Mapping the genetic landscape of DNA double-strand break repair.

Cells repair DNA double-strand breaks (DSBs) through a complex set of pathways critical for maintaining genomic integrity. To systematically map these pathways, we developed a high-throughput screening approach called Repair-seq that measures the effects of thousands of genetic perturbations on mutations introduced at targeted DNA lesions. Using Repair-seq, we profiled DSB repair products induced by two programmable nucleases (Cas9 and Cas12a) in the presence or absence of oligonucleotides for homology-directed repair (HDR) after knockdown of 476 genes involved in DSB repair or associated processes. The resulting data enabled principled, data-driven inference of DSB end joining and HDR pathways. Systematic interrogation of this data uncovered unexpected relationships among DSB repair genes and demonstrated that repair outcomes with superficially similar sequence architectures can have markedly different genetic dependencies. This work provides a foundation for mapping DNA repair pathways and for optimizing genome editing across diverse modalities.

Mapping the Genetic Landscape of Human Cells.

Seminal yeast studies have established the value of comprehensively mapping genetic interactions (GIs) for inferring gene function. Efforts in human cells using focused gene sets underscore the utility of this approach, but the feasibility of generating large-scale, diverse human GI maps remains unresolved. We developed a CRISPR interference platform for large-scale quantitative mapping of human GIs. We systematically perturbed 222,784 gene pairs in two cancer cell lines. The resultant maps cluster functionally related genes, assigning function to poorly characterized genes, including TMEM261, a new electron transport chain component. Individual GIs pinpoint unexpected relationships between pathways, exemplified by a specific cholesterol biosynthesis intermediate whose accumulation induces deoxynucleotide depletion, causing replicative DNA damage and a synthetic-lethal interaction with the ATR/9-1-1 DNA repair pathway. Our map provides a broad resource, establishes GI maps as a high-resolution tool for dissecting gene function, and serves as a blueprint for mapping the genetic landscape of human cells.

Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle.

Dissecting the regulatory mechanisms controlling mammalian transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm in human and mouse cells. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA-binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered a link between DDX3X and nuclear export. For hundreds of RNA metabolism genes, most transcripts with retained introns were degraded by the nuclear exosome, while the remaining molecules were exported with stable cytoplasmic lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, machine learning identified molecular features that predicted the diverse life cycles of mRNAs.

A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X.

The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled.

The regulatory protein SnoN antagonizes activin/Smad2 protein signaling and thereby promotes adipocyte differentiation and obesity in mice.

Ski-related oncogene SnoN (SnoN or SKIL) regulates multiple signaling pathways in a tissue- and developmental stage-dependent manner and has broad functions in embryonic angiogenesis, mammary gland alveologenesis, cancer, and aging. Here, we report that SnoN also plays a critical role in white adipose tissue (WAT) development by regulating mesenchymal stem cell (MSC) self-renewal and differentiation. We found that SnoN promotes MSC differentiation in the adipocyte lineage by antagonizing activin A/Smad2, but not TGFß/Smad3 signaling. Mice lacking SnoN or expressing a mutant SnoN defective in binding to the Smads were protected from high-fat diet-induced obesity and insulin resistance, and MSCs lacking a functional SnoN exhibited defective differentiation. We further demonstrated that activin, via Smad2, appears to be the major regulator of WAT development in vivo We also noted that activin A is abundantly expressed in WAT and adipocytes through an autocrine mechanism and promotes MSC self-renewal and inhibits adipogenic differentiation by inducing expression of the gene encoding the homeobox transcription factor Nanog. Of note, SnoN repressed activin/Smad2 signaling and activin A expression, enabling expression of adipocyte-specific transcription factors and promoting adipogenic differentiation. In conclusion, our study has revealed that SnoN plays an important in vivo role in adipocyte differentiation and WAT development in vivo by decreasing activity in the activin/Smad2 signaling pathway.

IGHMBP2 deletion suppresses translation and activates the integrated stress response.

IGHMBP2 is a nonessential, superfamily 1 DNA/RNA helicase that is mutated in patients with rare neuromuscular diseases SMARD1 and CMT2S. IGHMBP2 is implicated in translational and transcriptional regulation via biochemical association with ribosomal proteins, pre-rRNA processing factors, and tRNA-related species. To uncover the cellular consequences of perturbing IGHMBP2, we generated full and partial IGHMBP2 deletion K562 cell lines. Using polysome profiling and a nascent protein synthesis assay, we found that IGHMBP2 deletion modestly reduces global translation. We performed Ribo-seq and RNA-seq and identified diverse gene expression changes due to IGHMBP2 deletion, including ATF4 up-regulation. With recent studies showing the integrated stress response (ISR) can contribute to tRNA metabolism-linked neuropathies, we asked whether perturbing IGHMBP2 promotes ISR activation. We generated ATF4 reporter cell lines and found IGHMBP2 knockout cells demonstrate basal, chronic ISR activation. Our work expands upon the impact of IGHMBP2 in translation and elucidates molecular mechanisms that may link mutant IGHMBP2 to severe clinical phenotypes.

Protein-protein interactions with G3BPs drive stress granule condensation and gene expression changes under cellular stress.

Stress granules (SGs) are macromolecular assemblies that form under cellular stress. Formation of these condensates is driven by the condensation of RNA and RNA-binding proteins such as G3BPs. G3BPs condense into SGs following stress-induced translational arrest. Three G3BP paralogs (G3BP1, G3BP2A, and G3BP2B) have been identified in vertebrates. However, the contribution of different G3BP paralogs to stress granule formation and stress-induced gene expression changes is incompletely understood. Here, we identified key residues for G3BP condensation such as V11. This conserved amino acid is required for formation of the G3BP-Caprin-1 complex, hence promoting SG assembly. Total RNA sequencing and ribosome profiling revealed that disruption of G3BP condensation corresponds to changes in mRNA levels and ribosome engagement during the integrated stress response (ISR). Moreover, we found that G3BP2B preferentially condenses and promotes changes in mRNA expression under endoplasmic reticulum (ER) stress. Together, this work suggests that stress granule assembly promotes changes in gene expression under cellular stress, which is differentially regulated by G3BP paralogs.

A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X.

The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled.

IGHMBP2 deletion suppresses translation and activates the integrated stress response.

IGHMBP2 is a non-essential, superfamily 1 DNA/RNA helicase that is mutated in patients with rare neuromuscular diseases SMARD1 and CMT2S. IGHMBP2 is implicated in translational and transcriptional regulation via biochemical association with ribosomal proteins, pre-rRNA processing factors, and tRNA-related species. To uncover the cellular consequences of perturbing IGHMBP2, we generated full and partial IGHMBP2 deletion K562 cell lines. Using polysome profiling and a nascent protein synthesis assay, we found that IGHMBP2 deletion modestly reduces global translation. We performed Ribo-seq and RNA-seq and identified diverse gene expression changes due to IGHMBP2 deletion, including ATF4 upregulation. With recent studies showing the ISR can contribute to tRNA metabolism-linked neuropathies, we asked whether perturbing IGHMBP2 promotes ISR activation. We generated ATF4 reporter cell lines and found IGHMBP2 knockout cells demonstrate basal, chronic ISR activation. Our work expands upon the impact of IGHMBP2 in translation and elucidates molecular mechanisms that may link mutant IGHMBP2 to severe clinical phenotypes.

Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning.

Programmable C•G-to-G•C base editors (CGBEs) have broad scientific and therapeutic potential, but their editing outcomes have proved difficult to predict and their editing efficiency and product purity are often low. We describe a suite of engineered CGBEs paired with machine learning models to enable efficient, high-purity C•G-to-G•C base editing. We performed a CRISPR interference (CRISPRi) screen targeting DNA repair genes to identify factors that affect C•G-to-G•C editing outcomes and used these insights to develop CGBEs with diverse editing profiles. We characterized ten promising CGBEs on a library of 10,638 genomically integrated target sites in mammalian cells and trained machine learning models that accurately predict the purity and yield of editing outcomes (R = 0.90) using these data. These CGBEs enable correction to the wild-type coding sequence of 546 disease-related transversion single-nucleotide variants (SNVs) with >90% precision (mean 96%) and up to 70% efficiency (mean 14%). Computational prediction of optimal CGBE-single-guide RNA pairs enables high-purity transversion base editing at over fourfold more target sites than achieved using any single CGBE variant.

Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing.

Single-cell CRISPR screens enable the exploration of mammalian gene function and genetic regulatory networks. However, use of this technology has been limited by reliance on indirect indexing of single-guide RNAs (sgRNAs). Here we present direct-capture Perturb-seq, a versatile screening approach in which expressed sgRNAs are sequenced alongside single-cell transcriptomes. Direct-capture Perturb-seq enables detection of multiple distinct sgRNA sequences from individual cells and thus allows pooled single-cell CRISPR screens to be easily paired with combinatorial perturbation libraries that contain dual-guide expression vectors. We demonstrate the utility of this approach for high-throughput investigations of genetic interactions and, leveraging this ability, dissect epistatic interactions between cholesterol biogenesis and DNA repair. Using direct capture Perturb-seq, we also show that targeting individual genes with multiple sgRNAs per cell improves efficacy of CRISPR interference and activation, facilitating the use of compact, highly active CRISPR libraries for single-cell screens. Last, we show that hybridization-based target enrichment permits sensitive, specific sequencing of informative transcripts from single-cell RNA-seq experiments.

Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.

How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.

SnoN Antagonizes the Hippo Kinase Complex to Promote TAZ Signaling during Breast Carcinogenesis.

SnoN regulates multiple signaling pathways, including TGF-ß/Smad and p53, and displays both pro-oncogenic and anti-oncogenic activities in human cancer. We have observed previously that both its intracellular localization and expression levels are sensitive to cell density, suggesting that it may crosstalk with Hippo signaling. Here we report that, indeed, SnoN interacts with multiple components of the Hippo pathway to inhibit the binding of Lats2 to TAZ and the subsequent phosphorylation of TAZ, leading to TAZ stabilization. Consistently, SnoN enhances the transcriptional and oncogenic activities of TAZ, and reducing SnoN decreases TAZ expression as well as malignant progression of breast cancer cells. Interestingly, SnoN itself is downregulated by Lats2 that is activated by the Scribble basolateral polarity protein. Thus, SnoN is a critical component of the Hippo regulatory network that receives signals from the tissue architecture and polarity to coordinate the activity of intracellular signaling pathways.