Biology subjects

Pochet, N.

Publications and source records attributed to Pochet, N..

4 recordsLinked to original sources

bioRxiv2018

PorSignDB: a database of in vivo perturbation signatures for dissecting clinical outcome of PCV2 infection

Porcine Circovirus Type 2 (PCV2) is a pathogen that has the ability to cause often devastating disease manifestations in pig populations with major economic implications. How PCV2 establishes subclinical persistence and why certain individuals progress to lethal lymphoid depletion remain to be elucidated. Here we present PorSignDB, a gene signature database describing in vivo porcine tissue physiology that we generated from a large compendium of in vivo transcriptional profiles and that we subsequently leveraged for deciphering the distinct physiological states underlying PCV2-affected lymph nodes. This systems biology approach indicated that subclinical PCV2 infections shut down the immune system. A robust signature of PCV2 disease emphasized that immune activation is dysfunctional in subclinical infections, however, in contrast it is promoted in PCV2 patients with clinical manifestations. Functional genomics further uncovered IL-2 as a driver of PCV2-mediated disease and we identified STAT3 as a druggable PCV2 host factor candidate. Our systematic dissection of the mechanistic basis of PCV2 reveals that subclinical and clinical PCV2 display two diametrically opposed immunotranscriptomic recalibrations that represent distinct physiological states in vivo, which suggests a paradigm shift in this field. Finally, our PorSignDB signature database is publicly available as a community resource (http://www.vetvirology.ugent.be/PorSignDB/, included in Gene Sets from Community Contributors http://software.broadinstitute.org/gsea/msigdb/contributed_genesets.jsp) and provides systems biologists with a valuable tool for catalyzing studies of human and veterinary disease.\n\nAuthor SummaryPorcine Circovirus Type 2 (PCV2) is a small but economically important pathogen circulating endemically in pig populations. Although PCV2 causes mostly chronic subclinical infections, many individuals develop a lethal form of circoviral disease consisting of a collapse of lymphoid tissue. In order to provide a fresh look at how PCV2 reprograms host tissue, we created PorSignDB, a compendium of hundreds of transcriptomic gene-expression signatures derived from primary porcine tissue specimens of well over 1500 patients or lab animals. By leveraging PorSignDB on transcriptomic data of PCV2 patients, we uncover that subclinical PCV2 reprograms the host into a striking state of non-infection, which explains its failure to respond to an initial phase of circoviral presence. A PCV2 disease signature further demonstrates that the silenced immune system associated with subclinical PCV2 becomes fully activate in PCV2 patients, triggering severe circoviral disease. Further genomic and functional analysis demonstrate STAT3 as a druggable host factor and IL-2 as a disease driver. Together, this study demonstrates the mechanistic underpinnings of clinical outcome of PCV2 infections: subclinical and clinical PCV2 display two entirely opposing transcriptomic recalibrations of lymphoid tissue.

microbiology↗

bioRxiv2017

Module analysis captures pancancer (epi)genetically deregulated cancer driver genes for smoking and antiviral response

The availability of increasing volumes of multi-omics profiles across many cancers promises to improve our understanding of the regulatory mechanisms underlying cancer. The main challenge is to integrate these multiple levels of omics profiles and especially to analyze them across many cancers. Here we present AMARETTO, an algorithm that addresses both challenges in three steps. First, AMARETTO identifies potential cancer driver genes through integration of copy number, DNA methylation and gene expression data. Then AMARETTO connects these driver genes with co-expressed target genes that they control, defined as regulatory modules. Thirdly, we connect AMARETTO modules identified from different cancer sites into a pancancer network to identify cancer driver genes. Here we applied AMARETTO in a pancancer study comprising eleven cancer sites and confirmed that AMARETTO captures hallmarks of cancer. We also demonstrated that AMARETTO enables the identification of novel pancancer driver genes. In particular, our analysis led to the identification of pancancer driver genes of smoking-induced cancers and antiviral interferon-modulated innate immune response.\n\nSoftware availabilityAMARETTO is available as an R package at https://bitbucket.org/gevaertlab/pancanceramaretto\n\nHighlightsO_LIWe present an algorithm for pancancer identification of cancer driver genes based on multiomics data fusion\nC_LIO_LIGPX2 is a novel driver gene in smoking induced cancers and validated using knockdown of GPX2 in the A549 cell line.\nC_LIO_LIOAS2 is a novel driver gene defining cancers with an antiviral signature supported by increased infiltration of tumor-associated macrophages.\nC_LI\n\nResearch in contextWe present an algorithm that combines multiple sources of molecular data to identify novel genes that are involved in cancer development. We applied this algorithm on multiple cancers in a combined fashion and identified a network of pancancer driver genes. We highlighted two genes in detail GPX2 and OAS2. We showed that GPX2 is an important cancer gene in smoking induced cancers, and validated our predictions using experimental data where GPX2 was inactivated in a lung cancer cell line. Similarly we showed that OAS2 is an important cancer driver gene in cancers that show an antiviral signature.

bioinformatics↗

bioRxiv2017

STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq

MotivationFusion genes created by genomic rearrangements can be potent drivers of tumorigenesis. However, accurate identification of functionally fusion genes from genomic sequencing requires whole genome sequencing, since exonic sequencing alone is often insufficient. Transcriptome sequencing provides a direct, highly effective alternative for capturing molecular evidence of expressed fusions in the precision medicine pipeline, but current methods tend to be inefficient or insufficiently accurate, lacking in sensitivity or predicting large numbers of false positives. Here, we describe STAR-Fusion, a method that is both fast and accurate in identifying fusion transcripts from RNA-Seq data.\n\nResultsWe benchmarked STAR-Fusions fusion detection accuracy using both simulated and genuine Illumina paired-end RNA-Seq data, and show that it has superior performance compared to popular alternative fusion detection methods.\n\nAvailability and implementationSTAR-Fusion is implemented in Perl, freely available as open source software at http://star-fusion.github.io, and supported on Linux.\n\nContactbhaas@broadinstitute.org

bioinformatics↗

bioRxiv2016

Dissecting the role of non-coding RNAs in the accumulation of amyloid and tau neuropathologies in Alzheimer’s disease

BackgroundGiven multiple studies of brain microRNA (miRNA) in relation to Alzheimers disease (AD) with few consistent results and the heterogeneity of this disease, the objective of this study was to explore their mechanism by evaluating their relation to different elements of Alzheimers disease pathology, confounding factors and mRNA expression data from the same subjects in the same brain region.\n\nResultsWe report analyses of expression profiling of miRNA (n=700 subjects) and lincRNA (n=540 subjects) from the dorsolateral prefrontal cortex of individuals participating in two longitudinal cohort studies of aging. Evaluating well-established (miR-132, miR-129), we confirm their association with pathologic AD in our dataset, and then characterize their in disease role in terms of neuritic {beta}-amyloid plaques and neurofibrillary tangle pathology. Additionally, we identify one new miRNA (miR-99) and four lincRNA that are associated with these traits. Many other previously reported associations of microRNA with AD are associated with the confounders quantified in our longitudinal cohort. Finally, by performing analyses integrating both miRNA and RNA sequence data from the same individuals (525 samples), we characterize the impact of AD associated miRNA on human brain expression: we show that the effects of miR-132 and miR-129-5b converge on certain genes such as EP300 and find a role for miR200 and its target genes in AD using an integrated miRNA/mRNA analysis.\n\nConclusionsOverall, miRNAs play a modest role in human AD, but we observe robust evidence that a small number of miRNAs are responsible for specific alterations in the cortical transcriptome that are associated with AD.

neuroscience↗