The transcription factor BCL-6 controls early development of innate-like T cells.

Innate T cells, including invariant natural killer T (iNKT) and mucosal-associated innate T (MAIT) cells, are a heterogeneous T lymphocyte population with effector properties preprogrammed during their thymic differentiation. How this program is initiated is currently unclear. Here, we show that the transcription factor BCL-6 was transiently expressed in iNKT cells upon exit from positive selection and was required for their proper development beyond stage 0. Notably, development of MAIT cells was also impaired in the absence of Bcl6. BCL-6-deficient iNKT cells had reduced expression of genes that were associated with the innate T cell lineage, including Zbtb16, which encodes PLZF, and PLZF-targeted genes. BCL-6 contributed to a chromatin accessibility landscape that was permissive for the expression of development-related genes and inhibitory for genes associated with naive T cell programs. Our results revealed new functions for BCL-6 and illuminated how this transcription factor controls early iNKT cell development.

Catchet-MS identifies IKZF1-targeting thalidomide analogues as novel HIV-1 latency reversal agents.

A major pharmacological strategy toward HIV cure aims to reverse latency in infected cells as a first step leading to their elimination. While the unbiased identification of molecular targets physically associated with the latent HIV-1 provirus would be highly valuable to unravel the molecular determinants of HIV-1 transcriptional repression and latency reversal, due to technical limitations, this has been challenging. Here we use a dCas9 targeted chromatin and histone enrichment strategy coupled to mass spectrometry (Catchet-MS) to probe the differential protein composition of the latent and activated HIV-1 5'LTR. Catchet-MS identified known and novel latent 5'LTR-associated host factors. Among these, IKZF1 is a novel HIV-1 transcriptional repressor, required for Polycomb Repressive Complex 2 recruitment to the LTR. We find the clinically advanced thalidomide analogue iberdomide, and the FDA approved analogues lenalidomide and pomalidomide, to be novel LRAs. We demonstrate that, by targeting IKZF1 for degradation, these compounds reverse HIV-1 latency in CD4+ T-cells isolated from virally suppressed people living with HIV-1 and that they are able to synergize with other known LRAs.

Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population.

BACKGROUND: Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource. RESULTS: We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue. CONCLUSIONS: By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.

Integrative, normalization-insusceptible statistical analysis of RNA-Seq data, with improved differential expression and unbiased downstream functional analysis.

The study of differential gene expression patterns through RNA-Seq comprises a routine task in the daily lives of molecular bioscientists, who produce vast amounts of data requiring proper management and analysis. Despite widespread use, there are still no widely accepted golden standards for the normalization and statistical analysis of RNA-Seq data, and critical biases, such as gene lengths and problems in the detection of certain types of molecules, remain largely unaddressed. Stimulated by these unmet needs and the lack of in-depth research into the potential of combinatorial methods to enhance the analysis of differential gene expression, we had previously introduced the PANDORA P-value combination algorithm while presenting evidence for PANDORA's superior performance in optimizing the tradeoff between precision and sensitivity. In this article, we present the next generation of the algorithm along with a more in-depth investigation of its capabilities to effectively analyze RNA-Seq data. In particular, we show that PANDORA-reported lists of differentially expressed genes are unaffected by biases introduced by different normalization methods, while, at the same time, they comprise a reliable input option for downstream pathway analysis. Additionally, PANDORA outperforms other methods in detecting differential expression patterns in certain transcript types, including long non-coding RNAs.

recoup: flexible and versatile signal visualization from next generation sequencing.

BACKGROUND: The relentless continuing emergence of new genomic sequencing protocols and the resulting generation of ever larger datasets continue to challenge the meaningful summarization and visualization of the underlying signal generated to answer important qualitative and quantitative biological questions. As a result, the need for novel software able to reliably produce quick, comprehensive, and easily repeatable genomic signal visualizations in a user-friendly manner is rapidly re-emerging. RESULTS: recoup is a Bioconductor package for quick, flexible, versatile, and accurate visualization of genomic coverage profiles generated from Next Generation Sequencing data. Coupled with a database of precalculated genomic regions for multiple organisms, recoup offers processing mechanisms for quick, efficient, and multi-level data interrogation with minimal effort, while at the same time creating publication-quality visualizations. Special focus is given on plot reusability, reproducibility, and real-time exploration and formatting options, operations rarely supported in similar visualization tools in a profound way. recoup was assessed using several qualitative user metrics and found to balance the tradeoff between important package features, including speed, visualization quality, overall friendliness, and the reusability of the results with minimal additional calculations. CONCLUSION: While some existing solutions for the comprehensive visualization of NGS data signal offer satisfying results, they are often compromised regarding issues such as effortless tracking of processing and preparation steps under a common computational environment, visualization quality and user friendliness. recoup is a unique package presenting a balanced tradeoff for a combination of assessment criteria while remaining fast and friendly.

Distinct transcriptional profile of blood mononuclear cells in Behçet's disease: insights into the central role of neutrophil chemotaxis.

OBJECTIVES: Both innate and adaptive immune responses are reportedly increased in Behçet's disease (BD), a chronic, relapsing systemic vasculitis lying at the intersection between autoinflammation and autoimmunity. To further study pathophysiologic molecular mechanisms operating in BD, we searched for transcriptome-wide changes in blood mononuclear cells from these patients. METHODS: We performed 3' mRNA next-generation sequencing-based genome-wide transcriptional profiling followed by analysis of differential expression signatures, Kyoto Encyclopedia of Genes and Genomes pathways, GO biological processes and transcription factor signatures. RESULTS: Differential expression analysis clustered the transcriptomes of 13 patients and one healthy subject separately from those of 10 healthy age/gender-matched controls and one patient. Among the total of 17 591 expressed protein-coding genes, 209 and 31 genes were significantly upregulated and downregulated, respectively, in BD vs controls by at least 2-fold. The most upregulated genes comprised an abundance of CC- and CXC-chemokines. Remarkably, the 5 out of top 10 upregulated biological processes involved leucocyte recruitment to peripheral tissues, especially for neutrophils. Moreover, NF-kB, TNF and IL-1 signalling pathways were prominently enhanced in BD, while transcription factor activity analysis suggested that the NF-kB p65/RELA subunit action underlies the observed differences in the BD transcriptome. CONCLUSION: This RNA-sequencing analysis in peripheral blood mononuclear cells derived from patients with BD does not support a major pathogenetic role for adaptive immunity-driven mechanisms, but clearly points to the action of aberrant innate immune responses with a central role played by upregulated neutrophil chemotaxis.

Increased Autotaxin Levels in Severe COVID-19, Correlating with IL-6 Levels, Endothelial Dysfunction Biomarkers, and Impaired Functions of Dendritic Cells.

Autotaxin (ATX; ENPP2) is a secreted lysophospholipase D catalyzing the extracellular production of lysophosphatidic acid (LPA), a pleiotropic signaling phospholipid. Genetic and pharmacologic studies have previously established a pathologic role for ATX and LPA signaling in pulmonary injury, inflammation, and fibrosis. Here, increased ENPP2 mRNA levels were detected in immune cells from nasopharyngeal swab samples of COVID-19 patients, and increased ATX serum levels were found in severe COVID-19 patients. ATX serum levels correlated with the corresponding increased serum levels of IL-6 and endothelial damage biomarkers, suggesting an interplay of the ATX/LPA axis with hyperinflammation and the associated vascular dysfunction in COVID-19. Accordingly, dexamethasone (Dex) treatment of mechanically ventilated patients reduced ATX levels, as shown in two independent cohorts, indicating that the therapeutic benefits of Dex include the suppression of ATX. Moreover, large scale analysis of multiple single cell RNA sequencing datasets revealed the expression landscape of ENPP2 in COVID-19 and further suggested a role for ATX in the homeostasis of dendritic cells, which exhibit both numerical and functional deficits in COVID-19. Therefore, ATX has likely a multifunctional role in COVID-19 pathogenesis, suggesting that its pharmacological targeting might represent an additional therapeutic option, both during and after hospitalization.

Spontaneous development of hepatocellular carcinoma with cancer stem cell properties in PR-SET7-deficient livers.

PR-SET7-mediated histone 4 lysine 20 methylation has been implicated in mitotic condensation, DNA damage response and replication licensing. Here, we show that PR-SET7 function in the liver is pivotal for maintaining genome integrity. Hepatocyte-specific deletion of PR-SET7 in mouse embryos resulted in G2 phase arrest followed by massive cell death and defect in liver organogenesis. Inactivation at postnatal stages caused cell duplication-dependent hepatocyte necrosis, accompanied by inflammation, fibrosis and compensatory growth induction of neighboring hepatocytes and resident ductal progenitor cells. Prolonged necrotic regenerative cycles coupled with oncogenic STAT3 activation led to the spontaneous development of hepatic tumors composed of cells with cancer stem cell characteristics. These include a capacity to self-renew in culture or in xenografts and the ability to differentiate to phenotypically distinct hepatic cells. Hepatocellular carcinoma in PR-SET7-deficient mice displays a cancer stem cell gene signature specified by the co-expression of ductal progenitor markers and oncofetal genes.

Smyd3-associated regulatory pathways in cancer.

SMYD3 is a member of the SET and MYND-domain family of methyl-transferases, the increased expression of which correlates with poor prognosis in various types of cancer. In liver and colon tumors, SMYD3 is localized in the nucleus, where it interacts with RNA Pol II and H3K4me3 and functions as a selective transcriptional amplifier of oncogenes and genes that control cell proliferation and metastatic spread. Smyd3 expression has a high discriminative power for the characterization of liver tumors and positively correlates with poor prognosis. In lung and pancreatic cancer, SMYD3 acts in the cytoplasm, potentiating oncogenic Ras/ERK signaling through the methylation of the MAP3K2 kinase and the subsequent release from its inhibitor. A clinico-pathological analysis of lung cancer patients uncovers prognostic significance of SMYD3 only for first progression survival. However, stratification of patients according to their smoking history significantly expands the prognostic value of SMYD3 to overall survival and other features, suggesting that smoking-related effects saturate the clinical analysis and mask the function of SMYD3 as an oncogenic potentiator.

Refining a steroidogenic model: an analysis of RNA-seq datasets from insect prothoracic glands.

BACKGROUND: The prothoracic gland (PG), the principal steroidogenic organ of insects, has been proposed as a model for steroid hormone biosynthesis and regulation. RESULTS: To validate the robustness of the model, we present an analysis of accumulated transcriptomic data from PGs of two model species, Drosophila melanogaster and Bombyx mori. We identify that the common core components of the model in both species are encoded by nine genes. Five of these are Halloween genes whose expression differs substantially between the PGs of these species. CONCLUSIONS: We conclude that the PGs can be a model for steroid hormone synthesis and regulation within the context of mitochondrial cholesterol transport and steroid biosynthesis but beyond these core mechanisms, gene expression in insect PGs is too diverse to fit in a context-specific model and should be analysed within a species-specific framework.

Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns.

RNA-Seq is gradually becoming the standard tool for transcriptomic expression studies in biological research. Although considerable progress has been recorded in the development of statistical algorithms for the detection of differentially expressed genes using RNA-Seq data, the list of detected genes can differ significantly between algorithms. We present a new method (PANDORA) that combines multiple algorithms toward a summarized result, more efficiently reflecting true experimental outcomes. This is achieved through the systematic combination of several analysis algorithms, by weighting their outcomes according to their performance with realistically simulated data sets generated from real data. Results supported by the analysis of both simulated and real data from different organisms as well as correlation with PolII occupancy demonstrate that PANDORA improves the detection of differential expression. It accomplishes this by optimizing the tradeoff between standard performance measurements, such as precision and sensitivity.

Pretransplant urinary proteome analysis does not predict development of chronic kidney disease after liver transplantation.

BACKGROUND & AIMS: Chronic kidney disease (CKD) is a common complication after liver transplantation. Kidney biopsies cannot be easily performed before liver transplantation to predict patients at high risk for CKD. The aim of our study was to determine whether pre-, peri- and post-transplant factors, as well as peptides present in preliver transplant urine samples were associated with loss in kidney function at 6 months post-transplantation using proteome analysis. METHODS: Eighty patients who underwent a liver transplantation and that had pretransplant glomerular filtration rate (GFR) value of ≥60 mL/min/1.73 m² (MDRD) were included in the study. RESULTS: GFR decreased significantly after transplantation. At month 6 post-transplantation, 40 patients displayed a CKD, i.e. eGFR of <60 mL/min/1.73 m², while the other 40 patients did not. Although thousands of peptides were identified, none was significantly associated with the development of CKD at 6 months after liver transplantation. Moreover, using a urinary peptidome classifier to detect preexisting CKD, no difference was found in CKD scores between the 2 groups. After analysis of a large number of pre-, peri- and post-transplant parameters, viral hepatitis as a cause for liver transplantation was the sole independent predictive factor for CKD. No difference in peptides with differential urinary abundance between patients who received a graft for virus related liver disease vs. all other causes of liver disease was observed. CONCLUSION: Urinary peptidome analysis before liver transplantation failed to identify a peptide pattern associated with the development of CKD at 6 months after liver transplantation.

Coactivation of GR and NFKB alters the repertoire of their binding sites and target genes.

Glucocorticoid receptor (GR) exerts anti-inflammatory action in part by antagonizing proinflammatory transcription factors such as the nuclear factor kappa-b (NFKB). Here, we assess the crosstalk of activated GR and RELA (p65, major NFKB component) by global identification of their binding sites and target genes. We show that coactivation of GR and p65 alters the repertoire of regulated genes and results in their association with novel sites in a mutually dependent manner. These novel sites predominantly cluster with p65 target genes that are antagonized by activated GR and vice versa. Our data show that coactivation of GR and NFKB alters signaling pathways that are regulated by each factor separately and provide insight into the networks underlying the GR and NFKB crosstalk.

Evaluation of Polygenic Risk Scores for Prediction of Coronary Artery Disease in a Greek Case-Control Study.

Coronary artery disease (CAD) stands as the most predominant type of cardiovascular disease (CVD). Polygenic risk scores (PRSs) have become essential tools for quantifying genetic susceptibility, and researchers endeavor to improve their predictive precision. The aim of the present work is to assess the performance and the relative contribution of PRSs developed for CVD or CAD within a Greek population. The sample under study comprised 924 Greek individuals (390 cases with CAD and 534 controls) from the THISEAS study. Nine PRSs drawn from the PGS catalog were replicated and tested for CAD risk prediction. PRSs computations were performed in the R language, and snpStats was used to process genotypic data. Descriptive characteristics of the study were analyzed using the statistical software IBM SPSS Statistics v21.0. The effectiveness of each PRS was assessed using the PRS R2 metric provided by PRSice2. Among nine PRSs, PGS000747 greatly increased the predictive value of primary CAD risk factors by 21.6% (p-value = 2.63 × 10-25). PGS000012 was associated with a modest increase in CAD risk by 2.2% (p-value = 9.58 × 10-4). The remarkable risk discrimination capability of PGS000747 stands out as the most noteworthy outcome of our study.

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases.

BACKGROUND: Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases. RESULTS: In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease. CONCLUSIONS: The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner.

The KUPKB: a novel Web application to access multiomics data on kidney disease.

The information gathered from the large number of omics experiments in renal biology is underexplored, as it is scattered over many publications or held in supplemental data. To address this, we have developed an open-source Kidney and Urinary Pathway Knowledge Base (KUPKB) that facilitates simple exploration of these omics data. The KUPKB currently comprises 220 data sets (miRNA, mRNA, proteins, and metabolites) extracted from existing publications or databases. Researchers can explore the integrated data using the iKUP browser, and a simple template is provided to submit new omics data sets to the knowledge base. As an example of iKUP's use, we show how we identified, in silico, calreticulin as a protein induced in human interstitial fibrosis and tubular atrophy (IFTA) in chronic kidney transplant rejection; a link that would have been difficult to establish using existing Web-based tools. Using immunohistochemistry, we validated in vivo this in silico result in human and rat biopsies of IFTA, thus identifying calreticulin as a potential new player in chronic kidney transplant rejection. The KUPKB provides a simple tool that enables users to quickly survey a wide range of omics data sets and has been shown to facilitate rapid hypothesis generation in the context of renal pathophysiology.

In Vitro Fermentation of Pleurotus eryngii Mushrooms by Human Fecal Microbiota: Metataxonomic Analysis and Metabolomic Profiling of Fermentation Products.

Edible mushrooms contain biologically active compounds with antioxidant, antimicrobial, immunomodulatory and anticancer properties. The link between their anticancer and immunomodulatory properties with their possible prebiotic activity on gut micro-organisms has been the subject of intense research over the last decade. Lyophilized Pleurotus eryngii (PE) mushrooms, selected due to their strong lactogenic effect and anti-genotoxic, immunomodulatory properties, underwent in vitro static batch fermentation for 24 h by fecal microbiota from eight elderly apparently healthy volunteers (>65 years old). The fermentation-induced changes in fecal microbiota communities were examined using Next Generation Sequencing of the hypervariable regions of the 16S rRNA gene. Primary processing and analysis were conducted using the Ion Reporter Suite. Changes in the global metabolic profile were assessed by 1H NMR spectroscopy, and metabolites were assigned by 2D NMR spectroscopy and the MetaboMiner platform. PLS-DA analysis of both metataxonomic and metabolomic data showed a significant cluster separation of PE fermented samples relative to controls. DEseq2 analysis showed that the abundance of families such as Lactobacillaceae and Bifidobacteriaceae were increased in PE samples. Accordingly, in metabolomics, more than twenty metabolites including SCFAs, essential amino acids, and neurotransmitters discriminate PE samples from the respective controls, further validating the metataxonomic findings.

Robust Bioinformatics Approaches Result in the First Polygenic Risk Score for BMI in Greek Adults.

Quantifying the role of genetics via construction of polygenic risk scores (PRSs) is deemed a resourceful tool to enable and promote effective obesity prevention strategies. The present paper proposes a novel methodology for PRS extraction and presents the first PRS for body mass index (BMI) in a Greek population. A novel pipeline for PRS derivation was used to analyze genetic data from a unified database of three cohorts of Greek adults. The pipeline spans various steps of the process, from iterative dataset splitting to training and test partitions, calculation of summary statistics and PRS extraction, up to PRS aggregation and stabilization, achieving higher evaluation metrics. Using data from 2185 participants, implementation of the pipeline enabled consecutive repetitions in splitting training and testing samples and resulted in a 343-single nucleotide polymorphism PRS yielding an R2 = 0.3241 (beta = 1.011, p-value = 4 × 10-193) for BMI. PRS-included variants displayed a variety of associations with known traits (i.e., blood cell count, gut microbiome, lifestyle parameters). The proposed methodology led to creation of the first-ever PRS for BMI in Greek adults and aims at promoting a facilitating approach to reliable PRS development and integration in healthcare practice.

Protocol for unbiased, consolidated variant calling from whole exome sequencing data.

Whole Exome Sequencing (WES) is used for querying DNA variants using the protein coding parts of genomes (exomes). However, WES analysis can be challenging because of the complexity of the data. Here, we describe a consolidated protocol for unbiased WES analysis. The protocol uses three variant callers (HaplotypeCaller, FreeBayes, and DeepVariant), which have different underlying models. We provide detailed execution steps, as well as basic variant filtering, annotation, visualization, and consolidation aspects.

Interactive Analysis, Exploration, and Visualization of RNA-Seq Data with SeqCVIBE.

The rise of modern gene expression profiling techniques, such as RNA-Seq, has generated a wealth of high-quality datasets spanning all fields of current biological research. The large data sets and the continually expanding applications for which they can be mined, such as the investigation of alternative splicing and others, have created novel challenges for data management, exploration, analysis, and visualization. Although a large variety of RNA-Seq data analysis software packages has emerged, both open-source and commercial, most fail to simultaneously address the above challenges, while they lack obvious functionalities, such as estimating RNA abundance over non-annotated genomic regions of interest in real time. We have developed SeqCVIBE, an R Shiny web application for the interactive exploration, analysis, visualization, and genome browsing of large RNA-Seq datasets. SeqCVIBE allows for multiple on-the-fly visualizations and calculations, such as differential expression analysis, averaging genomic signals over specific regions of the genome, and calculating RNA abundances over custom, potentially non-annotated regions, such as novel long non-coding RNAs. In addition, SeqCVIBE comprises a database for pre-analyzed data, where users can navigate and explore results, as well as perform a variety of basic on-the-fly analyses and export the outcomes. Finally, we demonstrate the value of SeqCVIBE in the elucidation of the interplay of a novel lincRNA, WiNTRLINC1, and Wnt signaling in colon cancer.