Comprehensive mapping of the cell response to E. coli infection in porcine intestinal epithelial cells pretreated with exopolysaccharide derived from Lactobacillus reuteri.

Bacterial exopolysaccharides (EPSs) are known to modulate immunity. To date, a plethora of studies have reported the effect of EPSs on intestinal cells; however few works have revealed a complete picture of the signalling events in intestinal epithelial cells induced by bacterial EPSs. Here, using transcriptomics, we comprehensively mapped the biological processes in porcine intestinal epithelial cells challenged with EPS derived from Lactobacillus reuteri alone, enterotoxigenic Escherichia coli (ETEC) or ETEC after pretreatment with EPS. The Gene Ontology analysis of differentially expressed genes (DEGs) showed that ETEC is able to evoke biological processes specifically involved in cell junction reorganization, extracellular matrix degradation, and activation of the innate immune response through the activation of pattern recognition receptors, such as TLRs and CTRs. A total of 495 DEGs were induced in ETEC-challenged cells. On the other hand, EPS pretreatment was able to attenuate overexpression of the genes induced by ETEC infection. The most relevant finding of this study is that EPS has a suppressive effect on the inflammatory response evoked by ETEC infection. On the basis of high-throughput RNA-seq, this report is the first to describe the effects of EPSs derived from L. reuteri used as a pretreatment of global gene expression in porcine epithelial cells.

Linoleic acid potentiates CD8+ T cell metabolic fitness and antitumor immunity.

The metabolic state represents a major hurdle for an effective adoptive T cell therapy (ACT). Indeed, specific lipids can harm CD8+ T cell (CTL) mitochondrial integrity, leading to defective antitumor responses. However, the extent to which lipids can affect the CTL functions and fate remains unexplored. Here, we show that linoleic acid (LA) is a major positive regulator of CTL activity by improving metabolic fitness, preventing exhaustion, and stimulating a memory-like phenotype with superior effector functions. We report that LA treatment enhances the formation of ER-mitochondria contacts (MERC), which in turn promotes calcium (Ca2+) signaling, mitochondrial energetics, and CTL effector functions. As a direct consequence, the antitumor potency of LA-instructed CD8 T cells is superior in vitro and in vivo. We thus propose LA treatment as an ACT potentiator in tumor therapy.

Development of a bioinformatics platform for analysis of quantitative transcriptomics and proteomics data: the OMnalysis.

BACKGROUND: In the past decade, RNA sequencing and mass spectrometry based quantitative approaches are being used commonly to identify the differentially expressed biomarkers in different biological conditions. Data generated from these approaches come in different sizes (e.g., count matrix, normalized list of differentially expressed biomarkers, etc.) and shapes (e.g., sequences, spectral data, etc.). The list of differentially expressed biomarkers is used for functional interpretation and retrieve biological meaning, however, it requires moderate computational skills. Thus, researchers with no programming expertise find difficulty in data interpretation. Several bioinformatics tools are available to analyze such data; however, they are less flexible for performing the multiple steps of visualization and functional interpretation. IMPLEMENTATION: We developed an easy-to-use Shiny based web application (named as OMnalysis) that provides users with a single platform to analyze and visualize the differentially expressed data. The OMnalysis accepts the data in tabular form from edgeR, DESeq2, MaxQuant Perseus, R packages, and other similar software, which typically contains the list of differentially expressed genes or proteins, log of the fold change, log of the count per million, the P value, q-value, etc. The key features of the OMnalysis are multiple image type visualization and their dimension customization options, seven multiple hypothesis testing correction methods to get more significant gene ontology, network topology-based pathway analysis, and multiple databases support (KEGG, Reactome, PANTHER, biocarta, NCI-Nature Pathway Interaction Database PharmGKB and STRINGdb) for extensive pathway enrichment analysis. OMnalysis also fetches the literature information from PubMed to provide supportive evidence to the biomarkers identified in the analysis. In a nutshell, we present the OMnalysis as a well-organized user interface, supported by peer-reviewed R packages with updated databases for quick interpretation of the differential transcriptomics and proteomics data to biological meaning. AVAILABILITY: The OMnalysis codes are entirely written in R language and freely available at https://github.com/Punit201016/OMnalysis. OMnalysis can also be accessed from - http://lbmi.uvlf.sk/omnalysis.html. OMnalysis is hosted on a Shiny server at https://omnalysis.shinyapps.io/OMnalysis/. The minimum system requirements are: 4 gigabytes of RAM, i3 processor (or equivalent). It is compatible with any operating system (windows, Linux or Mac). The OMnalysis is heavily tested on Chrome web browsers; thus, Chrome is the preferred browser. OMnalysis works on Firefox and Safari.

Transcriptome analysis of human brain microvascular endothelial cells response to Neisseria meningitidis and its antigen MafA using RNA-seq.

Interaction of Neisseria meningitidis (NM) with human brain microvascular endothelial cells (hBMECs) initiates of multiple cellular processes, which allow bacterial translocation across the blood-brain barrier (BBB). NM is equipped with several antigens, which interacts with the host cell receptors. Recently we have shown that adhesin MafA (UniProtKB-X5EG71), relatively less studied protein, is one of those surface exposed antigens that adhere to hBMECs. The present study was designed to comprehensively map the undergoing biological processes in hBMECs challenged with NM or MafA using RNA sequencing. 708 and 726 differentially expressed genes (DEGs) were identified in hBMECs exposed to NM and MafA, respectively. Gene ontology analysis of the DEGs revealed that several biological processes, which may alter the permeability of BBB, were activated. Comparative analysis of DEGs revealed that MafA, alike NM, might provoke TLR-dependent pathway and augment cytokine response. Moreover, both MafA and NM were able to induce genes involved in cell surface modifications, endocytosis, extracellular matrix remodulation and anoikis/apoptosis. In conclusion, this study for the first time describes effect of NM on the global gene expression in hBMECs using high-throughput RNA-seq. It also presents ability of MafA to induce gene expression, which might aid NM in breaching the BBB.

Transcriptome Analysis of Two Rice Varieties Contrasting for Nitrogen Use Efficiency under Chronic N Starvation Reveals Differences in Chloroplast and Starch Metabolism-Related Genes.

The nitrogen use efficiency (NUE) of crop plants is limited and enhancing it in rice, a major cereal crop, would be beneficial for farmers and the environment alike. Here we report the genome-wide transcriptome analysis of two rice genotypes, IR 64 (IR64) and Nagina 22 (N22) under optimal (+N) and chronic starvation (-N) of nitrogen (N) from 15-day-old root and shoot tissues. The two genotypes were found to be contrasting in their response to -N; IR64 root architecture and root dry weight remained almost equivalent to that under +N conditions, while N22 showed high foraging ability but a substantial reduction in biomass under -N. Similarly, the photosynthetic pigments showed a drastic reduction in N22 under low N, while IR64 was more resilient. Nitrate reductase showed significantly low specific activity under -N in both genotypes. Glutamate synthase (GOGAT) and citrate synthase CS activity were highly reduced in N22 but not in IR64. Transcriptome analysis of these genotypes revealed nearly double the number of genes to be differentially expressed (DEGs) in roots (1016) compared to shoots (571). The response of the two genotypes to N starvation was distinctly different reflecting their morphological/biochemical response with just two and eight common DEGs in the root and shoot tissues. There were a total of 385 nitrogen-responsive DEGs (106 in shoots and 279 in roots) between the two genotypes. Fifty-two of the 89 DEGs identified as specific to N22 root tissues were also found to be differentially expressed between the two genotypes under -N. Most of these DEGs belonged to starch and chloroplast metabolism, followed by membrane and signaling proteins. Physical mapping of DEGs revealed 95 DEGs in roots and 76 in shoots to be present in quantitative trait loci (QTL) known for NUE.