Alemany, A. | Search bioRxiv

bioRxiv2020

A genome-wide CRISPR/Cas phenotypic screen formodulators of DUX4 cytotoxicity reveals screen complications

Facioscapulohumeral muscular dystrophy (FHSD), a fundamentally complex muscle disorder that thus far remains untreatable. As the name implies, FSHD starts in the muscles of the face and shoulder gridle. The main perturbator of the disease is the pioneer transcription factor DUX4, which is misexpressed in affected tissues due to a failure in epigenetic repressive mechanisms. In pursuit of unraveling the underlying mechanism of FSHD and finding potential therapeutic targets or treatment options, we performed an exhaustive genome-wide CRISPR/Cas9 phenotypic rescue screen to identify modulators of DUX4 cytotoxicity. We found no key effectors other than DUX4 itself, suggesting treatment efforts in FSHD should be directed towards its direct modulation. The screen did however reveal some rare and unexpected Cas9-induced genomic events, that may provide important considerations for planning future CRISPR/Cas9 knock-out screens.

molecular biology↗

bioRxiv2020

Functional modules from variable genes: Leveraging percolation to analyze noisy, high-dimensional data

While measurement advances now allow extensive surveys of gene activity (large numbers of genes across many samples), interpretation of these data is often confounded by noise — expression counts can differ strongly across samples due to variation of both biological and experimental origin. Complimentary to perturbation approaches, we extract functionally related groups of genes by analyzing the standing variation within a sampled population. To distinguish biologically meaningful patterns from uninterpretable noise, we focus on correlated variation and develop a novel density-based clustering approach that takes advantage of a percolation transition generically arising in random, uncorrelated data. We apply our approach to two contrasting RNA sequencing data sets that sample individual variation — across single cells of fission yeast and whole animals of C. elegans worms — and demonstrate robust applicability and versatility in revealing correlated gene clusters of diverse biological origin, including cell cycle phase, development/reproduction, tissue-specific functions, and feeding history. Our technique exploits generic features of noisy high-dimensional data and is applicable, beyond gene expression, to feature-rich data that sample population-level variability in the presence of noise.Significance Statement Gene expression largely determines the fate of each cell and ultimately the development and behavior of the whole organism. Whereas most of our knowledge on gene regulatory networks has been obtained from perturbation experiments (e.g. manipulating environmental conditions, genotype, or other physiological variables), here we develop an alternative approach based on the analysis of naturally occurring variations across individuals within a population. Using both single-cell and whole-animal RNA sequencing data, we demonstrate how a rich set of co-regulated gene modules can be uncovered from transcriptomic variability of individuals within unperturbed populations. To robustly extract interpretable clusters from the strong noise background, we devise a novel, versatile clustering approach based on network theory. With a foundation in the generic behavior of random networks near their percolation critical point, our method is broadly applicable, beyond gene expression, to any noisy, high-dimensional data that sample variation across individuals within a population.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗