Biology subjects

Khoroshkin, M. S.

Publications and source records attributed to Khoroshkin, M. S..

5 recordsLinked to original sources

bioRxiv2025

Discovery of a tRNA-regulatory transcription factor that suppresses breast cancer metastasis

Transfer RNAs (tRNAs), once viewed as static adaptors in translation, are now recognized as dynamic regulators of gene expression. While recent studies have illuminated roles for tRNA stability, the upstream mechanisms governing tRNA transcription remain poorly understood. To address this gap, we generated the EXpression atlas of tRNA (EXTRNA), a high-resolution tRNA expression dataset spanning 24 cell lines across 9 human tissues. EXTRNA revealed both tissue-type-specific expression programs ("tRNAomes") and unexpected intra-tissue heterogeneity across breast cancer samples. Integrating EXTRNA with computational network analysis and data from other publicly available datasets, we identified Zinc Finger ZZ-Type And EF-Hand Domain Containing 1 (ZZEF1) as the first sequence-specific transcription factor of a particular tRNA. ZZEF1 promoted tRNA-LysUUU transcription by partnering with the ATP-dependent chromatin remodeler Chromodomain Helicase DNA Binding Protein 6 (CHD6), enhancing chromatin accessibility at tRNA-Lys-TTT-3 loci. ZZEF1 deficiency reduced tRNA-LysUUU abundance, decreased the translational efficiency of AAR codon-enriched mRNAs--including the tumor suppressor Serine/Threonine Kinase 3 (STK3)--and promoted metastatic progression in breast cancer in vivo. Together, our findings establish a previously unrecognized mechanism for RNA polymerase III-mediated tRNA transcription and define a regulatory circuit linking chromatin remodeling, codon-specific translation, and tumor suppression. More broadly, this work introduces a framework for dissecting the regulatory logic of the tRNAome and highlights tRNA expression control as a promising avenue for therapeutic intervention.

genomics↗

bioRxiv2024

A generative framework for enhanced cell-type specificity in rationally designed mRNAs

mRNA delivery offers new opportunities for disease treatment by directing cells to produce therapeutic proteins. However, designing highly stable mRNAs with programmable cell type-specificity remains a challenge. To address this, we measured the regulatory activity of 60,000 5 and 3 untranslated regions (UTRs) across six cell types and developed PARADE (Prediction And RAtional DEsign of mRNA UTRs), a generative AI framework to engineer untranslated RNA regions with tailored cell type-specific activity. We validated PARADE by testing 15,800 de novo-designed sequences across these cell lines and identified many sequences that demonstrated superior specificity and activity compared to existing RNA therapeutics. mRNAs with PARADE-engineered UTRs also exhibited robust tissue-specific activity in animal models, achieving selective expression in the liver and spleen. We also leveraged PARADE to enhance mRNA stability, significantly increasing protein output and therapeutic durability in vivo. These advancements translated to notable increases in therapeutic efficacy, as PARADE-designed UTRs in oncosuppressor mRNAs, namely PTEN and P16, effectively reduced tumor growth in patient-derived neuroglioma xenograft models and orthotopic mouse models. Collectively, these findings establish PARADE as a versatile platform for designing safer, more precise, and highly stable mRNA therapies.

systems biology↗

bioRxiv2023

Gene networks reveal stem-cell state convergence during preneoplasia and progression to malignancy in multistage skin carcinogenesis

Adult mammalian stem cells play critical roles in normal tissue homeostasis, as well as in tumor development, by contributing to cell heterogeneity, plasticity, and development of drug resistance. The relationship between different types of normal and cancer stem cells is highly controversial and poorly understood. Here, we carried out gene expression network analysis of normal and tumor samples from genetically heterogeneous mice to create network metagenes for visualization of stem-cell networks, rather than individual stem-cell markers, at the single-cell level during multistage carcinogenesis. We combined this approach with lineage tracing and single-cell RNASeq of stem cells and their progeny, identifying a previously unrecognized hierarchy in which Lgr6+ stem cells from tumors generate progeny that express a range of other stem-cell markers including Sox2, Pitx1, Foxa1, Klf5, and Cd44. Our data identify a convergence of multiple stem-cell and tumor-suppressor pathways in benign tumor cells expressing markers of lineage plasticity and oxidative stress. This same single-cell population expresses network metagenes corresponding to markers of cancer drug resistance in human tumors of the skin, lung and prostate. Treatment of mouse squamous carcinomas in vivo with the chemotherapeutic cis-platin resulted in elevated expression of the genes that mark this cell population. Our data have allowed us to create a simplified model of multistage carcinogenesis that identifies distinct stem-cell states at different stages of tumor progression, thereby identifying networks involved in lineage plasticity, drug resistance, and immune surveillance, providing a rich source of potential targets for cancer therapy. One-Sentence SummaryGenes act in networks to drive cancer, and we identify these groups of genes from bulk-tissue and trace them at single-cell resolution.

cancer biology↗

bioRxiv2023

Systematic Identification of Post-Transcriptional Regulatory Modules

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we performed a systematic annotation of RBP combinatorial interactions via multimodal data integration. We built a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we used CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generated an integrated map of functional RBP interactions. We then used this map to match RBPs to their context-specific functions and validated the predicted functions biochemically for four RBPs. This study highlights the previously underappreciated scale of the inter-RBP interactions, be it genetic or physical, and is a first step towards a more comprehensive understanding of post-transcriptional regulatory processes and their underlying molecular grammar.

systems biology↗

bioRxiv2017

Extending Chemical Perturbations Of The Ubiquitin Fitness Landscape In A Classroom Setting

Although the primary protein sequence of ubiquitin (Ub) is extremely stable over evolutionary time, it is highly tolerant to mutation during selection experiments performed in the laboratory. We have proposed that this discrepancy results from the difference between fitness under laboratory culture conditions and the selective pressures in changing environments over evolutionary time scales. Building on our previous work (Mavor et al 2016), we used deep mutational scanning to determine how twelve new chemicals (3-Amino-1,2,4-triazole, 5-fluorocytosine, Amphotericin B, CaCl2, Cerulenin, Cobalt Acetate, Menadione, Nickel Chloride, p-fluorophenylalanine, Rapamycin, Tamoxifen, and Tunicamycin) reveal novel mutational sensitivities of ubiquitin residues. We found sensitization of Lys63 in eight new conditions. In total, our experiments have uncovered a sensitizing condition for every position in Ub except Ser57 and Gln62. By determining the Ubiquitin fitness landscape under different chemical constraints, our work helps to resolve the inconsistencies between deep mutational scanning experiments and sequence conservation over evolutionary timescales.\n\nBuilds onMavor D, Barlow KA, Thompson S, Barad BA, Bonny AR, Cario CL, Gaskins G, Liu Z, Deming L, Axen SD, Caceres E, Chen W, Cuesta A, Gate R, Green EM, Hulce KR, Ji W, Kenner LR, Mensa B, Morinishi LS, Moss SM, Mravic M, Muir RK, Niekamp S, Nnadi CI, Palovcak E, Poss EM, Ross TD, Salcedo E, See S, Subramaniam M, Wong AW, Li J, Thorn KS, Conchuir SO, Roscoe BP, Chow ED, DeRisi JL, Kortemme T, Bolon DN, Fraser JS. Determination of Ubiquitin Fitness Landscapes Under Different Chemical Stresses in a Classroom Setting. eLife. 2016.\n\nImpact StatementWe organized a project-based course that used deep mutational scanning in multiple chemical conditions to resolve the inconsistencies between tolerance to mutations in laboratory conditions and sequence conservation over evolutionary timescales.

bioinformatics↗