Large-scale Gene Expression Profiling Research Articles

Transcriptional Changes and Preservation of Muscle Protein Content in Hibernating Black Bears

Physical inactivity decreases mechanical load on the skeleton, which, when prolonged, leads to loss of muscle mass and strength in most mammalian species. In contrast, hibernating bears demonstrate limited loss of muscle mass and protein content over the prolonged periods (5-6 months) of inactivity and fasting during winter. This suggests that bears have natural adaptive mechanisms preserving muscle mass and functionality. To identify transcriptional changes that underlie molecular mechanisms attenuating muscle loss, we conducted a large-scale gene expression profiling (14,194 genes) in quadriceps muscle of adult black bear males, comparing hibernating animals (n=5) sampled in March, about 1 month before expected emergence, and summer active animals (n=5) using next generation sequencing (RNA-seq) of the transcriptome. In total, 336 differentially expressed (FDR<0.05) genes were up-regulated and 677 genes were down-regulated in muscle of hibernating bears. Gene set enrichment analysis with Reactome database showed significantly elevated proportion of overexpressed genes involved in eukaryotic translation initiation (FDR<0.0001) and elongation (FDR<0.0001) as well as in the mTORC1 mediated signaling pathway (FDR=0.021). In contrast, down-regulated genes were overrepresented among genes involved in catabolism of branched chain amino acid (BCAA: leucine, isoleucine and valine, FDR<0.0001) that suggests preservation of BCAA. These findings imply induction of protein biosynthesis through the mTORC1 signaling positively activated by availability of leucine in muscle during hibernation. Support for this conclusion comes from significant overexpression of Ras-related GTP-binding protein D (RRAGD, FC=2.12, FDR<0.0001), crucial regulator of the mTORC1 response to leucine availability, and up-regulation of eukaryotic translation initiation factor 4B (EIF4B, FC=1.95, FDR=0.012), downstream target of the mTORC1 mediated signaling. In addition to regulation of protein biosynthesis, activation of the mTOR signaling suppresses autophagy-dependent protein degradation. Consistent with reduction of autophagy, microtubule associated protein 1 light chain (MAP1LC3A, FC=-2.46, FDR<0.0001) and unc-51 like autophagy activating kinase (ULK1, FC=-1.69, FDR=0.052) were down-regulated in hibernating muscle. No expression differences were detected for two muscle atrophy markers, key members of the ubiquitin proteasome degradation, FBXO32 (Atrogin-1, MAFBX, FDR=0.85) and TRIM63 (MURF-1, FDR=0.63). The induction of protein biosynthesis and decrease in protein catabolism through the mTORC1 mediated signaling as response to BCAA availability likely contribute to the maintenance of muscle protein content through prolonged periods of immobility and fasting during hibernation. The follow up studies need to include metabolomic quantification of BCAA, phospho-proteomic assessment of key genes (RPS6KB1, RPS6) involved in the mTORC1 signaling and identification of upstream negative regulators suppressing BCAA catabolism in hibernating muscle. The work was supported by NIH COBRE under grant number [P20GM130443]. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Molecular pathogenesis of ameloblastoma.

Ameloblastoma (AM) is a benign, although aggressive, epithelial odontogenic tumour originating from tooth-forming tissues or remnants. Its aetiopathogenesis remains unclear; however, molecular analysis techniques have allowed researchers to progress in understanding its genetic basis. The high frequency of BRAF p.V600E as a main driver mutation in AM is well established; nevertheless, it is insufficient to explain its tumourigenesis. In this review, we aimed to integrate the current knowledge about the biology of AM and to describe the main genetic alterations reported, focusing on the findings of large-scale sequencing and gene expression profiling techniques. Current evidence shows that besides BRAF mutation and activation of the MAPK pathway, alterations in Hedgehog and Wnt/β-catenin pathway-related genes are also involved in AM pathogenesis. Recently, a tumour suppressor gene, KMT2D, has been reported as mutated by different research groups. The biological impact of these mutations in the pathogenesis of AM has yet to be elucidated. Further studies are needed to clarify the impact of these findings in the identification of novel biomarkers that could be useful for diagnosing, classifying, and molecular targeting this neoplasm.

Construction of Gene Expression Patterns to Identify Critical Genes Under SARS-CoV-2 Infection Conditions.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a positive-stranded single-stranded RNA virus with an envelope frequently altered by unstable genetic material, making it extremely difficult for vaccines, drugs, and diagnostics to work. Understanding SARS-CoV-2 infection mechanisms requires studying gene expression changes. Deep learning methods are often considered for large-scale gene expression profiling data. Data feature-oriented analysis, however, neglects the biological process nature of gene expression, making it difficult to describe gene expression behaviors accurately. In this article, we propose a novel scheme for modeling gene expression during SARS-CoV-2 infection as networks (gene expression modes, GEM), to characterize their expression behaviors. On this basis, we investigated the relationships among GEMs to determine SARS-CoV-2's core radiation mode. Our final experiments identified key COVID-19 genes by gene function enrichment, protein interaction, and module mining. Experimental results show that ATG10, ATG14, MAP1LC3B, OPTN, WDR45, and WIPI1 genes contribute to SARS-CoV-2 virus spread by affecting autophagy.

DNA Methylation Landscape of Peripheral T-Cell Lymphomas Not Otherwise Specified

DNA methylation is an epigenetic modification catalyzed by DNA methyltransferases that regulate gene transcription in mammalian cells. DNA methylation in gene promoters is associated with repressive histone marks and contributes to gene repression. This process is involved in various physiological processes, including development, X chromosome inactivation, genomic imprinting, differentiation, and hematopoiesis. The global, genome-wide deregulation of methylation patterns contributes to the pathogenesis of immune disorders, hematologic malignancies, and cancer. Because aberrations in DNA methylation can deregulate spatiotemporal gene expression in a cell type-specific manner, genome-wide changes in this epigenetic modification may contribute to tumor development. Specifically, the downregulation of tumor suppressors through promoter hypermethylation is a widely accepted mechanism in cancer progression. Due to its distinct patterns in cancer, DNA methylation can be used as a diagnostic and predictive biomarker. Peripheral T-cell lymphomas, Not Otherwise Specified (PTCL-NOS), are a group of heterogeneous T-cell malignancies with diverse cytogenic, molecular, and morphological phenotypes. Despite large-scale gene expression profiling, the molecular features-and DNA methylation in particular-of these rare but aggressive malignancies remain poorly understood. To better understand the molecular landscape of PTCL-NOS, we performed global methylation profiling via Whole Genome Bisulfite Sequencing on 10 lymphomas and compared those to patterns seen in normal T-cells and Anaplastic Large Cell Lymphoma (ALCL). Our analysis revealed large-scale deregulation of methylation in all tumors at levels of both differentially methylated cytosines and regions. The dominant molecular change was hypomethylation, which was, on average, 7 times more frequent than hypermethylation. Hypomethylation was not limited to repetitive elements but was also seen in all genomic elements, including promoters, enhancers, gene bodies, and exons. Out of 1,286 genes whose promoters were consistently hypomethylated in Peripheral T-cell lymphomas (PTCL), 39 genes were associated with an increase in expression, suggesting that the loss of methylation may have contributed to their deregulation. This hypomethylated and overexpressed signature contained genes that might be involved in tumor progression or maintenance, such as Rac GTPase activating protein 1 ( RACGAP1), regulator of chromosome condensation 1 ( RCC1), anillin actin-binding protein ( ANLN), and others. Out of 578 genes whose promoters were consistently hypermethylated in PTCL, the expression of 56 genes was decreased. These included genes with tumor suppressor function, such as splicing factor 3b subunit 1 ( SF3B1) and lymphoid enhancer-binding factor 1 ( LEF1). Our analysis also identified differential methylation between PTCL-NOS and ALCL in various genomic features. For example, 71 hypo- and 59 hypermethylated promoters were found only in ALCL but not PTCL-NOS. Conversely, 326 hypo- and 254 hypermethylated promoters were observed only in PTCL-NOS. Analysis of gene expression also revealed specific changes in both PTCL subtypes, manifested by the activation of different signaling pathways as identified by Ingenuity Pathway Analysis. PTCL-NOS was characterized by the suppression of the SUMOylation pathway, PTEN, and EIF2 Signaling, and the activation of NF-κB and Notch signaling. ALCLs were characterized by the suppression of the Role of CHK Proteins in Cell Cycle Control, Erythropoietin, ERBB signaling, the Coronavirus Pathogenesis pathway, IGF-1 signaling, and the activation of Glutathione-mediated Detoxification and Triacylglycerol Biosynthesis. Altogether, we identified recurrent specific methylation and expression changes that could serve as the basis for the development of tools facilitating the diagnosis and prognosis of PTCL-NOS. In addition, our data may contribute to the identification of epigenetically-regulated drivers of tumor initiation, progression, and maintenance.

Factor-specific generative pattern from large-scale drug-induced gene expression profile

Drug discovery is a complex and interdisciplinary field that requires the identification of potential drug targets for specific diseases. In this study, we present FacPat, a novel approach that identifies the optimal factor-specific pattern explaining the drug-induced gene expression profile. FacPat uses a genetic algorithm based on pattern distance to mine the optimal factor-specific pattern for each gene in the LINCS L1000 dataset. We applied Benjamini–Hochberg correction to control the false discovery rate and identified significant and interpretable factor-specific patterns consisting of 480 genes, 7 chemical compounds, and 38 human cell lines. Using our approach, we identified genes that show context-specific effects related to chemical compounds and/or human cell lines. Furthermore, we performed functional enrichment analysis to characterize biological features. We demonstrate that FacPat can be used to reveal novel relationships among drugs, diseases, and genes.

Identification of a 5-lncRNA-Based Signature for Immune Characteristics and Prognosis of Lung Squamous Cell Carcinoma and Verification of the Function of lncRNA SPATA41.

Lung squamous cell carcinoma (LUSC) is one of the most lethal cancers worldwide. Traditional tumor-node-metastasis (TNM) staging system has many insufficiencies in predicting immune characteristics, overall survival (OS), and prognosis of LUSC. LncRNA is currently found involved in tumor development and effectively predicts tumor prognosis. We screened potential tumor-related lncRNAs for immune characteristics and constructed a nomogram combining lncRNA and traditional clinical indicators for prognosis prediction. We obtained the large-scale gene expression profiles of samples from 492 LUSC patients in The Cancer Genome Atlas database. SPATA41, AL034550.2, AP003721.2, AC106786.1, and AC078889.1 were finally screened to construct a 5-lncRNA-based signature. The risk score of the signature divided patients into subgroups of high-risk and low-risk with significant differences in OS. Their area under the curve (AUC) reached more than 0.70 in 1, 3, and 5 years. In addition, compared with the high-risk subgroup, the low-risk subgroup exhibited a remarkably favorable prognosis and TME score, along with a higher immune infiltration score and lower TIDE score. The signature also significantly related to chemotherapy response, especially in cisplatin, vinorelbine, and paclitaxel. Importantly, the nomogram we constructed had good reliability with the assessment of the calibration chart and consistency index (c-index). GO and KEGG enrichment analysis indicated that co-expression mRNAs of the 5 lncRNAs were mainly focused on RNA splicing, DNA replication, and protein serine/threonine kinase activity. Functional assays demonstrated that SPATA41, one of the five OS-related lncRNAs, regulated invasion, migration, proliferation, and programmed death in vitro. In summary, our 5-lncRNA-based signature has a good performance in predicting immune characteristics and prognosis of LUSC patients.

Differential Gene Expression in the Hippocampi of Nonhuman Primates Chronically Exposed to Methamphetamine, Cocaine, or Heroin.

Objective Methamphetamine (MA), cocaine, and heroin cause severe public health problems as well as impairments in neural plasticity and cognitive function in the hippocampus. This study aimed to identify the genes differentially expressed in the hippocampi of cynomolgus monkeys in response to these drugs. Methods After the monkeys were chronically exposed to MA, cocaine, and heroin, we performed large-scale gene expression profiling of the hippocampus using RNA-Seq technology and functional annotation of genes differentially expressed. Some genes selected from RNA-Seq analysis data were validated with reverse transcription-quantitative polymerase chain reaction (RT-qPCR). And the expression changes of ADAM10 protein were assessed using immunohistochemistry. Results The changes in genes related to axonal guidance (PTPRP and KAL1), the cell cycle (TLK2), and the regulation of potassium ions (DPP10) in the drug-treated groups compared to the control group were confirmed using RT-qPCR. Comparative analysis of all groups showed that among genes related to synaptic long-term potentiation, CREBBP and GRIN3A were downregulated in both the MA- and heroin-treated groups compared to the control group. In particular, the mRNA and protein expression levels of ADAM10 were decreased in the MA-treated group but increased in the cocaine-treated group compared to the control group. Conclusion These results provide insights into the genes that are upregulated and downregulated in the hippocampus by the chronic administration of MA, cocaine, or heroin and basic information for developing novel drugs for the treatment of hippocampal impairments caused by drug abuse.

M6Acancer-Net: Identification of m6A-mediated cancer driver genes from gene-site heterogeneous network.

N6-methyladenosine (m6A) is the most abundant eukaryotic modification internal mRNA, which plays the crucial roles in the occurrence and development of cancer. However, current knowledge about m6A-mediated functional circuit and key genes targeted by m6A methylation in cancer is mostly elusive. Thus, here we proposed a novel network-based approach (called m6Acancer-Net) to identify m6A-mediated driver genes and their associated network in specific type of cancer, such as acute myeloid leukemia. m6A-mediated cancer driver genes are defined as genes mediated by m6A methylation, significantly mutated, and functionally interacted in cancer. m6Acancer-Net identified the m6A-mediated cancer driver genes by combining gene functional interaction network with RNA methylation, gene expression and mutation information. A cancer-specific gene-site heterogeneous network was firstly constructed by connecting the m6A site co-methylation network with the functional interaction pruned gene co-expression network generated from large scale gene expression profile of specific cancer. Then, the functional m6A-mediated genes were identified by selecting the m6A regulators as seed genes to perform the random walk with restart algorithm on the gene-site heterogeneous network. Finally, m6A-mediated cancer driver gene subnetworks were constructed by performing the heat diffusion of mutation frequency for functional m6A-mediated genes in protein-protein interaction networks. The experimental results of m6Acancer-Net on the acute myeloid leukemia (AML) and glioblastoma multiforme (GBM) data from TCGA project show that the m6A-mediated caner driver genes identified by m6Acancer-Net are targeted by m6A regulators, and mediate significant cancer-related pathways. They play crucial roles in development and prognostic stratification of cancer. Moreover, 15 m6A-mediated cancer driver genes identified in AML are validated by literatures to mediate AML progress, and 14 m6A-mediated cancer driver genes identified in GBM are validated by literatures to participate in development of GBM. m6Acancer-Net is reliable to identify the functionally significant m6A-mediated driver genes in specific cancer, and it can effectively facilitate the understanding of regulatory and therapeutic mechanism of cancer driver genes in epitranscriptome layer.

A critical role of the podocyte cytoskeleton in the pathogenesis of glomerular proteinuria and autoimmune podocytopathies.

Selective glomerular filtration relies on the membrane separating the glomerular arterioles from the Bowman space. As a major component of the glomerular filtration barrier, podocytes form foot processes by the actin cytoskeleton, which dynamically adjusts in response to environmental changes to maintain filtration barrier integrity. The slit diaphragms bridge the filtration slits between neighboring foot processes and act as signaling hubs interacting with the actin cytoskeleton. Focal adhesions relay signals to regulate actin dynamics while allowing podocyte adherence to the basement membrane. Mutations in actin regulatory and signaling proteins may disrupt the actin cytoskeleton, resulting in foot process retraction, effacement, and proteinuria. Large-scale gene expression profiling platforms, transgenic animal models, and other in vivo gene delivery methods now enhance our understanding of the interactions among podocyte focal adhesions, slit diaphragms, and actin dynamics. In addition, our team found that at least 66% of idiopathic nephrotic syndrome (INS) children have podocyte autoantibodies, which was defined as a new disease subgroup-, autoimmune podocytopathies. This review outlines the pathophysiological mechanisms of podocyte cytoskeleton protein interactions in proteinuria and glomerular podocytopathy.

Single-Cell Regulatory Network Inference and Clustering Identifies Cell-Type Specific Expression Pattern of Transcription Factors in Mouse Sciatic Nerve.

Advances in single-cell RNA sequencing technologies and bioinformatics methods allow for both the identification of cell types in a complex tissue and the large-scale gene expression profiling of various cell types in a mixture. In this report, we analyzed a single-cell RNA sequencing (scRNA-seq) dataset for the intact adult mouse sciatic nerve and examined cell-type specific transcription factor expression and activity during peripheral nerve homeostasis. In total, we identified 238 transcription factors expressed in nine different cell types of intact mouse sciatic nerve. Vascular smooth muscle cells have the lowest number of transcription factors expressed with 17 transcription factors identified. Myelinating Schwann cells (mSCs) have the highest number of transcription factors expressed, with 61 transcription factors identified. We created a cell-type specific expression map for the identified 238 transcription factors. Our results not only provide valuable information about the expression pattern of transcription factors in different cell types of adult peripheral nerves but also facilitate future studies to understand the function of key transcription factors in the peripheral nerve homeostasis and disease.

RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis.

RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. With RNfuzzyApp, we provide a user-friendly, web-based R shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, fully automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, cluster overlap analysis, Mfuzz loop computations, as well as cluster enrichments. RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its assignment of orthologs, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

Informative Gene Selection Based on Cost-Sensitive Fast Correlation- Based Filter Feature Selection

Background: Informative gene selection is an essential step in performing tumor classification. However, it is difficult to select informative genes related to tumors from large-scale gene expression profiles because of their characteristics, such as high dimensionality, relatively small samples, and class imbalance, and some genes are superfluous and irrelevant. Objective: Many researchers analyze and process gene expression data to obtain classified gene subsets by using machine learning methods. However, the gene expression profiles of tumors often have massive computational challenges. In addition, when improving feature importance and classification accuracy, cost estimation is often ignored in traditional feature selection algorithms, which makes tumor classification more difficult. Methods: In this study, a novel informative gene selection method based on cost-sensitive fast correlation- based filter (CS-FCBF) feature selection is proposed. Results: First, the symmetric uncertainty index is used to evaluate the correlation between informativeFirst, the symmetric uncertainty index is used to evaluate the correlation between informative genes and class labels, then a large number of irrelevant and redundant genes are quickly filtered according to importance. Thereby, a candidate gene subset is generated. Second, costsensitive learning, which introduces the misclassification cost matrix and support vector machine attribute evaluation, is used to obtain the top-ranked gene subset with minimum misclassification loss. Finally, the candidate gene subset is optimized. genes and class labels, then a large number of irrelevant and redundant genes are quickly filtered according to importance. Thereby, a candidate gene subset is generated. Second, costsensitive learning, which introduces the misclassification cost matrix and support vector machine attribute evaluation, is used to obtain the top-ranked gene subset with minimum misclassification loss. Finally, the candidate gene subset is optimized. Conclusion: This experiment was verified in eight independent tumor datasets. By comparing and analyzing CS-FCBF with another three hybrids of typical gene selection algorithms combined with cost-sensitive learning, we found that the method proposed in this study has a better classification performance with fewer selected genes, which might provide guidance in tumor diagnosis and research.

RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis

RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. With RNfuzzyApp, we provide a user-friendly, web-based R shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, fully automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, cluster overlap analysis, Mfuzz loop computations, as well as cluster enrichments. RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its assignment of orthologs, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

Transcriptomics Analysis Indicates Trifarotene Reverses Acne-Related Gene Expression Changes.

Background and Objectives: Trifarotene is a topical retinoid selective for retinoic acid receptor gamma that was recently approved for treatment of acne vulgaris. We performed a gene expression analysis to identify the molecular and cellular impact of trifarotene treatment on acne papules.Methods: In this open-label prospective study, subjects with moderate inflammatory acne of the back were treated with trifarotene 0.005% or vehicle cream on dedicated areas for 27 days, and 4 biopsies were collected from each subject (1 from skin without a visible acne lesion and three at the site of an acne papule: one baseline, one after vehicle treatment, and one after trifarotene treatment). Large scale gene expression profiling of the biopsies was performed using Affymetrix technology, treatment-specific gene expression profiles were generated using statistical modeling, and pathway analysis was performed. Using single-cell RNAseq data, in silico deconvolution of transcriptomics data was performed to identify cellular signatures.Results: We discovered a unique set of 67 genes modulated by trifarotene that are primarily involved in cellular migration, inflammation, and extracellular matrix reorganization. Changes in cellular expression were similar in both trifarotene-treated and spontaneously-resolving lesions. However, only trifarotene treatment impacted SPP1+ macrophages, a subset of highly proliferative macrophages recently identified in fibrotic tissue.Conclusions: These results show that trifarotene has a novel action in acne treatment by affecting epidermal and immune components of acne pathogenesis.

27862 Trifarotene transcriptomics analysis and acne-related gene expression

Trifarotene cream, 0.005% is an FDA approved topical retinoid indicated for treatment of facial and truncal acne and has received positive outcome through the European Decentralized Procedure. Trifarotene is the first novel topical retinoid molecule approved for acne treatment in 20 years, and the only approved retinoid that specifically acts through retinoic acid receptor gamma. This study investigated new potential pathways through which trifarotene exerts its efficacy in acne by analyzing the changes in gene expression in the skin of acne patients. Subjects with moderate inflammatory acne of the back were treated with trifarotene 0.005% or vehicle cream on dedicated back areas for 27 days, and 4 biopsies were collected on each subject (non-involved skin, acne papule, trifarotene, and vehicle-treated site). Large-scale gene expression profiling of the biopsies was performed using Affymetrix technology, and treatment specific gene expression profiles were generated using statistical modeling. This analysis highlighted a specific gene expression profile, comprising of 67 genes uniquely driven by Trifarotene treatment with most of these genes known to affect immune responses as well as extracellular matrix reorganization. Interestingly, those genes were downregulated as compared with the papule signature suggesting that trifarotene reverses some inflammatory processes of the papule. Altogether, these results show that trifarotene has a unique action in acne treatment by acting on epidermal differentiation and proliferation (Aubert et al., 2018) as well as on innate and adaptive immune cells in acne pathogenesis.

RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis.

RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. With RNfuzzyApp, we provide a user-friendly, web-based R shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, fully automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, cluster overlap analysis, Mfuzz loop computations, as well as cluster enrichments. RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals

The global efforts in the past year have led to the discovery of nearly 200 drug repurposing candidates for COVID-19. Gaining more insights into their mechanisms of action could facilitate a better understanding of infection and the development of therapeutics. Leveraging large-scale drug-induced gene expression profiles, we found 36% of the active compounds regulate genes related to cholesterol homeostasis and microtubule cytoskeleton organization. Following bioinformatics analyses revealed that the expression of these genes is associated with COVID-19 patient severity and has predictive power on anti-SARS-CoV-2 efficacy in vitro. Monensin, a top new compound that regulates these genes, was further confirmed as an inhibitor of SARS-CoV-2 replication in Vero-E6 cells. Interestingly, drugs co-targeting cholesterol homeostasis and microtubule cytoskeleton organization processes more likely present a synergistic effect with antivirals. Therefore, potential therapeutics could be centered around combinations of targeting these processes and viral proteins.

Mining influential genes based on deep learning

BackgroundCurrently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome.ResultsHere, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information. Specifically, an AutoEncoder framework is first constructed to learn the non-linear relationship between genes, and then DeepLIFT is applied to calculate gene importance scores. Using this data-driven approach, we have re-obtained a landmark gene set. The result shows that our landmark genes can predict target genes more accurately and robustly than that of L1000 based on two metrics [mean absolute error (MAE) and Pearson correlation coefficient (PCC)]. This reveals that the landmark genes detected by our method contain more genomic information.ConclusionsWe believe that our proposed framework is very suitable for the analysis of biological big data to reveal the mysteries of life. Furthermore, the landmark genes inferred from this study can be used for the explosive amplification of gene expression profiles to facilitate research into functional connections.

Identification of IL10RA by Weighted Correlation Network Analysis and in vitro Validation of Its Association With Prognosis of Metastatic Melanoma.

Skin cutaneous melanoma (SKCM) is the major cause of death for skin cancer patients, its high metastasis often leads to poor prognosis of patients with malignant melanoma. However, the molecular mechanisms underlying metastatic melanoma remain to be elucidated. In this study we aim to identify and validate prognostic biomarkers associated with metastatic melanoma. We first construct a co-expression network using large-scale public gene expression profiles from GEO, from which candidate genes are screened out using weighted gene co-expression network analysis (WGCNA). A total of eight modules are established via the average linkage hierarchical clustering, and 111 hub genes are identified from the clinically significant modules. Next, two other datasets from GEO and TCGA are used for further screening of biomarker genes related to prognosis of metastatic melanoma, and identified 11 key genes via survival analysis. We find that IL10RA has the highest correlation with clinically important modules among all identified biomarker genes. Further in vitro biochemical experiments, including CCK8 assays, wound-healing assays and transwell assays, have verified that IL10RA can significantly inhibit the proliferation, migration and invasion of melanoma cells. Furthermore, gene set enrichment analysis shows that PI3K-AKT signaling pathway is significantly enriched in metastatic melanoma with highly expressed IL10RA, indicating that IL10RA mediates in metastatic melanoma via PI3K-AKT pathway.

Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform

BackgroundQuerying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression databases. However, due to the lack of code-free and user friendly applications, it is not easy for biologists and pharmacologists to model MOAs with state-of-art deep learning approach.ResultsIn this work, a newly developed online collaborative tool, Genetic profile-activity relationship (GPAR) was built to help modeling and predicting MOAs easily via deep learning. The users can use GPAR to customize their training sets to train self-defined MOA prediction models, to evaluate the model performances and to make further predictions automatically. Cross-validation tests show GPAR outperforms Gene set enrichment analysis in predicting MOAs.ConclusionGPAR can serve as a better approach in MOAs prediction, which may facilitate researchers to generate more reliable MOA hypothesis.