Biology subjects

Elkon, R.

Publications and source records attributed to Elkon, R..

10 recordsLinked to original sources

bioRxiv2026

Mapping Genetic Risk Associations to Cellular Contexts via Deep Learning and Biological Ontologies

Translating genome-wide association studies (GWAS) signals into trait-relevant cellular contexts remains challenging due to the complexity of the genomic regulatory code and linkage disequilibrium among associated variants. We present a novel computational framework that aggregates deep learning-based predictions of the functional effects of noncoding variants on transcriptional regulatory elements across GWAS loci and empirically evaluates their statistical significance. By organizing these aggregated signals within biological ontologies, our approach enables statistically calibrated interpretation of GWAS associations, highlighting relevant cell-type and tissue contexts across human traits.

bioinformatics↗

bioRxiv2026

Histone H1 Variants Regulate Neurodevelopmental Transcriptional Programs in Autism with 16p11.2 deletion

BackgroundNeurodevelopmental disorders, including autism spectrum disorder, involve widespread transcriptional dysregulation. Copy number variations at 16p11.2 are among the strongest genetic risk factors for autism spectrum disorder, yet the molecular mechanisms by which these copy number variations contribute to neurodevelopmental pathology remain unclear. ResultsWe identify significant genetic associations between autism spectrum disorder susceptibility and the HIST1 histone gene cluster through genome-wide analysis. Transcriptomic profiling across post-mortem brain tissue, patient-derived neural progenitor cells, neurons, and cerebral organoids reveals consistent upregulation of linker histone variants H1.2 and H1.5 in idiopathic autism spectrum disorder and 16p11.2 hemi-deletion carriers, but not in schizophrenia or bipolar disorder. Functional assays demonstrate that dysregulated H1 expression disrupts gene networks involved in synaptic signaling, chromatin remodeling, and neural differentiation. Mechanistically, we link H1 upregulation to MAZ, a transcription factor encoded within the 16p11.2 locus. MAZ binds the promoter regions of H1 genes and represses their transcription. Knockdown of MAZ leads to H1 overexpression. H1 upregulation alone is sufficient to alter the expression of autism spectrum disorder-associated genes. ConclusionsOur findings define a MAZ-dependent regulation of H1 dosage as a critical chromatin-mediated mechanism contributing to transcriptional pathology in 16p11.2-associated autism spectrum disorder.

genomics↗

bioRxiv2025

Multimodal profiling reveals a Notch-responsive regenerative subpopulation of cochlear supporting cells

Regeneration enables organisms to repair damaged tissues, yet this capacity is strikingly limited in the cochlear sensory epithelium, essential for sound detection. A major cause of hearing loss arises from the irreversible loss of sensory hair cells (HCs) in the cochlea. While supporting cells (SCs) have a latent ability to transdifferentiate into HCs, this regenerative potential is rapidly lost after development. Using live imaging and single-cell multi-omics of cochlear explants, we uncovered the cellular and molecular heterogeneity underlying the limited regenerative capacity of the neonatal mouse cochlea. Notch repression broadly silenced key SC genes, yet only a rare subpopulation of Deiters cells (DC), termed transdifferentiating DCs (tDCs), initiated the transdifferentiation into HC fate. These cells underwent coordinated transcriptional and enhancer remodeling, linking epigenetic priming with morphological plasticity, while other SCs remained refractory despite robust Notch targets downregulation. Our study provides a molecular definition of an early induced transitional DC to HC state, revealing Notch inhibition as a selective trigger that unmasks rare regenerative competence.

developmental biology↗

bioRxiv2021

The DOMINO web-server for active module identification analysis

Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal ("active modules"). Such modules can point out key molecular processes in the analyzed biological conditions. ResultsWe recently introduced a novel AMI algorithm called DOMINO, and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations, and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms. Availability and implementationThe server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.

bioinformatics↗

bioRxiv2021

Long Reads Capture Simultaneous Enhancer-Promoter Methylation Status for Cell-type Deconvolution

MotivationWhile promoter methylation is associated with reinforcing fundamental tissue identities, the methylation status of distant enhancers was shown by genome-wide association studies to be a powerful determinant of cell-state and cancer. With recent availability of long-reads that report on the methylation status of enhancer-promoter pairs on the same molecule, we hypothesized that probing these pairs on the single-molecule level may serve the basis for detection of rare cancerous transformations in a given cell population. We explore various analysis approaches for deconvolving cell-type mixtures based on their genome-wide enhancer-promoter methylation profiles. ResultsTo evaluate our hypothesis we examine long-read optical methylome data for the GM12787 cell line and myoblast cell lines from two donors. We identified over 100,000 enhancer-promoter pairs that co-exist on at least 30 individual DNA molecules per pair. We developed a detailed methodology for mixture deconvolution and applied it to estimate the proportional cell compositions in synthetic mixtures based on analyzing their enhancer-promoter pairwise methylation. We found our methodology to lead to very accurate estimates, outperforming our promoter-based deconvolutions. Moreover, we show that it can be generalized from deconvolving different cell types to subtle scenarios where one wishes to deconvolve different cell populations of the same cell-type. AvailabilityThe code used in this work to analyze single-molecule Bionano Genomics optical maps is available via the GitHub repository https://github.com/ebensteinLab/Single_molecule_methylation_in_EP. Contactuv@post.tau.ac.il (Y.E), roded@tauex.tau.ac.il (R.S)

bioinformatics↗

bioRxiv2020

DOMINO: a novel network-based module detection algorithm with reduced rate offalse calls

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes activity scores as input and report sub-networks that show significant over-representation of accrued activity signal ( active modules), thus representing biological processes that presumably play key roles in the analyzed biological conditions. Although such methods exist for almost two decades, only a handful of studies attempted to compare the biological signals captured by different methods. Here, we systematically evaluated six popular AMI methods on gene expression (GE) and GWAS data. Notably, we observed that GO terms enriched in modules detected by these methods on the real data were often also enriched on modules found on randomly permuted input data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation-based method that evaluates the empirical significance of GO terms reported as enriched in modules. We used the method to fashion five novel performance criteria for evaluating AMI methods. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir-Lab.

bioinformatics↗

bioRxiv2019

Systematic identification of functional SNPs interrupting 3’ UTR polyadenylation signals

Alternative polyadenylation (APA) is emerging as a widespread regulatory layer as the majority of human protein-coding genes contain several polyadenylation (p(A)) sites in their 3 UTRs. By generating isoforms with different 3 UTR length, APA potentially affects mRNA stability, translation efficiency, nuclear export, and cellular localization. Polyadenylation sites are regulated by adjacent RNA cis-regulatory elements, the principals among them are the polyadenylation signal (PAS) AAUAAA and its main variant AUUAAA, typically located ~20- nt upstream of the p(A) site. Mutations in PAS and other auxiliary poly(A) cis-elements in the 3 UTR of several genes have been shown to cause human Mendelian diseases, and to date, only a few common SNPs that regulate APA were associated with complex diseases. Here, we systematically searched for SNPs that affect gene expression and human traits by modulation of 3 UTR APA. Focusing on the variants most likely to exert the strongest effect, we identified 2,305 SNPs that interrupt the canonical PAS or its main variant. Implementing pA-QTL tests using GTEx RNA-seq data, we identified 139 PAS SNPs significantly associated with the usage of their p(A) site. As expected, PAS-interrupting alleles were significantly linked with decreased cleavage at their p(A) site and the consequential 3 UTR lengthening. As an indication for a functional effect of these PAS SNPs on gene expression, 65 of the pA-QTLs were also detected as eQTLs of the same gene in the same tissue. Furthermore, we observed that PAS-interrupting alleles linked with 3 UTR lengthening were also strongly associated with decreased gene expression, pointing that shorter isoforms generated by APA are generally more stable than longer ones. Last, indicative of the impact of PAS SNPs on human phenotypes, 53 pA-QTLs overlapped GWAS SNPs that are significantly linked with human traits.

genomics↗

bioRxiv2019

CT-FOCS: a novel method for inferring cell type-specific enhancer-promoter maps

Spatiotemporal gene expression patterns are governed to a large extent by the activity of enhancer elements, which engage in physical contacts with their target genes. Identification of enhancer-promoter (EP) links that are functional only in a specific subset of cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS, a statistical inference method that uses linear mixed effect models to infer EP links that show marked activity only in a single or a small subset of cell types out of a large panel of probed cell types. Analyzing 808 samples from FANTOM5, covering 472 cell lines, primary cells, and tissues, CT-FOCS inferred such EP links more accurately than recent state-of-the-art methods. Furthermore, we show that strictly cell type-specific EP links are very uncommon in the human genome.

bioinformatics↗

bioRxiv2018

Genomic meta-analysis of the interplay between 3D chromatin organization and gene expression programs under basal and stress conditions

BackgroundOur appreciation of the critical role of the 3D organization of the genome in gene regulation is steadily increasing. Recent 3C-based deep sequencing techniques elucidated a hierarchy of structures that underlie the spatial organization of the genome in the nucleus. At the top of this hierarchical organization are chromosomal territories and the megabase-scale A/B compartments that correlate with transcriptional activity within cells. Below them are the relatively cell-type invariant topologically associated domains (TADs), characterized by high frequency of physical contacts between loci within the same TAD and are assumed to function as regulatory units. Within TADs, chromatin loops bring enhancers and target promoters to close spatial proximity. Yet, we still have only rudimentary understanding how differences in chromatin organization between different cell types affect cell-type specific gene expression programs that are executed under basal and challenged conditions.\n\nResultsHere, we carried out a large-scale meta-analysis that integrated Hi-C data from thirteen different cell lines and dozens of ChIP-seq and RNA-seq datasets measured on these cells, either under basal conditions or after treatment. Pairwise comparisons between cell lines demonstrated the strong association between modulation of A/B compartmentalization, differential gene expression and transcription factor (TF) binding events. Furthermore, integrating the analysis of transcriptomes of different cell lines in response to various challenges, we show that 3D organization of cells under basal conditions constrains not only gene expression programs and TF binding profiles that are active under the basal condition but also those induced in response to treatment.\n\nConclusionsOur results further elucidate the role of dynamic genome organization in regulation of differential gene expression between different cell types, and indicate the impact of intra-TAD enhancer-promoter interactions that are established under basal conditions on both the basal and treatment-induced gene expression programs.

genomics↗

bioRxiv2017

An extensive enhancer-promoter map generated by genome-scale analysis of enhancer and gene activity patterns

Massive efforts have documented hundreds of thousands of putative enhancers in the human genome. A pressing genomic challenge is to identify which of these enhancers are functional and map them to the genes they regulate. We developed a novel method for inferring enhancer-promoter (E-P) links based on correlated activity patterns across many samples. Our method, called FOCS, uses rigorous statistical validation tailored for zero-inflated data, identifying the most important E-P links in each gene model. We applied FOCS to the wide epigenomic and transcriptomic datasets recorded by the ENCODE, Roadmap Epigenomics and FANTOM5 projects, together covering 2,630 samples of human primary cells, tissues and cell lines. In addition, building on expression of enhancer RNAs (eRNAs) as an exquisite mark of enhancer activity and on the robust detection of eRNAs by the GRO-seq technique, we compiled a compendium of eRNA and gene expression profiles based on public GRO-seq data from 245 samples and 23 human cell types. Applying FOCS to this compendium further expanded the coverage of our inferred E-P map. Benchmarking against gold standard E-P links from ChIA-PET and eQTL data, we demonstrate that FOCS prediction of E-P links outperforms extant methods. Collectively, we inferred >300,000 cross-validated E-P links spanning ~16K known genes. Our study presents an improved method for inferring regulatory links between enhancers and promoters, and provides an extensive resource of E-P maps that could greatly assist the functional interpretation of the noncoding regulatory genome. FOCS and our predicted E-P map are publicly available at http://acgt.cs.tau.ac.il/focs.

bioinformatics↗