IgA Plasma Cells Are Long-Lived Residents of Gut and Bone Marrow That Express Isotype- and Tissue-Specific Gene Expression Patterns

Antibody secreting plasma cells are made in response to a variety of pathogenic and commensal microbes. While all plasma cells express a core gene transcription program that allows them to secrete large quantities of immunoglobulin, unique transcriptional profiles are linked to plasma cells expressing different antibody isotypes. IgA expressing plasma cells are generally thought of as short-lived in mucosal tissues and they have been understudied in systemic sites like the bone marrow. We find that IgA+ plasma cells in both the small intestine lamina propria and the bone marrow are long-lived and transcriptionally related compared to IgG and IgM expressing bone marrow plasma cells. IgA+ plasma cells show signs of shared clonality between the gut and bone marrow, but they do not recirculate at a significant rate and are found within bone marrow plasma cells niches. These data suggest that systemic and mucosal IgA+ plasma cells are from a common source, but they do not migrate between tissues. However, comparison of the plasma cells from the small intestine lamina propria to the bone marrow demonstrate a tissue specific gene transcription program. Understanding how these tissue specific gene networks are regulated in plasma cells could lead to increased understanding of the induction of mucosal versus systemic antibody responses and improve vaccine design.

Identification of Potential Genes in Pathogenesis and Diagnostic Value Analysis of Partial Androgen Insensitivity Syndrome Using Bioinformatics Analysis

BackgroundAndrogen insensitivity syndrome (AIS) is a rare X-linked genetic disease and one of the causes of 46,XY disorder of sexual development. The unstraightforward diagnosis of AIS and the gender assignment dilemma still make a plague for this disorder due to the overlapping clinical phenotypes.MethodsPeripheral blood mononuclear cells (PBMCs) of partial AIS (PAIS) patients and healthy controls were separated, and RNA-seq was performed to investigate transcriptome variance. Then, tissue-specific gene expression, functional enrichment, and protein–protein interaction (PPI) network analyses were performed; and the key modules were identified. Finally, the RNA expression of differentially expressed genes (DEGs) of interest was validated by quantitative real-time PCR (qRT-PCR).ResultsIn our dataset, a total of 725 DEGs were captured, with functionally enriched reproduction and immune-related pathways and Gene Ontology (GO) functions. The most highly specific systems centered on hematologic/immune and reproductive/endocrine systems. We finally filtered out CCR1, PPBP, PF4, CLU, KMT2D, GP6, and SPARC by the key gene clusters of the PPI network and manual screening of tissue-specific gene expression. These genes provide novel insight into the pathogenesis of AIS in the immune system or metabolism and bring forward possible molecular markers for clinical screening. The qRT-PCR results showed a consistent trend in the expression levels of related genes between PAIS patients and healthy controls.ConclusionThe present study sheds light on the molecular mechanisms underlying the pathogenesis and progression of AIS, providing potential targets for diagnosis and future investigation.

Integrative Network-Based Analysis Reveals Gene Networks and Novel Drug Repositioning Candidates for Alzheimer Disease

Background and ObjectivesTo integrate genome-wide association study data with tissue-specific gene expression information to identify coexpression networks, biological pathways, and drug repositioning candidates for Alzheimer disease.MethodsWe integrated genome-wide association summary statistics for Alzheimer disease with tissue-specific gene coexpression networks from brain tissue samples in the Genotype-Tissue Expression study. We identified gene coexpression networks enriched with genetic signals for Alzheimer disease and characterized the associated networks using biological pathway analysis. The disease-implicated modules were subsequently used as a molecular substrate for a computational drug repositioning analysis, in which we (1) imputed genetically regulated gene expression within Alzheimer disease implicated modules; (2) integrated the imputed gene expression levels with drug-gene signatures from the connectivity map to identify compounds that normalize dysregulated gene expression underlying Alzheimer disease; and (3) prioritized drug compounds and mechanisms of action based on the extent to which they normalize dysregulated expression signatures.ResultsGenetic factors for Alzheimer disease are enriched in brain gene coexpression networks involved in the immune response. Computational drug repositioning analyses of expression changes within the disease-associated networks retrieved known Alzheimer disease drugs (e.g., memantine) as well as biologically meaningful drug categories (e.g., glutamate receptor antagonists).DiscussionOur results improve the biological interpretation of genetic data for Alzheimer disease and provide a list of potential antidementia drug repositioning candidates for which the efficacy should be investigated in functional validation studies.

A novel feature extraction method based on highly expressed SNPs for tissue-specific gene prediction

Background Gene expression provides a means for an organism to produce gene products necessary for the organism to live. Variation in the significant gene expression levels can distinguish the gene and the tissue in which the gene is expressed. Tissue-specific gene expression, often determined by single nucleotide polymorphisms (SNPs), provides potential molecular markers or therapeutic targets for disease progression. Therefore, SNPs are good candidates for identifying disease progression. The current bioinformatics literature uses gene network modeling to summarize complex interactions between transcription factors, genes, and gene products. Here, our focus is on the SNPs’ impact on tissue-specific gene expression levels. To the best of our knowledge, we are not aware of any studies that distinguish tissue-specific genes using SNP expression levels. Method We propose a novel feature extraction method based on highly expressed SNPs using k-mers as features. We also propose optimal k-mer and feature sizes used in our approach. Determining the optimal sizes is still an open research question as it depends on the dataset and purpose of the analysis. Therefore, we evaluate our algorithm’s performance on a range of k-mer and feature sizes using a multinomial naive Bayes (MNB) classifier on genes in the 49 human tissues from the Genotype-Tissue Expression (GTEx) portal. Conclusions Our approach achieves practical performance results with k-mers of size 3. Based on the purpose of the analysis and the number of tissue-specific genes under study, feature sizes [7, 8, 9] and [8, 9, 10] are typically optimal for the machine learning model.

PEGS: An efficient tool for gene set enrichment within defined sets of genomic intervals

Many biological studies of transcriptional control mechanisms produce lists of genes and non-coding genomic intervals from corresponding gene expression and epigenomic assays. In higher organisms, such as eukaryotes, genes may be regulated by distal elements, with these elements lying 10s–100s of kilobases away from a gene transcription start site. To gain insight into these distal regulatory mechanisms, it is important to determine comparative enrichment of genes of interest in relation to genomic regions of interest, and to be able to do so at a range of distances. Existing bioinformatics tools can annotate genomic regions to nearest known genes, or look for transcription factor binding sites in relation to gene transcription start sites. Here, we present PEGS (Peak set Enrichment in Gene Sets). This tool efficiently provides an exploratory analysis by calculating enrichment of multiple gene sets, associated with multiple non-coding elements (peak sets), at multiple genomic distances, and within topologically associated domains. We apply PEGS to gene sets derived from gene expression studies, and genomic intervals from corresponding ChIP-seq and ATAC-seq experiments to derive biologically meaningful results. We also demonstrate an extended application to tissue-specific gene sets and publicly available GWAS data, to find enrichment of sleep trait associated SNPs in relation to tissue-specific gene expression profiles.

Tissue-specific 5-hydroxymethylcytosine landscape of the human genome

Abstract5-Hydroxymethylcytosine (5hmC) is an important epigenetic mark that regulates gene expression. Charting the landscape of 5hmC in human tissues is fundamental to understanding its regulatory functions. Here, we systematically profiled the whole-genome 5hmC landscape at single-base resolution for 19 types of human tissues. We found that 5hmC preferentially decorates gene bodies and outperforms gene body 5mC in reflecting gene expression. Approximately one-third of 5hmC peaks are tissue-specific differentially-hydroxymethylated regions (tsDhMRs), which are deposited in regions that potentially regulate the expression of nearby tissue-specific functional genes. In addition, tsDhMRs are enriched with tissue-specific transcription factors and may rewire tissue-specific gene expression networks. Moreover, tsDhMRs are associated with single-nucleotide polymorphisms identified by genome-wide association studies and are linked to tissue-specific phenotypes and diseases. Collectively, our results show the tissue-specific 5hmC landscape of the human genome and demonstrate that 5hmC serves as a fundamental regulatory element affecting tissue-specific gene expression programs and functions.

Transcrptome analysis via photo-isolation chemistry

To gain insights into tissue-specific gene expression in multicellular systems, gene expression profiles are required to be precisely linked with spatial information. Here, we establish a hight-depth spatial transcriptomics method, photo-isolation chemistrty (PIC), which is able to isolate gene expression profiles only from UV-irradiatied region out of whole tissue section. This method performs reverse transcription on tissue sections using photocaged oligo DNAs. After the UV irradiation, the cDNAs in the irradiated regions are allowed to be amplified and sequenced, thereby providing gene expression profiles linked with spatial information.