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.

Neuroinflammation underlies the development of social stress induced cognitive deficit in sickle cell disease.

Cognitive deficit is a debilitating complication of SCD with multifactorial pathobiology. Here we show that neuroinflammation and dysregulation in lipidomics and transcriptomics profiles are major underlying mechanisms of social stress-induced cognitive deficit in SCD. Townes sickle cell (SS) mice and controls (AA) were exposed to social stress using the repeat social defeat (RSD) paradigm concurrently with or without treatment with minocycline. Mice were tested for cognitive deficit using novel object recognition (NOR) and fear conditioning (FC) tests. SS mice exposed to RSD without treatment had worse performance on cognitive tests compared to SS mice exposed to RSD with treatment or to AA controls, irrespective of their RSD or treatment disposition. Additionally, compared to SS mice exposed to RSD with treatment, SS mice exposed to RSD without treatment had significantly more cellular evidence of neuroinflammation coupled with a significant shift in the differentiation of neural progenitor cells towards astrogliogenesis. Additionally, brain tissue from SS mice exposed to RSD was significantly enriched for genes associated with blood-brain barrier dysfunction, neuron excitotoxicity, inflammation, and significant dysregulation in sphingolipids important to neuronal cell processes. We demonstrate in this study that neuroinflammation and lipid dysregulation are potential underlying mechanisms of social stress-related cognitive deficit in SS mice.

Targeted Assessment of Mucosal Immune Gene Expression Predicts Clinical Outcomes in Children with Ulcerative Colitis.

BACKGROUND AND AIMS: We aimed to determine whether a targeted gene expression panel could predict clinical outcomes in paediatric ulcerative colitis [UC] and investigated putative pathogenic roles of predictive genes. METHODS: In total, 313 rectal RNA samples from a cohort of newly diagnosed paediatric UC patients (PROTECT) were analysed by a real-time PCR microfluidic array for expression of type 1, 2 and 17 inflammation genes. Associations between expression and clinical outcomes were assessed by logistic regression. Identified prognostic markers were further analysed using existing RNA sequencing (RNA-seq) data sets and tissue immunostaining. RESULTS: IL13RA2 was associated with a lower likelihood of corticosteroid-free remission (CSFR) on mesalamine at week 52 (p = .002). A model including IL13RA2 and only baseline clinical parameters was as accurate as an established clinical model, which requires week 4 remission status. RORC was associated with a lower likelihood of colectomy by week 52. A model including RORC and PUCAI predicted colectomy by 52 weeks (area under the receiver operating characteristic curve 0.71). Bulk RNA-seq identified IL13RA2 and RORC as hub genes within UC outcome-associated expression networks related to extracellular matrix and innate immune response, and lipid metabolism and microvillus assembly, respectively. Adult UC single-cell RNA-seq data revealed IL13RA2 and RORC co-expressed genes were localized to inflammatory fibroblasts and undifferentiated epithelial cells, respectively, which was supported by protein immunostaining. CONCLUSION: Targeted assessment of rectal mucosal immune gene expression predicts 52-week CSFR in treatment-naïve paediatric UC patients. Further exploration of IL-13Rɑ2 as a therapeutic target in UC and future studies of the epithelial-specific role of RORC in UC pathogenesis are warranted.

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.