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.