Massively parallel assessment of human variants with base editor screens

Target Identification of Small Molecules Using Large-Scale CRISPR-Cas Mutagenesis Scanning of Essential Genes

Target deconvolution of new bioactive agents identified from phenotypic screens remains a challenging task. The discovery of mutations that confer resistance to such agents is regarded as the gold standard proof of target identification. Here, we describe a method that exploits the error-prone repair of CRISPR-induced DNA double-strand breaks to enhance mutagenesis and increase the incidence of drug resistance mutations in essential genes. As each DNA double-strand break is introduced at a targeted genomic site predefined by the presence of a protospacer adjacent motif (PAM) and a particular CRISPR single guide RNA (sgRNA), the genetic location of drug resistance mutations can be easily uncovered through targeted sequencing of CRISPR sgRNAs. Moreover, the method allows for the identification of not only the drug target gene, but also the drug-binding domain within the target gene.

CRISPR Guide RNA Library Screens in Human Induced Pluripotent Stem Cells

High-throughput CRISPR guide RNA (gRNA) library screen, that is, CRISPR/Cas9 screen, enables the unbiased identification of gene functions in a variety of biological processes. Typical pooled CRISPR/Cas9 screen couples a gRNA library and a guided Cas9 or dCas9 endonuclease to target specific gene loci, and then systematically uncover the causal link between candidate genes and observed cellular phenotypes via gRNA depletion or enrichment in screens. Here, we describe a detailed method of puromycin (PURO) concentration titration and lentiviral CRISPR gRNA library titration in Cas9 expressing monoclonal human iPSC line (Cas9+MNhiPSC) prior to performing the screens, conducting pooled CRISPR gRNA library screens in Cas9+MNhiPSC, genomic DNA extraction from the selected cell subpopulation and sequencing library preparation as well as next generation sequencing (NGS) to generate gRNA read counts. In CRISPR/Cas9 screen, we aim for 30% transduction efficiency (i.e., multiplicity of infection = 0.3) to ensure most of infected cells receive only one gRNA. The principles in this method can be applied to CRISPR perturbation (knockout, activation, repression or base editing) screens with other CRISPR gRNA libraries across many other cell models and other species.

Multiparametric and accurate functional analysis of genetic sequence variants using CRISPR-Select

Determining the functional role of thousands of genetic sequence variants (mutations) associated with genetic diseases is a major challenge. Here we present clustered regularly interspaced short palindromic repeat (CRISPR)-Select^TIME, CRISPR-Select^SPACE and CRISPR-Select^STATE, a set of flexible knock-in assays that introduce a genetic variant in a cell population and track its absolute frequencies relative to an internal, neutral control mutation as a function of time, space or a cell state measurable by flow cytometry. Phenotypically, CRISPR-Select can thereby determine, for example, pathogenicity, drug responsiveness/resistance or in vivo tumor promotion by a specific variant. Mechanistically, CRISPR-Select can dissect how the variant elicits the phenotype by causally linking the variant to motility/invasiveness or any cell state or biochemical process with a flow cytometry marker. The method is applicable to organoids, nontransformed or cancer cell lines. It is accurate, quantitative, fast and simple and works in single-well or 96-well higher throughput format. CRISPR-Select provides a versatile functional variant assay for research, diagnostics and drug development for genetic disorders.

OUP accepted manuscript

CRISPR/Cas base editors promise nucleotide-level control over DNA sequences, but the determinants of their activity remain incompletely understood. We measured base editing frequencies in two human cell lines for two cytosine and two adenine base editors at ∼14 000 target sequences and find that base editing activity is sequence-biased, with largest effects from nucleotides flanking the target base. Whether a base is edited depends strongly on the combination of its position in the target and the preceding base, acting to widen or narrow the effective editing window. The impact of features on editing rate depends on the position, with sequence bias efficacy mainly influencing bases away from the center of the window. We use these observations to train a machine learning model to predict editing activity per position, with accuracy ranging from 0.49 to 0.72 between editors, and with better generalization across datasets than existing tools. We demonstrate the usefulness of our model by predicting the efficacy of disease mutation correcting guides, and find that most of them suffer from more unwanted editing than pure outcomes. This work unravels the position-specificity of base editing biases and allows more efficient planning of editing campaigns in experimental and therapeutic contexts.

The CRISPR-Cas toolbox and gene editing technologies

The emergence of CRISPR-Cas systems has accelerated the development of gene editing technologies, which are widely used in the life sciences. To improve the performance of these systems, workers have engineered and developed a variety of CRISPR-Cas tools with a broader range of targets, higher efficiency and specificity, and greater precision. Moreover, CRISPR-Cas-related technologies have also been expanded beyond making cuts in DNA by introducing functional elements that permit precise gene modification, control gene expression, make epigenetic changes, and so on. In this review, we introduce and summarize the characteristics and applications of different types of CRISPR-Cas tools. We discuss certain limitations of current approaches and future prospects for optimizing CRISPR-Cas systems.

CRISPR-based therapeutics: current challenges and future applications

The discovery, only a decade ago, of the genome editing power of clustered regularly interspaced short palindromic repeats (CRISPR)-associated nucleases is already reinventing the therapeutic process, from how new drugs are discovered to novel ways to treat diseases. CRISPR-based screens can aid therapeutic development by quickly identifying a drug's mechanism of action and escape mutants. Additionally, CRISPR-Cas has advanced emerging ex vivo therapeutics, such as cell replacement therapies. However, Cas9 is limited as an in vivo therapeutic due to ineffective delivery, unwanted immune responses, off-target effects, unpredictable repair outcomes, and cellular stress. To address these limitations, controls that inhibit or degrade Cas9, biomolecule-Cas9 conjugates, and base editors have been developed. Herein, we discuss CRISPR-Cas systems that advance both conventional and emerging therapeutics.

Tools for Decoding Ubiquitin Signaling in DNA Repair

The maintenance of genome stability requires dedicated DNA repair processes and pathways that are essential for the faithful duplication and propagation of chromosomes. These DNA repair mechanisms counteract the potentially deleterious impact of the frequent genotoxic challenges faced by cells from both exogenous and endogenous agents. Intrinsic to these mechanisms, cells have an arsenal of protein factors that can be utilised to promote repair processes in response to DNA lesions. Orchestration of the protein factors within the various cellular DNA repair pathways is performed, in part, by post-translational modifications, such as phosphorylation, ubiquitin, SUMO and other ubiquitin-like modifiers (UBLs). In this review, we firstly explore recent advances in the tools for identifying factors involved in both DNA repair and ubiquitin signaling pathways. We then expand on this by evaluating the growing repertoire of proteomic, biochemical and structural techniques available to further understand the mechanistic basis by which these complex modifications regulate DNA repair. Together, we provide a snapshot of the range of methods now available to investigate and decode how ubiquitin signaling can promote DNA repair and maintain genome stability in mammalian cells.

Mechanistic insights into genetic susceptibility to prostate cancer

Prostate cancer (PCa) is the second most common cancer in men and is a highly heritable disease that affects millions of individuals worldwide. Genome-wide association studies have to date discovered nearly 270 genetic loci harboring hundreds of single nucleotide polymorphisms (SNPs) that are associated with PCa susceptibility. In contrast, the functional characterization of the mechanisms underlying PCa risk association is still growing. Given that PCa risk-associated SNPs are highly enriched in noncoding cis-regulatory genomic regions, accumulating evidence suggests a widespread modulation of transcription factor chromatin binding and allelic enhancer activity by these noncoding SNPs, thereby dysregulating gene expression. Emerging studies have shown that a proportion of noncoding variants can modulate the formation of transcription factor complexes at enhancers and CTCF-mediated 3D genome architecture. Interestingly, DNA methylation-regulated CTCF binding could orchestrate a long-range chromatin interaction between PCa risk enhancer and causative genes. Additionally, one-causal-variant-two-risk genes or multiple-risk-variant-multiple-genes are prevalent in some PCa risk-associated loci. In this review, we will discuss the current understanding of the general principles of SNP-mediated gene regulation, experimental advances, and functional evidence supporting the mechanistic roles of several PCa genetic loci with potential clinical impact on disease prevention and treatment.

Facilitating Machine Learning-Guided Protein Engineering with Smart Library Design and Massively Parallel Assays

Protein design plays an important role in recent medical advances from antibody therapy to vaccine design. Typically, exhaustive mutational screens or directed evolution experiments are used for the identification of the best design or for improvements to the wild-type variant. Even with a high-throughput screening on pooled libraries and Next-Generation Sequencing to boost the scale of read-outs, surveying all the variants with combinatorial mutations for their empirical fitness scores is still of magnitudes beyond the capacity of existing experimental settings. To tackle this challenge, in-silico approaches using machine learning to predict the fitness of novel variants based on a subset of empirical measurements are now employed. These machine learning models turn out to be useful in many cases, with the premise that the experimentally determined fitness scores and the amino-acid descriptors of the models are informative. The machine learning models can guide the search for the highest fitness variants, resolve complex epistatic relationships, and highlight bio-physical rules for protein folding. Using machine learning-guided approaches, researchers can build more focused libraries, thus relieving themselves from labor-intensive screens and fast-tracking the optimization process. Here, we describe the current advances in massive-scale variant screens, and how machine learning and mutagenesis strategies can be integrated to accelerate protein engineering. More specifically, we examine strategies to make screens more economical, informative, and effective in discovery of useful variants.

Discovering new biology with drug-resistance alleles

Small molecule drugs form the backbone of modern medicine's therapeutic arsenal. Often less appreciated is the role that small molecules have had in advancing basic biology. In this Review, we highlight how resistance mutations have unlocked the potential of small molecule chemical probes to discover new biology. We describe key instances in which resistance mutations and related genetic variants yielded foundational biological insight and categorize these examples on the basis of their role in the discovery of novel molecular mechanisms, protein allostery, physiology and cell signaling. Next, we suggest ways in which emerging technologies can be leveraged to systematically introduce and characterize resistance mutations to catalyze basic biology research and drug discovery. By recognizing how resistance mutations have propelled biological discovery, we can better harness new technologies and maximize the potential of small molecules to advance our understanding of biology and improve human health.

Towards a CRISPeR understanding of homologous recombination with high-throughput functional genomics

CRISPR-dependent genome editing enables the study of genes and mutations on a large scale. Here we review CRISPR-based functional genomics technologies that generate gene knockouts and single nucleotide variants (SNVs) and discuss how their use has provided new important insights into the function of homologous recombination (HR) genes. In particular, we highlight discoveries from CRISPR screens that have contributed to define the response to PARP inhibition in cells deficient for the HR genes BRCA1 and BRCA2, uncover genes whose loss causes synthetic lethality in combination with BRCA1/2 deficiency, and characterize the function of BRCA1/2 SNVs of uncertain clinical significance. Further use of these approaches, combined with next-generation CRISPR-based technologies, will aid to dissect the genetic network of the HR pathway, define the impact of HR mutations on cancer etiology and treatment, and develop novel targeted therapies for HR-deficient tumors.

Applications of single-cell genomics and computational strategies to study common disease and population-level variation

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.

Linking genome variants to disease: scalable approaches to test the functional impact of human mutations

The application of genomics to medicine has accelerated the discovery of mutations underlying disease and has enhanced our knowledge of the molecular underpinnings of diverse pathologies. As the amount of human genetic material queried via sequencing has grown exponentially in recent years, so too has the number of rare variants observed. Despite progress, our ability to distinguish which rare variants have clinical significance remains limited. Over the last decade, however, powerful experimental approaches have emerged to characterize variant effects orders of magnitude faster than before. Fueled by improved DNA synthesis and sequencing and, more recently, by CRISPR/Cas9 genome editing, multiplex functional assays provide a means of generating variant effect data in wide-ranging experimental systems. Here, I review recent applications of multiplex assays that link human variants to disease phenotypes and I describe emerging strategies that will enhance their clinical utility in coming years.

A new era in functional genomics screens

The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. Despite this, the function of many genes and gene regulatory elements remains poorly characterized, which limits our ability to apply these insights to human disease. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on a library of 28,294 lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.

Base editors enable precise genetic alterations but vary in efficiency at different loci. Here the authors analyse ABEs and CBEs at over 28,000 integrated sequences to train BE-DICT, a machine learning model capable of predicting base editing outcomes.

BARBEKO'ing in the lab: Versatile CRISPR screens with barcoded base editors

Xu et al. (2021) describe a novel two-pronged CRISPR screen, termed BARBEKO, by coupling cytosine base editors and internally barcoded sgRNAs to eliminate double-stranded break-induced toxicity, enable high multiplicities of infection, and ensure experimental reproducibility.

Our Tangled Family Tree: New Genomic Methods Offer Insight into the Legacy of Archaic Admixture

The archaic ancestry present in the human genome has captured the imagination of both scientists and the wider public in recent years. This excitement is the result of new studies pushing the envelope of what we can learn from the archaic genetic information that has survived for over 50,000 years in the human genome. Here, we review the most recent ten years of literature on the topic of archaic introgression, including the current state of knowledge on Neanderthal and Denisovan introgression, as well as introgression from other as-yet unidentified archaic populations. We focus this review on four topics: 1) a reimagining of human demographic history, including evidence for multiple admixture events between modern humans, Neanderthals, Denisovans, and other archaic populations; 2) state-of-the-art methods for detecting archaic ancestry in population-level genomic data; 3) how these novel methods can detect archaic introgression in modern African populations; and 4) the functional consequences of archaic gene variants, including how those variants were co-opted into novel function in modern human populations. The goal of this review is to provide a simple-to-access reference for the relevant methods and novel data, which has changed our understanding of the relationship between our species and its siblings. This body of literature reveals the large degree to which the genetic legacy of these extinct hominins has been integrated into the human populations of today.

Advancing drug discovery using the power of the human genome

Human genetics plays an increasingly important role in drug development and population health. Here we review the history of human genetics in the context of accelerating the discovery of therapies, present examples of how human genetics evidence supports successful drug targets, and discuss how polygenic risk scores could be beneficial in various clinical settings. We highlight the value of direct-to-consumer platforms in the era of fast-paced big data biotechnology, and how diverse genetic and health data can benefit society. © 2021 23andMe, Inc. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis

Single-cell transcriptomics provide a systematic map of gene expression in different human cell types. The next challenge is to systematically understand cell-type-specific gene function. The integration of CRISPR-based functional genomics and stem cell technology enables the scalable interrogation of gene function in differentiated human cells. Here we present the first genome-wide CRISPR interference and CRISPR activation screens in human neurons. We uncover pathways controlling neuronal response to chronic oxidative stress, which is implicated in neurodegenerative diseases. Unexpectedly, knockdown of the lysosomal protein prosaposin strongly sensitizes neurons, but not other cell types, to oxidative stress by triggering the formation of lipofuscin, a hallmark of aging, which traps iron, generating reactive oxygen species and triggering ferroptosis. We also determine transcriptomic changes in neurons after perturbation of genes linked to neurodegenerative diseases. To enable the systematic comparison of gene function across different human cell types, we establish a data commons named CRISPRbrain.

CRISPR base editor screens identify variant function at scale

Cuella-Martin et al. (2021) and Hanna et al. (2021) showcase CRISPR base editing in large-scale pooled screens in human cells to discover both loss- and gain-of-function variants, enabling protein structure/function insights and clinical variant interpretation.

Functional interrogation of DNA damage response variants with base editing screens

Mutations in DNA damage response (DDR) genes endanger genome integrity and predispose to cancer and genetic disorders. Here, using CRISPR-dependent cytosine base editing screens, we identify > 2,000 sgRNAs that generate nucleotide variants in 86 DDR genes, resulting in altered cellular fitness upon DNA damage. Among those variants, we discover loss- and gain-of-function mutants in the Tudor domain of the DDR regulator 53BP1 that define a non-canonical surface required for binding the deubiquitinase USP28. Moreover, we characterize variants of the TRAIP ubiquitin ligase that define a domain, whose loss renders cells resistant to topoisomerase I inhibition. Finally, we identify mutations in the ATM kinase with opposing genome stability phenotypes and loss-of-function mutations in the CHK2 kinase previously categorized as variants of uncertain significance for breast cancer. We anticipate that this resource will enable the discovery of additional DDR gene functions and expedite studies of DDR variants in human disease.