Tracking global gene expression responses in T cell differentiation

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided significant impact on our understanding of biological systems. They contribute to almost all aspects of biomedical research, including studying developmental biology, host-parasite relationships, disease progression and drug effects. However, the high-throughput data generations present challenges for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for high-throughput gene expression analysis that extends the functionality of our previous ABioTrans with several new tools, including protein datasets analysis, and a web interface. GeneCloudOmics allows both microarray and RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications. Furthermore, GeneCloudOmics allows the direct import of gene expression data from the NCBI Gene Expression Omnibus database. The user can perform all tasks rapidly through an intuitive graphical user interface that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. Thus, GeneCloudOmics is a one-stop open-source tool for gene expression data analysis and visualization. It is freely available at http://combio-sifbi.org/GeneCloudOmics.

ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes

Differential expressed (DE) genes analysis is valuable for understanding comparative transcriptomics between cells, conditions or time evolution. However, the predominant way of identifying DE genes is to use arbitrary threshold fold or expression changes as cutoff. Here, we developed a more objective method, Scatter Overlay or ScatLay, to extract and graphically visualize DE genes across any two samples by utilizing their pair-wise scatter or transcriptome-wide noise, while factoring replicate variabilities. We tested ScatLay for 3 cell types: between time points for Escherichia coli aerobiosis and Saccharomyces cerevisiae hypoxia, and between untreated and Etomoxir treated Mus Musculus embryonic stem cell. As a result, we obtain 1194, 2061 and 2932 DE genes, respectively. Next, we compared these data with two widely used current approaches (DESeq2 and NOISeq) with typical twofold expression changes threshold, and show that ScatLay reveals significantly larger number of DE genes. Hence, our method provides a wider coverage of DE genes, and will likely pave way for finding more novel regulatory genes in future works.

Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli

For any dynamical system, like living organisms, an attractor state is a set of variables or mechanisms that converge towards a stable system behavior despite a wide variety of initial conditions. Here, using multi-dimensional statistics, we investigate the global gene expression attractor mechanisms shaping anaerobic to aerobic state transition (AAT) of Escherichia coli in a bioreactor at early times. Out of 3,389 RNA-Seq expression changes over time, we identified 100 sharply changing genes that are key for guiding 1700 genes into the AAT attractor basin. Collectively, these genes were named as attractor genes constituting of 6 dynamic clusters. Apart from the expected anaerobic (glycolysis), aerobic (TCA cycle) and fermentation (succinate pathways) processes, sulphur metabolism, ribosome assembly and amino acid transport mechanisms together with 332 uncharacterised genes are also key for AAT. Overall, our work highlights the importance of multi-dimensional statistical analyses for revealing novel processes shaping AAT.

ABioTrans: A Biostatistical Tool for Transcriptomics Analysis

Here we report a bio-statistical/informatics tool, ABioTrans, developed in R for gene expression analysis. The tool allows the user to directly read RNA-Seq data files deposited in the Gene Expression Omnibus or GEO database. Operated using any web browser application, ABioTrans provides easy options for multiple statistical distribution fitting, Pearson and Spearman rank correlations, PCA, k-means and hierarchical clustering, differential expression (DE) analysis, Shannon entropy and noise (square of coefficient of variation) analyses, as well as Gene ontology classifications.

Blood handling and leukocyte isolation methods impact the global transcriptome of immune cells

Background

Transcriptional profiling with ultra-low input methods can yield valuable insights into disease, particularly when applied to the study of immune cells using RNA-sequencing. The advent of these methods has allowed for their use in profiling cells collected in clinical trials and other studies that involve the coordination of human-derived material. To date, few studies have sought to quantify what effects that collection and handling of this material can have on resulting data.

Results

We characterized the global effects of blood handling, methods for leukocyte isolation, and preservation media on low numbers of immune cells isolated from blood. We found overall that storage/shipping temperature of blood prior to leukocyte isolation and sorting led to global changes in both CD8⁺ T cells and monocytes, including alterations in immune-related gene sets. We found that the use of a leukocyte filtration system minimized these alterations and we applied this method to generate high-quality transcriptional data from sorted immune cells isolated from the blood of intracerebral hemorrhage patients and matched healthy controls.

Conclusions

Our data underscore the necessity of processing samples with comparably defined protocols prior to transcriptional profiling and demonstrate that a filtration method can be applied to quickly isolate immune cells of interest while minimizing transcriptional bias.

Electronic supplementary material

The online version of this article (10.1186/s12865-018-0268-6) contains supplementary material, which is available to authorized users.

Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells

Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models.

Chromatin Remodeling Protein SMAR1 Is a Critical Regulator of T Helper Cell Differentiation and Inflammatory Diseases

T cell differentiation from naïve T cells to specialized effector subsets of mature cells is determined by the iterative action of transcription factors. At each stage of specific T cell lineage differentiation, transcription factor interacts not only with nuclear proteins such as histone and histone modifiers but also with other factors that are bound to the chromatin and play a critical role in gene expression. In this review, we focus on one of such nuclear protein known as tumor suppressor and scaffold matrix attachment region-binding protein 1 (SMAR1) in CD4⁺ T cell differentiation. SMAR1 facilitates Th1 differentiation by negatively regulating T-bet expression via recruiting HDAC1–SMRT complex to its gene promoter. In contrast, regulatory T (T_reg) cell functions are dependent on inhibition of Th17-specific genes mainly IL-17 and STAT3 by SMAR1. Here, we discussed a critical role of chromatin remodeling protein SMAR1 in maintaining a fine-tuned balance between effector CD4⁺ T cells and T_reg cells by influencing the transcription factors during allergic and autoimmune inflammatory diseases.

Gene expression profiling of CD8+ T cells induced by ovarian cancer cells suggests a possible mechanism for CD8+ Treg cell production

OBJECTIVES

The aim of this study was to investigate a possible mechanism of CD8⁺ regulatory T-cell (Treg) production in an ovarian cancer (OC) microenvironment.

MATERIALS AND METHODS

Agilent microarray was used to detect changes in gene expression between CD8⁺ T cells cultured with and without the SKOV3 ovarian adenocarcinoma cell line. QRT-PCR was performed to determine glycolysis gene expression in CD8⁺ T cells from a transwell culturing system and OC patients. We also detected protein levels of glycolysis-related genes using Western blot analysis.

RESULTS

Comparing gene expression profiles revealed significant differences in expression levels of 1420 genes, of which 246 were up-regulated and 1174 were down-regulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that biological processes altered in CD8⁺ Treg are particularly associated with energy metabolism. CD8⁺ Treg cells induced by co-culture with SKOV3 had lower glycolysis gene expression compared to CD8⁺ T cells cultured alone. Glycolysis gene expression was also decreased in the CD8⁺ T cells of OC patients.

CONCLUSIONS

These findings provide a comprehensive bioinformatics analysis of DEGs in CD8⁺ T cells cultured with and without SKOV3 and suggests that metabolic processes may be a possible mechanism for CD8⁺ Treg induction.