Biology subjects

Linial, M.

Publications and source records attributed to Linial, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Profiles and Dynamics of the Transcriptome of Microglial Cells Reveal their Inflammatory Status

The primary role of microglia in the maintenance of brain homeostasis is to respond to disturbances in the microenvironment. In this study, we cultured murine neonatal microglia and activated them with lipopolysaccharide (LPS) and benzoyl ATP (bzATP) to characterize changes in the transcriptome in response to various in vivo stimuli caused by pathogens, injury, or toxins. Activation by bzATP, an agonist of purinergic receptors, induces a transient wave of transcriptional changes. However, a long-lasting transcriptional profile affecting thousands of genes occurs only following a combination of bzATP and LPS. This profile is dominated by a coordinated induction of cytokines (e.g., IL1- and IL1-{beta}), chemokines, and their direct regulators. Many of these inflammatory-related genes are up-regulated by several orders of magnitude. We identified the TNF- and NF-{kappa}B pathways as the principal hubs for signaling of interleukin and chemokine induction in this cell system. We propose that primary microglia under controlled activation paradigms can be used for testing reagents that could attenuate their activated state. Such a microglial system could serve as a model for changes occurring in the aging brain and neurodegenerative diseases. Highlight points* Primary murine microglia cultures release cytokines following activation with bzATP and LPS * The wave of changes in gene expression by bzATP is transient. * bzATP+LPS causes a transcription program dominated by the induction of interleukins and chemokines.

neuroscience↗

bioRxiv2021

Ladostigil attenuates the oxidative and ER stress in human neuroblast-like SH-SY5Y cells

A hallmark of the aging brain is the robust inflammation mediated by microglial activation. Neuroinflammation resulting from the induction of oxidative stress in neurodegenerative diseases and following brain injury. Chronic treatment of aging rats by ladostigil, a compound with antioxidant and anti-inflammatory function, prevented microglial activation and learning deficits. In this study, we investigate the effect of ladostigil on neuronal-like SH-SY5Y cells. We show that SH-SY5Y cells exposed to acute (by H2O2) or chronic oxidative stress (by Sin1, 3-morpholinosydnonimine) induced apoptotic cell death. However, in the presence of ladostigil, the decline in cell viability and the oxidative levels were partially reversed. RNA-seq analysis showed that chronic oxidation by Sin1 resulted in coordinated suppression of endoplasmic reticulum (ER) quality control and ER stress response gene sets. Chronic oxidative stress impacted ER proteostasis and induced the expression of numerous lncRNAs. Pre-incubation with ladostigil before exposing SH-SY5Y cells to Sin1 induced Clk1 (Cdc2-like kinase 1) which was implicated in psychophysiological stress in mice and Alzheimer disease. Ladostigil also suppressed the expression of Ccpg1 (Cell cycle progression 1) and Synj1 (Synaptojanin 1) that function in ER-autophagy and endocytic pathways. We postulate that ladostigil alleviated cell damage by oxidation and ER stress. Therefore, it may attenuate neurotoxicity and cell death that accompany chronic stress conditions in the aging brain.

cell biology↗

bioRxiv2021

ProteinBERT: A universal deep-learning model of protein sequence and function

Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a deep language model specifically designed for proteins. Our pretraining scheme consists of masked language modeling combined with a novel task of Gene Ontology (GO) annotation prediction. We introduce novel architectural elements that make the model highly efficient and flexible to very large sequence lengths. The architecture of ProteinBERT consists of both local and global representations, allowing end-to-end processing of these types of inputs and outputs. ProteinBERT obtains state-of-the-art performance on multiple benchmarks covering diverse protein properties (including protein structure, post translational modifications and biophysical attributes), despite using a far smaller model than competing deep-learning methods. Overall, ProteinBERT provides an efficient framework for rapidly training protein predictors, even with limited labeled data. Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert.

bioinformatics↗

bioRxiv2021

Chromoanagenesis landscape in 10,000 TCGA patients

During the past decade, whole-genome sequencing of tumor biopsies and individuals with congenital disorders highlighted the phenomenon of chromoanagenesis, a single chaotic event of chromosomal rearrangement. Chromoanagenesis was shown to be frequent in many types of cancers, to occur in early stages of cancer development, and significantly impact the tumors nature. However, an in-depth, cancer-type dependent analysis has been somewhat incomplete due to the shortage in whole genome sequencing of cancerous samples. In this study, we extracted data from The Pan-Cancer Analysis of Whole Genome (PCAWG) and The Cancer Genome Atlas (TCGA) to construct a machine learning algorithm that can detect chromoanagenesis with high accuracy (86%). The algorithm was applied to ~10,000 TCGA cancer patients. We utilize the chromoanagenesis assignment results, to analyze cancer-type specific chromoanagenesis characteristics in 20 TCGA cancer types. Our results unveil prominent genes affected in either chromoanagenesis or non-chromoanagenesis tumorigenesis. The analysis reveals a mutual exclusivity relationship between the genes impaired in chromoanagenesis versus non-chromoanagenesis cases. We offer the discovered characteristics as possible targets for cancer diagnostic and therapeutic purposes.

cancer biology↗

bioRxiv2021

Targeted in situ cross-linking mass spectrometry and integrative modeling reveal the architectures of Nsp1, Nsp2, and Nucleocapsid proteins from SARS-CoV-2

Atomic structures of several proteins from the coronavirus family are still partial or unavailable. A possible reason for this gap is the instability of these proteins outside of the cellular context, thereby prompting the use of in-cell approaches. In situ cross-linking and mass spectrometry (in situ CLMS) can provide information on the structures of such proteins as they occur in the intact cell. Here, we applied targeted in situ CLMS to structurally probe Nsp1, Nsp2, and Nucleocapsid (N) proteins from SARS-CoV-2, and obtained cross-link sets with an average density of one cross-link per twenty residues. We then employed integrative modeling that computationally combined the cross-linking data with domain structures to determine full-length atomic models. For the Nsp2, the cross-links report on a complex topology with long-range interactions. Integrative modeling with structural prediction of individual domains by the AlphaFold2 system allowed us to generate a single consistent all-atom model of the full-length Nsp2. The model reveals three putative metal binding sites, and suggests a role for Nsp2 in zinc regulation within the replication-transcription complex. For the N protein, we identified multiple intra- and inter-domain cross-links. Our integrative model of the N dimer demonstrates that it can accommodate three single RNA strands simultaneously, both stereochemically and electrostatically. For the Nsp1, cross-links with the 40S ribosome were highly consistent with recent cryo-EM structures. These results highlight the importance of cellular context for the structural probing of recalcitrant proteins and demonstrate the effectiveness of targeted in situ CLMS and integrative modeling.

biochemistry↗

bioRxiv2020

The FABRIC Cancer Portal: A Ranked Catalogue of Gene Selection in Tumors over the Human Coding Genome

We present The FABRIC Cancer Portal, a comprehensive catalogue of gene selection in cancer covering the entire human coding genome (~18,000 protein-coding genes) across 33 cancer types and pan-cancer. Genes in the portal are ranked according to the strength of evidence for selection in tumor, based on rigorous and robust statistics. Gene selection is quantified by combining genomic data with rich proteomic annotations. The portal includes a selected set of cross-references to the most relevant resources providing genomic, proteomic and cancer-related information. We showcase the portal with known and overlooked cancer genes, demonstrating the usefulness of the portal via its simple visual interface that allows to pivot between gene-centric and cancer-type views. The portal is available at fabric-cancer.huji.ac.il.

cancer biology↗

bioRxiv2020

Recessive effects in cancer predisposition exposed by genome-wide and proteome-wide association studies

The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Current attempts to detect cancer predisposition genomic regions are typically based on small-scale familial studies or genome-wide association studies (GWAS) over dedicated case-control cohorts. In this study, we utilized the UK Biobank as a large-scale prospective cohort to conduct a comprehensive analysis of cancer predisposition using both GWAS and proteome-wide association study (PWAS), a method that highlights genetic associations mediated by functional alterations to protein-coding genes. We discovered 137 unique genomic loci implicated with cancer risk in the white British population across nine cancer types and pan-cancer. While most of these genomic regions are supported by external evidence, our results highlight novel loci as well. We performed a comparative analysis of cancer predisposition between cancer types, finding that most of the implicated regions are cancer-type specific. We further analyzed the role of recessive genetic effects in cancer predisposition. We found that 30 of the 137 cancer regions were recovered only by a recessive model, highlighting the importance of recessive inheritance outside of familial studies. Finally, we show that many of the cancer associations exert substantial cancer risk in the studied cohort, suggesting their clinical relevance.

genetics↗

bioRxiv2020

Spliceosome-Associated MicroRNAs Identified in Breast Cancer Cells Act on Nuclear Targets and Are Potential Indicators for Tumorigenicity

MicroRNAs (miRNAs) act as negative regulators of gene expression in the cytoplasm. Previous studies identified miRNAs associated with the spliceosome. Here we study three breast-derived cell-lines with increased tumorigenicity (from MCF-10A to MCF-7 and MDA-MB-231) and compared their miRNA sequences at the spliceosome fraction (SF). We report that the SF-miRNAs expression, identity, and pre-miRNA segmental composition vary across these cell-lines. The expression of the majority of the abundant SF-miRNAs (e.g. miR-100, miR-30a, and let-7 members) shows an opposite trend in view of the literature and breast cancer large cohorts. The results suggest that SF-miRNAs act in the nucleus on alternative targets than in the cytoplasm. One such miRNA is miR-7704 whose genomic position overlaps HAGLR, a cancer-related lncRNA. We found an inverse expression of miR-7704 and HAGLR in the tested cell lines. Moreover, inhibition of miR-7704 caused an increase in HAGLR expression. Furthermore, increasing miR-7704 levels attenuated the MDA-MB-231 cell-division rate. While miR-7704 acts as oncomiR in breast cancer patients, it has a tumor-suppressing function in SF, with HAGLR being its nuclear target. Manipulating miR-7704 levels is a potential lead for altering tumorigenicity. Altogether, we report on the potential of manipulating SF-miRNAs as an unexplored route for breast cancer therapeutics.

cancer biology↗

bioRxiv2020

Evolutionary and Functional Lessons from Human-Specific Amino-Acid Substitution Matrices

The characterization of human genetic variation in coding regions is fundamental to our understanding of protein function, structure, and evolution. Amino-acid (AA) substitution matrices such as BLOSUM (BLOcks SUbstitution Matrix) and PAM (Point Accepted Mutations) encapsulate the stochastic nature of such proteomic variation and are used in studying protein families and evolutionary processes. However, these matrices were constructed from protein sequences spanning long evolutionary distances and are not designed to reflect polymorphism within species. To accurately represent proteomic variation within the human population, we constructed a set of human-centric substitution matrices derived from genetic variations by analyzing the frequencies of >4.8M single nucleotide variants (SNVs). These human-specific matrices expose short-term evolutionary trends at both codon and AA resolution and therefore present an evolutionary perspective that differs from that implicated in the traditional matrices. Specifically, our matrices consider the directionality of variants, and uncover a set of AA pairs that exhibit a strong tendency to substitute in a specific direction. We further demonstrate that the substitution rates of nucleotides only partially determine AA substitution rates. Finally, we investigate AA substitutions in post-translational modification (PTM) and ion-binding sites. We confirm a strong propensity towards conservation of the identity of the AA that participates in such functions. The empirically-derived human-specific substitution matrices expose purifying selection over a range of residue-based protein properties. The new substitution matrices provide a robust baseline for the analysis of protein variations in health and disease. The underlying methodology is available as an open-access to the biomedical community.

genomics↗

bioRxiv2020

Sensitivity of Gene Sets to miRNA Regulation: A Cell-Based Probabilistic Approach

Mature microRNAs (miRNAs) are small, non-coding RNA molecules that function by base-pairing with mRNAs. In multicellular organisms, miRNAs lead to mRNA destabilization and translation arrest. Importantly, the quantities and stichometry of miRNAs/mRNAs determine the miRNA regulation characteristics of specific cells. In this study, we used COMICS (Competition of miRNA Interactions in Cell Systems), a stochastic computational iterative framework to characterize genes by their sensitivity and robustness to miRNA regulation. We monitor the cell state by quantifying the retention level for all mRNAs, at the end of 100,000 simulation iterations. In HeLa cells, we partitioned all genes to five classes according to their decay rates. We show that the largest class (69% of genes) is apparently resistant to miRNA regulation. We created in silico perturbations using overexpressing of all major miRNAs (248 types) at various levels relative to the basal level (x1 to x1000). We further classified genes according to the differential behaviour for any pair of conditions of miRNA expression profile. Based on such measure (OvereXpression Ratio, OXR), we identified a small number of gene sets that are especially sensitive to OXR. Our results expose an overlooked quantitative dimension for set of genes and miRNA regulation in living cells.

bioinformatics↗

bioRxiv2020

The SARS-CoV-2 exerts a distinctive strategy for interacting with the ACE2 human receptor

The COVID-19 disease has plagued over 110 countries and has resulted in over 4,000 deaths within 10 weeks. We compare the interaction between the human ACE2 receptor and the SARS-CoV-2 spike protein with that of other pathogenic coronaviruses using molecular dynamics simulations. SARS-CoV, SARS-CoV-2, and HCoV-NL63 recognize ACE2 as the natural receptor but present a distinct binding interface to ACE2 and a different network of residue-residue contacts. SARS-CoV and SARS-CoV-2 have comparable binding affinities achieved by balancing energetics and dynamics. The SARS-CoV-2-ACE2 complex contains a higher number of contacts, a larger interface area, and decreased interface residue fluctuations relative to SARS-CoV. These findings expose an exceptional evolutionary exploration exerted by coronaviruses toward host recognition. We postulate that the versatility of cell receptor binding strategies has immediate implications on therapeutic strategies. One Sentence SummaryMolecular dynamics simulations reveal a temporal dimension of coronaviruses interactions with the host receptor.

molecular biology↗

bioRxiv2019

PWAS: Proteome-Wide Association Study

Over the last two decades, GWAS (Genome-Wide Association Study) has become a canonical tool for exploratory genetic research, generating countless gene-phenotype associations. Despite its accomplishments, several limitations and drawbacks still hinder its success, including low statistical power and obscurity about the causality of implicated variants. We introduce PWAS (Proteome-Wide Association Study), a new method for detecting protein-coding genes associated with phenotypes through protein function alterations. PWAS aggregates the signal of all variants jointly affecting a protein-coding gene and assesses their overall impact on the proteins function using machine-learning and probabilistic models. Subsequently, it tests whether the gene exhibits functional variability between individuals that correlates with the phenotype of interest. By collecting the genetic signal across many variants in light of their rich proteomic context, PWAS can detect subtle patterns that standard GWAS and other methods overlook. It can also capture more complex modes of heritability, including recessive inheritance. Furthermore, the discovered associations are supported by a concrete molecular model, thus reducing the gap to inferring causality. To demonstrate its applicability for a wide range of human traits, we applied PWAS on a cohort derived from the UK Biobank (~330K individuals) and evaluated it on 49 prominent phenotypes. We compared PWAS to existing methods, proving its capacity to recover causal protein-coding genes and highlighting new associations with plausible biological mechanism.

bioinformatics↗

bioRxiv2019

Allele Specific Expression in Human - Genomic Makeup and Phenotypic Implications

The allele-specific expression phenomenon refers to unbalanced expression from the two parental alleles in a tissue of a diploid organism. AlleleDB is a high-quality resource that reports on about 30,000 ASE variants (ASE-V) from hundreds of human samples. In this study, we present the genomic characteristics and phenotypic implications of ASE. We identified tens of segments with extreme density of ASE-V, many of them are located at the major histocompatibility complex (MHC) locus. Notably, at a resolution of 100 nucleotides, the likelihood of ASE-V increases with the density of polymorphic sites. Another dominant trend of ASE is a strong bias of the expression to the major allele. This observation relies on the known allele frequencies in the healthy human population. Overlap of ASE-V and GWAS associations was calculated for 48 phenotypes from the UK-Biobank. ASE-V were significantly associated with a risk for inflammation (e.g. asthma), autoimmunity (e.g., rheumatoid arthritis, multiple sclerosis, and type 1 diabetes) and several blood cell traits (e.g., red cell distribution width). At the level of the ASE-genes, we seek association with all traits and conditions reported in the GWAS catalog. The statistical significance of ASE-genes to GWAS catalog reveals association with the susceptibility to virus infection, autoimmunity, inflammation, allergies, blood cancer and more. We postulate that ASE determines phenotype diversity between individuals and the risk for a variety of immune-related conditions.

genomics↗

bioRxiv2019

BIRD: Identifying Cell Doublets via Biallelic Expression from Single cells

MotivationCurrent technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods led to a higher risk of cell doublets contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell expression data focus on cell doublets from different species, cell types or individuals.\n\nResultsIn this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power towards doublets identification. We present a pipeline called BIRD (BIallelic Ratio for Doublets) that relies on heterologous genetic variations extracted from single-cell RNA-seq (scRNA-seq). For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-Seq data from 163 primary fibroblasts. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets from female-origin fibroblasts were verified by the unexpected biallelic expression from X-chromosome. Data from 10X Genomics microfluidics of peripheral blood cells analyzed by BIRD achieved in average 83% ({+/-} 3.7%) accuracy with an area under the curve of 0.88 ({+/-} 0.04) for a collection of [~]13,300 single cells.\n\nConclusionsBIRD addresses instances of doublets which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved with high coverage data. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage, and depth.

genomics↗

bioRxiv2019

Structural analysis of de novo STXBP1 mutation in complex with syntaxin 1A reveals a major alteration in the interaction interface in a child with developmental delay and spasticity

STXBP1, also known as Munc-18, is a master regulator of neurotransmitter release and synaptic function in the human brain through its direct interaction with syntaxin 1A. STXBP1 related disorders are well characterized and cover a diverse range of neurological and neurodevelopmental conditions. Through exome sequencing of a child with developmental delay, hypotonia and spasticity, we found a novel de novo insertion mutation of three nucleotides in the STXBP1 coding region, resulting in an additional arginine after position 39 (R39dup). Inconclusive results from state-of-the-art variant prediction tools mandated a structure-based approach using molecular dynamics (MD) simulations of the STXBP1-syntaxin 1A complex. Comparison of the interaction interfaces of the wild type and the R39dup complexes revealed a reduced interaction surface area in the mutant, leading to destabilization of the interaction. We applied the same MD methodology to 7 additional previously reported STXBP1 mutations. We find that the stability of the STXBP1-syntaxin 1A interface correlates with the reported clinical phenotypes. We illustrate a direct link between a patients genetic variations and the observed clinical phenotype through protein structure, dynamics, and function.

genetics↗

bioRxiv2018

Human Genes Escaping X-inactivation Revealed by Single Cell Expression Data

BackgroundIn mammals, sex chromosomes pose an inherent imbalance of gene expression between sexes. In each female somatic cell, random inactivation of one of the X-chromosomes restores this balance. While most genes from the inactivated X-chromosome are silenced, 15-25% are known to escape X-inactivation (termed escapees). The expression levels of these genes are attributed to sex-dependent phenotypic variability.\n\nResultsWe used single-cell RNA-Seq to detect escapees in somatic cells. As only one X-chromosome is inactivated in each cell, the origin of expression from the active or inactive chromosome can be determined from the variation of sequenced RNAs. We analyzed primary, healthy fibroblasts (n=104), and clonal lymphoblasts with sequenced parental genomes (n=25) by measuring the degree of allelic-specific expression (ASE) from heterozygous sites. We identified 24 and 49 candidate escapees, at varying degree of confidence, from the fibroblast and lymphoblast transcriptomes, respectively. We critically test the validity of escapee annotations by comparing our findings with a large collection of independent studies. We find that most genes (66%) from the unified set were previously reported as escapees. Furthermore, out of the overlooked escapees, 11 are long noncoding RNA (lncRNAs).\n\nConclusionsX-chromosome inactivation and escaping from it are robust, permanent phenomena that are best studies at a single-cell resolution. The cumulative information from individual cells increases the potential of identifying escapees. Moreover, despite the use of a limited number of cells, clonal cells (i.e., same X-chromosomes are coordinately inhibited) with genomic phasing are valuable for detecting escapees at high confidence. Generalizing the method to uncharacterized genomic loci resulted in lncRNAs escapees which account for 20% of the listed candidates. By confirming genes as escapees and propose others as candidates from two different cell types, we contribute to the cumulative knowledge and reliability of human escapees.

genomics↗

bioRxiv2018

Substantial Batch Effects in TCGA Exome Sequences Undermine Pan-Cancer Analysis of Germline Variants

BackgroundIn recent years, research on cancer predisposition germline variants has emerged as a prominent field. The identity of somatic mutations is based on a reliable mapping of the patient germline variants. In addition, the statistics of germline variants frequencies in healthy individuals and cancer patients is the basis for seeking candidates for cancer predisposition genes. The Cancer Genome Atlas (TCGA) is one of the main sources of such data, providing a diverse collection of molecular data including deep sequencing for more than 30 types of cancer from >10,000 patients.\n\nMethodsOur hypothesis in this study is that whole exome sequences from healthy blood samples of cancer patients are not expected to show systematic differences among cancer types. To test this hypothesis, we analyzed common and rare germline variants across six cancer types, covering 2,241 samples from TCGA. In our analysis we accounted for inherent variables in the data including the different variant calling protocols, sequencing platforms, and ethnicity.\n\nResultsWe report on substantial batch effects in germline variants associated with cancer types. We attribute the effect to the specific sequencing centers that produced the data. Specifically, we measured 30% variability in the number of reported germline variants per sample across sequencing centers. The batch effect is further expressed in nucleotide composition and variant frequencies. Importantly, the batch effect causes substantial differences in germline variant distribution patterns across numerous genes, including prominent cancer predisposition genes such as BRCA1, RET, MAX, and KRAS. For most of known cancer predisposition genes, we found a distinct batch-dependent difference in germline variants.\n\nConclusionTCGA germline data is exposed to strong batch effects with substantial variabilities among TCGA sequencing centers. We claim that those batch effects are consequential for numerous TCGA pan-cancer studies. In particular, these effects may compromise the reliability and the potency to detect new cancer predisposition genes. Furthermore, interpretation of pan-cancer analyses should be revisited in view of the source of the genomic data after accounting for the reported batch effects.

cancer biology↗

bioRxiv2018

The Translation Machinery Is Immune from miRNA Perturbations: A Cell-Based Probabilistic Approach

Mature microRNAs (miRNAs) regulate most human genes through direct base-pairing with mRNAs. We investigate some underlying principles of such regulation. To this end, we overexpressed miRNAs in different cell types and measured the mRNA decay rate under transcriptional arrest. Parameters extracted from these experiments were incorporated into a computational stochastic framework which was developed to simulate the cooperative action of miRNAs in living cells. We identified gene sets that exhibit coordinated behavior with respect to all major miRNAs, over a broad range of overexpression levels. While a small set of genes is highly sensitive to miRNA manipulations, about 180 genes are insensitive to miRNA manipulations as measured by their degree of mRNA retention. The insensitive genes are associated with the translation machinery. We conclude that the stochastic nature of miRNAs reveals an unexpected robustness of gene expression in living cells. Moreover, the use of a systematic probabilistic approach exposes design principles of cells states and in particular, the translational machinery.\n\nHighlightsO_LIA probabilistic-based simulator assesses the cellular response to thousands of miRNA overexpression manipulations\nC_LIO_LIThe translational machinery displays an exceptional resistance to manipulations of miRNAs.\nC_LIO_LIThe insensitivity of the translation machinery to miRNA manipulations is shared by different cell types\nC_LIO_LIThe composition of the most abundant miRNAs dominates cell identity\nC_LI

systems biology↗