Gene Expression Meta-analysis Research Articles

Meta-analysis of active tuberculosis gene expression ascertains host directed drug targets.

Multi-drug resistant tuberculosis still remains a major public health crisis globally. With the emergence of newer active tuberculosis disease, the requirement of prolonged treatment time and adherence to therapy till its completion necessitates the search of newer therapeutics, targeting human host factors. The current work utilized statistical meta-analysis of human gene transcriptomes of active pulmonary tuberculosis disease obtained from six public datasets. The meta-analysis resulted in the identification of 2038 significantly differentially expressed genes (DEGs) in the active tuberculosis disease. The gene ontology (GO) analysis revealed that these genes were major contributors in immune responses. The pathway enrichment analyses identified from various human canonical pathways are related to other infectious diseases. In addition, the comparison of the DEGs with the tuberculosis genome wide association study (GWAS) datasets revealed the presence of few genetic variants in their proximity. The analysis of protein interaction networks (human and Mycobacterium tuberculosis) and host directed drug-target interaction network led to new candidate drug targets for drug repurposing studies. The current work sheds light on host genes and pathways enriched in active tuberculosis disease and suggest potential drug repurposing targets for host-directed therapies.

Recent developments and future directions in meta-analysis of differential gene expression in livestock RNA-Seq.

Decreases in the costs of high-throughput sequencing technologies have led to continually increasing numbers of livestock RNA-Seq studies in the last decade. Although the number of studies has increased dramatically, most livestock RNA-Seq experiments are limited by cost to a small number of biological replicates. Meta-analysis procedures can be used to integrate and jointly analyze data from multiple independent studies. Meta-analyses increase the sample size, which in turn increase both statistical power and robustness of the results. In this work, we discuss cutting edge approaches to combining results from multiple independent RNA-Seq studies to improve livestock transcriptomics research. We review currently published RNA-Seq meta-analyses in livestock, describe many of the key issues specific to RNA-Seq meta-analysis in livestock species, and discuss future perspectives.

DExMA: An R Package for Performing Gene Expression Meta-Analysis with Missing Genes

Meta-analysis techniques allow researchers to jointly analyse different studies to determine common effects. In the field of transcriptomics, these methods have gained popularity in recent years due to the increasing number of datasets that are available in public repositories. Despite this, there is a limited number of statistical software packages that implement proper meta-analysis functionalities for this type of data. This article describes DExMA, an R package that provides a set of functions for performing gene expression meta-analyses, from data downloading to results visualization. Additionally, we implemented functions to control the number of missing genes, which can be a major issue when comparing studies generated with different analytical platforms. DExMA is freely available in the Bioconductor repository.

Integrative study of gene expression datasets in retinal samples of Diabetic Retinopathy

Diabetic Retinopathy is prevalent among patients with uncontrolled hyperglycemia resulting in vision loss. Despite numerous challenges to create a link among these conditions, the characterization of pathological neovascularization causing retinal damage due to the prognosis of early non-proliferative diabetic retinopathy to late proliferative diabetic retinopathy needs deep understanding. In this study, meta-analysis-based integration of gene expression datasets for the fibrovascular membrane of PDR and neural retina of NPDR were compared, to investigate the differentially expressed genes involved in retinal angiogenesis. Human samples with gene expression profiling of the same experiment type and platform with sufficient information for analysis were included in the study. The studies from cell lines and non-human studies, human samples that include serum, cornea, lens, and/or other ocular tissues or fluids, and studies that lack basic information for analysis were excluded. The microarray datasets available in the Gene Expression Omnibus database of the early and late stages in DR were screened to find common gene expression profiles. Using the INMEX bioinformatics tool, significantly upregulated and downregulated genes in the neural retina of Non-Proliferative Diabetic Retinopathy and fibrovascular membrane of Proliferative Diabetic Retinopathy were compared and studied by the combine effect size method. Using the STRING database PPI network, 50 upregulated and 50 downregulated genes were used to find the key candidate genes involved in retinal disease/degeneration in eye/retinal tissues. In the extensive gene expression meta-analysis performed using INMEX bioinformatics tool, overall, 7935 differentially expressed genes were identified and the respective heatmap was created by using the visualization tools of INVEX. STRING database PPI network identified Retinol Binding Protein 3, Neural Retina Leucine Zipper, S-Antigen Visual Arrestin, Peripherin 2, and Aryl Hydrocarbon Receptor Interacting Protein Like-1 to be the most highly ranked hub genes. The newly discovered potential genes related to retinal angiogenesis causing FVM formation in DR may provide insight into the cellular pathogenesis of NPDR to PDR.

Panzootic chytrid fungus exploits diverse amphibian host environments through plastic infection strategies.

While some pathogens are limited to single species, others can colonize many hosts, likely contributing to the emergence of novel disease outbreaks. Despite this biodiversity threat, traits associated with host niche expansions are not well understood in multihost pathogens. Here, we aimed to uncover functional machinery driving multihost invasion by focusing on Batrachochytrium dendrobatidis (Bd), a pathogen that infects the skin of hundreds of amphibians worldwide. We performed a meta-analysis of Bd gene expression using data from published infection experiments and newly generated profiles. We analysed Bd transcriptomic landscapes across the skin of 14 host species, reconstructed Bd isolates phylogenetic relationships, and inferred the origin and evolutionary history of differentially expressed genes under a phylogenetic framework comprising other 12 zoosporic fungi. Bd displayed plastic infection strategies when challenged by hosts with different disease susceptibility. Our analyses identified sets of differentially expressed genes under host environments with similar infection outcome. We stressed nutritional immunity and gene silencing as important processes required to overcome challenging skin environments in less susceptible hosts. Overall, Bd genes expressed during amphibian skin exploitation have arisen mainly via gene duplications with great family expansions, increasing the gene copy events previously described for this fungal species. Finally, we provide a comprehensive gene data set that can be used to further examine eco-evolutionary hypotheses for this host-pathogen system. Our study supports the idea that host environments exert contrasting selective pressures, such that gene expression plasticity could be one of the evolutionary keys leading to the success of multihost pathogens.

Natural high-avidity T-cell receptor efficiently mediates regression of cancer/testis antigen 83 positive common solid cancers

BackgroundT-cell receptor-engineered T cells (TCR-Ts) have achieved encouraging success in anticancer clinical trials. The antigenic targets, however, were primarily focused on human leukocyte antigen (HLA) A*02:01 restricted epitopes from a...

Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology.

A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.

MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies.

Simple SummaryIn this work we present MAGE, an open-source Python package developed for the meta-analysis of gene expression data. It contains functions to convert probes to gene identifiers, and to perform standard meta-analysis, meta-analysis with bootstrap standard errors, and meta-analysis of multiple outcomes, as well as functional enrichment analysis. Additionally, visualizations for every function of this software package are provided. MAGE is available both in a standalone version and as a webserver.MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package.

Gene Expression Meta-Analysis of Potential Shared and Unique Pathways between Autoimmune Diseases under Anti-TNFα Therapy.

While anti-TNFα has been established as an effective therapeutic approach for several autoimmune diseases, results from clinical trials have uncovered heterogeneous patients’ response to therapy. Here, we conducted a meta-analysis on the publicly available gene expression cDNA microarray datasets that examine the differential expression observed in response to anti-TNFα therapy with psoriasis (PsO), inflammatory bowel disease (IBD) and rheumatoid arthritis (RA). Five disease-specific meta-analyses and a single combined random-effects meta-analysis were performed through the restricted maximum likelihood method. Gene Ontology and Reactome Pathways enrichment analyses were conducted, while interactions between differentially expressed genes (DEGs) were determined with the STRING database. Four IBD, three PsO and two RA datasets were identified and included in our analyses through our search criteria. Disease-specific meta-analyses detected distinct pro-inflammatory down-regulated DEGs for each disease, while pathway analyses identified common inflammatory patterns involved in the pathogenesis of each disease. Combined meta-analyses further revealed DEGs that participate in anti-inflammatory pathways, namely IL-10 signaling. Our analyses provide the framework for a transcriptomic approach in response to anti-TNFα therapy in the above diseases. Elucidation of the complex interactions involved in such multifactorial phenotypes could identify key molecular targets implicated in the pathogenesis of IBD, PsO and RA.

Cumulus cell antioxidant system is modulated by patients' clinical characteristics and correlates with embryo development.

To study whether the cumulus cell antioxidant system varies accordingly to patients clinical characteristics' as age, infertility diagnosis, BMI, and stimulation protocol applied and if the antioxidant profile of cumulus cells could be used as a predictor of embryo development. A prospective study including 383 human cumulus samples provided by 191 female patients undergoing intracytoplasmic sperm injection during in vitro fertilization treatments from a local in vitro fertilization center and processed in university laboratories. Catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), and glutathione S-transferase (GST) enzyme activity levels and reduced glutathione (GSH) levels were measured in cumulus oophorus cells individually collected from each aspirated cumulus-oocyte complex, and the results of each sample were compared considering the oocytes outcome after ICSI and patients clinical characteristics. A total of 223 other human cumulus samples from previous studies were submitted to a gene expression meta-analysis. The antioxidant system changes dramatically depending on patients' age, infertility diagnosis, stimulation protocol applied, and oocyte quality. SOD activity in cumulus cells revealed to be predictive of top-quality blastocysts for young patients with male factor infertility (P < 0.05), while GST levels were shown to be extremely influenced by infertility cause (P < 0.0001) and stimulation protocol applied (P < 0.05), but nonetheless, it can be used as a complementary tool for top-quality blastocyst prediction in patients submitted to intracytoplasmic sperm injection technique (ICSI) by male factor infertility (P < 0.05). Through a simple and non-invasive analysis, the evaluation of redox enzymes in cumulus cells could be used to predict embryo development, in a personalized matter in specific patient groups, indicating top-quality oocytes and improving success rates in in vitro fertilization treatments. The trial was registered at UFRGS Research Ethics Committee and Plataforma Brasil under approval number 68081017.2.0000.5347 in June 6, 2019.

Investigation of genes and pathways involved in breast cancer subtypes through gene expression meta-analysis

BackgroundMolecular-based studies have revealed heterogeneity in Breast cancer BC while also improving classification and treatment. However, efforts are underway to distinguish between distinct subtypes of breast cancer. In this study, the results of several microarray studies were combined to identify genes and pathways specific to each BC subtype. MethodsMeta-analysis of multiple gene expression profile datasets was screened to find differentially expressed genes (DEGs) across subtypes of BC and normal breast tissue samples. Protein-protein interaction network and gene set enrichment analysis were used to identify critical genes and pathways associated with BC subtypes. The differentially expressed genes from meta-analysis was validated using an independent comprehensive breast cancer RNA-sequencing dataset obtained from the Cancer Genome Atlas (TCGA). ResultsWe identified 110 DEGs (13 DEGs in all and 97 DEGs in each subtype) across subtypes of BC. All subtypes had a small set of shared DEGs enriched in the Chemokine receptor bind chemokine pathway. Luminal A specific were enriched in the translational elongation process in mitochondria, and the enhanced process in luminal B subtypes was interferon-alpha/beta signaling. Cell cycle and mitotic DEGs were enriched in the basal-like group. All subtype-specific DEG genes (100%) were successfully validated for Luminal A, Luminal B, ERBB2, and Normal-like. However, the validation percentage for Basal-like group was 77.8%. ConclusionIntegrating researches such as a meta-analysis of gene expression might be more effective in uncovering subtype-specific DEGs and pathways than a single-study analysis. It would be more beneficial to increase the number of studies that use matched BC subtypes along with GEO profiling approaches to reach a better result regarding DEGs and reduce probable biases. However, achieving 77.8% overlap in basal-specific genes and complete concordance in specific genes related to other subtypes can implicate the strength of our analysis for discovering the subtype-specific genes.

Abstract P5-06-02: A gene expression meta-analysis identifies microenvironment differences in cellular composition and cell-cell interactions associated with breast cancer invasion

Abstract Background: The invasion of malignant cells into surrounding stroma is the histological hallmark differentiating invasive ductal carcinoma (IDC) from ductal carcinoma in situ (DCIS). The associated variations in the cellular composition and interactions in the tumor microenvironment, and how those mediate invasion are poorly understood. Systematic analyses of gene expression differences between DCIS and IDC are rare, confounded by cellular heterogeneity, and limited to few cases unable to distinguish stromal from epithelial contributions or compare pure DCIS from those synchronous with IDC. To address these problems we have assembled multiple datasets from publicly available gene expression studies of microdissected DCIS and IDC samples. Methods: The transcriptome from 192 regions microdissected from 140 cases was assembled from 5 published studies and included DCIS (83 epithelial, 22 stromal) and IDC (67 epithelial, 20 stromal) cases. Differences in infiltration of 10 immune cell types were measured using expression signatures. Expression subtypes were determined from the PAM50 signatures of epithelial regions using the genefu package. Gene Set Enrichment Analysis (GSEA) was performed using the gene sets in the Hallmark and Reactome collections. Cell-cell interactions between stromal and epithelial regions were measured using the co-expression of 1175 curated pairs of ligand-receptor genes and compared between DCIS and IDC using a permutation test. The corresponding interacting cell types were identified using RNA sequencing of 5444 single nuclei extracted from fine needle aspirates of two excised DCIS specimens. Results: Of the measured immune cell types, B cells showed a progressive depletion from normal to DCIS to IDC stroma. Pure IDC regions were significantly less likely to be the Luminal A subtype compared to synchronous IDC/DCIS and pure DCIS (p=0.0008). IFN signaling was higher in IDC epithelium suggesting possible differences in immune visibility between these two diseases. Higher calcium and glutamate signaling is observed in the DCIS stroma compared to IDC, calcium signaling has been implicated as both essential to normal breast function and as a positive effector of IDC proliferation. The expression-based measurement of stromal-epithelial interactions identified 99 and 115 ligand-receptor interactions enriched in DCIS and IDC, respectively (FDR&amp;lt;0.1). To precisely identify the corresponding interacting cell types, the expression profile of cells within the DCIS microenvironment was determined using single-nucleus RNA sequencing from two DCIS patients. The analysis identified 9 cell types, including luminal, basal, macrophage, adipocyte and endothelial, and 54% of the candidate cell-cell interactions could be mapped to at least one cell type pair. Based on these cell type mappings, interactions between luminal cells and fibroblasts were gained in IDC while those involving the vascular endothelium were lost, including interactions between CD24 and P-selectin, an interaction involved in leukocyte recruitment. Increased macrophage autocrine interactions were identified in both IDC stroma and epithelium through urokinase-urokinase receptor gene co-expression, an interaction previously associated with a transition to the M2 phenotype. Conclusions: The meta-analysis combined with novel computational methods, improves our ability to characterize the micro-environment of DCIS specimens, typically hard to study. Changes in cellular dynamics involving both immune and non-immune cell types suggest that mechanisms other than direct immune escape accompany progression. Citation Format: Adam Officer, Andre Dempsey, Farnaz Hasteh, Michal Slyper, Asa Segerstolpe, Joanna Klughammer, Judit Jane-Valbuena, Orit Rozenblatt-Rosen, Lyndsay Murrow, Zev Gartner, Aviv Regev, Christina Yau, Pablo Tamayo, Olivier Harismendy. A gene expression meta-analysis identifies microenvironment differences in cellular composition and cell-cell interactions associated with breast cancer invasion [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-06-02.

Meta-Analysis of IBD Gut Samples Gene Expression Identifies Specific Markers of Ileal and Colonic Diseases.

Inflammatory bowel diseases (IBDs) are characterized by chronic inflammation and tissue damages in limited segments of the digestive tract. Pathogenesis in the tissue and mucosal inflammation probably differs according to disease location. Our aim was to further analyze transcriptomic profiles in different locations of IBD, differentiating ulcerative colitis (UC), colonic Crohn's disease (CD), ileal CD, and pouchitis, with respect to normal colonic and ileal mucosa. We thus performed a meta-analysis focusing on specific transcriptomic signatures of ileal and colonic diseases. We identified 5 cohorts with available transcriptomic data in ileal or colonic samples from active IBD and non-IBD control samples. The meta-analysis was performed on 1047 samples. In each cohort separately, we compared gene expression in CD ileitis and normal ileum; in CD colitis, UC, and normal colon; and finally in pouchitis and normal ileum. We identified specific markers of ileal (FOLH1, CA2) and colonic (REG3A) inflammation and showed that, with disease, some cells from the ileum start to express colonic markers. We confirmed by immunohistochemistry that these markers were specifically present in ileal or colonic diseases. We highlighted that, overall, colonic CD resembles UC and is distinct from ileal CD, which is in turn closer to pouchitis. We demonstrated that ileal and colonic diseases exhibit specific signatures, independent of their initial clinical classification. This supports molecular, rather than clinical, disease stratification, and may be used to design drugs specifically targeting ileal or colonic diseases.

A review and meta-analysis of gene expression profiles in suicide

Suicide claims over 800,000 deaths worldwide, making it a serious public health problem. The etiopathophysiology of suicide remains unclear and is highly complex, and postmortem gene expression studies can offer insights into the molecular biological mechanism underlying suicide. In the current study, we conducted a meta-analysis of postmortem brain gene expression in relation to suicide. We identified five gene expression datasets for postmortem orbitofrontal, prefrontal, or dorsolateral prefrontal cortical brain regions from the Gene Expression Omnibus repository. After quality control, the total sample size was 380 (141 suicide deaths and 239 deaths from other causes). We performed the analyses using two meta-analytic approaches. We further performed pathway and cell-set enrichment analyses. We found reduced expression of the KCNJ2 (Potassium Inwardly Rectifying Channel Subfamily J Member 2), A2M (Alpha-2-Macroglobulin), AGT (Angiotensinogen), PMP2 (Peripheral Myelin Protein 2), and VEZF1 (Vascular Endothelial Zinc Finger 1) genes (FDR p<0.05). Our findings support the involvement of astrocytes, stress response, immune system, and microglia in suicide. These findings will require further validation in additional large datasets.

Bioinformatic Analysis and Integration of Transcriptome and Proteome Results Identify Key Coding and Noncoding Genes Predicting Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas.

Background Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic ductal adenocarcinoma (PDAC). IPMNs are generally associated with high risk of developing malignancy and therefore need to be diagnosed and assessed accurately, once detected. Existing diagnostic methods are inadequate, and identification of efficient biomarker capable of detecting high-risk IPMNs is necessitated. Moreover, the mechanism of development of malignancy in IPMNs is also elusive. Methods Gene expression meta-analysis conducted using 12 low-risk IPMN and 23 high-risk IPMN tissue samples. We have also listed all the altered miRNAs and long noncoding RNAs (lncRNAs), identified their target genes, and performed pathway analysis. We further enlisted cyst fluid proteins detected to be altered in high-risk or malignant IPMNs and compared them with fraction of differentially expressed genes secreted into cyst fluid. Results Our meta-analysis identified 270 upregulated and 161 downregulated genes characteristically altered in high-risk IPMNs. We further identified 61 miRNAs and 14 lncRNAs and their target genes and key pathways contributing towards understanding of the gene regulation during the progression of the disease. Most importantly, we have detected 12 genes altered significantly both in cystic lesions and cyst fluid. Conclusion Our study reports, for the first time, a meta-analysis identifying key changes in gene expression between low-risk and high-risk IPMNs and also explains the regulatory aspect through construction of a miRNA-lncRNA-mRNA interaction network. The 12-gene-signature could function as potential biomarker in cyst fluid for detection of IPMN with a high risk of developing malignancy.

A comprehensive study on sars-cov-2 through gene expression meta-analysis and network biology approach

Introduction: Coronaviruses are significant pathogens of both human and animals and are globally distributed. Out of seven CoVs strains, the most lethal coronavirus strains being portrayed is SARS-CoV-2. It can cause bronchial asthma, and severe pneumonia and acute respiratory disease. Due to its contagion in infants, adults, and immunocompromised patients which further results in making this a deadly disease, thus there is an urgent need to develop effective and safe therapeutics against it.Materials and Methods: Meta-analysis of publicly available gene expression datasets belonging to SARS-CoV-2, SARS-CoV, MERS-CoV, and HCoV-229E were carried out to identify the potential differentially expressed genes exclusively associated with SARS-CoV-2, and then a network model was developed to decipher significant drug targets, associated pathways and drug candidates which can be repurposed for this infection.Results: The COVID-19 infection mainly targets immune responses and regulatory processes. A novel role of Relaxin signaling pathway was identified in SARS-CoV-2 infection. Anti-inflammatory, anti-tumor, nutraceutical and anthelmintic agents were found to be good prospective candidates for repurposing against COVID-19.Conclusions: This theoretical study resulted in the identification of approved drug targets that may have the potential to be repurposed for COVID 19 treatment.

Comprehensive transcriptome mining identified the gene expression signature and differentially regulated pathways of the late-onset preeclampsia.

Preeclampsia (PE) is categorized as a pregnancy-related hypertensive disorder and is a serious concern in pregnancies. Several factors, including genetic factors (placenta gene expression, and imprinting), oxidative stress, the inaccurate immune response of the mother, and the environmental factors are responsible for PE development, but still, the exact mechanism of the pathogenesis has remained unknown. The main aim of the present study is to identify the gene expression signature in placenta tissue, to unveil disease etiology mechanisms. The GEO, PubMed, and ArrayExpress databases have selected to identify gene expression datasets on placenta samples of both preeclampsia and the normotensive controls. A comprehensive gene expression meta-analysis of fourteen publicly available microarray data of preeclampsia disease has performed to identify gene expression signature and responsible biological pathways and processes. Using two different meta-analysis pipeline (in-house and INMEX) we have identified a total of 1234 differentially expressed genes (DEGs) with in-house method, including 713 overexpressed and 356 under-expressed genes whereas 272 DEGs (131 over and 141 under-expressed) have identified with INMEX, across PEs and healthy controls. Comprehensive functional enrichment and pathway analysis was performed by EnrichR library, whic revealed "Asparagine N-linked glycosylation Homo sapiens", "Nef and signal transduction", "Hemostasis", and "immune system" among the most enriched terms. The present study sets out to explain a novel database of candidate genetic markers and biological pathways that play a critical role in PE development, which might aid in the identification of diagnostic, prognostic, and therapeutic informative molecules.

Gene Expression Meta-Analysis Reveals Interferon-Induced Genes Associated With SARS Infection in Lungs.

BackgroundSevere Acute Respiratory Syndrome (SARS) corona virus (CoV) infections are a serious public health threat because of their pandemic-causing potential. This work is the first to analyze mRNA expression data from SARS infections through meta-analysis of gene signatures, possibly identifying therapeutic targets associated with major SARS infections.MethodsThis work defines 37 gene signatures representing SARS-CoV, Middle East Respiratory Syndrome (MERS)-CoV, and SARS-CoV2 infections in human lung cultures and/or mouse lung cultures or samples and compares them through Gene Set Enrichment Analysis (GSEA). To do this, positive and negative infectious clone SARS (icSARS) gene panels are defined from GSEA-identified leading-edge genes between two icSARS-CoV derived signatures, both from human cultures. GSEA then is used to assess enrichment and identify leading-edge icSARS panel genes between icSARS gene panels and 27 other SARS-CoV gene signatures. The meta-analysis is expanded to include five MERS-CoV and three SARS-CoV2 gene signatures. Genes associated with SARS infection are predicted by examining the intersecting membership of GSEA-identified leading-edges across gene signatures.ResultsSignificant enrichment (GSEA p<0.001) is observed between two icSARS-CoV derived signatures, and those leading-edge genes defined the positive (233 genes) and negative (114 genes) icSARS panels. Non-random significant enrichment (null distribution p<0.001) is observed between icSARS panels and all verification icSARSvsmock signatures derived from human cultures, from which 51 over- and 22 under-expressed genes are shared across leading-edges with 10 over-expressed genes already associated with icSARS infection. For the icSARSvsmock mouse signature, significant, non-random significant enrichment held for only the positive icSARS panel, from which nine genes are shared with icSARS infection in human cultures. Considering other SARS strains, significant, non-random enrichment (p<0.05) is observed across signatures derived from other SARS strains for the positive icSARS panel. Five positive icSARS panel genes, CXCL10, OAS3, OASL, IFIT3, and XAF1, are found across mice and human signatures regardless of SARS strains.ConclusionThe GSEA-based meta-analysis approach used here identifies genes with and without reported associations with SARS-CoV infections, highlighting this approach’s predictability and usefulness in identifying genes that have potential as therapeutic targets to preclude or overcome SARS infections.

Multisystem inflammatory syndrome in children: a microcosm of challenges and opportunities for translational bioinformatics in pediatric research.

Despite significant progress in our understanding and clinical management of multisystem inflammatory syndrome in children (MIS-C), significant challenges remain. Here, we review recently published studies on the clinical diagnosis, risk stratification, and treatment of MIS-C, highlighting key gaps in research progress that are a microcosm for challenges in translational pediatric research. We then discuss potential solutions in the realm of translational bioinformatics. Current case definitions are inconsistent and do not capture the underlying pathophysiology of MIS-C, which remains poorly understood. Although overall mortality is low, some patients rapidly decompensate, and a test to identify those at risk for severe outcomes remains an unmet need. Treatment consists of various combinations of immunoglobulins, corticosteroids, and biologics, based on extrapolated data and expert opinion, while the benefits remain unclear as we await the completion of clinical trials. The small size and heterogeneity of the pediatric population contribute to unmet needs because of financial and logistical constraints of the current research infrastructure focused on eliminating most sources of heterogeneity, leading to ungeneralizable results. Data sharing and meta-analysis of gene expression shows promise to accelerate progress in the field of MIS-C as well as other childhood diseases beyond the current pandemic.

Gene Expression Meta-Analysis of Cerebellum Samples Supports the FKBP5 Gene-Environment Interaction Model for Schizophrenia.

Background: One of the most studied molecular models of gene-environment interactions is that of FKBP5, which has been shown to interact with childhood adversity to increase the risk of psychiatric disorders, and has been implicated in schizophrenia. While the model predicts up-regulation of FKBP5, previous brain samples gene expression studies yielded inconsistent results. Methods: We performed a systematic gene expression meta-analysis of FKBP5 and NR3C1, a glucocorticoid receptor inhibited by FKBP5, in cerebellum samples of patients with schizophrenia. The gene expression databases GEO, SMRI and those of NIMH were searched, and out of six screened datasets, three were eligible for the meta-analysis (overall 69 with schizophrenia and 78 controls). Results: We detected up-regulation of FKBP5 and down-regulation of NR3C1 in schizophrenia, and a negative correlation between their expression patterns. Correlation analysis suggested that the detected differential expression did not result from potential confounding factors. Conclusions: Our results give significant support to the FKBP5 gene-environment interaction model for schizophrenia, which provides a molecular mechanism by which childhood adversity is involved in the development of the disorder. To explore FKBP5’s potential as a therapeutic target, a mapping of its differential expression patterns in different brain regions of schizophrenia patients is needed.