Multiple Transcriptome Datasets Research Articles

ALDH2 mediates the effects of sodium-glucose cotransporter 2 inhibitors (SGLT2i) on improving cardiac remodeling

BackgroundSodium-glucose cotransporter-2 inhibitors (SGLT2i) are now recommended for patients with heart failure, but the mechanisms that underlie the protective role of SGLT2i in cardiac remodeling remain unclear. Aldehyde dehydrogenase 2 (ALDH2) effectively prevents cardiac remodeling. Here, the key role of ALDH2 in the efficacy of SGLT2i on cardiac remodeling was studied.MethodsAnalysis of multiple transcriptomic datasets and two-sample Mendelian randomization were performed to find out the differentially expressed genes between pathological cardiac hypertrophy models (patients) and controls. A pathological cardiac hypertrophy mouse model was established via transverse aortic constriction (TAC) or isoproterenol (ISO). Cardiomyocyte-specific ALDH2 knockout mice (ALDH2CMKO) and littermate control mice (ALDH2flox/flox) were generated to determine the critical role of ALDH2 in the preventive effects of dapagliflozin (DAPA) on cardiac remodeling. RNA sequencing, gene knockdown or overexpression, bisulfite sequencing PCR, and luciferase reporter assays were performed to explore the underlying molecular mechanisms involved.ResultsOnly ALDH2 was differentially expressed when the differentially expressed genes obtained via Mendelian analysis and the differentially expressed genes obtained from the multiple transcriptome datasets were combined. Mendelian analysis revealed that ALDH2 was negatively related to the severity of myocardial hypertrophy in patients. DAPA alleviated cardiac remodeling in mouse hearts subjected to TAC or ISO. ALDH2 expression was reduced, whereas ALDH2 expression was restored by DAPA in hypertrophic hearts. Cardiomyocyte specific ALDH2 knockout abolished the protective role of DAPA in preventing cardiac remodeling. ALDH2 expression and activity were increased in DAPA-treated neonatal rat primary cardiomyocytes (NRCMs), H9C2 cells and AC16 cells. Moreover, DAPA upregulated ALDH2 in peripheral blood mononuclear cells (PBMCs) from patients with type 2 diabetes. Sodium/proton exchanger 1 (NHE1) inhibition contributed to the regulation of ALDH2 by DAPA. DAPA suppressed the production of reactive oxygen species (ROS), downregulated DNA methyltransferase 1 (DNMT1) and subsequently reduced the ALDH2 promoter methylation level. Further studies revealed that DAPA enhanced the binding of nuclear transcription factor Y, subunit A (NFYA) to the promoter region of ALDH2, which was due to the decreased promoter methylation level of ALDH2.ConclusionsThe upregulation of ALDH2 plays a critical role in the protection of DAPA against cardiac remodeling. DAPA enhances the binding of NFYA to the ALDH2 promoter by reducing the ALDH2 promoter methylation level through NHE1/ROS/DNMT1 pathway.Graphical abstract

Hippocampal tau-induced GRIN3A deficiency in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by significant alterations in hippocampal function and structure, but the molecular mechanisms underlying the hippocampal region remain elusive. We integrated multiple transcriptome datasets including human or rat hippocampus (GSE173955, GSE129051, GSE84422) to identify candidate genes. Subsequent analyses including gene ontology analysis and protein-protein interaction mapping were performed to identify key genes and pathways. We found that glutamate ionotropic receptor NMDA-type subunit 3A (GRIN3A) and glutamate metabotropic receptor 8 (GRM8), which are related to the glutamatergic system, were the top two annotated genes and directly related to MAPT, which encodes a tau protein. Since there is no direct evidence of interaction between tauopathy and these genes in AD, further transcriptomic data(GSE125957, GSE56772) from tau transgenic mice and experimental validations through primary rat hippocampal neurons and induced pluripotent stem cell (iPSC)-derived brain organoids were performed. Interestingly, we identified that decreased NR3A (encoded by GRIN3A) and mGluR8 (encoded by GRM8) are correlated with tauopathy and loss of postsynaptic function in AD. Taken together, our results identified a novel tauopathy biomarker GRIN3A in AD. Furthermore, our findings suggest that an integrated approach combining public databases and diverse experimental validations can contribute to the advancement of precision medicine therapies.

GEDI: An R Package for Integration of Transcriptomic Data from Multiple Platforms for Bioinformatics Applications.

Transcriptomic data is often expensive and difficult to generate in large cohorts relative to genomic data; therefore, it is often important to integrate multiple transcriptomic datasets from both microarray- and next generation sequencing (NGS)-based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including reannotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically reannotating the data and removing the batch effect. The removal of the batch effect is verified with principal component analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. These transcriptomic datasets were from multiple high-throughput platforms, namely, array-based Affymetrix and Agilent platforms, and NGS-based Illumina paired-end RNA-seq platform. Furthermore, we compared the GEDI package to existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration for downstream genomic and bioinformatics applications. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: ReadGE, a function to import gene expression datasets Basic Protocol 2: GEDI, a function to reannotate and merge gene expression datasets Basic Protocol 3: BatchCorrection, a function to remove batch effects from gene expression data Basic Protocol 4: VerifyGEDI, a function to confirm successful integration of gene expression data.

MetaFun: unveiling sex-based differences in multiple transcriptomic studies through comprehensive functional meta-analysis

BackgroundWhile sex-based differences in various health scenarios have been thoroughly acknowledged in the literature, we lack sufficient tools and methods that allow for an in-depth analysis of sex as a variable in biomedical research. To fill this knowledge gap, we created MetaFun as an easy-to-use web-based tool to meta-analyze multiple transcriptomic datasets with a sex-based perspective to gain major statistical power and biological soundness.DescriptionMetaFun is a complete suite that allows the analysis of transcriptomics data and the exploration of the results at all levels, performing single-dataset exploratory analysis, differential gene expression, gene set functional enrichment, and finally, combining results in a functional meta-analysis. Which biological processes, molecular functions or cellular components are altered in a common pattern in different transcriptomic studies when comparing male and female patients? This and other biological questions of interest can be answered with the use of MetaFun. This tool is available at https://bioinfo.cipf.es/metafun while additional help can be found at https://gitlab.com/ubb-cipf/metafunweb/-/wikis/Summary.ConclusionsOverall, Metafun is the first open-access web-based tool to identify consensus biological functions across multiple transcriptomic datasets, helping to elucidate sex differences in numerous diseases. Its use will facilitate the generation of novel biological knowledge that can be used in the research and application of Personalized Medicine considering the sex of patients.

KAKU4 regulates leaf senescence through modulation of H3K27me3 deposition in the Arabidopsis genome

Lamins are the major components of the nuclear lamina, which regulate chromatin structure and gene expression. KAKU4 is a unique nuclear lamina component in the nuclear periphery, modulates nuclear shape and size in Arabidopsis. The knowledge about the regulatory role of KAKU4 in leaf development remains limited. Here we found that knockdown of KAKU4 resulted in an accelerated leaf senescence phenotype, with elevated levels of H2O2 and hormones, particularly SA, JA, and ABA. Our results demonstrated the importance of KAKU4 as a potential negative regulator in age-triggered leaf senescence in Arabidopsis. Furthermore, we conducted combination analyses of transcriptomic and epigenomic data for the kaku4 mutant and WT leaves. The knockdown of KAKU4 lowered H3K27me3 deposition in the up-regulated genes associated with hormone pathways, programmed cell death, and leaf senescence, including SARD1, SAG113/HAI1, PR2, and so forth. In addition, we found the functional crosstalks between KAKU4 and its associated proteins (CRWN1/4, PNET2, GBPL3, etc.) through comparing multiple transcriptome datasets. Overall, our results indicated that KAKU4 may inhibit the expression of a series of genes related to hormone signals and H2O2 metabolism by affecting the deposition of H3K27me3, thereby suppressing leaf senescence.

Single-cell analysis of psoriasis resolution demonstrates an inflammatory fibroblast state targeted by IL-23 blockade

Biologic therapies targeting the IL-23/IL-17 axis have transformed the treatment of psoriasis. However, the early mechanisms of action of these drugs remain poorly understood. Here, we perform longitudinal single-cell RNA-sequencing in affected individuals receiving IL-23 inhibitor therapy. By profiling skin at baseline, day 3 and day 14 of treatment, we demonstrate that IL-23 blockade causes marked gene expression shifts, with fibroblast and myeloid populations displaying the most extensive changes at day 3. We also identify a transient WNT5A+/IL24+ fibroblast state, which is only detectable in lesional skin. In-silico and in-vitro studies indicate that signals stemming from these WNT5A+/IL24+ fibroblasts upregulate multiple inflammatory genes in keratinocytes. Importantly, the abundance of WNT5A+/IL24+ fibroblasts is significantly reduced after treatment. This observation is validated in-silico, by deconvolution of multiple transcriptomic datasets, and experimentally, by RNA in-situ hybridization. These findings demonstrate that the evolution of inflammatory fibroblast states is a key feature of resolving psoriasis skin.

TOMM40 Correlates with Cholesterol and is Predictive of a Favorable Prognosis in Endometrial Carcinoma.

A link between cholesterol and endometrial cancer has been established, but current studies have been limited in their findings. We aimed to elucidate the causal relationship between cholesterol and endometrial cancer and to find prognostic genes for endometrial cancer. We first explored the causal relationship between total cholesterol and endometrial cancer using two-sample Mendelian randomization and then obtained differential genes to screen for prognosis-related genes in endometrial cancer. Then, we utilized pan-cancer analysis based on RNA sequencing data to evaluate the expression pattern and immunological role of the Translocase of Outer Mitochondrial Membrane 40 (TOMM40). Through multiple transcriptome datasets and multi-omics in-depth analysis, we comprehensively explore the relationship of TOMM40 expression with clinicopathologic characteristics, clinical outcomes and mutations in endometrial cancer. Lastly, we systematically associated the TOMM40 with different cancers from immunological properties from numerous perspectives, such as immune cell infiltration, immune checkpoint inhibitors, immunotherapy, gene mutation load and microsatellite instability. We found a negative association between cholesterol and endometrial cancer. A total of 78 genes were enriched by relevant single nucleotide polymorphisms (SNPs), of which 12 upregulated genes and 5 downregulated genes in endometrial cancer. TOMM40 was found to be a prognostic gene associated with endometrial cancer by prognostic analysis. TOMM40 was found to be positively correlated with the infiltration of most immune cells and immunization checkpoints in a subsequent study. Meanwhile, TOMM40 also was an oncogene in many cancer types. High TOMM40 was associated with lower genome stability. The findings of our study indicate that the maintenance of normal total cholesterol metabolism is associated with a decreased risk of developing endometrial cancer. Moreover, TOMM40 may have potential as a prognostic indicator for endometrial cancer.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer.

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Candidate biomarkers and persistent transcriptional responses after low and high dose ionizing radiation at high dose rate

Purpose Development of an integrated time and dose model to explore the dynamics of gene expression alterations and identify biomarkers for biodosimetry following low- and high-dose irradiations at high dose rate. Material and methods We utilized multiple transcriptome datasets (GSE8917, GSE43151, and GSE23515) from Gene Expression Omnibus (GEO) for identifying candidate biological dosimeters. A linear mixed-effects model with random intercept was used to explore the dose-time dynamics of transcriptional responses and to functionally characterize the time- and dose-dependent changes in gene expression. Results We identified genes that are correlated with dose and time and discovered two clusters of genes that are either positively or negatively correlated with both dose and time based on the parameters of the model. Genes in these two clusters may have persistent transcriptional alterations. Twelve potential transcriptional markers for dosimetry—ARHGEF3, BAX, BBC3, CCDC109B, DCP1B, DDB2, F11R, GADD45A, GSS, PLK3, TNFRSF10B, and XPC were identified. Of these genes, BAX, GSS, and TNFRSF10B are positively associated with both dose and time course, have a persistent transcriptional response, and might be better biological dosimeters. Conclusions With the proposed approach, we may identify candidate biomarkers that change monotonically in relation to dose, have a persistent transcriptional response, and are reliable over a wide dose range.

An integrated genomic approach identifies follistatin as a target of the p63-epidermal growth factor receptor oncogenic network in head and neck squamous cell carcinoma.

Although numerous putative oncogenes have been associated with the etiology of head and neck squamous cell carcinoma (HNSCC), the mechanisms by which these oncogenes and their downstream targets mediate tumor progression have not been fully elucidated. We performed an integrative analysis to identify a crucial set of targets of the oncogenic transcription factor p63 that are common across multiple transcriptomic datasets obtained from HNSCC patients, and representative cell line models. Notably, our analysis revealed FST which encodes follistatin, a secreted glycoprotein that inhibits the transforming growth factor TGFβ/activin signaling pathways, to be a direct transcriptional target of p63. In addition, we found that FST expression is also driven by epidermal growth factor receptor EGFR signaling, thus mediating a functional link between the TGF-β and EGFR pathways. We show through loss- and gain-of-function studies that FST predominantly imparts a tumor-growth and migratory phenotype in HNSCC cells. Furthermore, analysis of single-cell RNA sequencing data from HNSCC patients unveiled cancer cells as the dominant source of FST within the tumor microenvironment and exposed a correlation between the expression of FST and its regulators with immune infiltrates. We propose FST as a prognostic biomarker for patient survival and a compelling candidate mediating the broad effects of p63 on the tumor and its associated microenvironment.

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections.

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

Comprehensive pan-cancer analysis of role of GPRASP1, associated with clinical outcomes, immune microenvironment, and immunotherapeutic efficiency in pancreatic cancer.

GPRASP1 (G-protein-coupled receptor-associated sorting protein 1) plays an important role in tumorigenesis. However, GPRASP1 specific role has not been clearly clarified in cancer, particularly in pancreatic cancer(PC). Firstly, we utilized pan-cancer analysis based on RNA sequencing data from TCGA (The Cancer Genome Atlas) to evaluate the expression pattern and immunological role of GPRASP1. Then, through multiple transcriptome datasets (TCGA and Gene Expression Omnibus (GEO)) and multi-omics (RNA-seq, DNA methylation, copy number variations (CNV), somatic mutation data) in-depth analysis, we comprehensively explore the relationship of GPRASP1 expression with clinicopathologic characteristics, clinical outcomes, CNV, and DNA methylation in pancreatic cancer. Additionally, we employed immunohistochemistry (IHC) to further confirm GPRASP1 expression pattern between PC tissues and paracancerous tissues. Lastly, we systematically associated the GPRASP1 with immunological properties from numerous perspectives, such as immune cell infiltration, immune-related pathways, immune checkpoint inhibitors, immunomodulators, immunogenicity, and immunotherapy. Through pan-cancer analysis, we identified that GPRASP1 plays a critical role in the occurrence and prognosis of PC, and is closely related to immunological characteristics in PC. IHC analysis confirmed that GPRASP1 is significantly down-regulated in PC compared with normal tissues. The expression of GPRASP1 is significantly negatively correlated with clinical features (histologic grade, T stage, and TNM stage), and is an independent predictor of favorable prognosis, regardless of other clinicopathological features (HR: 0.69, 95% CI 0.54-0.92, p= 0.011). The etiological investigation found that the abnormal expression of GPRASP1 was related to DNA methylation and CNV frequency. Subsequently, the high expression of GPRASP1 was significantly correlated with immune cell infiltration (CD8+ T cell, tumor-infiltrating lymphocyte(TIL)), immune-related pathways(cytolytic activity, check-point, human leukocyte antigen (HLA)), immune checkpoint inhibitors (CTLA4, HAVCR2, LAG3, PDCD1 and TIGIT), immunomodulators ( CCR4/5/6, CXCL9, CXCR4/5), and immunogenicity(immune score, neoantigen, TMB(tumor mutation burden)). Finally, IPS (immunophenoscore) and TIDE (tumor immune dysfunction and exclusion) analysis demonstrated that GPRASP1 expression levels can accurately predict the immunotherapeutic response. GPRASP1 is a promising candidate biomarker that plays a role in the occurrence, development, and prognosis of PC. Evaluating GPRASP1 expression will aid in the characterization of tumor microenvironment (TME) infiltration and orient more efficient immunotherapy strategies.

MeCP2 regulates Gdf11, a dosage-sensitive gene critical for neurological function.

Loss- and gain-of-function of MeCP2 causes Rett syndrome (RTT) and MECP2 duplication syndrome (MDS), respectively. MeCP2 binds methyl-cytosines to finely tune gene expression in the brain, but identifying genes robustly regulated by MeCP2 has been difficult. By integrating multiple transcriptomics datasets, we revealed that MeCP2 finely regulates growth differentiation factor 11 (Gdf11). Gdf11 is down-regulated in RTT mouse models and, conversely, up-regulated in MDS mouse models. Strikingly, genetically normalizing Gdf11 dosage levels improved several behavioral deficits in a mouse model of MDS. Next, we discovered that losing one copy of Gdf11 alone was sufficient to cause multiple neurobehavioral deficits in mice, most notably hyperactivity and decreased learning and memory. This decrease in learning and memory was not due to changes in proliferation or numbers of progenitor cells in the hippocampus. Lastly, loss of one copy of Gdf11 decreased survival in mice, corroborating its putative role in aging. Our data demonstrate that Gdf11 dosage is important for brain function.

Involvement of Epithelial-Mesenchymal Transition Genes in Small Cell Lung Cancer Phenotypic Plasticity.

Small cell lung cancer (SCLC) is an aggressive cancer recalcitrant to treatment, arising predominantly from epithelial pulmonary neuroendocrine (NE) cells. Intratumor heterogeneity plays critical roles in SCLC disease progression, metastasis, and treatment resistance. At least five transcriptional SCLC NE and non-NE cell subtypes were recently defined by gene expression signatures. Transition from NE to non-NE cell states and cooperation between subtypes within a tumor likely contribute to SCLC progression by mechanisms of adaptation to perturbations. Therefore, gene regulatory programs distinguishing SCLC subtypes or promoting transitions are of great interest. Here, we systematically analyze the relationship between SCLC NE/non-NE transition and epithelial to mesenchymal transition (EMT)-a well-studied cellular process contributing to cancer invasiveness and resistance-using multiple transcriptome datasets from SCLC mouse tumor models, human cancer cell lines, and tumor samples. The NE SCLC-A2 subtype maps to the epithelial state. In contrast, SCLC-A and SCLC-N (NE) map to a partial mesenchymal state (M1) that is distinct from the non-NE, partial mesenchymal state (M2). The correspondence between SCLC subtypes and the EMT program paves the way for further work to understand gene regulatory mechanisms of SCLC tumor plasticity with applicability to other cancer types.

Identification of Novel Core Genes Involved in Malignant Transformation of Inflamed Colon Tissue Using a Computational Biology Approach and Verification in Murine Models.

Inflammatory bowel disease (IBD) is a complex and multifactorial systemic disorder of the gastrointestinal tract and is strongly associated with the development of colorectal cancer. Despite extensive studies of IBD pathogenesis, the molecular mechanism of colitis-driven tumorigenesis is not yet fully understood. In the current animal-based study, we report a comprehensive bioinformatics analysis of multiple transcriptomics datasets from the colon tissue of mice with acute colitis and colitis-associated cancer (CAC). We performed intersection of differentially expressed genes (DEGs), their functional annotation, reconstruction, and topology analysis of gene association networks, which, when combined with the text mining approach, revealed that a set of key overexpressed genes involved in the regulation of colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Adam8, Mmp7, Mmp13) occupied hub positions within explored colitis- and CAC-related regulomes. Further validation of obtained data in murine models of dextran sulfate sodium (DSS)-induced colitis and azoxymethane/DSS-stimulated CAC fully confirmed the association of revealed hub genes with inflammatory and malignant lesions of colon tissue and demonstrated that genes encoding matrix metalloproteinases (acute colitis: Mmp3, Mmp9; CAC: Mmp7, Mmp13) can be used as a novel prognostic signature for colorectal neoplasia in IBD. Finally, using publicly available transcriptomics data, translational bridge interconnecting of listed colitis/CAC-associated core genes with the pathogenesis of ulcerative colitis, Crohn's disease, and colorectal cancer in humans was identified. Taken together, a set of key genes playing a core function in colon inflammation and CAC was revealed, which can serve both as promising molecular markers and therapeutic targets to control IBD and IBD-associated colorectal neoplasia.

Emergent dynamics of underlying regulatory network links EMT and androgen receptor-dependent resistance in prostate cancer

Advanced prostate cancer patients initially respond to hormone therapy, be it in the form of androgen deprivation therapy or second-generation hormone therapies, such as abiraterone acetate or enzalutamide. However, most men with prostate cancer eventually develop hormone therapy resistance. This resistance can arise through multiple mechanisms, such as through genetic mutations, epigenetic mechanisms, or through non-genetic pathways, such as lineage plasticity along epithelial-mesenchymal or neuroendocrine-like axes. These mechanisms of hormone therapy resistance often co-exist within a single patient’s tumor and can overlap within a single cell. There exists a growing need to better understand how phenotypic heterogeneity and plasticity results from emergent dynamics of the regulatory networks governing androgen independence. Here, we investigated the dynamics of a regulatory network connecting the drivers of androgen receptor (AR) splice variant-mediated androgen independence and those of epithelial-mesenchymal transition. Model simulations for this network revealed four possible phenotypes: epithelial-sensitive (ES), epithelial-resistant (ER), mesenchymal-resistant (MR) and mesenchymal-sensitive (MS), with the latter phenotype occurring rarely. We observed that well-coordinated “teams” of regulators working antagonistically within the network enable these phenotypes. These model predictions are supported by multiple transcriptomic datasets both at single-cell and bulk levels, including in vitro EMT induction models and clinical samples. Further, our simulations reveal spontaneous stochastic switching between the ES and MR states. Addition of the immune checkpoint molecule, PD-L1, to the network was able to capture the interactions between AR, PD-L1, and the mesenchymal marker SNAIL, which was also confirmed through quantitative experiments. This systems-level understanding of the driver of androgen independence and EMT could aid in understanding non-genetic transitions and progression of such cancers and help in identifying novel therapeutic strategies or targets.

Genome-Wide Identification of Auxin-Responsive GH3 Gene Family in Saccharum and the Expression of ScGH3-1 in Stress Response.

Gretchen Hagen3 (GH3), one of the three major auxin-responsive gene families, is involved in hormone homeostasis in vivo by amino acid splicing with the free forms of salicylic acid (SA), jasmonic acid (JA) or indole-3-acetic acid (IAA). Until now, the functions of sugarcane GH3 (SsGH3) family genes in response to biotic stresses have been largely unknown. In this study, we performed a systematic identification of the SsGH3 gene family at the genome level and identified 41 members on 19 chromosomes in the wild sugarcane species, Saccharum spontaneum. Many of these genes were segmentally duplicated and polyploidization was the main contributor to the increased number of SsGH3 members. SsGH3 proteins can be divided into three major categories (SsGH3-I, SsGH3-II, and SsGH3-III) and most SsGH3 genes have relatively conserved exon-intron arrangements and motif compositions. Diverse cis-elements in the promoters of SsGH3 genes were predicted to be essential players in regulating SsGH3 expression patterns. Multiple transcriptome datasets demonstrated that many SsGH3 genes were responsive to biotic and abiotic stresses and possibly had important functions in the stress response. RNA sequencing and RT-qPCR analysis revealed that SsGH3 genes were differentially expressed in sugarcane tissues and under Sporisorium scitamineum stress. In addition, the SsGH3 homolog ScGH3-1 gene (GenBank accession number: OP429459) was cloned from the sugarcane cultivar (Saccharum hybrid) ROC22 and verified to encode a nuclear- and membrane-localization protein. ScGH3-1 was constitutively expressed in all tissues of sugarcane and the highest amount was observed in the stem pith. Interestingly, it was down-regulated after smut pathogen infection but up-regulated after MeJA and SA treatments. Furthermore, transiently overexpressed Nicotiana benthamiana, transduced with the ScGH3-1 gene, showed negative regulation in response to the infection of Ralstonia solanacearum and Fusarium solani var. coeruleum. Finally, a potential model for ScGH3-1-mediated regulation of resistance to pathogen infection in transgenic N. benthamiana plants was proposed. This study lays the foundation for a comprehensive understanding of the sequence characteristics, structural properties, evolutionary relationships, and expression of the GH3 gene family and thus provides a potential genetic resource for sugarcane disease-resistance breeding.

HCoCena: horizontal integration and analysis of transcriptomics datasets.

MotivationTranscriptome-based gene co-expression analysis has become a standard procedure for structured and contextualized understanding and comparison of different conditions and phenotypes. Since large study designs with a broad variety of conditions are costly and laborious, extensive comparisons are hindered when utilizing only a single dataset. Thus, there is an increased need for tools that allow the integration of multiple transcriptomic datasets with subsequent joint analysis, which can provide a more systematic understanding of gene co-expression and co-functionality within and across conditions. To make such an integrative analysis accessible to a wide spectrum of users with differing levels of programming expertise it is essential to provide user-friendliness and customizability as well as thorough documentation.ResultsThis article introduces horizontal CoCena (hCoCena: horizontal construction of co-expression networks and analysis), an R-package for network-based co-expression analysis that allows the analysis of a single transcriptomic dataset as well as the joint analysis of multiple datasets. With hCoCena, we provide a freely available, user-friendly and adaptable tool for integrative multi-study or single-study transcriptomics analyses alongside extensive comparisons to other existing tools.Availability and implementationThe hCoCena R-package is provided together with R Markdowns that implement an exemplary analysis workflow including extensive documentation and detailed descriptions of data structures and objects. Such efforts not only make the tool easy to use but also enable the seamless integration of user-written scripts and functions into the workflow, creating a tool that provides a clear design while remaining flexible and highly customizable. The package and additional information including an extensive Wiki are freely available on GitHub: https://github.com/MarieOestreich/hCoCena. The version at the time of writing has been added to Zenodo under the following link: https://doi.org/10.5281/zenodo.6911782.Supplementary information Supplementary data are available at Bioinformatics online.

Deletion of Abi3/Gngt2 influences age-progressive amyloid β and tau pathologies in distinctive ways

BackgroundThe S209F variant of Abelson Interactor Protein 3 (ABI3) increases risk for Alzheimer’s disease (AD), but little is known about its function in relation to AD pathogenesis.MethodsHere, we use a mouse model that is deficient in Abi3 locus to study how the loss of function of Abi3 impacts two cardinal neuropathological hallmarks of AD—amyloid β plaques and tau pathology. Our study employs extensive neuropathological and transcriptomic characterization using transgenic mouse models and adeno-associated virus-mediated gene targeting strategies.ResultsAnalysis of bulk RNAseq data confirmed age-progressive increase in Abi3 levels in rodent models of AD-type amyloidosis and upregulation in AD patients relative to healthy controls. Using RNAscope in situ hybridization, we localized the cellular distribution of Abi3 in mouse and human brains, finding that Abi3 is expressed in both microglial and non-microglial cells. Next, we evaluated Abi3−/− mice and document that both Abi3 and its overlapping gene, Gngt2, are disrupted in these mice. Using multiple transcriptomic datasets, we show that expression of Abi3 and Gngt2 are tightly correlated in rodent models of AD and human brains, suggesting a tight co-expression relationship. RNAseq of the Abi3-Gngt2−/− mice revealed upregulation of Trem2, Plcg2, and Tyrobp, concomitant with induction of an AD-associated neurodegenerative signature, even in the absence of AD-typical neuropathology. In APP mice, loss of Abi3-Gngt2 resulted in a gene dose- and age-dependent reduction in Aβ deposition. Additionally, in Abi3-Gngt2−/− mice, expression of a pro-aggregant form of human tau exacerbated tauopathy and astrocytosis. Further, using in vitro culture assays, we show that the AD-associated S209F mutation alters the extent of ABI3 phosphorylation.ConclusionsThese data provide an important experimental framework for understanding the role of Abi3-Gngt2 function and early inflammatory gliosis in AD. Our studies also demonstrate that inflammatory gliosis could have opposing effects on amyloid and tau pathology, highlighting the unpredictability of targeting immune pathways in AD.

Construction of an HLA Classifier for Early Diagnosis, Prognosis, and Recognition of Immunosuppression in Sepsis by Multiple Transcriptome Datasets

Background: Sepsis is a clinical syndrome, due to a dysregulated inflammatory response to infection. Accumulating evidence shows that human leukocyte antigen (HLA) genes play a key role in the immune responses to sepsis. Nevertheless, the effects of HLA genes in sepsis have still not been comprehensively understood. Methods: A systematical search was performed in the Gene Expression Omnibus (GEO) and ArrayExpress databases from inception to 10 September 2021. Random forest (RF) and modified Lasso penalized regression were conducted to identify hub genes in multi-transcriptome data, thus we constructed a prediction model, namely the HLA classifier. ArrayExpress databases, as external validation, were utilized to evaluate its diagnostic, prognostic, and predictive performance. Immune cell infiltration score was calculated via CIBERSORTx tools and single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis (GSVA) and ssGSEA were conducted to determine the pathways that are significantly enriched in different subgroups. Next, we systematically correlated the HLA classifier with immunological characteristics from multiple perspectives, such as immune-related cell infiltration, pivotal molecular pathways, and cytokine expression. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to validate the expression level of HLA genes in clinical samples. Results: A total of nine datasets comprising 1,251 patients were included. Based on RF and modified Lasso penalized regression in multi-transcriptome datasets, five HLA genes (B2M, HLA-DQA1, HLA-DPA1, TAP1, and TAP2) were identified as hub genes, which were used to construct an HLA classifier. In the discovery cohort, the HLA classifier exhibited superior diagnostic value (AUC = 0.997) and performed better in predicting mortality (AUC = 0.716) than clinical characteristics or endotypes. Encouragingly, similar results were observed in the ArrayExpress databases. In the E-MTAB-7581 dataset, the use of hydrocortisone in the HLA high-risk subgroup (OR: 2.84, 95% CI 1.07–7.57, p = 0.037) was associated with increased risk of mortality, but not in the HLA low-risk subgroup. Additionally, immune infiltration analysis by CIBERSORTx and ssGSEA revealed that B cells, activated dendritic cells, NK cells, T helper cells, and infiltrating lymphocytes (ILs) were significantly richer in HLA low-risk phenotypes, while Tregs and myeloid-derived suppressor cells (MDSCs) were more abundant in HLA high-risk phenotypes. The HLA classifier was significantly negatively correlated with B cells, activated dendritic cells, NK cells, T helper cells, and ILs, yet was significantly positively correlated with Tregs and MDSCs. Subsequently, molecular pathways analysis uncovered that cytokine-cytokine receptor (CCR) interaction, human leukocyte antigen (HLA), and antigen-presenting cell (APC) co-stimulation were significantly enriched in HLA low-risk endotypes, which was significantly negatively correlated with the HLA classifier in multi-transcriptome data. Finally, the expression levels of several cytokines (IL-10, IFNG, TNF) were significantly different between the HLA subgroups, and the ratio of IL-10/TNF was significantly positively correlated with HLA score in multi-transcriptome data. Results of qRT-PCR validated the higher expression level of B2M as well as lower expression level of HLA-DQA1, HLA-DPA1, TAP1, and TAP2 in sepsis samples compared to control sample. Conclusion: Based on five HLA genes, a diagnostic and prognostic model, namely the HLA classifier, was established, which is closely correlated with responses to hydrocortisone and immunosuppression status and might facilitate personalized counseling for specific therapy.