Biology subjects

Gheorghe, V.

Publications and source records attributed to Gheorghe, V..

4 recordsLinked to original sources

bioRxiv2026

Mapping the genetic landscape of the DNA damage response with Cas12a-based combinatorial knockout screens

The DNA damage response (DDR) is a complex network of cellular pathways that ensures the faithful maintenance of our genomes upon a wide array of genomic insults. To elucidate the functional architecture of this network, we conducted unbiased genetic interaction screens using the Cas12a genome editor to disrupt 233 DDR genes frequently mutated in cancer and other genetic diseases, either individually or in pairwise combinations. This approach enabled us to assess the phenotypic effects induced by the disruption of >27,000 DDR gene pair combinations under unperturbed cell growth conditions. From this analysis, we identified over 750 high-confidence positive (buffering) or negative (synthetic lethal/sick) gene-gene interactions, along with multiple connections between previously unlinked DDR pathways and modules, allowing us to define novel aspects of the cellular response to spontaneous, DNA replication-associated DNA damage. Among the identified genetic interactions, we uncovered profound synthetic lethal interactions between genes encoding 1) the translesion polymerase REV1-Pol {zeta} complex and the MCM8-MCM9-HROB DNA helicase complex; 2) Fanconi Anemia (FA) proteins and the mitotic DNA repair factors GEN1, CIP2A, and RHINO; and 3) the DNA translocase SMARCAL1 and components of the FANCM complex, suggesting novel opportunities for targeted therapies in tumors carrying mutations in these genes. Additionally, we identified robust suppressor interactions between the DCLRE1B gene encoding the nuclease APOLLO and the core non-homologous end joining (NHEJ) genes XRCC4, LIG4, and NHEJ1, suggesting that NHEJ impairs the fitness of APOLLO-deficient cells. This work provides a functional map of the DDR network and demonstrates the power of Cas12a-based screens for identifying synthetic lethal and buffering interactions with therapeutic potential.

genetics↗

bioRxiv2025

Functional modules predict cancer-relevant genetic interactions in mammalian cells

Genetic interactions can reveal gene function and identify disease-relevant synthetic lethals, but resource-intensive screening in human cells constrains experimental scope and limits our understanding of how generalizable sparse hits are. Here, we leverage principles from yeast genetic networks to identify human gene modules involved in receptor tyrosine kinase signaling and DNA damage response that are predicted to be enriched for genetic interactions, then use our enCas12a-based In4mer combinatorial knockout platform to efficiently screen these modules across diverse cancer cell lines. We identify hundreds of unreported synthetic lethals, including a dense network within the protein N-linked glycosylation machinery that shows both evolutionary conservation and network rewiring, and we confirm that interactions discovered in 2D cell culture are maintained in more physiologically relevant models. Critically, a meta-analysis within and between screens highlights sources of variation in published results, reveals the potential for false positives in screens with many essential genes, and demonstrates the need to move beyond DDR for actionable synthetic lethalities.

systems biology↗

bioRxiv2022

Optimal construction of a functional interaction network from pooled library CRISPR fitness screens

Functional interaction networks, where edges connect genes likely to operate in the same biological process or pathway, can be inferred from CRISPR knockout screens in cancer cell lines. Genes with similar knockout fitness profiles across a sufficiently diverse set of cell line screens are likely to be co-functional, and these "coessentiality" networks are increasingly powerful predictors of gene function and biological modularity. While several such networks have been published, most use different algorithms for each step of the network construction process. In this study, we identify an optimal measure of functional interaction and test all combinations of options at each step - essentiality scoring, sample variance and covariance normalization, and similarity measurement - to identify best practices for generating a functional interaction network from CRISPR knockout data. We show that Bayes Factor and Ceres scores give the best results, that Ceres outperforms the newer Chronos scoring scheme, and that covariance normalization is a critical step in network construction. We further show that Pearson correlation, mathematically identical to ordinary least squares after covariance normalization, can be extended by using partial correlation to detect and amplify signals from "moonlighting" proteins which show context-dependent interaction with different partners.

systems biology↗

bioRxiv2021

Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions

Coessentiality networks derived from CRISPR screens in cell lines provide a powerful framework for identifying functional modules in the cell and for inferring the role of uncharacterized genes. However, these networks integrate signal across all underlying data, and can mask strong interactions that occur in only a subset of the cell lines analyzed. Here we decipher dynamic functional interactions by identifying significant cellular contexts, primarily by oncogenic mutation, lineage, and tumor type, and discovering coessentiality relationships that depend on these contexts. We recapitulate well-known gene-context interactions such as oncogene-mutation, paralog buffering, and tissue-specific essential genes, show how mutation rewires known signal transduction pathways, including RAS/RAF and IGF1R-PIK3CA, and illustrate the implications for drug targeting. We further demonstrate how context-dependent functional interactions can elucidate lineage-specific gene function, as illustrated by the maturation of proreceptors IGF1R and MET by proteases FURIN and CPD. This approach advances our understanding of context-dependent interactions and how they can be gleaned from these data. We provide an online resource to explore these context-dependent interactions at diffnet.hart-lab.org.

systems biology↗