Biology subjects

Aregger, M.

Publications and source records attributed to Aregger, M..

6 recordsLinked to original sources

bioRxiv2025

A global genetic interaction map of a human cell reveals conserved principles of genetic networks

We generated a genome-scale, genetic interaction network from the analysis of more than 4 million double mutants in the haploid human cell line, HAP1. The network maps [~]90,000 genetic interactions, including thousands of extreme synthetic lethal and genetic suppression interactions. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes, pathways, biological processes, and cellular compartments. Comparative analyses showed that general principles of genetic networks are conserved from yeast to human cells. A genetic interaction network mapped in a single genetic background complements the DepMap gene co-essentiality network, recapitulating many of the same biological connections and also capturing unique functional information to reveal roles of uncharacterized genes and molecular determinants of specific cancer cell line genetic dependencies.

systems biology↗

bioRxiv2025

Quantitative analysis of genetic interactions in human cells from genome-wide CRISPR-Cas9 screens

Genetic interaction (GI) networks in model organisms have revealed how combinations of genome variants can impact phenotypes. To advance efforts toward a reference human GI network, we developed the quantitative Genetic Interaction (qGI) score, a method for precise GI measurement from genome-wide CRISPR-Cas9 screens in different query mutants constructed in a single human cell line. We found surprising prevalent systematic variation unrelated to GIs in CRISPR screen data, including both genomically linked effects and functionally coherent covariation. Leveraging [~]40 control screens in wild-type cells and half a billion differential fitness effect measurements, we developed a pipeline for CRISPR screen data processing and normalization to correct these artifacts and measure accurate, quantitative GIs. We also comprehensively characterized GI reproducibility by characterizing 4 - 5 biological replicates for [~]125,000 unique gene pairs. The qGI framework enables systematic identification of human GIs and provides broadly applicable strategies for analyzing context-specific CRISPR screen data.

bioinformatics↗

bioRxiv2022

Reproducibility metrics for CRISPR screens

CRISPR screens are used extensively to systematically interrogate the phenotype-to-genotype problem. In contrast to early CRISPR screens, which defined core cell fitness genes, most current efforts now aim to identify context-specific phenotypes that differentiate a cell line, genetic background or condition of interest, such as a drug treatment. While CRISPR-related technologies have shown great promise and a fast pace of innovation, a better understanding of standards and methods for quality assessment of CRISPR screen results is crucial to guide technology development and application. Specifically, many commonly used metrics for quantifying screen quality do not accurately measure the reproducibility of context-specific hits. We highlight the importance of reporting reproducibility statistics that directly relate to the purpose of the screen and suggest the use of metrics that are sensitive to context-specific signal.

genomics↗

bioRxiv2020

Major functional bias for mitochondrial complexes in genome-wide CRISPR screens

We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome-wide CRISPR screen data and methods for analyzing them. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a dominant mitochondria-associated signal, which our time-resolved CRISPR screens and analysis suggests may reflect screen dynamics and protein stability effects rather than genetic dependencies.

systems biology↗

bioRxiv2019

Systematic mapping of genetic interactions for de novo fatty acid synthesis

The de novo synthesis of fatty acids has emerged as a therapeutic target for various diseases including cancer. While several translational efforts have focused on direct perturbation of de novo fatty acid synthesis, only modest responses have been associated with mono-therapies. Since cancer cells are intrinsically buffered to combat metabolic stress, cells may adapt to loss of de novo fatty acid biosynthesis. To explore cellular response to defects in fatty acid synthesis, we used pooled genome-wide CRISPR screens to systematically map genetic interactions (GIs) in human HAP1 cells carrying a loss-of-function mutation in FASN, which catalyzes the formation of long-chain fatty acids. FASN mutant cells showed a strong dependence on lipid uptake that was reflected by negative GIs with genes involved in the LDL receptor pathway, vesicle trafficking, and protein glycosylation. Further support for these functional relationships was derived from additional GI screens in query cell lines deficient for other genes involved in lipid metabolism, including LDLR, SREBF1, SREBF2, ACACA. Our GI profiles identified a potential role for a previously uncharacterized gene LUR1 (C12orf49) in exogenous lipid uptake regulation. Overall, our data highlights the genetic determinants underlying the cellular adaptation associated with loss of de novo fatty acid synthesis and demonstrate the power of systematic GI mapping for uncovering metabolic buffering mechanisms in human cells.

systems biology↗

bioRxiv2017

Evaluation and Design of Genome-wide CRISPR/Cas9 Knockout Screens

The adaptation of CRISPR/Cas9 technology to mammalian cell lines is transforming the study of human functional genomics. Pooled libraries of CRISPR guide RNAs (gRNAs), targeting human protein-coding genes and encoded in viral vectors, have been used to systematically create gene knockouts in a variety of human cancer and immortalized cell lines, in an effort to identify whether these knockouts cause cellular fitness defects. Previous work has shown that CRISPR screens are more sensitive and specific than pooled library shRNA screens in similar assays, but currently there exists significant variability across CRISPR library designs and experimental protocols. In this study, we re-analyze 17 genome-scale knockout screens in human cell lines from three research groups using three different genome-scale gRNA libraries, using the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm to identify essential genes, to refine and expand our previously defined set of human core essential genes, from 360 to 684 genes. We use this expanded set of reference Core Essential Genes (CEG2), plus empirical data from six CRISPR knockout screens, to guide the design of a sequence-optimized gRNA library, the Toronto KnockOut version 3.0 (TKOv3) library. We demonstrate the high effectiveness of the library relative to reference sets of essential and nonessential genes as well as other screens using similar approaches. The optimized TKOv3 library, combined with the CEG2 reference set, provide an efficient, highly optimized platform for performing and assessing gene knockout screens in human cell lines.

systems biology↗