SummaryTumor-infiltrating regulatory T lymphocytes (Treg) can suppress effector T cells specific for tumor antigens. Deeper molecular definitions of tumor-infiltrating-lymphocytes could thus offer therapeutic opportunities. Transcriptomes of T helper 1 (Th1), Th17, and Treg cells infiltrating colorectal or non-small-cell lung cancers were compared to transcriptomes of the same subsets from normal tissues and validated at the single-cell level. We found that tumor-infiltrating Treg cells were highly suppressive, upregulated several immune-checkpoints, and expressed on the cell surfaces specific signature molecules such as interleukin-1 receptor 2 (IL1R2), programmed death (PD)-1 Ligand1, PD-1 Ligand2, and CCR8 chemokine, which were not previously described on Treg cells. Remarkably, high expression in whole-tumor samples of Treg cell signature genes, such as LAYN, MAGEH1, or CCR8, correlated with poor prognosis. Our findings provide insights into the molecular identity and functions of human tumor-infiltrating Treg cells and define potential targets for tumor immunotherapy.

Long non-coding-RNAs are emerging as important regulators of cellular functions but little is known on their role in human immune system. Here we investigated long intergenic non-coding-RNAs (lincRNAs) in thirteen T and B lymphocyte subsets by RNA-seq analysis and de novo transcriptome reconstruction. Over five hundred new lincRNAs were identified and lincRNAs signatures were described. Expression of linc-MAF-4, a chromatin-associated TH1-specific lincRNA, was inversely correlated with MAF, a TH2-associated transcription factor. Linc-MAF-4 down-regulation skewed T cell differentiation toward TH2. We identified a long-distance interaction between linc-MAF-4 and MAF genomic regions, where linc-MAF-4 associates with LSD1 and EZH2, suggesting linc-MAF-4 regulated MAF transcription by recruitment of chromatin modifiers. Our results demonstrate a key role of lincRNAs in T lymphocyte differentiation.

CD4(+) T lymphocytes orchestrate adaptive immune responses by differentiating into various subsets of effector T cells such as T-helper 1 (Th1), Th2, Th17, and regulatory T cells. These subsets have been generally described by master transcription factors that dictate the expression of cytokines and receptors, which ultimately define lymphocyte effector functions. However, the view of T-lymphocyte subsets as stable and terminally differentiated lineages has been challenged by increasing evidence of functional plasticity within CD4(+) T-cell subsets, which implies flexible programming of effector functions depending on time and space of T-cell activation. An outstanding question with broad basic and traslational implications relates to the mechanisms, besides transcriptional regulation, which define the plasticity of effector functions. In this study, we discuss the emerging role of regulatory non-coding RNAs in T-cell differentiation and plasticity. Not only microRNAs have been proven to be important for CD4(+) T-cell differentiation, but it is also likely that the overall T-cell functioning is the result of a multilayered network composed by coding RNAs as well as by short and long non-coding RNAs. The integrated study of all the nodes of this network will provide a comprehensive view of the molecular mechanisms underlying T-cell functions in health and disease.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is an invaluable tool for mapping chromatin-associated proteins. Current barcoding strategies aim to improve assay throughput and scalability but intense sample handling and lack of standardization over cell types, cell numbers and epitopes hinder wide-spread use in the field. Here, we present a barcoding method to enable high-throughput ChIP-seq using common molecular biology techniques. The method, called RELACS (restriction enzyme-based labeling of chromatin in situ) relies on standardized nuclei extraction from any source and employs chromatin cutting and barcoding within intact nuclei. Barcoded nuclei are pooled and processed within the same ChIP reaction, for maximal comparability and workload reduction. The innovative barcoding concept is particularly user-friendly and suitable for implementation to standardized large-scale clinical studies and scarce samples. Aiming to maximize universality and scalability, RELACS can generate ChIP-seq libraries for transcription factors and histone modifications from hundreds of samples within three days.

To help better understand the role of long noncoding RNAs in the human immune system, we recently generated a comprehensive RNA-seq data set using 63 RNA samples from 13 subsets of T (CD4+ naive, CD4+ TH1, CD4+ TH2, CD4+ TH17, CD4+ Treg, CD4+ TCM, CD4+ TEM, CD8+ TCM, CD8+ TEM, CD8+ naive) and B (B naive, B memory, B CD5+) lymphocytes. There were five biological replicates for each subset except for CD8+ TCM and B CD5+ populations that included 4 replicates. RNA-Seq data were generated by an Illumina HiScanSQ sequencer using the TruSeq v3 Cluster kit. 2.192 billion of paired-ends reads, 2×100 bp, were sequenced and after filtering a total of about 1.7 billion reads were mapped. Using different de novo transcriptome reconstruction techniques over 500 previously unknown lincRNAs were identified. The current data set could be exploited to drive the functional characterization of lincRNAs, identify novel genes and regulatory networks associated with specific cells subsets of the human immune system.

BackgroundThe alarming rise of obesity and its associated comorbidities represents a medical burden and a major global health and economic issue. Understanding etiological mechanisms underpinning susceptibility and therapeutic response is of primary importance. Obesity, diabetes, and metabolic diseases are complex trait disorders with only partial genetic heritability, indicating important roles for environmental programing and epigenetic effects.Scope of the reviewWe will highlight some of the reasons for the scarce predictability of metabolic diseases. We will outline how genetic variants generate phenotypic variation in disease susceptibility across populations. We will then focus on recent conclusions about epigenetic mechanisms playing a fundamental role in increasing variability and subsequently disease triggering.Major conclusionsCurrently, we are unable to predict or mechanistically define how “missing heritability” drives disease. Unravelling this black box of regulatory processes will allow us to move towards a truly personalized and precision medicine.

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RNA-Seq is an approach to transcriptome profiling that uses deep-sequencing technologies to detect and accurately quantify RNA molecules originating from a genome at a given moment in time. In recent years, the advent of RNA-Seq has facilitated genome-wide expression profiling, including the identification of novel and rare transcripts like noncoding RNAs and novel alternative splicing isoforms.Here, we describe the analytical steps required for the identification and characterization of noncoding RNAs starting from RNA-Seq raw samples, with a particular emphasis on long noncoding RNAs (lncRNAs).