iGEAK: an interactive gene expression analysis kit for seamless workflow using the R/shiny platform.

BACKGROUND: The use of microarrays and RNA-seq technologies is ubiquitous for transcriptome analyses in modern biology. With proper analysis tools, the differential gene expression analysis process can be significantly accelerated. Many open-source programs provide cutting-edge techniques, but these often require programming skills and lack intuitive and interactive or graphical user interfaces. To avoid bottlenecks impeding seamless analysis processing, we have developed an Interactive Gene Expression Analysis Kit, we term iGEAK, focusing on usability and interactivity. iGEAK is designed to be a simple, intuitive, light-weight that contrasts with heavy-duty programs. RESULTS: iGEAK is an R/Shiny-based client-side desktop application, providing an interactive gene expression data analysis pipeline for microarray and RNA-seq data. Gene expression data can be intuitively explored using a seamless analysis pipeline consisting of sample selection, differentially expressed gene prediction, protein-protein interaction, and gene set enrichment analyses. For each analysis step, users can easily alter parameters to mine more relevant biological information. CONCLUSION: iGEAK is the outcome of close collaboration with wet-bench biologists who are eager to easily explore, mine, and analyze new or public microarray and RNA-seq data. We designed iGEAK as a gene expression analysis pipeline tool to provide essential analysis steps and a user-friendly interactive graphical user interface. iGEAK enables users without programing knowledge to comfortably perform differential gene expression predictions and downstream analyses. iGEAK packages, manuals, tutorials, sample datasets are available at the iGEAK project homepage ( https://sites.google.com/view/iGEAK ).

The leptin receptor has no role in delta-cell control of beta-cell function in the mouse.

Introduction: Leptin inhibits insulin secretion from isolated islets from multiple species, but the cell type that mediates this process remains elusive. Several mouse models have been used to explore this question. Ablation of the leptin receptor (Lepr) throughout the pancreatic epithelium results in altered glucose homeostasis and ex vivo insulin secretion and Ca2+ dynamics. However, Lepr removal from neither alpha nor beta cells mimics this result. Moreover, scRNAseq data has revealed an enrichment of LEPR in human islet delta cells. Methods: We confirmed LEPR upregulation in human delta cells by performing RNAseq on fixed, sorted beta and delta cells. We then used a mouse model to test whether delta cells mediate the diminished glucose-stimulated insulin secretion in response to leptin. Results: Ablation of Lepr within mouse delta cells did not change glucose homeostasis or insulin secretion, whether mice were fed a chow or high-fat diet. We further show, using a publicly available scRNAseq dataset, that islet cells expressing Lepr lie within endothelial cell clusters. Conclusions: In mice, leptin does not influence beta-cell function through delta cells.

A systematic computational analysis of Mycobacterium tuberculosis H37Rv and human CD34+ genomic expression reveals crucial molecular entities involved in infection progression.

The co-evolution of Mycobacterium tuberculosis H37Rv along with its host systems enables the pathogenic bacterium to emerge as a multi-drug resistant form. This creates challenges for a more efficacious treatment strategy that can mitigate the infection. Working towards the same, our study followed a mathematical and statistical approach proposing that mycobacterial transcription factors regulating virulence and adaptation, host cell cytoplasmic component metabolism, oxidoreductase activity and respiratory ETC would be targets for antibiotics against Mycobacterium tuberculosis. Simultaneously, extending the statistical study on Mycobacterium-infected human cord blood CD34+ cells revealed that the human CD34+ genes, S100A8 and FGR (tyrosine-protein kinase, Src2), might be affected in the infection pathogenesis by Mycobacterium. Further, the deduced Mycobacterium-human gene interaction network proposed that mycobacterial coregulators Rv0452 (MarR family regulator) and Rv3862c (WhiB6) triggered genes controlling bacterial metabolism, which influences human immunological pathways involving TLR2 and CXCL8/MAPK8.Communicated by Ramaswamy H. Sarma.

Genes and Diseases: Insights from Transcriptomics Studies.

Results of expression studies can be useful to clarify the genotype-phenotype relationship. However, according to data from recent literature, there is a large group of genes that are revealed as differentially expressed (DE) in many studies, regardless of the biological context. Additional analyses could shed more light on the relationships between genes, their differential expression, and diseases. We generated a set of 9972 disease genes from five gene-phenotype databases (OMIM, ORPHANET, DDG2P, DisGeNet and MalaCards) and a report of the International Union of Immunological Societies. To study transcriptomics of disease and non-disease genes in healthy tissues, we obtained data from the Human Protein Atlas (HPA) website. We analyzed the dependency between expression in healthy tissues and gene occurrence in Gene Expression Omnibus series using tools within the Enrichr libraries. The results of expression studies were annotated with Gene Ontology (GO) and Human Phenotype Ontology (HPO) terms. Using transcriptomics analysis of healthy tissues, we validated the previous findings of higher expression levels of disease genes in pathologically linked tissues compared to other tissues. Preferentially DE genes were generally highly expressed in one or multiple tissues and were enriched for disease genes. According to the results of GO enrichment analyses, both down- and up-regulated DE genes most often took part in immune response, translation and tissue-specific processes. A connection between DE-related pathology and the diversity of HPO terms was found. Investigating a link between expression and phenotype contributes to understanding the mode of development and progression of human diseases.

The Impact of Varying Food Availability on Gene Expression in the Liver: Testing the Match-Mismatch Hypothesis.

Background: During early phases of life, such as prenatal or early postnatal development and adolescence, an organism's phenotype can be shaped by the environmental conditions it experiences. According to the Match-Mismatch hypothesis (MMH), changes to this environment during later life stages can result in a mismatch between the individual's adaptations and the prevailing environmental conditions. Thus, negative consequences in welfare and health can occur. We aimed to test the MMH in the context of food availability, assuming adolescence as a sensitive period of adaptation. Methods: We have previously reported a study of the physiological and behavioral effects of match and mismatch conditions of high (ad libitum) and low (90% of ad libitum intake) food availability from adolescence to early adulthood in female C57BL/6J mice (n = 62). Here, we performed RNA-sequencing of the livers of a subset of these animals (n = 16) to test the effects of match and mismatch feeding conditions on the liver transcriptome. Results: In general, we found no effect of the match-mismatch situations. Contrarily, the amount of food available during early adulthood (low vs. high) drove the differences we observed in final body weight and gene expression in the liver, regardless of the amount of food available to the animals during adolescence. Many of the differentially expressed genes and the corresponding biological processes found to be overrepresented overlapped, implicating common changes in various domains. These included metabolism, homeostasis, cellular responses to diverse stimuli, transport of bile acids and other molecules, cell differentiation, major urinary proteins, and immunity and inflammation. Conclusions: Our previous and present observations found no support for the MMH in the context of low vs high food availability from adolescence to early adulthood in female C57BL/6J mice. However, even small differences of approximately 10% in food availability during early adulthood resulted in physiological and molecular changes with potential beneficial implications for metabolic diseases.

Biomarker detection and categorization in ribonucleic acid sequencing meta-analysis using Bayesian hierarchical models.

Meta-analysis combining multiple transcriptomic studies increases statistical power and accuracy in detecting differentially expressed genes. As the next-generation sequencing experiments become mature and affordable, increasing number of RNA-seq datasets are available in the public domain. The count-data based technology provides better experimental accuracy, reproducibility and ability to detect low-expressed genes. A naive approach to combine multiple RNA-seq studies is to apply differential analysis tools such as edgeR and DESeq to each study and then combine the summary statistics of p-values or effect sizes by conventional meta-analysis methods. Such a two-stage approach loses statistical power, especially for genes with short length or low expression abundance. In this paper, we propose a full Bayesian hierarchical model (namely, BayesMetaSeq) for RNA-seq meta-analysis by modelling count data, integrating information across genes and across studies, and modelling potentially heterogeneous differential signals across studies via latent variables. A Dirichlet process mixture (DPM) prior is further applied on the latent variables to provide categorization of detected biomarkers according to their differential expression patterns across studies, facilitating improved interpretation and biological hypothesis generation. Simulations and a real application on multi-brain-region HIV-1 transgenic rats demonstrate improved sensitivity, accuracy and biological findings of the proposed method.

A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data.

As the sequencing cost continued to drop in the past decade, RNA sequencing (RNA-seq) has replaced microarray to become the standard high-throughput experimental tool to analyze transcriptomic profile. As more and more datasets are generated and accumulated in the public domain, meta-analysis to combine multiple transcriptomic studies to increase statistical power has received increasing popularity. In this article, we propose a Bayesian hierarchical model to jointly integrate microarray and RNA-seq studies. Since systematic fold change differences across RNA-seq and microarray for detecting differentially expressed genes have been previously reported, we replicated this finding in several real datasets and showed that incorporation of a normalization procedure to account for the bias improves the detection accuracy and power. We compared our method with the popular two-stage Fisher's method using simulations and two real applications in a histological subtype (invasive lobular carcinoma) of breast cancer comparing PR+ versus PR- and early-stage versus late-stage patients. The result showed improved detection power and more significant and interpretable pathways enriched in the detected biomarkers from the proposed Bayesian model.

DEApp: an interactive web interface for differential expression analysis of next generation sequence data.

BACKGROUND: A growing trend in the biomedical community is the use of Next Generation Sequencing (NGS) technologies in genomics research. The complexity of downstream differential expression (DE) analysis is however still challenging, as it requires sufficient computer programing and command-line knowledge. Furthermore, researchers often need to evaluate and visualize interactively the effect of using differential statistical and error models, assess the impact of selecting different parameters and cutoffs, and finally explore the overlapping consensus of cross-validated results obtained with different methods. This represents a bottleneck that slows down or impedes the adoption of NGS technologies in many labs. RESULTS: We developed DEApp, an interactive and dynamic web application for differential expression analysis of count based NGS data. This application enables models selection, parameter tuning, cross validation and visualization of results in a user-friendly interface. CONCLUSIONS: DEApp enables labs with no access to full time bioinformaticians to exploit the advantages of NGS applications in biomedical research. This application is freely available at https://yanli.shinyapps.io/DEAppand https://gallery.shinyapps.io/DEApp.