Biology subjects

Barash, Y.

Publications and source records attributed to Barash, Y..

6 recordsLinked to original sources

bioRxiv2018

RNA-binding protein A1CF modulates plasma triglyceride levels through posttranscriptional regulation of stress-induced VLDL secretion

BackgroundA recent human exome-chip study on plasma lipids identified a missense mutation in the A1CF (APOBEC1 complementation factor) gene that is associated with elevated triglyceride (TG) levels, but how A1CF, an RNA binding protein, influences plasma TG is unknown.\n\nMethodsWe generated A1cf knockout (A1cf -/-) mice and knock-in mice homozygous for the TG-associated Gly398Ser mutation (A1cfGS/GS), determined lipid phenotypes, and assessed TG physiology through measurements of clearance and secretion. We further identified A1CFs RNA binding targets using enhanced cross-linking and immunoprecipitation sequencing of cultured HepG2 cells and investigated pathways enriched for these targets. Transcriptomic effects of A1CF deficiency were evaluated through RNA sequencing and analyses for differential expression, alternative splicing, and RNA editing.\n\nResultsBoth A1cf -/-and A1cfGS/GS mice exhibited increased fasting plasma TG, establishing that the TG phenotype is due to A1CF loss of function. In vivo TG secretion and clearance studies revealed increased TG secretion without changes in clearance in A1cf -/-mice. Increased VLDL-apoB secretion was also seen in A1cf -/-rat hepatoma cells, but no increase in apoB synthesis was observed. This phenotype was seen without significant shifts in apoB-100/apoB-48 in A1CF deficiency. To discover novel pathways for A1CFs role in TG metabolism, we identified A1CFs RNA binding targets, which were enriched for pathways related to proteasomal catabolism and endoplasmic reticulum (ER) stress. Indeed, proteasomal inhibition led to increased cellular stress in A1cf -/-cells, and higher expression of ER-stress protein GRP78 was observed in resting A1cf -/-cells. RNA-seq of whole livers from wild-type and A1cf -/-mice revealed that pro-inflammatory, not lipogenesis, genes were upregulated as a secondary effect of A1CF deficiency. Differential alternative splicing (AS) analysis and RNA editing analysis revealed that genes involved in cellular stress and metabolism underwent differential changes in A1CF deficiency, and top A1CF binding target proteins with relevance to intracellular stress were differentially expressed on the protein but not mRNA level, implicating multiple mechanisms by which A1CF influences TG secretion.\n\nConclusionsThese data suggest an important role for A1CF in mediating VLDL-TG secretion through regulating intracellular stress.

genomics↗

bioRxiv2017

Aberrant splicing in B-cell acute lymphoblastic leukemia

Aberrant splicing is a hallmark of leukemias with mutations in splicing factor (SF)-encoding genes. Here we investigated its prevalence in pediatric B-cell acute lymphoblastic leukemias (B-ALL), where SFs are not mutated. By comparing them to normal pro-B cells, we found thousands of aberrant local splice variations (LSVs) per sample, with 279 LSVs in 241 genes present in every comparison. These genes were enriched in RNA processing pathways and encoded ~100 SFs, e.g. hnRNPA1. hnRNPA1 3UTR was pervasively misspliced, yielding the transcript subject to nonsense-mediated decay. Thus, we knocked it down in B-lymphoblastoid cells, identified 213 hnRNPA1-dependent splicing events, and defined the hnRNPA1 splicing signature in pediatric leukemias. One of its elements was DICER1, a known tumor suppressor gene; its LSVs were consistent with reduced translation of DICER1 mRNA. Additionally, we searched for LSVs in other leukemia and lymphoma drivers and discovered 81 LSVs in 41 genes. 77 LSVs were confirmed using two large independent B-ALL RNA-seq datasets. In fact, the twenty most common B-ALL drivers showed higher prevalence of aberrant splicing than of somatic mutations. Thus, post-transcriptional deregulation of SF can drive widespread changes in B-ALL splicing and likely contribute to disease pathogenesis.

cancer biology↗

bioRxiv2017

MAJIQ-SPEL: Web-Tool To Interrogate Classical And Complex Splicing Variations From RNA-Seq Data

Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret, and experimentally validate. To address these challenges, We developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex (non-binary) splicing variations. Using a matching primer design algorithm it also suggests users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis.\n\nAvailability: Program and code will be available at http://majiq.biociphers.org/majiq-spel

bioinformatics↗

bioRxiv2017

Integrative Deep Models for Alternative Splicing

Advancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The First involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. The second involves probabilistic models, also known as splicing codes, which infer regulatory mechanisms and predict splicing outcome directly from genomic sequence. To date, these models have utilized only expression data. In this work we address two related challenges: Can we improve on previous models for AS outcome prediction and can we integrate additional sources of data to improve predictions for AS regulatory factors. We perform a detailed comparison of two previous modeling approaches, Bayesian and Deep Neural networks, dissecting the confounding effects of datasets and target functions. We then develop a new target function for AS prediction and show that it significantly improves model accuracy. Next, we develop a modeling framework to incorporate CLIP-Seq, knockdown and over-expression experiments, which are inherently noisy and suffer from missing values. Using several datasets involving key splice factors in mouse brain, muscle and heart we demonstrate both the prediction improvements and biological insights offered by our new models. Overall, the framework we propose offers a scalable integrative solution to improve splicing code modeling as vast amounts of relevant genomic data become available.\n\nAvailability: code and data will be available on Github following publication.

bioinformatics↗

bioRxiv2017

Outlier detection for improved differential splicing quantification from RNA-Seq experiments withreplicates

A key component in many RNA-Seq based studies is the production of multiple replicates for varying experimental conditions. Such replicates allow to capture underlying biological variability and control for experimental ones. However, during data production researchers often lack clear definitions to what constitutes a \"bad\" replicate which should be discarded and if data from failed replicates is published downstream analysis by groups using this data can be hampered. Here we develop a probability model to weigh a given RNA-Seq experiment as a representative of an experimental condition when performing alternative splicing analysis. Using both synthetic and real life data we demonstrate that this model detects outlier samples which are consistently and significantly different compared to samples from the same condition. Using both synthetic and real life data we perform extensive evaluation of the algorithm in different scenarios involving perturbed samples, mislabeled samples, no-signal groups, and different levels of coverage, and show it compares favorably with current state of the art tools.\n\nAvailabilityProgram and code will be available at majiq.biociphers.org

bioinformatics↗

bioRxiv2017

Ancient antagonism between CELF and RBFOX families tunes mRNA splicing outcomes

Over 95% of human multi-exon genes undergo alternative splicing, a process important in normal development and often dysregulated in disease. We sought to analyze the global splicing regulatory network of CELF2 in human T cells, a well-studied splicing regulator critical to T cell development and function. By integrating high-throughput sequencing data for binding and splicing quantification with sequence features and probabilistic splicing code models, we find evidence of splicing antagonism between CELF2 and the RBFOX family of splicing factors. We validate this functional antagonism through knockdown and overexpression experiments in human cells and find CELF2 represses RBFOX2 mRNA and protein levels. Because both families of proteins have been implicated in the development and maintenance of neuronal, muscle, and heart tissues, we analyzed publicly available data in these systems. Our analysis suggests global, antagonistic co-regulation of splicing by the CELF and RBFOX proteins in mouse muscle and heart in several physiologically relevant targets including proteins involved in calcium signaling and members of the MEF2 family of transcription factors. Importantly, a number of these co-regulated events are aberrantly spliced in mouse models and human patients with diseases that affect these tissues including heart failure, diabetes, or myotonic dystrophy. Finally, analysis of exons regulated by ancient CELF family homologs in chicken, and Drosophila suggests this antagonism is conserved through evolution.

genomics↗