Wave

Research

The overarching goals of our research are to decipher the noncoding human genome, understand different modes of gene regulation and elucidate mechanisms by which noncoding genetic variants affect human traits and disease risk.

Transcriptional regulation

Large-scale projects including ENCODE and Roadmap Epigenomics have already documented ~1M putative enhancer elements in the human genome. These regions were demonstrated to be strongly enriched for disease risk SNPs detected by GWAS studies. Key current challenges that research in our lab tries to advance are:

(1) Improve global mapping between enhancers and their target genes;

(2) Pinpoint causal genetic variants which modulate binding affinity of transcription factors thereby affecting enhancer activity; and

(3) Characterize target genes and biological processes that are affected by risk SNPs, thereby elucidating molecular pathways that are relevant for the etiology of complex diseases.

(4) Delineate interplays between the genome 3D organization and transcriptional regulation.   

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We recently developed a novel method for detecting transcriptional programs that link cell differentiation and disease risk, by integrative analysis of single-cell RNA-seq and GWAS data. [PubMed]

References:

  • Shulman ED, Elkon R. Genetic mapping of developmental trajectories for complex traits and diseases. Comput Struct Biotechnol J. 2021 Jun 6;19:3458-3469. [PubMed]

 

  • Hait TA, Amar D, Shamir R, Elkon R. FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map. Genome Biol. 2018 May 1;19(1):56.  

 

  • Nurick I, Shamir R, Elkon R. Genomic meta-analysis of the interplay between 3D chromatin organization and gene expression programs under basal and stress conditions. Epigenetics Chromatin. 2018 Aug 29;11(1):49. [PubMed]

  • Elkon R, Agami R. Characterization of noncoding regulatory DNA in the human genome. Nat Biotechnol. 2017 Aug 8;35(8):732-746. [PubMed]

  • Korkmaz G, Lopes R, Ugalde AP, Nevedomskaya E, Han R, Myacheva K, Zwart W, Elkon R, Agami R. Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9. Nat Biotechnol. 2016 Feb;34(2):192-8. [PubMed]

Regulation of transcript stability and translation efficiency.

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Gene regulation is a multi-layered process. While the main layer is transcriptional regulation, other layers remain under-explored. We aim at elucidating post-transcriptional mechanisms that control gene expression by modulating mRNA stability and translation efficiency. These layers are mainly regulated by cis-elements embedded in transcripts’ untranslated regions (UTRs). We study how genetic variation in UTRs affect gene expression and human traits.

We introduced the concept of poly-adenylation QTLs (pA-QTLs) and discovered hundreds of SNPs that affect gene expression by interfering with the canonical signal that control mRNA polyadenylation (the PAS signal in 3’UTRs). [PubMed]

References:

  • Shulman ED, Elkon R. Systematic identification of functional SNPs interrupting 3'UTR polyadenylation signals. PLoS Genet. 2020 Aug 17;16(8):e1008977. [PubMed]

 

  • Shulman ED, Elkon R. Cell-type-specific analysis of alternative polyadenylation using single-cell transcriptomics data. Nucleic Acids Res. 2019 Nov 4;47(19):10027-10039. [PubMed]

  • Elkon R, Loayza-Puch F, Korkmaz G, Lopes R, van Breugel PC, Bleijerveld OB, Altelaar AF, Wolf E, Lorenzin F, Eilers M, Agami R. Myc coordinates transcription and translation to enhance transformation and suppress invasiveness. EMBO Rep. 2015 Dec;16(12):1723-36. [PubMed]

  • Elkon R, Ugalde AP, Agami R. Alternative cleavage and polyadenylation: extent, regulation and function. Nat Rev Genet. 2013 Jul;14(7):496-506. [PubMed]

Functional interpretation of omics data

With the ever-increasing volume of publicly available genomic, transcriptomic, and proteomic data, it remains a great challenge to uncover biological and biomedical insights out of it. We analyze different systems-biology approaches for enhancing the functional interpretation of such data, including 

(1) network-based and (2) gene-set analyses.

 

Both approaches build on the amplification of weak signals, achieved by consideration of the coordinated response of many genes that function in the same biological process, where individually most of them show only mild signal that does not reach statistical significance in individual gene-level tests.

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A gene network involved in increased risk for Schizophrenia detected by our DOMINO algorithm 

[PubMed]

References:

  • Levi H, Elkon R, Shamir R. DOMINO: a network-based active module identification algorithm with reduced rate of false calls. Mol Syst Biol. 2021 Jan;17(1):e9593. [PubMed]

 

  • Mandelboum S, Manber Z, Elroy-Stein O, Elkon R. Recurrent functional misinterpretation of RNA-seq data caused by sample-specific gene length bias. PLoS Biol. 2019 Nov 12;17(11):e3000481. [PubMed]

  • Hait TA, Maron-Katz A, Sagir D, Amar D, Ulitsky I, Linhart C, Tanay A, Sharan R, Shiloh Y, Elkon R, Shamir R. The EXPANDER Integrated Platform for Transcriptome Analysis. J Mol Biol. 2019 Jun 14;431(13):2398-2406. [PubMed]

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