Biology subjects

Lahav, T.

Publications and source records attributed to Lahav, T..

6 recordsLinked to original sources

bioRxiv2026

RBM25 exhibits distinct RNA- and chromatin-associated target programs in human embryonic stem cells

RNA-binding proteins (RBPs) are increasingly recognized to perform chromatin-associated functions in addition to their canonical roles in RNA regulation, yet how these activities are coordinated in human embryonic stem cells (hESCs) remain poorly understood. Here, we characterize distinct RNA- and chromatin-associated activities for RBM25 in hESCs. Transcriptome-wide profiling showed that RBM25 preferentially associates with transcripts involved in RNA processing and splicing, consistent with its canonical role as an RBP involved in post-transcriptional regulation. Genome-wide chromatin profiling further uncovered a distinct chromatin-associated program, with RBM25 occupying accessible promoters of actively expressed cell-cycle genes and introns predominantly associated with transcriptionally inactive neuronal genes. Interestingly, RBM25 chromatin-binding sites showed substantial co-occupancy with regions bound by the transcription factor CEBPZ in hESCs. We further show that RBM25 and CEBPZ physically interact and exhibit shared chromatin-binding patterns at both promoter and intronic regions. Importantly, reciprocal depletion experiments revealed that RBM25 is required for CEBPZ occupancy at CCAAT-containing promoters of highly expressed cell-cycle genes, whereas CEBPZ is not similarly required for RBM25 chromatin occupancy. Together, our findings establish RBM25 as a multifunctional factor with distinct roles in RNA processing and chromatin-associated regulation and reveal a context-dependent RBP-TF interaction that contributes to transcriptional regulation in hESCs.

molecular biology↗

bioRxiv2023

Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6's role in hematopoietic differentiation and leukemia

BackgroundLong non-coding RNAs (lncRNAs) are pivotal players in cellular processes, and their unique cell-type specific expression patterns make them attractive biomarkers and therapeutic targets. Yet, the functional roles of most lncRNAs remain enigmatic. To address the need to identify new druggable lncRNAs, we developed a comprehensive approach integrating transcription factor binding data with other genetic features to generate a machine learning model, which we have called INFLAMeR (Identifying Novel Functional LncRNAs with Advanced Machine Learning Resources). MethodsINFLAMeR was trained on high-throughput CRISPR interference (CRISPRi) screens across seven cell lines, and the algorithm was based on 71 genetic features. To validate the predictions, we selected candidate lncRNAs in the K562 leukemia cell line and determined the effect of their knockdown on cell proliferation and chemotherapy drug resistance. We further performed transcriptomic analysis for candidate genes. Based on these findings, we assessed the lncRNA Small Nucleolar RNA Host Gene 6 (SNHG6) for its role in myeloid differentiation by incubation with Phorbol 12-myristate 13-acetate (PMA) to induce megakaryocyte differentiation, or with hemin to induce erythrocyte differentiation. ResultsThe INFLAMeR model successfully reconstituted CRISPRi screening data and predicted functional lncRNAs that were previously overlooked. Intensive cell-based and transcriptomic validation of nearly fifty genes in K562 revealed cell type-specific functionality for 85% of the predicted lncRNAs. Our cell-based and transcriptomic analyses predicted a role for SNHG6 in hematopoiesis and leukemia. Consistent with its predicted role in hematopoietic differentiation, SNHG6 transcription is regulated by hematopoiesis-associated transcription factors. Knockdown of SNHG6 reduced the proliferation of leukemia cells and sensitized them to differentiation. Treatment of K562 leukemic cells with hemin and PMA, respectively, demonstrated that SNHG6 inhibits red blood cell differentiation but strongly promotes megakaryocyte differentiation. Despite SNHG6 transcripts showing strong cytoplasmic enrichment, SNHG6 regulates the expression of hematopoietic genes such as PPBP (Pro-Platelet Basic Protein) and PF4 (Platelet Factor 4). ConclusionsOur approach not only improved the identification and characterization of functional lncRNAs through genomic approaches in a cell type-specific manner, but also identified new lncRNAs with a role in hematopoiesis and leukemia. Such approaches cab be used to identify new targets for precision therapy.

genetics↗

bioRxiv2023

The Unexpected Match: STAT3-NORAD Interaction, a Novel Link in Antiviral Defense

In the intricate biological landscape, evolution often repurposes familiar elements for novel roles. This study reveals an interaction between the long non-coding RNA NORAD, noted for its role in DNA stability, and the immune-related transcription factor STAT3. Our findings indicate that NORADs binding to STAT3 facilitates its nuclear entry, suppressing the antiviral response. In the absence of NORAD, STAT3 remains cytoplasmic, enabling STAT1 to activate this response. Evidence from viral infections and clinical samples reinforces this unique role for NORAD. Intriguingly, while other functions of NORAD are conserved in evolution, this newly discovered role is unique to humans, owing to the introduction of an ALU element in hominoids. This discovery sheds new light on the evolution of antiviral defenses.

molecular biology↗

bioRxiv2023

Proteasome gene expression is controlled by the coordinated functions of multiple transcription factors

Proteasome activity is crucial for cellular integrity, but how tissues adjust proteasome content in response to catabolic stimuli is uncertain. Here, we demonstrate that transcriptional coordination by multiple transcription factors is required to increase proteasome content and activate proteolysis in catabolic states. Using denervated mouse muscle as a model system for accelerated proteolysis in vivo, we reveal that a two-phase transcriptional program activates genes encoding proteasome subunits and assembly chaperones to boost an increase in proteasome content. Initially, gene induction is necessary to maintain basal proteasome levels, and in a more delayed phase (7-10 d after denervation) it stimulates proteasome assembly to meet cellular demand for excessive proteolysis. Intriguingly, the transcription factors PAX4 and -PALNRF-1 control the expression of proteasome among other genes in a combinatorial manner, driving cellular adaptation to muscle denervation. Consequently, PAX4 and -PALNRF-1 represent new therapeutic targets to inhibit proteolysis in catabolic diseases (e.g. type-2 diabetes, cancer).

cell biology↗

bioRxiv2019

Genome-Scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation

Atrazine is an herbicide and pollutant of great environmental concern that is naturally biodegraded by microbial communities. The efficiency of biodegradation can be improved through the stimulating addition of fertilizers, electron acceptors, etc. In recent years, metabolic modelling approaches have become widely used as an in silico tool for organism-level phenotyping and the subsequent development of metabolic engineering strategies including biodegradation improvement. Here, we constructed a genome scale metabolic model, iRZ960, for Paenarthrobacter aurescens TC1 - a widely studied atrazine degrader - aiming at simulating its degradation activity. A mathematical stoichiometric metabolic model was constructed based on a published genome sequence of P. aurescens TC1. An Initial draft model was automatically constructed using the RAST and KBase servers. The draft was developed into a predictive model through semi-automatic gap-filling procedures including manual curation. In addition to growth predictions under different conditions, model simulations were used to identify optimized media for enhancing the natural degradation of atrazine without a need in strain design via genetic modifications. Model predictions for growth and atrazine degradation efficiency were tested in myriad of media supplemented with different combinations of carbon and nitrogen sources that were verified in vitro. Experimental validations support the reliability of the models predictions for both bacterial growth (biomass accumulation) and atrazine degradation. Predictive tools, such as the presented model, can be applied for achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation in changing environments.

bioinformatics↗

bioRxiv2016

Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment

Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. Metabolic interactions with bacteria have been key drivers in the evolution of sap-feeding insects, enabling complementation of their deprived nutrition. The sap-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. We took advantage of the well-defined bacterial community in B. tabaci as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented frequency of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species combinations, and (3) dependencies of each species on different media components.\n\nThe automatic-based predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggested several previously un-reported routes for complementary interactions. Highly abundant symbiont combinations were found to have the potential to produce a diverse set of complementary metabolites, in comparison to un-detected combinations. No clear association was detected between metabolic codependencies and co-occurrence patterns. The findings indicate a potential key role for metabolic exchanges as key determinants shaping community structure in this system.

systems biology↗