives) have a reduced risk of developing rheumatoid arthritis and vice versa. Vishwajit Nimgaonkar, Madhavi Ganapathiraju and colleagues at the University of Pittsburgh compared single nucleotide variations in the genetic code that are significantly associated with these diseases. They found that the inverse risk between these disorders could be due to differences harbored in eight genes. Computational analyses of the protein interactomes of genes associated exclusively with rheumatoid arthritis and those with schizophrenia revealed common interacting partners that are known to be involved in immune and inflammatory responses, and shared disease pathways. Additional investigation of these proteins could shed further light on shared disease mechanisms.

Impaired apoptotic clearance of myofibroblasts can result in the continuous expansion of scar tissue during the persistent injury in the lung. However, the molecular and cellular mechanisms underlying the apoptotic clearance of multiple mesenchymal cells including fibrocytes, fibroblasts and myofibroblasts in severe fibrotic lung diseases such as idiopathic pulmonary fibrosis (IPF) remain largely unknown. We analyzed the apoptotic pathways activated in mesenchymal cells of IPF and in a mouse model of TGFα-induced pulmonary fibrosis. We found that fibrocytes and myofibroblasts in fibrotic lung lesions have acquired resistance to Fas-induced apoptosis, and an FDA-approved anti-fibrotic agent, nintedanib, effectively induced apoptotic cell death in both. In support, comparative gene expression analyses suggest that apoptosis-linked gene networks similarly dysregulated in both IPF and a mouse model of TGFα-induced pulmonary fibrosis. TGFα mice treated with nintedanib show increased active caspase 3-positive cells in fibrotic lesions and reduced fibroproliferation and collagen production. Further, the long-term nintedanib therapy attenuated fibrocyte accumulation, collagen deposition, and lung function decline during TGFα-induced pulmonary fibrosis. These results highlight the importance of inhibiting survival pathways and other pro-fibrotic processes in the various types of mesenchymal cells and suggest that the TGFα mouse model is relevant for testing of anti-fibrotic drugs either alone or in combination with nintedanib.

Objectives: To test whether genetic associations with behavioral outcomes after early childhood traumatic brain injury (TBI) are enriched for biologic pathways underpinning neurocognitive and behavioral networks.Design: Cross-sectional evaluation of the association of genetic factors with early (~ 6 months) and long-term (~ 7 years) post-TBI behavioral outcomes. We combined systems biology and genetic association testing methodologies to identify biologic pathways associated with neurocognitive and behavior outcomes after TBI. We then evaluated whether genes/single nucleotide polymorphism (SNPs) associated with these biologic pathways were more likely to demonstrate a relationship (i.e., enrichment) with short and long-term behavioral outcomes after early childhood TBI compared to genes/SNPs not associated with these biologic pathways.Setting: Outpatient research setting.Participants:140 children, ages 3–6:11 years at time of injury, admitted for a TBI or orthopedic injury (OI).Interventions: Not Applicable.Main Outcome Measures: Child behavior checklist total problems T score.Results: Systems biology methodology identified neuronal systems and neurotransmitter signaling (Glutamate receptor, dopamine, serotonin, and calcium signaling), inflammatory response, cell death, immune systems, and brain development as important biologic pathways to neurocognitive and behavioral outcomes after TBI. At 6 months post injury, the group (TBI versus OI) by polymorphism interaction was significant when the aggregate signal from the highest ranked 40% of case gene associations was compared to the control set of genes. At ~ 7 years post injury, the selected polymorphisms had a significant main effect after controlling for injury type when the aggregate signal from the highest ranked 10% of the case genes were compared to the control set of genesConclusions: Findings demonstrate the promise of applying a genomics approach, informed by systems biology, to understanding behavioral recovery after pediatric TBI. A mixture of biologic pathways and processes are associated with behavioral recovery, specifically genes associated with cell death, inflammatory response, neurotransmitter signaling, and brain development. These results provide insights into the complex biology of TBI recovery.

Intestinal epithelial stem cells (IESCs) control the intestinal homeostatic response to inflammation and regeneration. The underlying mechanisms are unclear. Cytokine-STAT5 signaling regulates intestinal epithelial homeostasis and responses to injury. We link STAT5 signaling to IESC replenishment upon injury by depletion or activation of Stat5 transcription factor. We found that depletion of Stat5 led to deregulation of IESC marker expression and decreased LGR5+ IESC proliferation. STAT5-deficient mice exhibited worse intestinal histology and impaired crypt regeneration after γ-irradiation. We generated a transgenic mouse model with inducible expression of constitutively active Stat5. In contrast to Stat5 depletion, activation of STAT5 increased IESC proliferation, accelerated crypt regeneration, and conferred resistance to intestinal injury. Furthermore, ectopic activation of STAT5 in mouse or human stem cells promoted LGR5+ IESC self-renewal. Accordingly, STAT5 promotes IESC proliferation and regeneration to mitigate intestinal inflammation. STAT5 is a functional therapeutic target to improve the IESC regenerative response to gut injury.

Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated—“The most fruitful basis for the discovery of a new drug is to start with an old drug”. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of “serendipity”, discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules—pharmacologically relevant sub-networks of biomolecules and/or pathways—from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.