Saturation variant interpretation using CRISPR prime editing

Saturation genome editing maps the functional spectrum of pathogenic VHL alleles

To maximize the impact of precision medicine approaches, it is critical to identify genetic variants underlying disease and to accurately quantify their functional effects. A gene exemplifying the challenge of variant interpretation is the von Hippel-Lindautumor suppressor (VHL). VHL encodes an E3 ubiquitin ligase that regulates the cellular response to hypoxia. Germline pathogenic variants in VHL predispose patients to tumors including clear cell renal cell carcinoma (ccRCC) and pheochromocytoma, and somatic VHL mutations are frequently observed in sporadic renal cancer. Here we optimize and apply saturation genome editing to assay nearly all possible single-nucleotide variants (SNVs) across VHL's coding sequence. To delineate mechanisms, we quantify mRNA dosage effects and compare functional effects in isogenic cell lines. Function scores for 2,268 VHL SNVs identify a core set of pathogenic alleles driving ccRCC with perfect accuracy, inform differential risk across tumor types and reveal new mechanisms by which variants impact function. These results have immediate utility for classifying VHL variants encountered clinically and illustrate how precise functional measurements can resolve pleiotropic and dosage-dependent genotype-phenotype relationships across complete genes.

SynDesign: web-based prime editing guide RNA design and evaluation tool for saturation genome editing

Saturation genome editing (SGE) enables in-depth functional evaluation of disease-associated genes and variants by generating all possible single nucleotide variants (SNVs) within a given coding region. Although prime editing can be employed for inducing these SNVs, designing efficient prime editing guide RNAs (pegRNAs) can be challenging and time-consuming. Here, we present SynDesign, an easy-to-use webtool for the design, evaluation, and construction precision pegRNA libraries for SGE with synonymous mutation markers. SynDesign offers a simple yet powerful interface that automates the generation of all feasible pegRNA designs for a target gene or variant of interest. The pegRNAs are selected using the state-of-the-art models to predict prime editing efficiencies for various prime editors and cell types. Top-scoring pegRNA designs are further enhanced using synonymous mutation markers which improve pegRNA efficiency by diffusing the cellular mismatch repair mechanism and serve as sequence markers for improved identification of intended edits following deep sequencing. SynDesign is expected to facilitate future research using SGE to investigate genes or variants of interest associated with human diseases. SynDesign is freely available at https://deepcrispr.info/SynDesign without a login process.

Accelerated drug-resistant variant discovery with an enhanced, scalable mutagenic base editor platform

Personalized cancer therapeutics bring directed treatment options to patients based on their tumor's genetic signature. Unfortunately, tumor genomes are remarkably adaptable, and acquired resistance through gene mutation frequently occurs. Identifying mutations that promote resistance within drug-treated patient populations can be cost, resource, and time intensive. Accordingly, base editing, enabled by Cas9-deaminase domain fusions, has emerged as a promising approach for rapid, large-scale gene variant screening in situ. Here, we adapt and optimize a conditional activation-induced cytidine deaminase (AID)-dead Cas9 (dCas9) system, which demonstrates greater heterogeneity of edits with an expanded footprint compared to the most commonly utilized cytosine base editor, BE4. In combination with a custom single guide RNA (sgRNA) library, we identify individual and compound variants in epidermal growth factor receptor (EGFR) and v-raf murine sarcoma viral oncogene homolog B1 (BRAF) that confer resistance to established EGFR inhibitors. This system and analytical pipeline provide a simple, highly scalable platform for cis or trans drug-modifying variant discovery and for uncovering valuable insights into protein structure-function relationships.

Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF)

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods hamper crowd-sourcing approaches toward genome-wide resolution of variants in disease-related genes. Our framework, Saturation Mutagenesis-Reinforced Functional assays (SMuRF), addresses these issues by offering simple and cost-effective saturation mutagenesis, as well as streamlining functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes FKRP and LARGE1, we generated functional scores for all possible coding single nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Our approach opens new directions for enabling variant-to-function insights for disease genes in a manner that is broadly useful for crowd-sourcing implementation across standard research laboratories.

Multimodal framework to resolve variants of uncertain significance in TSC2

Efforts to resolve the functional impact of variants of uncertain significance (VUS) have lagged behind the identification of new VUS; as such, there is a critical need for scalable VUS resolution technologies. Computational variant effect predictors (VEPs), once trained, can predict pathogenicity for all missense variants in a gene, set of genes, or the exome. Existing tools have employed information on known pathogenic and benign variants throughout the genome to predict pathogenicity of VUS. We hypothesize that taking a gene-specific approach will improve pathogenicity prediction over globally-trained VEPs. We tested this hypothesis using the gene TSC2, whose loss of function results in tuberous sclerosis, a multisystem mTORopathy affecting about 1 in 6,000 individuals born in the United States. TSC2 has been identified as a high-priority target for VUS resolution, with (1) well-characterized molecular and patient phenotypes associated with loss-of-function variants, and (2) more than 2,700 VUS already documented in ClinVar. We developed Tuberous sclerosis classifier to Resolve variants of Uncertain Significance in T SC2 (TRUST), a machine learning model to predict pathogenicity of TSC2 missense VUS. To test whether these predictions are accurate, we further introduce curated loci prime editing (cliPE) as an accessible strategy for performing scalable multiplexed assays of variant effect (MAVEs). Using cliPE, we tested the effects of more than 200 TSC2 variants, including 106 VUS. It is highly likely this functional data alone would be sufficient to reclassify 92 VUS with most being reclassified as likely benign. We found that TRUST's classifications were correlated with the functional data, providing additional validation for the in silico predictions. We provide our pathogenicity predictions and MAVE data to aid with VUS resolution. In the near future, we plan to host these data on a public website and deposit into relevant databases such as MAVEdb as a community resource. Ultimately, this study provides a framework to complete variant effect maps of TSC1 and TSC2 and adapt this approach to other mTORopathy genes.

Analyzing the functional effects of DNA variants with gene editing

Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.

Massively parallel CRISPR-assisted homologous recombination enables saturation editing of full-length endogenous genes in yeast

Performing saturation editing of chromosomal genes will enable the study of genetic variants in situ and facilitate protein and cell engineering. However, current in vivo editing of endogenous genes either lacks flexibility or is limited to discrete codons and short gene fragments, preventing a comprehensive exploration of genotype-phenotype relationships. To enable facile saturation editing of full-length genes, we used a protospacer adjacent motif-relaxed Cas9 variant and homology-directed repair to achieve above 60% user-defined codon replacement efficiencies in Saccharomyces cerevisiae genome. Coupled with massively parallel DNA design and synthesis, we developed a saturation gene editing method termed CRISPR-Cas9- and homology-directed repair-assisted saturation editing (CHASE) and achieved highly saturated codon swapping of long genomic regions. By applying CHASE to massively edit a well-studied global transcription factor gene, we found known and unreported genetic variants affecting an industrially relevant microbial trait. The user-defined codon editing capability and wide targeting windows of CHASE substantially expand the scope of saturation gene editing.

Chromatin context-dependent regulation and epigenetic manipulation of prime editing

We set out to exhaustively characterize the impact of the cis-chromatin environment on prime editing, a precise genome engineering tool. Using a highly sensitive method for mapping the genomic locations of randomly integrated reporters, we discover massive position effects, exemplified by editing efficiencies ranging from ∼0% to 94% for an identical target site and edit. Position effects on prime editing efficiency are well predicted by chromatin marks, e.g., positively by H3K79me2 and negatively by H3K9me3. Next, we developed a multiplex perturbational framework to assess the interaction of trans-acting factors with the cis-chromatin environment on editing outcomes. Applying this framework to DNA repair factors, we identify HLTF as a context-dependent repressor of prime editing. Finally, several lines of evidence suggest that active transcriptional elongation enhances prime editing. Consistent with this, we show we can robustly decrease or increase the efficiency of prime editing by preceding it with CRISPR-mediated silencing or activation, respectively.

How CRISPR Is Revolutionizing the Generation of New Models for Cancer Research

Cancers arise through acquisition of mutations in genes that regulate core biological processes like cell proliferation and cell death. Decades of cancer research have led to the identification of genes and mutations causally involved in disease development and evolution, yet defining their precise function across different cancer types and how they influence therapy responses has been challenging. Mouse models have helped define the in vivo function of cancer-associated alterations, and genome-editing approaches using CRISPR have dramatically accelerated the pace at which these models are developed and studied. Here, we highlight how CRISPR technologies have impacted the development and use of mouse models for cancer research and discuss the many ways in which these rapidly evolving platforms will continue to transform our understanding of this disease.

MOSAIC enables in situ saturation mutagenesis of genes and CRISPR prime editing guide RNA optimization in human cells

CRISPR prime editing offers unprecedented versatility and precision for the installation of genetic edits in situ . Here we describe the development and characterization of the Multiplexing Of Site-specific Alterations for In situ Characterization ( MOSAIC ) method, which leverages a non-viral PCR-based prime editing method to enable rapid installation of thousands of defined edits in pooled fashion. We show that MOSAIC can be applied to perform in situ saturation mutagenesis screens of: (1) the BCR-ABL1 fusion gene, successfully identifying known and potentially new imatinib drug-resistance variants; and (2) the IRF1 untranslated region (UTR), re-confirming non-coding regulatory elements involved in transcriptional initiation. Furthermore, we deployed MOSAIC to enable high-throughput, pooled screening of hundreds of systematically designed prime editing guide RNA ( pegRNA ) constructs for a large series of different genomic loci. This rapid screening of >18,000 pegRNA designs identified optimized pegRNAs for 89 different genomic target modifications and revealed the lack of simple predictive rules for pegRNA design, reinforcing the need for experimental optimization now greatly simplified and enabled by MOSAIC. We envision that MOSAIC will accelerate and facilitate the application of CRISPR prime editing for a wide range of high-throughput screens in human and other cell systems.

Deconstructing cancer with precision genome editing

Recent advances in genome editing technologies are allowing investigators to engineer and study cancer-associated mutations in their endogenous genetic contexts with high precision and efficiency. Of these, base editing and prime editing are quickly becoming gold-standards in the field due to their versatility and scalability. Here, we review the merits and limitations of these precision genome editing technologies, their application to modern cancer research, and speculate how these could be integrated to address future directions in the field.

Recent advances in prime editing technologies and their promises for therapeutic applications

Prime editing (PE) is a groundbreaking genome editing technology offering unparalleled precision in targeted genome modifications and has great potential for therapeutic applications. This review delves into the core principles of PE and emphasizes its advancements, applications, and prospects. We begin with a brief introduction to PE principles, followed by a detailed examination of recent improvements in efficiency, precision, and the scale of feasible edits. These improvements have been made to the PE systems through guide RNA engineering, protein engineering, DNA repair pathway screening, chromosomal or epigenomic modification, and in silico design and optimization tools. Furthermore, we highlight in vivo studies showcasing the therapeutic potential of PE to model and treat genetic diseases. Moreover, we discuss PE's versatile applications in saturation genome editing and its applicability to nonhuman organisms. In conclusion, we address the challenges and opportunities linked with PE, emphasizing its profound impact on biological research and therapeutics.

A benchmarked, high-efficiency prime editing platform for multiplexed dropout screening

Prime editing installs precise edits into the genome with minimal unwanted byproducts, but low and variable editing efficiencies have complicated application of the approach to high-throughput functional genomics. Leveraging several recent advances, we assembled a prime editing platform capable of high-efficiency substitution editing across a set of engineered prime editing guide RNAs (epegRNAs) and corresponding target sequences (80% median intended editing). Then, using a custom library of 240,000 epegRNAs targeting >17,000 codons with 175 different substitution types, we benchmarked our platform for functional interrogation of small substitution variants (1-3 nucleotides) targeted to essential genes. Resulting data identified negative growth phenotypes for nonsense mutations targeted to ~8,000 codons, and comparing those phenotypes to results from controls demonstrated high specificity. We also observed phenotypes for synonymous mutations that disrupted splice site motifs at 3' exon boundaries. Altogether, we establish and benchmark a high-throughput prime editing approach for functional characterization of genetic variants with simple readouts from multiplexed experiments.

High-throughput evaluation of genetic variants with prime editing sensor libraries

Tumor genomes often harbor a complex spectrum of single nucleotide alterations and chromosomal rearrangements that can perturb protein function. Prime editing has been applied to install and evaluate genetic variants, but previous approaches have been limited by the variable efficiency of prime editing guide RNAs. Here we present a high-throughput prime editing sensor strategy that couples prime editing guide RNAs with synthetic versions of their cognate target sites to quantitatively assess the functional impact of endogenous genetic variants. We screen over 1,000 endogenous cancer-associated variants of TP53-the most frequently mutated gene in cancer-to identify alleles that impact p53 function in mechanistically diverse ways. We find that certain endogenous TP53 variants, particularly those in the p53 oligomerization domain, display opposite phenotypes in exogenous overexpression systems. Our results emphasize the physiological importance of gene dosage in shaping native protein stoichiometry and protein-protein interactions, and establish a framework for studying genetic variants in their endogenous sequence context at scale.

Functional analysis and clinical classification of 462 germline BRCA2 missense variants affecting the DNA binding domain

Variants of uncertain significance (VUSs) in BRCA2 are a common result of hereditary cancer genetic testing. While more than 4,000 unique VUSs, comprised of missense or intronic variants, have been identified in BRCA2, the few missense variants now classified clinically as pathogenic or likely pathogenic are predominantly located in the region encoding the C-terminal DNA binding domain (DBD). We report on functional evaluation of the influence of 462 BRCA2 missense variants affecting the DBD on DNA repair activity of BRCA2 using a homology-directed DNA double-strand break repair assay. Of these, 137 were functionally abnormal, 313 were functionally normal, and 12 demonstrated intermediate function. Comparisons with other functional studies of BRCA2 missense variants yielded strong correlations. Sequence-based in silico prediction models had high sensitivity, but limited specificity, relative to the homology-directed repair assay. Combining the functional results with clinical and genetic data in an American College of Medical Genetics (ACMG)/Association for Molecular Pathology (AMP)-like variant classification framework from a clinical testing laboratory, after excluding known splicing variants and functionally intermediate variants, classified 431 of 442 (97.5%) missense variants (129 as pathogenic/likely pathogenic and 302 as benign/likely benign). Functionally abnormal variants classified as pathogenic by ACMG/AMP rules were associated with a slightly lower risk of breast cancer (odds ratio [OR] 5.15, 95% confidence interval [CI] 3.43-7.83) than BRCA2 DBD protein truncating variants (OR 8.56, 95% CI 6.03-12.36). Overall, functional studies of BRCA2 variants using validated assays substantially improved the variant classification yield from ACMG/AMP models and are expected to improve clinical management of many individuals found to harbor germline BRCA2 missense VUS.

A prime editor mouse to model a broad spectrum of somatic mutations in vivo

Genetically engineered mouse models only capture a small fraction of the genetic lesions that drive human cancer. Current CRISPR-Cas9 models can expand this fraction but are limited by their reliance on error-prone DNA repair. Here we develop a system for in vivo prime editing by encoding a Cre-inducible prime editor in the mouse germline. This model allows rapid, precise engineering of a wide range of mutations in cell lines and organoids derived from primary tissues, including a clinically relevant Kras mutation associated with drug resistance and Trp53 hotspot mutations commonly observed in pancreatic cancer. With this system, we demonstrate somatic prime editing in vivo using lipid nanoparticles, and we model lung and pancreatic cancer through viral delivery of prime editing guide RNAs or orthotopic transplantation of prime-edited organoids. We believe that this approach will accelerate functional studies of cancer-associated mutations and complex genetic combinations that are challenging to construct with traditional models.

Precise genome-editing in human diseases: mechanisms, strategies and applications

Precise genome-editing platforms are versatile tools for generating specific, site-directed DNA insertions, deletions, and substitutions. The continuous enhancement of these tools has led to a revolution in the life sciences, which promises to deliver novel therapies for genetic disease. Precise genome-editing can be traced back to the 1950s with the discovery of DNA's double-helix and, after 70 years of development, has evolved from crude in vitro applications to a wide range of sophisticated capabilities, including in vivo applications. Nonetheless, precise genome-editing faces constraints such as modest efficiency, delivery challenges, and off-target effects. In this review, we explore precise genome-editing, with a focus on introduction of the landmark events in its history, various platforms, delivery systems, and applications. First, we discuss the landmark events in the history of precise genome-editing. Second, we describe the current state of precise genome-editing strategies and explain how these techniques offer unprecedented precision and versatility for modifying the human genome. Third, we introduce the current delivery systems used to deploy precise genome-editing components through DNA, RNA, and RNPs. Finally, we summarize the current applications of precise genome-editing in labeling endogenous genes, screening genetic variants, molecular recording, generating disease models, and gene therapy, including ex vivo therapy and in vivo therapy, and discuss potential future advances.

Functional assay for assessment of pathogenicity of BAP1 variants

BACKGROUND

Pathogenic germline variants in BRCA1-Associated Protein 1 (BAP1) cause BAP1 tumor predisposition syndrome (BAP1-TPDS). Carriers run especially a risk of uveal (UM) and cutaneous melanoma, malignant mesothelioma, and clear cell renal carcinoma. Approximately half of increasingly reported BAP1 variants lack accurate classification. Correct interpretation of pathogenicity can improve prognosis of the patients through tumor screening with better understanding of BAP1-TPDS.

METHODS

We edited five rare BAP1 variants with differing functional characteristics identified from patients with UM in HAP1 cells using CRISPR-Cas9 and assayed their effect on cell adhesion/spreading (at 4 h) and proliferation (at 48 h), measured as cell index (CI), using xCELLigence real-time analysis system.

RESULTS

In BAP1 knockout HAP1 cultures, cell number was half of wild type (WT) cultures at 48 h (p = 0.00021), reaching confluence later, and CI was 78% reduced (p < 0.0001). BAP1-TPDS-associated null variants c.67+1G>T and c.1780_1781insT, and a likely pathogenic missense variant c.281A>G reduced adhesion (all p ≤ 0.015) and proliferation by 74%-83% (all p ≤ 0.032). Another likely pathogenic missense variant c.680G>A reduced both by at least 50% (all p ≤ 0.032), whereas cells edited with likely benign one c.1526C>T grew similarly to WT.

CONCLUSIONS

BAP1 is essential for optimal fitness of HAP1 cells. Pathogenic and likely pathogenic BAP1 variants reduced cell fitness, reflected in adhesion/spreading and proliferation properties. Further, moderate effects were quantifiable. Variant modelling in HAP1 with CRISPR-Cas9 enabled functional analysis of coding and non-coding region variants in an endogenous expression system.

Functional annotation of variants of the BRCA2 gene via locally haploid human pluripotent stem cells

Mutations in the BRCA2 gene are associated with sporadic and familial cancer, cause genomic instability and sensitize cancer cells to inhibition by the poly(ADP-ribose) polymerase (PARP). Here we show that human pluripotent stem cells (hPSCs) with one copy of BRCA2 deleted can be used to annotate variants of this gene and to test their sensitivities to PARP inhibition. By using Cas9 to edit the functional BRCA2 allele in the locally haploid hPSCs and in fibroblasts differentiated from them, we characterized essential regions in the gene to identify permissive and loss-of-function mutations. We also used Cas9 to directly test the function of individual amino acids, including amino acids encoded by clinical BRCA2 variants of uncertain significance, and identified alleles that are sensitive to PARP inhibitors used as a standard of care in BRCA2-deficient cancers. Locally haploid human pluripotent stem cells can facilitate detailed structure-function analyses of genes and the rapid functional evaluation of clinically observed mutations.

Germline POT1 Variants: A Critical Perspective on POT1 Tumor Predisposition Syndrome

The Protection of Telomere 1 (POT1) gene was identified as a melanoma predisposition candidate nearly 10 years ago. Thereafter, various cancers have been proposed as associated with germline POT1 variants in the context of the so-called POT1 Predisposition Tumor Syndrome (POT1-TPD). While the key role, and related risks, of the alterations in POT1 in melanoma are established, the correlation between germline POT1 variants and the susceptibility to other cancers partially lacks evidence, due also to the rarity of POT1-TPD. Issues range from the absence of functional or segregation studies to biased datasets or the need for a revised classification of variants. Furthermore, a proposal of a surveillance protocol related to the cancers associated with POT1 pathogenic variants requires reliable data to avoid an excessive, possibly unjustified, burden for POT1 variant carriers. We propose a critical perspective regarding data published over the last 10 years that correlate POT1 variants to various types of cancer, other than cutaneous melanoma, to offer food for thought for the specialists who manage cancer predisposition syndromes and to stimulate a debate on the grey areas that have been exposed.

scSNV-seq: high-throughput phenotyping of single nucleotide variants by coupled single-cell genotyping and transcriptomics

CRISPR screens with single-cell transcriptomic readouts are a valuable tool to understand the effect of genetic perturbations including single nucleotide variants (SNVs) associated with diseases. Interpretation of these data is currently limited as genotypes cannot be accurately inferred from guide RNA identity alone. scSNV-seq overcomes this limitation by coupling single-cell genotyping and transcriptomics of the same cells enabling accurate and high-throughput screening of SNVs. Analysis of variants across the JAK1 gene with scSNV-seq demonstrates the importance of determining the precise genetic perturbation and accurately classifies clinically observed missense variants into three functional categories: benign, loss of function, and separation of function.

The expanded CRISPR toolbox for constructing microbial cell factories

Microbial cell factories (MCFs) convert low-cost carbon sources into valuable compounds. The CRISPR/Cas9 system has revolutionized MCF construction as a remarkable genome editing tool with unprecedented programmability. Recently, the CRISPR toolbox has been significantly expanded through the exploration of new CRISPR systems, the engineering of Cas effectors, and the incorporation of other effectors, enabling multi-level regulation and gene editing free of double-strand breaks. This expanded CRISPR toolbox powerfully promotes MCF construction by facilitating pathway construction, enzyme engineering, flux redistribution, and metabolic burden control. In this article, we summarize different CRISPR tool designs and their applications in MCF construction for gene editing, transcriptional regulation, and enzyme modulation. Finally, we also discuss future perspectives for the development and application of the CRISPR toolbox.

Prime editing: Its systematic optimization and current applications in disease treatment and agricultural breeding

CRISPR/Cas-mediated genome-editing technology has accelerated the development of the life sciences. Prime editing has raised genome editing to a new level because it allows for all 12 types of base substitutions, targeted insertions and deletions, large DNA fragment integration, and even combinations of these edits without generating DNA double-strand breaks. This versatile and game-changing technology has successfully been applied to human cells and plants, and it currently plays important roles in basic research, gene therapy, and crop breeding. Although prime editing has substantially expanded the range of possibilities for genome editing, its efficiency requires improvement. In this review, we briefly introduce prime editing and highlight recent optimizations that have improved the efficiency of prime editors. We also describe how the dual-pegRNA strategy has expanded current editing capabilities, and we summarize the potential of prime editing in treating mammalian diseases and improving crop breeding. Finally, we discuss the limitations of current prime editors and future prospects for optimizing these editors.

Mapping cis- and trans-regulatory target genes of human-specific deletions

Deletion of functional sequence is predicted to represent a fundamental mechanism of molecular evolution1,2. Comparative genetic studies of primates2,3 have identified thousands of human-specific deletions (hDels), and the cis-regulatory potential of short (≤31 base pairs) hDels has been assessed using reporter assays4. However, how structural variant-sized (≥50 base pairs) hDels influence molecular and cellular processes in their native genomic contexts remains unexplored. Here, we design genome-scale libraries of single-guide RNAs targeting 7.2 megabases of sequence in 6,358 hDels and present a systematic CRISPR interference (CRISPRi) screening approach to identify hDels that modify cellular proliferation in chimpanzee pluripotent stem cells. By intersecting hDels with chromatin state features and performing single-cell CRISPRi (Perturb-seq) to identify their cis- and trans-regulatory target genes, we discovered 19 hDels controlling gene expression. We highlight two hDels, hDel_2247 and hDel_585, with tissue-specific activity in the liver and brain, respectively. Our findings reveal a molecular and cellular role for sequences lost in the human lineage and establish a framework for functionally interrogating human-specific genetic variants.

A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data

Accurately predicting the effect of missense variants is a central problem in interpretation of genomic variation. Commonly used computational methods does not capture the quantitative impact on fitness in populations. We developed MisFit to estimate missense fitness effect using biobank-scale human population genome data. MisFit jointly models the effect at molecular level ( d ) and population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . MisFit is a probabilistic graphical model that integrates deep neural network components and population genetics models efficiently with inductive bias based on biological causality of variant effect. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that s is informative in predicting frequency across ancestries and consistent with the fraction of de novo mutations given s . Finally, MisFit outperforms previous methods in prioritizing missense variants in individuals with neurodevelopmental disorders.

High-throughput PRIME-editing screens identify functional DNA variants in the human genome

Despite tremendous progress in detecting DNA variants associated with human disease, interpreting their functional impact in a high-throughput and single-base resolution manner remains challenging. Here, we develop a pooled prime-editing screen method, PRIME, that can be applied to characterize thousands of coding and non-coding variants in a single experiment with high reproducibility. To showcase its applications, we first identified essential nucleotides for a 716 bp MYC enhancer via PRIME-mediated single-base resolution analysis. Next, we applied PRIME to functionally characterize 1,304 genome-wide association study (GWAS)-identified non-coding variants associated with breast cancer and 3,699 variants from ClinVar. We discovered that 103 non-coding variants and 156 variants of uncertain significance are functional via affecting cell fitness. Collectively, we demonstrate that PRIME is capable of characterizing genetic variants at single-base resolution and scale, advancing accurate genome annotation for disease risk prediction, diagnosis, and therapeutic target identification.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

BACKGROUND

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

RESULTS

We benchmark eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compare experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, is lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieve the best overall performance at distinguishing disruptive and neutral variants, and controlling for overall call rate genome-wide, SpliceAI and Pangolin have superior sensitivity. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

CONCLUSION

SpliceAI and Pangolin show the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

Rewriting regulatory DNA to dissect and reprogram gene expression

Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants (≤10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.

Saturation genome editing-based functional evaluation and clinical classification of BRCA2 single nucleotide variants

Germline BRCA2 loss-of function (LOF) variants identified by clinical genetic testing predispose to breast, ovarian, prostate and pancreatic cancer. However, variants of uncertain significance (VUS) (n>4000) limit the clinical use of testing results. Thus, there is an urgent need for functional characterization and clinical classification of all BRCA2 variants. Here we report on comprehensive saturation genome editing-based functional characterization of 97% of all possible single nucleotide variants (SNVs) in the BRCA2 DNA Binding Domain hotspot for pathogenic missense variants that is encoded by exons 15 to 26. The assay was based on deep sequence analysis of surviving endogenously targeted haploid cells. A total of 7013 SNVs were characterized as functionally abnormal (n=955), intermediate/uncertain, or functionally normal (n=5224) based on 95% agreement with ClinVar known pathogenic and benign standards. Results were validated relative to batches of nonsense and synonymous variants and variants evaluated using a homology directed repair (HDR) functional assay. Breast cancer case-control association studies showed that pooled SNVs encoding functionally abnormal missense variants were associated with increased risk of breast cancer (odds ratio (OR) 3.89, 95%CI: 2.77-5.51). In addition, 86% of tumors associated with abnormal missense SNVs displayed loss of heterozygosity (LOH), whereas 26% of tumors with normal variants had LOH. The functional data were added to other sources of information in a ClinGen/ACMG/AMP-like model and 700 functionally abnormal SNVs, including 220 missense SNVs, were classified as pathogenic or likely pathogenic, while 4862 functionally normal SNVs, including 3084 missense SNVs, were classified as benign or likely benign. These classified variants can now be used for risk assessment and clinical care of variant carriers and the remaining functional scores can be used directly for clinical classification and interpretation of many additional variants.

Summary

Germline BRCA2 loss-of function (LOF) variants identified by clinical genetic testing predispose to several types of cancer. However, variants of uncertain significance (VUS) limit the clinical use of testing results. Thus, there is an urgent need for functional characterization and clinical classification of all BRCA2 variants to facilitate current and future clinical management of individuals with these variants. Here we show the results from a saturation genome editing (SGE) and functional analysis of all possible single nucleotide variants (SNVs) from exons 15 to 26 that encode the BRCA2 DNA Binding Domain hotspot for pathogenic missense variants. The assay was based on deep sequence analysis of surviving endogenously targeted human haploid HAP1 cells. The assay was calibrated relative to ClinVar known pathogenic and benign missense standards and 95% prevalence thresholds for functionally abnormal and normal variants were identified. Thresholds were validated based on nonsense and synonymous variants. SNVs encoding functionally abnormal missense variants were associated with increased risks of breast and ovarian cancer. The functional assay results were integrated into a ClinGen/ACMG/AMP-like model for clinical classification of the majority of BRCA2 SNVs as pathogenic/likely pathogenic or benign/likely benign. The classified variants can be used for improved clinical management of variant carriers.

Protocol for the saturation and multiplexing of genetic variants using CRISPR-Cas9

Here, we present a multiplexed assay for variant effect protocol to assess the functional impact of all possible genetic variations within a particular genomic region. We describe steps for saturation genome editing by designing and cloning of single-guide RNA (sgRNA). We then detail steps for nucleofection of sgRNA, testing drug response on variants, and amplification of genomic DNA for next-generation sequencing. For complete details on the use and execution of this protocol, please refer to Sahu et al.1.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

Pooled Genome-Scale CRISPR Screens in Single Cells

Assigning functions to genes and learning how to control their expression are part of the foundation of cell biology and therapeutic development. An efficient and unbiased method to accomplish this is genetic screening, which historically required laborious clone generation and phenotyping and is still limited by scale today. The rapid technological progress on modulating gene function with CRISPR-Cas and measuring it in individual cells has now relaxed the major experimental constraints and enabled pooled screening with complex readouts from single cells. Here, we review the principles and practical considerations for pooled single-cell CRISPR screening. We discuss perturbation strategies, experimental model systems, matching the perturbation to the individual cells, reading out cell phenotypes, and data analysis. Our focus is on single-cell RNA sequencing and cell sorting-based readouts, including image-enabled cell sorting. We expect this transformative approach to fuel biomedical research for the next several decades.

Sequencing-based functional assays for classification of BRCA2 variants in mouse ESCs

Sequencing of genes, such as BRCA1 and BRCA2, is recommended for individuals with a personal or family history of early onset and/or bilateral breast and/or ovarian cancer or a history of male breast cancer. Such sequencing efforts have resulted in the identification of more than 17,000 BRCA2 variants. The functional significance of most variants remains unknown; consequently, they are called variants of uncertain clinical significance (VUSs). We have previously developed mouse embryonic stem cell (mESC)-based assays for functional classification of BRCA2 variants. We now developed a next-generation sequencing (NGS)-based approach for functional evaluation of BRCA2 variants using pools of mESCs expressing 10-25 BRCA2 variants from a given exon. We use this approach for functional evaluation of 223 variants listed in ClinVar. Our functional classification of BRCA2 variants is concordant with the classification reported in ClinVar or those reported by other orthogonal assays.

Deep mutational scanning of proteins in mammalian cells

Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.

Emerging insights into ethnic-specific TP53 germline variants

The recent expansion of human genomics repositories has facilitated the discovery of novel TP53 variants in populations of different ethnic origins. Interpreting TP53 variants is a major clinical challenge because they are functionally diverse, confer highly variable predisposition to cancer (including elusive low-penetrance alleles), and interact with genetic modifiers that alter tumor susceptibility. Here, we discuss how a cancer risk continuum may relate to germline TP53 mutations on the basis of our current review of genotype-phenotype studies and an integrative analysis combining functional and sequencing datasets. Our study reveals that each ancestry contains a distinct TP53 variant landscape defined by enriched ethnic-specific alleles. In particular, the discovery and characterization of suspected low-penetrance ethnic-specific variants with unique functional consequences, including P47S (African), G334R (Ashkenazi Jewish), and rs78378222 (Icelandic), may provide new insights in terms of managing cancer risk and the efficacy of therapy. Additionally, our analysis highlights infrequent variants linked to milder cancer phenotypes in various published reports that may be underdiagnosed and require further investigation, including D49H in East Asians and R181H in Europeans. Overall, the sequencing and projected functions of TP53 variants arising within ethnic populations and their interplay with modifiers, as well as the emergence of CRISPR screens and AI tools, are now rapidly improving our understanding of the cancer susceptibility spectrum, leading toward more accurate and personalized cancer risk assessments.

Molecular breeding of livestock for disease resistance

Animal infectious diseases pose a significant threat to the global agriculture and biomedicine industries, leading to significant economic losses and public health risks. The emergence and spread of viral infections such as African swine fever virus (ASFV), porcine reproductive and respiratory syndrome virus (PRRSV), porcine epidemic diarrhea virus (PEDV), and avian influenza virus (AIV) have highlighted the need for innovative approaches to develop resilient and disease-resistant animal populations. Gene editing technologies, such as CRISPR/Cas9, offer a promising avenue for generating animals with enhanced disease resistance. This review summarizes recent advances in molecular breeding strategies for generating disease-resistant animals, focusing on the development of disease-resistant livestock. We also highlight the potential applications of genome-wide CRISPR/Cas9 library screening and base editors in producing precise gene modified livestock for disease resistance in the future. Overall, gene editing technologies have the potential to revolutionize animal breeding and improve animal health and welfare.

Prime editor-mediated functional reshaping of ACE2 prevents the entry of multiple human coronaviruses, including SARS-CoV-2 variants

The spike protein of SARS-CoV-2 hijacks the host angiotensin converting enzyme 2 (ACE2) to meditate its entry and is the primary target for vaccine development. Nevertheless, SARS-CoV-2 keeps evolving and the latest Omicron subvariants BQ.1 and XBB have gained exceptional immune evasion potential through mutations in their spike proteins, leading to sharply reduced efficacy of current spike-focused vaccines and therapeutics. Compared with the fast-evolving spike protein, targeting host ACE2 offers an alternative antiviral strategy that is more resistant to viral evolution and can even provide broad prevention against SARS-CoV and HCoV-NL63. Here, we use prime editor (PE) to precisely edit ACE2 at structurally selected sites. We demonstrated that residue changes at Q24/D30/K31 and/or K353 of ACE2 could completely ablate the binding of tested viruses while maintaining its physiological role in host angiotensin II conversion. PE-mediated ACE2 editing at these sites suppressed the entry of pseudotyped SARS-CoV-2 major variants of concern and even SARS-CoV or HCoV-NL63. Moreover, it significantly inhibited the replication of the Delta variant live virus. Our work investigated the unexplored application potential of prime editing in high-risk infectious disease control and demonstrated that such gene editing-based host factor reshaping strategy can provide broad-spectrum antiviral activity and a high barrier to viral escape or resistance.

Prediction of prime editing insertion efficiencies using sequence features and DNA repair determinants

Most short sequences can be precisely written into a selected genomic target using prime editing; however, it remains unclear what factors govern insertion. We design a library of 3,604 sequences of various lengths and measure the frequency of their insertion into four genomic sites in three human cell lines, using different prime editor systems in varying DNA repair contexts. We find that length, nucleotide composition and secondary structure of the insertion sequence all affect insertion rates. We also discover that the 3' flap nucleases TREX1 and TREX2 suppress the insertion of longer sequences. Combining the sequence and repair features into a machine learning model, we can predict relative frequency of insertions into a site with R = 0.70. Finally, we demonstrate how our accurate prediction and user-friendly software help choose codon variants of common fusion tags that insert at high efficiency, and provide a catalog of empirically determined insertion rates for over a hundred useful sequences.

Accurate proteome-wide missense variant effect prediction with AlphaMissense

The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.

PINE-TREE enables highly efficient genetic modification of human cell lines

Prime editing technologies enable precise genome editing without the caveats of CRISPR nuclease-based methods. Nonetheless, current approaches to identify and isolate prime-edited cell populations are inefficient. Here, we established a fluorescence-based system, prime-induced nucleotide engineering using a transient reporter for editing enrichment (PINE-TREE), for real-time enrichment of prime-edited cell populations. We demonstrated the broad utility of PINE-TREE for highly efficient introduction of substitutions, insertions, and deletions at various genomic loci. Finally, we employ PINE-TREE to rapidly and efficiently generate clonal isogenic human pluripotent stem cell lines, a cell type recalcitrant to genome editing.

Manipulating and studying gene function in human pluripotent stem cell models

Human pluripotent stem cells (hPSCs) are uniquely suited to study human development and disease and promise to revolutionize regenerative medicine. These applications rely on robust methods to manipulate gene function in hPSC models. This comprehensive review aims to both empower scientists approaching the field and update experienced stem cell biologists. We begin by highlighting challenges with manipulating gene expression in hPSCs and their differentiated derivatives, and relevant solutions (transfection, transduction, transposition, and genomic safe harbor editing). We then outline how to perform robust constitutive or inducible loss-, gain-, and change-of-function experiments in hPSCs models, both using historical methods (RNA interference, transgenesis, and homologous recombination) and modern programmable nucleases (particularly CRISPR/Cas9 and its derivatives, i.e., CRISPR interference, activation, base editing, and prime editing). We further describe extension of these approaches for arrayed or pooled functional studies, including emerging single-cell genomic methods, and the related design and analytical bioinformatic tools. Finally, we suggest some directions for future advancements in all of these areas. Mastering the combination of these transformative technologies will empower unprecedented advances in human biology and medicine.

Saturation genome editing of 11 codons and exon 13 of BRCA2 coupled with chemotherapeutic drug response accurately determines pathogenicity of variants

The unknown pathogenicity of a significant number of variants found in cancer-related genes is attributed to limited epidemiological data, resulting in their classification as variant of uncertain significance (VUS). To date, Breast Cancer gene-2 (BRCA2) has the highest number of VUSs, which has necessitated the development of several robust functional assays to determine their functional significance. Here we report the use of a humanized-mouse embryonic stem cell (mESC) line expressing a single copy of the human BRCA2 for a CRISPR-Cas9-based high-throughput functional assay. As a proof-of-principle, we have saturated 11 codons encoded by BRCA2 exons 3, 18, 19 and all possible single-nucleotide variants in exon 13 and multiplexed these variants for their functional categorization. Specifically, we used a pool of 180-mer single-stranded donor DNA to generate all possible combination of variants. Using a high throughput sequencing-based approach, we show a significant drop in the frequency of non-functional variants, whereas functional variants are enriched in the pool of the cells. We further demonstrate the response of these variants to the DNA-damaging agents, cisplatin and olaparib, allowing us to use cellular survival and drug response as parameters for variant classification. Using this approach, we have categorized 599 BRCA2 variants including 93-single nucleotide variants (SNVs) across the 11 codons, of which 28 are reported in ClinVar. We also functionally categorized 252 SNVs from exon 13 into 188 functional and 60 non-functional variants, demonstrating that saturation genome editing (SGE) coupled with drug sensitivity assays can enhance functional annotation of BRCA2 VUS.

PE-STOP: A versatile tool for installing nonsense substitutions amenable for precise reversion

The rapid advances in genome editing technologies have revolutionized the study of gene functions in cell or animal models. The recent generation of double-stranded DNA cleavage-independent base editors has been suitably adapted for interrogation of protein-coding genes on the basis of introducing premature stop codons or disabling the start codons. However, such versions of stop/start codon-oriented genetic tools still present limitations on their versatility, base-level precision, and target specificity. Here, we exploit a newly developed prime editor (PE) that differs from base editors by its adoption of a reverse transcriptase activity, which enables incorporation of various types of precise edits templated by a specialized prime editing guide RNA. Based on such a versatile platform, we established a prime editing-empowered method (PE-STOP) for installation of nonsense substitutions, providing a complementary approach to the present gene-targeting tools. PE-STOP is bioinformatically predicted to feature substantially expanded coverage in the genome space. In practice, PE-STOP introduces stop codons with good efficiencies in human embryonic kidney 293T and N2a cells (with medians of 29% [ten sites] and 25% [four sites] editing efficiencies, respectively), while exhibiting minimal off-target effects and high on-target precision. Furthermore, given the fact that PE installs prime editing guide RNA-templated mutations, we introduce a unique strategy for precise genetic rescue of PE-STOP-dependent nonsense mutation via the same PE platform. Altogether, the present work demonstrates a versatile and specific tool for gene inactivation and for functional interrogation of nonsense mutations.

Breaking enhancers to gain insights into developmental defects

Despite ground-breaking genetic studies that have identified thousands of risk variants for developmental diseases, how these variants lead to molecular and cellular phenotypes remains a gap in knowledge. Many of these variants are non-coding and occur at enhancers, which orchestrate key regulatory programs during development. The prevailing paradigm is that non-coding variants alter the activity of enhancers, impacting gene expression programs, and ultimately contributing to disease risk. A key obstacle to progress is the systematic functional characterization of non-coding variants at scale, especially since enhancer activity is highly specific to cell type and developmental stage. Here, we review the foundational studies of enhancers in developmental disease and current genomic approaches to functionally characterize developmental enhancers and their variants at scale. In the coming decade, we anticipate systematic enhancer perturbation studies to link non-coding variants to molecular mechanisms, changes in cell state, and disease phenotypes.

High throughput PRIME editing screens identify functional DNA variants in the human genome

Despite tremendous progress in detecting DNA variants associated with human disease, interpreting their functional impact in a high-throughput and base-pair resolution manner remains challenging. Here, we develop a novel pooled prime editing screen method, PRIME, which can be applied to characterize thousands of coding and non-coding variants in a single experiment with high reproducibility. To showcase its applications, we first identified essential nucleotides for a 716 bp MYC enhancer via PRIME-mediated saturation mutagenesis. Next, we applied PRIME to functionally characterize 1,304 non-coding variants associated with breast cancer and 3,699 variants from ClinVar. We discovered that 103 non-coding variants and 156 variants of uncertain significance are functional via affecting cell fitness. Collectively, we demonstrate PRIME capable of characterizing genetic variants at base-pair resolution and scale, advancing accurate genome annotation for disease risk prediction, diagnosis, and therapeutic target identification.

Base editor screens for in situ mutational scanning at scale

A fundamental challenge in biology is understanding the molecular details of protein function. How mutations alter protein activity, regulation, and response to drugs is of critical importance to human health. Recent years have seen the emergence of pooled base editor screens for in situ mutational scanning: the interrogation of protein sequence-function relationships by directly perturbing endogenous proteins in live cells. These studies have revealed the effects of disease-associated mutations, discovered novel drug resistance mechanisms, and generated biochemical insights into protein function. Here, we discuss how this "base editor scanning" approach has been applied to diverse biological questions, compare it with alternative techniques, and describe the emerging challenges that must be addressed to maximize its utility. Given its broad applicability toward profiling mutations across the proteome, base editor scanning promises to revolutionize the investigation of proteins in their native contexts.

High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo

The vast number of genomic and molecular alterations in cancer pose a substantial challenge to uncovering the mechanisms of tumorigenesis and identifying therapeutic targets. High-throughput functional genomic methods in genetically engineered mouse models allow for rapid and systematic investigation of cancer driver genes. In this review, we discuss the basic concepts and tools for multiplexed investigation of functionally important cancer genes in vivo using autochthonous cancer models. Furthermore, we highlight emerging technical advances in the field, potential opportunities for future investigation, and outline a vision for integrating multiplexed genetic perturbations with detailed molecular analyses to advance our understanding of the genetic and molecular basis of cancer.

High-throughput library transgenesis in Caenorhabditis elegans via Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS)

High-throughput transgenesis using synthetic DNA libraries is a powerful method for systematically exploring genetic function. Diverse synthesized libraries have been used for protein engineering, identification of protein-protein interactions, characterization of promoter libraries, developmental and evolutionary lineage tracking, and various other exploratory assays. However, the need for library transgenesis has effectively restricted these approaches to single-cell models. Here, we present Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS), a simple yet powerful approach to large-scale transgenesis that overcomes typical limitations encountered in multicellular systems. TARDIS splits the transgenesis process into a two-step process: creation of individuals carrying experimentally introduced sequence libraries, followed by inducible extraction and integration of individual sequences/library components from the larger library cassette into engineered genomic sites. Thus, transformation of a single individual, followed by lineage expansion and functional transgenesis, gives rise to thousands of genetically unique transgenic individuals. We demonstrate the power of this system using engineered, split selectable TARDIS sites in Caenorhabditis elegans to generate (1) a large set of individually barcoded lineages and (2) transcriptional reporter lines from predefined promoter libraries. We find that this approach increases transformation yields up to approximately 1000-fold over current single-step methods. While we demonstrate the utility of TARDIS using C. elegans, in principle the process is adaptable to any system where experimentally generated genomic loci landing pads and diverse, heritable DNA elements can be generated.

CRISPR-based functional genomics screening in human-pluripotent-stem-cell-derived cell types

While our knowledge of gene expression in different human cell types is rapidly expanding with advances in transcriptomic profiling technologies, the next challenge is to understand gene function in each cell type. CRISPR-Cas9-based functional genomics screening offers a powerful approach to determine gene function in a high-throughput manner. With the maturation of stem cell technology, a variety of human cell types can be derived from human pluripotent stem cells (hPSCs). Recently, the integration of CRISPR screening with hPSC differentiation technologies opens up unprecedented opportunities to systematically examine gene function in different human cell types and identify mechanisms and therapeutic targets for human diseases. This review highlights recent progress in the development and applications of CRISPR-Cas9-based functional genomics screening in hPSC-derived cell types, discusses current challenges and limitations, and outlines future directions for this emerging field.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Background

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

Results

We benchmarked eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compared experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, was lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieved the best overall performance at distinguishing disruptive and neutral variants. Controlling for overall call rate genome-wide, SpliceAI and Pangolin also showed superior overall sensitivity for identifying SDVs. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

Conclusion

SpliceAI and Pangolin showed the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.