Genetic interaction mapping in mammalian cells using CRISPR interference

High-fidelity CRISPR/Cas12a dual-crRNA screening reveals novel synergistic interactions in hepatocellular carcinoma

 : CRISPR/Cas12a-based combinational screening has shown remarkable potential for identifying genetic interactions. Here, we describe an innovative method for combinational genetic screening with rapid construction of a dual-CRISPR RNA (crRNA) library using gene splicing through overlap extension PCR (SOE PCR) and the adoption of CeCas12a, which we previously identified with strict PAM recognition and low off-targeting to guarantee fidelity and efficiency. The custom-pooled SOE crRNA array (SOCA) library for double-knockout screening could be conveniently constructed in the laboratory for widespread use, and the CeCas12a-mediated high-fidelity screen displayed good performance even under a negative selection screen. By designing a SOCA dual-crRNA library that covered most of the kinase and metabolism-associated gene targets of FDA-approved drugs implicated in hepatocellular carcinoma (HCC) tumourigenesis, novel cross-talk between the two gene sets was negatively selected to inhibit HCC cell growth in vitro and in vivo and was validated using virtual double-knockdown screening based on TCGA databases. Thus, this rapid, efficient and high-fidelity double-knockout screening system is promising for systemically identifying potential genetic interactions between multiple gene sets or combinations of FDA- approved drugs for clinical translational medicine in the future.

CRISPRi functional genomics in bacteria and its application to medical and industrial research

SUMMARYFunctional genomics is the use of systematic gene perturbation approaches to determine the contributions of genes under conditions of interest. Although functional genomic strategies have been used in bacteria for decades, recent studies have taken advantage of CRISPR (clustered regularly interspaced short palindromic repeats) technologies, such as CRISPRi (CRISPR interference), that are capable of precisely modulating expression of all genes in the genome. Here, we discuss and review the use of CRISPRi and related technologies for bacterial functional genomics. We discuss the strengths and weaknesses of CRISPRi as well as design considerations for CRISPRi genetic screens. We also review examples of how CRISPRi screens have defined relevant genetic targets for medical and industrial applications. Finally, we outline a few of the many possible directions that could be pursued using CRISPR-based functional genomics in bacteria. Our view is that the most exciting screens and discoveries are yet to come.

Bidirectional epigenetic editing reveals hierarchies in gene regulation

CRISPR perturbation methods are limited in their ability to study non-coding elements and genetic interactions. In this study, we developed a system for bidirectional epigenetic editing, called CRISPRai, in which we apply activating (CRISPRa) and repressive (CRISPRi) perturbations to two loci simultaneously in the same cell. We developed CRISPRai Perturb-seq by coupling dual perturbation gRNA detection with single-cell RNA sequencing, enabling study of pooled perturbations in a mixed single-cell population. We applied this platform to study the genetic interaction between two hematopoietic lineage transcription factors, SPI1 and GATA1, and discovered novel characteristics of their co-regulation on downstream target genes, including differences in SPI1 and GATA1 occupancy at genes that are regulated through different modes. We also studied the regulatory landscape of IL2 (interleukin-2) in Jurkat T cells, primary T cells and chimeric antigen receptor (CAR) T cells and elucidated mechanisms of enhancer-mediated IL2 gene regulation. CRISPRai facilitates investigation of context-specific genetic interactions, provides new insights into gene regulation and will enable exploration of non-coding disease-associated variants.

Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform

Genetic interactions mediate the emergence of phenotype from genotype, but technologies for combinatorial genetic perturbation in mammalian cells are challenging to scale. Here, we identify background-independent paralog synthetic lethals from previous CRISPR genetic interaction screens, and find that the Cas12a platform provides superior sensitivity and assay replicability. We develop the in4mer Cas12a platform that uses arrays of four independent guide RNAs targeting the same or different genes. We construct a genome-scale library, Inzolia, that is ~30% smaller than a typical CRISPR/Cas9 library while also targeting ~4000 paralog pairs. Screens in cancer cells demonstrate discrimination of core and context-dependent essential genes similar to that of CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes. Importantly, the in4mer platform offers a fivefold reduction in library size compared to other genetic interaction methods, substantially reducing the cost and effort required for these assays.

Efficient gene knockout and genetic interactions: the IN4MER CRISPR/Cas12a multiplex knockout platform

Genetic interactions mediate the emergence of phenotype from genotype, but initial technologies for combinatorial genetic perturbation in mammalian cells suffer from inefficiency and are challenging to scale. Recent focus on paralog synthetic lethality in cancer cells offers an opportunity to evaluate different approaches and improve on the state of the art. Here we report a meta-analysis of CRISPR genetic interactions screens, identifying a candidate set of background-independent paralog synthetic lethals, and find that the Cas12a platform provides superior sensitivity and assay replicability. We demonstrate that Cas12a can independently target up to four genes from a single guide array, and we build on this knowledge by constructing a genome-scale library that expresses arrays of four guides per clone, a platform we call 'in4mer'. Our genome-scale human library, with only 49k clones, is substantially smaller than a typical CRISPR/Cas9 monogenic library while also targeting more than four thousand paralog pairs, triples, and quads. Proof of concept screens in four cell lines demonstrate discrimination of core and context-dependent essential genes similar to that of state-of-the-art CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes, a capability not offered by any extant library. Importantly, the in4mer platform offers a fivefold reduction in the number of clones required to assay genetic interactions, dramatically improving the cost and effort required for these studies.

Mapping the Genetic Interaction Network of PARP inhibitor Response

Genetic interactions have long informed our understanding of the coordinated proteins and pathways that respond to DNA damage in mammalian cells, but systematic interrogation of the genetic network underlying that system has yet to be achieved. Towards this goal, we measured 147,153 pairwise interactions among genes implicated in PARP inhibitor (PARPi) response. Evaluating genetic interactions at this scale, with and without exposure to PARPi, revealed hierarchical organization of the pathways and complexes that maintain genome stability during normal growth and defined changes that occur upon accumulation of DNA lesions due to cytotoxic doses of PARPi. We uncovered unexpected relationships among DNA repair genes, including context-specific buffering interactions between the minimally characterized AUNIP and BRCA1-A complex genes. Our work thus establishes a foundation for mapping differential genetic interactions in mammalian cells and provides a comprehensive resource for future studies of DNA repair and PARP inhibitors.

Theory for High-Throughput Genetic Interaction Screening

Systematic, genome-scale genetic screens have been instrumental for elucidating genotype-phenotype relationships, but approaches for probing genetic interactions have been limited to at most ∼100 pre-selected gene combinations in mammalian cells. Here, we introduce a theory for high-throughput genetic interaction screens. The theory extends our recently developed Multiplexing using Spectral Imaging and Combinatorics (MuSIC) approach to propose ∼105 spectrally unique, genetically encoded MuSIC barcodes from 18 currently available fluorescent proteins. Simulation studies based on constraints imposed by spectral flow cytometry equipment suggest that genetic interaction screens at the human genome-scale may be possible if MuSIC barcodes can be paired to guide RNAs. While experimental testing of this theory awaits, it offers transformative potential for genetic perturbation technology and knowledge of genetic function. More broadly, the availability of a genome-scale spectral barcode library for non-destructive identification of single cells could find more widespread applications such as traditional genetic screening and high-dimensional lineage tracing.

Environmental challenge rewires functional connections among human genes

A fundamental question in biology is how a limited number of genes combinatorially govern cellular responses to environmental changes. While the prevailing hypothesis is that relationships between genes, processes, and ontologies could be plastic to achieve this adaptability, quantitatively comparing human gene functional connections between specific environmental conditions at scale is very challenging. Therefore, it remains unclear whether and how human genetic interaction networks are rewired in response to changing environmental conditions. Here, we developed a framework for mapping context-specific genetic interactions, enabling us to measure the plasticity of human genetic architecture upon environmental challenge for ~250,000 interactions, using cell cycle interruption, genotoxic perturbation, and nutrient deprivation as archetypes. We discover large-scale rewiring of human gene relationships across conditions, highlighted by dramatic shifts in the functional connections of epigenetic regulators (TIP60), cell cycle regulators (PP2A), and glycolysis metabolism. Our study demonstrates that upon environmental perturbation, intra-complex genetic rewiring is rare while inter-complex rewiring is common, suggesting a modular and flexible evolutionary genetic strategy that allows a limited number of human genes to enable adaptation to a large number of environmental conditions.

Finding meaning in chaos: a selection signature for functional interactions and its use in molecular biology

A primary goal of biomedical research is to elucidate molecular mechanisms, particularly those responsible for human traits, either normal or pathological. Yet achieving this goal is difficult if not impossible when the traits of interest lack tractable models and so cannot be dissected through time-honoured approaches like forward genetics or reconstitution. Arguably, no biological problem has hindered scientific progress more than this: the inability to dissect a trait's mechanism without a tractable likeness of the trait. At root, forward genetics and reconstitution are powerful approaches because they assay for specific molecular functions. Here, we discuss an alternative way to uncover important mechanistic interactions, namely, to assay for positive natural selection. If an interaction has been selected for, then it must perform an important function, a function that significantly promotes reproductive success. Accordingly, selection is a consequence and indicator of function, and uncovering multimolecular selection will reveal important functional interactions. We propose a selection signature for interactions and review recent selection-based approaches through which to dissect traits that are not inherently tractable. The review includes proof-of-principle studies in which important interactions were uncovered by screening for selection. In sum, screens for selection appear feasible when screens for specific functions are not. Selection screens thus constitute a novel tool through which to reveal the mechanisms that shape the fates of organisms.

Resolving Deleterious and Near-Neutral Effects Requires Different Pooled Fitness Assay Designs

Pooled sequencing-based fitness assays are a powerful and widely used approach to quantifying fitness of thousands of genetic variants in parallel. Despite the throughput of such assays, they are prone to biases in fitness estimates, and errors in measurements are typically larger for deleterious fitness effects, relative to neutral effects. In practice, designing pooled fitness assays involves tradeoffs between the number of timepoints, the sequencing depth, and other parameters to gain as much information as possible within a feasible experiment. Here, we combined simulations and reanalysis of an existing experimental dataset to explore how assay parameters impact measurements of near-neutral and deleterious fitness effects using a standard fitness estimator. We found that sequencing multiple timepoints at relatively modest depth improved estimates of near-neutral fitness effects, but systematically biased measurements of deleterious effects. We showed that a fixed total number of reads, deeper sequencing at fewer timepoints improved resolution of deleterious fitness effects. Our results highlight a tradeoff between measurement of deleterious and near-neutral effect sizes for a fixed amount of data and suggest that fitness assay design should be tuned for fitness effects that are relevant to the specific biological question.

Fit-Seq2.0: An Improved Software for High-Throughput Fitness Measurements Using Pooled Competition Assays

The fitness of a genotype is defined as its lifetime reproductive success, with fitness itself being a composite trait likely dependent on many underlying phenotypes. Measuring fitness is important for understanding how alteration of different cellular components affects a cell's ability to reproduce. Here, we describe an improved approach, implemented in Python, for estimating fitness in high throughput via pooled competition assays.

Macromolecular crowding impact on anti-CRISPR AcrIIC3/NmeCas9 complex: Insights from scaled particle theory, molecular dynamics, and elastic networks models

The coupling of Cas9 and its inhibitor AcrIIC3, both from the bacterium Neisseria meningitidis (Nme), form a homodimer of the (NmeCas9/AcrIIC3)₂ type. This coupling was studied to assess the impact of their interaction with the crowders in the following environments: (1) homogeneous crowded, (2) heterogeneous, and (3) microheterogeneous cytoplasmic. For this, statistical thermodynamic models based on the scaled particle theory (SPT) were used, considering the attractive and repulsive protein-crowders contributions and the stability of the formation of spherocylindrical homodimers and the effects of changes in the size of spherical dimers were estimated. Studies based on models of dynamics, elastic networks, and statistical potentials to the formation of complexes NmeCas9/AcrIIC3 using PEG as the crowding agent support the predictions from SPT. Macromolecular crowding stabilizes the formation of the dimers, being more significant when the attractive protein-crowder interactions are weaker and the crowders are smaller. The coupling is favored towards the formation of spherical and compact dimers due to crowding addition (excluded-volume effects) and the thermodynamic stability of the dimers is markedly dependent on the size of the crowders. These results support the experimental mechanistic proposal of inhibition of NmeCas9 mediated by AcrIIC3.

Integrative structure determination of histones H3 and H4 using genetic interactions

Integrative structure modeling is increasingly used for determining the architectures of biological assemblies, especially those that are structurally heterogeneous. Recently, we reported on how to convert in vivo genetic interaction measurements into spatial restraints for structural modeling: first, phenotypic profiles are generated for each point mutation and thousands of gene deletions or environmental perturbations. Following, the phenotypic profile similarities are converted into distance restraints on the pairs of mutated residues. We illustrate the approach by determining the structure of the histone H3-H4 complex. The method is implemented in our open-source IMP program, expanding the structural biology toolbox by allowing structural characterization based on in vivo data without the need to purify the target system. We compare genetic interaction measurements to other sources of structural information, such as residue coevolution and deep-learning structure prediction of complex subunits. We also suggest that determining genetic interactions could benefit from new technologies, such as CRISPR-Cas9 approaches to gene editing, especially for mammalian cells. Finally, we highlight the opportunity for using genetic interactions to determine recalcitrant biomolecular structures, such as those of disordered proteins, transient protein assemblies, and host-pathogen protein complexes.

Prediction of efficiencies for diverse prime editing systems in multiple cell types

Applications of prime editing are often limited due to insufficient efficiencies, and it can require substantial time and resources to determine the most efficient pegRNAs and prime editors (PEs) to generate a desired edit under various experimental conditions. Here, we evaluated prime editing efficiencies for a total of 338,996 pairs of pegRNAs including 3,979 epegRNAs and target sequences in an error-free manner. These datasets enabled a systematic determination of factors affecting prime editing efficiencies. Then, we developed computational models, named DeepPrime and DeepPrime-FT, that can predict prime editing efficiencies for eight prime editing systems in seven cell types for all possible types of editing of up to 3 base pairs. We also extensively profiled the prime editing efficiencies at mismatched targets and developed a computational model predicting editing efficiencies at such targets. These computational models, together with our improved knowledge about prime editing efficiency determinants, will greatly facilitate prime editing applications.

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

MOTIVATION

Synthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed.

RESULTS

In this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability.

AVAILABILITY AND IMPLEMENTATION

SLGNN is freely available at https://github.com/zy972014452/SLGNN.

Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction

Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model's successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.

The CRISPR/Cas System in Human Cancer

The use of CRISPR as a genetic editing tool modified the oncology field from its basic to applied research for opening a simple, fast, and cheaper way to manipulate the genome. This chapter reviews some of the major uses of this technique for in vitro- and in vivo-based biological screenings, for cellular and animal model generation, and new derivative tools applied to cancer research. CRISPR has opened new frontiers increasing the knowledge about cancer, pointing to new solutions to overcome several challenges to better understand the disease and design better treatments.

Maximizing CRISPRi efficacy and accessibility with dual-sgRNA libraries and optimal effectors

CRISPR interference (CRISPRi) enables programmable, reversible, and titratable repression of gene expression (knockdown) in mammalian cells. Initial CRISPRi-mediated genetic screens have showcased the potential to address basic questions in cell biology, genetics, and biotechnology, but wider deployment of CRISPRi screening has been constrained by the large size of single guide RNA (sgRNA) libraries and challenges in generating cell models with consistent CRISPRi-mediated knockdown. Here, we present next-generation CRISPRi sgRNA libraries and effector expression constructs that enable strong and consistent knockdown across mammalian cell models. First, we combine empirical sgRNA selection with a dual-sgRNA library design to generate an ultra-compact (1-3 elements per gene), highly active CRISPRi sgRNA library. Next, we compare CRISPRi effectors to show that the recently published Zim3-dCas9 provides an excellent balance between strong on-target knockdown and minimal non-specific effects on cell growth or the transcriptome. Finally, we engineer a suite of cell lines with stable expression of Zim3-dCas9 and robust on-target knockdown. Our results and publicly available reagents establish best practices for CRISPRi genetic screening.

Integration of CRISPR/Cas9 with artificial intelligence for improved cancer therapeutics

Gene editing has great potential in treating diseases caused by well-characterized molecular alterations. The introduction of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)–based gene-editing tools has substantially improved the precision and efficiency of gene editing. The CRISPR/Cas9 system offers several advantages over the existing gene-editing approaches, such as its ability to target practically any genomic sequence, enabling the rapid development and deployment of novel CRISPR-mediated knock-out/knock-in methods. CRISPR/Cas9 has been widely used to develop cancer models, validate essential genes as druggable targets, study drug-resistance mechanisms, explore gene non-coding areas, and develop biomarkers. CRISPR gene editing can create more-effective chimeric antigen receptor (CAR)-T cells that are durable, cost-effective, and more readily available. However, further research is needed to define the CRISPR/Cas9 system’s pros and cons, establish best practices, and determine social and ethical implications. This review summarizes recent CRISPR/Cas9 developments, particularly in cancer research and immunotherapy, and the potential of CRISPR/Cas9-based screening in developing cancer precision medicine and engineering models for targeted cancer therapy, highlighting the existing challenges and future directions. Lastly, we highlight the role of artificial intelligence in refining the CRISPR system's on-target and off-target effects, a critical factor for the broader application in cancer therapeutics.

Combinatorial CRISPR Interference Library for Enhancing 2,3-BDO Production and Elucidating Key Genes in Cyanobacteria

Cyanobacteria can convert CO₂ to chemicals such as 2,3-butanediol (2,3-BDO), rendering them promising for renewable production and carbon neutralization, but their applications are limited by low titers. To enhance cyanobacterial 2,3-BDO production, we developed a combinatorial CRISPR interference (CRISPRi) library strategy. We integrated the 2,3-BDO pathway genes and a CRISPRi library into the cyanobacterium PCC7942 using the orthogonal CRISPR system to overexpress pathway genes and attenuate genes that inhibit 2,3-BDO formation. The combinatorial CRISPRi library strategy allowed us to inhibit fbp, pdh, ppc, and sps (which catalyzes the synthesis of fructose-6-phosphate, acetyl-coenzyme A, oxaloacetate, and sucrose, respectively) at different levels, thereby allowing for rapid screening of a strain that enhances 2,3-BDO production by almost 2-fold to 1583.8 mg/L. Coupled with a statistical model, we elucidated that differentially inhibiting all the four genes enhances 2,3-BDO synthesis to varying degrees. fbp and pdh suppression exerted more profound effects on 2,3-BDO production than ppc and sps suppression, and these four genes can be repressed simultaneously without mutual interference. The CRISPRi library approach paves a new avenue to combinatorial metabolic engineering of cyanobacteria.

CRISPR screening in cancer stem cells

Cancer stem cells (CSCs) are a subpopulation of tumor cells with self-renewal ability. Increasing evidence points to the critical roles of CSCs in tumorigenesis, metastasis, therapy resistance, and cancer relapse. As such, the elimination of CSCs improves cancer treatment outcomes. However, challenges remain due to limited understanding of the molecular mechanisms governing self-renewal and survival of CSCs. Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 screening has been increasingly used to identify genetic determinants in cancers. In this primer, we discuss the progress made and emerging opportunities of coupling advanced CRISPR screening systems with CSC models to reveal the understudied vulnerabilities of CSCs.

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

High-content CRISPR screening

CRISPR screens are a powerful source of biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications and species. In pooled CRISPR screens, various genetically encoded perturbations are introduced into pools of cells. The targeted cells proliferate under a biological challenge such as cell competition, drug treatment or viral infection. Subsequently, the perturbation-induced effects are evaluated by sequencing-based counting of the guide RNAs that specify each perturbation. The typical results of such screens are ranked lists of genes that confer sensitivity or resistance to the biological challenge of interest. Contributing to the broad utility of CRISPR screens, adaptations of the core CRISPR technology make it possible to activate, silence or otherwise manipulate the target genes. Moreover, high-content read-outs such as single-cell RNA sequencing and spatial imaging help characterize screened cells with unprecedented detail. Dedicated software tools facilitate bioinformatic analysis and enhance reproducibility. CRISPR screening has unravelled various molecular mechanisms in basic biology, medical genetics, cancer research, immunology, infectious diseases, microbiology and other fields. This Primer describes the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development - with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen.

Application of prime editing to the correction of mutations and phenotypes in adult mice with liver and eye diseases

The use of prime editing-a gene-editing technique that induces small genetic changes without the need for donor DNA and without causing double strand breaks-to correct pathogenic mutations and phenotypes needs to be tested in animal models of human genetic diseases. Here we report the use of prime editors 2 and 3, delivered by hydrodynamic injection, in mice with the genetic liver disease hereditary tyrosinemia, and of prime editor 2, delivered by an adeno-associated virus vector, in mice with the genetic eye disease Leber congenital amaurosis. For each pathogenic mutation, we identified an optimal prime-editing guide RNA by using cells transduced with lentiviral libraries of guide-RNA-encoding sequences paired with the corresponding target sequences. The prime editors precisely corrected the disease-causing mutations and led to the amelioration of the disease phenotypes in the mice, without detectable off-target edits. Prime editing should be tested further in more animal models of genetic diseases.

CRISPRi-seq for genome-wide fitness quantification in bacteria

CRISPR interference (CRISPRi) is a powerful tool to link essential and nonessential genes to specific phenotypes and to explore their functions. Here we describe a protocol for CRISPRi screenings to assess genome-wide gene fitness in a single sequencing step (CRISPRi-seq). We demonstrate the use of the protocol in Streptococcus pneumoniae, an important human pathogen; however, the protocol can easily be adapted for use in other organisms. The protocol includes a pipeline for single-guide RNA library design, workflows for pooled CRISPRi library construction, growth assays and sequencing steps, a read analysis tool (2FAST2Q) and instructions for fitness quantification. We describe how to make an IPTG-inducible system with small libraries that are easy to handle and cost-effective and overcome bottleneck issues, which can be a problem when using similar, transposon mutagenesis-based methods. Ultimately, the procedure yields a fitness score per single-guide RNA target for any given growth condition. A genome-wide screening can be finished in 1 week with a constructed library. Data analysis and follow-up confirmation experiments can be completed in another 2-3 weeks.

Identification of Genes Regulating Hepatocyte Injury by a Genome-Wide CRISPR-Cas9 Screen

Gene editing introduces stable mutations into the genome and has powerful applications extending from research to clinical gene therapy. CRISPR-Cas9 gene editing can be employed to study directly the functional impact of stable gene knockout, activation, and knockdown. Here, we describe the end-to-end methodology by which we employ genome-wide CRISPR-Cas9 knockout to study drug toxicity using acetaminophen (APAP) in a hepatocellular carcinoma liver model as an example. This methodology can be extended to other proliferative cell types and chemical metabolic and toxicity models. By employing a massively parallelized genome-wide knockout model, the genes responsible for cellular toxicity and proliferation may be assayed concurrently. Resultant data are interrogated in the context of existing gene expression data, pathway analysis, drug-gene interactions, and orthogonal confirmatory assays to better understand the metabolic mechanisms.

Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells

CRISPR knockout fitness screens in cancer cell lines reveal many genes whose loss of function causes cell death or loss of fitness or, more rarely, the opposite phenotype of faster proliferation. Here we demonstrate a systematic approach to identify these proliferation suppressors, which are highly enriched for tumor suppressor genes, and define a network of 145 such genes in 22 modules. One module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of acute myeloid leukemia cell lines. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe loss of fitness phenotype for genes in the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirm biochemically. Overexpression of this module is associated with a survival advantage in juvenile leukemias, suggesting a clinically relevant subtype.

CRISPR-based knockout screens in cancer cells have suggested the existence of proliferation suppressor genes (PSG). Here, the authors develop an approach to systematically identify them, and reveal a PSG module involved in fatty acid synthesis and tumour suppression in acute myeloid leukemia cell lines.

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.

dCas9 techniques for transcriptional repression in mammalian cells: Progress, applications and challenges

Innovative loss-of-function techniques developed in recent years have made it much easier to target specific genomic loci at transcriptional levels. CRISPR interference (CRISPRi) has been proven to be the most effective and specific tool to knock down any gene of interest in mammalian cells. The catalytically deactivated Cas9 (dCas9) can be fused with transcription repressors to downregulate gene expression specified by sgRNA complementary to target genomic sequence. Although CRISPRi has huge potential for gene knockdown, there is still a lack of systematic guidelines for efficient and widespread use. Here we describe the working mechanism and development of CRISPRi, designing principles of sgRNA, delivery methods and applications in mammalian cells in detail. Finally, we propose possible solutions and future directions with regard to current challenges.

KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers

Motivation

Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. Wet-lab screening of SL pairs is afflicted with high cost, batch-effect, and off-target problems. Current computational methods for SL prediction include gene knock-out simulation, knowledge-based data mining and machine learning methods. Most of the existing methods tend to assume that SL pairs are independent of each other, without taking into account the shared biological mechanisms underlying the SL pairs. Although several methods have incorporated genomic and proteomic data to aid SL prediction, these methods involve manual feature engineering that heavily relies on domain knowledge.

Results

Here, we propose a novel graph neural network (GNN)-based model, named KG4SL, by incorporating knowledge graph (KG) message-passing into SL prediction. The KG was constructed using 11 kinds of entities including genes, compounds, diseases, biological processes and 24 kinds of relationships that could be pertinent to SL. The integration of KG can help harness the independence issue and circumvent manual feature engineering by conducting message-passing on the KG. Our model outperformed all the state-of-the-art baselines in area under the curve, area under precision-recall curve and F1. Extensive experiments, including the comparison of our model with an unsupervised TransE model, a vanilla graph convolutional network model, and their combination, demonstrated the significant impact of incorporating KG into GNN for SL prediction.

Availability and implementation

: KG4SL is freely available at https://github.com/JieZheng-ShanghaiTech/KG4SL.

Supplementary information

Supplementary data are available at Bioinformatics online.

Network Analysis Reveals Synergistic Genetic Dependencies for Rational Combination Therapy in Philadelphia Chromosome-Like Acute Lymphoblastic Leukemia

PURPOSE

Systems biology approaches can identify critical targets in complex cancer signaling networks to inform new therapy combinations that may overcome conventional treatment resistance.

EXPERIMENTAL DESIGN

We performed integrated analysis of 1,046 childhood B-ALL cases and developed a data-driven network controllability-based approach to identify synergistic key regulator targets in Philadelphia chromosome-like B-acute lymphoblastic leukemia (Ph-like B-ALL), a common high-risk leukemia subtype associated with hyperactive signal transduction and chemoresistance.

RESULTS

We identified 14 dysregulated network nodes in Ph-like ALL involved in aberrant JAK/STAT, Ras/MAPK, and apoptosis pathways and other critical processes. Genetic cotargeting of the synergistic key regulator pair STAT5B and BCL2-associated athanogene 1 (BAG1) significantly reduced leukemia cell viability in vitro. Pharmacologic inhibition with dual small molecule inhibitor therapy targeting this pair of key nodes further demonstrated enhanced antileukemia efficacy of combining the BCL-2 inhibitor venetoclax with the tyrosine kinase inhibitors ruxolitinib or dasatinib in vitro in human Ph-like ALL cell lines and in vivo in multiple childhood Ph-like ALL patient-derived xenograft models. Consistent with network controllability theory, co-inhibitor treatment also shifted the transcriptomic state of Ph-like ALL cells to become less like kinase-activated BCR-ABL1-rearranged (Ph+) B-ALL and more similar to prognostically favorable childhood B-ALL subtypes.

CONCLUSIONS

Our study represents a powerful conceptual framework for combinatorial drug discovery based on systematic interrogation of synergistic vulnerability pathways with pharmacologic inhibitor validation in preclinical human leukemia models.

Systematic screening reveals synergistic interactions that overcome MAPK inhibitor resistance in cancer cells

OBJECTIVE

Effective adjuvant therapeutic strategies are urgently needed to overcome MAPK inhibitor (MAPKi) resistance, which is one of the most common forms of resistance that has emerged in many types of cancers. Here, we aimed to systematically identify the genetic interactions underlying MAPKi resistance, and to further investigate the mechanisms that produce the genetic interactions that generate synergistic MAPKi resistance.

METHODS

We conducted a comprehensive pair-wise sgRNA-based high-throughput screening assay to identify synergistic interactions that sensitized cancer cells to MAPKi, and validated 3 genetic combinations through competitive growth, cell viability, and spheroid formation assays. We next conducted Kaplan-Meier survival analysis based on The Cancer Genome Atlas database and conducted immunohistochemistry to determine the clinical relevance of these synergistic combinations. We also investigated the MAPKi resistance mechanisms of these validated synergistic combinations by using co-immunoprecipitation, Western blot, qRT-PCR, and immunofluorescence assays.

RESULTS

We constructed a systematic interaction network of MAPKi resistance and identified 3 novel synergistic combinations that effectively targeted MAPKi resistance (ITGB3 + IGF1R, ITGB3 + JNK, and HDGF + LGR5). We next analyzed their clinical relevance and the mechanisms by which they sensitized cancer cells to MAPKi exposure. Specifically, we discovered a novel protein complex, HDGF-LGR5, that adaptively responded to MAPKi to enhance cancer cell stemness, which was up- or downregulated by the inhibitors of ITGB3 + JNK or ITGB3 + IGF1R.

CONCLUSIONS

Pair-wise sgRNA library screening provided systematic insights into elucidating MAPKi resistance in cancer cells. ITGB3- + IGF1R-targeting drugs (cilengitide + linsitinib) could be used as an effective therapy for suppressing the adaptive formation of the HDGF-LGR5 protein complex, which enhanced cancer stemness during MAPKi stress.

An Overview of RNA-Based Scaffolds for Osteogenesis

Tissue engineering provides new hope for the combination of cells, scaffolds, and bifactors for bone osteogenesis. This is achieved by mimicking the bone's natural behavior in recruiting the cell's molecular machinery for our use. Many researchers have focused on developing an ideal scaffold with specific features, such as good cellular adhesion, cell proliferation, differentiation, host integration, and load bearing. Various types of coating materials (organic and non-organic) have been used to enhance bone osteogenesis. In the last few years, RNA-mediated gene therapy has captured attention as a new tool for bone regeneration. In this review, we discuss the use of RNA molecules in coating and delivery, including messenger RNA (mRNA), RNA interference (RNAi), and long non-coding RNA (lncRNA) on different types of scaffolds (such as polymers, ceramics, and metals) in osteogenesis research. In addition, the effect of using gene-editing tools-particularly CRISPR systems-to guide RNA scaffolds in bone regeneration is also discussed. Given existing knowledge about various RNAs coating/expression may help to understand the process of bone formation on the scaffolds during osseointegration.

Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions

Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .

A genome-wide atlas of co-essential modules assigns function to uncharacterized genes

A central question in the post-genomic era is how genes interact to form biological pathways. Measurements of gene dependency across hundreds of cell lines have been used to cluster genes into 'co-essential' pathways, but this approach has been limited by ubiquitous false positives. In the present study, we develop a statistical method that enables robust identification of gene co-essentiality and yields a genome-wide set of functional modules. This atlas recapitulates diverse pathways and protein complexes, and predicts the functions of 108 uncharacterized genes. Validating top predictions, we show that TMEM189 encodes plasmanylethanolamine desaturase, a key enzyme for plasmalogen synthesis. We also show that C15orf57 encodes a protein that binds the AP2 complex, localizes to clathrin-coated pits and enables efficient transferrin uptake. Finally, we provide an interactive webtool for the community to explore our results, which establish co-essentiality profiling as a powerful resource for biological pathway identification and discovery of new gene functions.

Surrogate broodstock to enhance biotechnology research and applications in aquaculture

Aquaculture is playing an increasingly important role in meeting global demands for seafood, particularly in low and middle income countries. Genetic improvement of aquaculture species has major untapped potential to help achieve this, with selective breeding and genome editing offering exciting avenues to expedite this process. However, limitations to these breeding and editing approaches include long generation intervals of many fish species, alongside both technical and regulatory barriers to the application of genome editing in commercial production. Surrogate broodstock technology facilitates the production of donor-derived gametes in surrogate parents, and comprises transplantation of germ cells of donors into sterilised recipients. There are many successful examples of intra- and inter-species germ cell transfer and production of viable offspring in finfish, and this leads to new opportunities to address the aforementioned limitations. Firstly, surrogate broodstock technology raises the opportunity to improve genome editing via the use of cultured germ cells, to reduce mosaicism and potentially enable in vivo CRISPR screens in the progeny of surrogate parents. Secondly, the technology has pertinent applications in preservation of aquatic genetic resources, and in facilitating breeding of high-value species which are otherwise difficult to rear in captivity. Thirdly, it holds potential to drastically reduce the effective generation interval in aquaculture breeding programmes, expediting the rate of genetic gain. Finally, it provides new opportunities for dissemination of tailored, potentially genome edited, production animals of high genetic merit for farming. This review focuses on the state-of-the-art of surrogate broodstock technology, and discusses the next steps for its applications in research and production. The integration and synergy of genomics, genome editing, and reproductive technologies have exceptional potential to expedite genetic gain in aquaculture species in the coming decades.

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Genetic improvement in aquaculture species has a major role in global food security. Advances in biotechnology provide new opportunities to support aquaculture breeding.

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Advances in biotechnology provide new opportunities to support aquaculture breeding.

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Donor-derived gametes can be produced from surrogate broodstock of several aquaculture species.

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Surrogate broodstock technology provides new opportunities for application of genome editing.

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Surrogate broodstock can accelerate genetic gain, and improve dissemination of elite germplasm.

Approach for in vivo delivery of CRISPR/Cas system: a recent update and future prospect

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) system provides a groundbreaking genetic technology that allows scientists to modify genes by targeting specific genomic sites. Due to the relative simplicity and versatility of the CRISPR/Cas system, it has been extensively applied in human genetic research as well as in agricultural applications, such as improving crops. Since the gene editing activity of the CRISPR/Cas system largely depends on the efficiency of introducing the system into cells or tissues, an efficient and specific delivery system is critical for applying CRISPR/Cas technology. However, there are still some hurdles remaining for the translatability of CRISPR/Cas system. In this review, we summarized the approaches used for the delivery of the CRISPR/Cas system in mammals, plants, and aquacultures. We further discussed the aspects of delivery that can be improved to elevate the potential for CRISPR/Cas translatability.

Graph contextualized attention network for predicting synthetic lethality in human cancers

MOTIVATION

Synthetic Lethality (SL) plays an increasingly critical role in the targeted anticancer therapeutics. In addition, identifying SL interactions can create opportunities to selectively kill cancer cells without harming normal cells. Given the high cost of wet-lab experiments, in silico prediction of SL interactions as an alternative can be a rapid and cost-effective way to guide the experimental screening of candidate SL pairs. Several matrix factorization-based methods have recently been proposed for human SL prediction. However, they are limited in capturing the dependencies of neighbors. In addition, it is also highly challenging to make accurate predictions for new genes without any known SL partners.

RESULTS

In this work, we propose a novel graph contextualized attention network named GCATSL to learn gene representations for SL prediction. First, we leverage different data sources to construct multiple feature graphs for genes, which serve as the feature inputs for our GCATSL method. Second, for each feature graph, we design node-level attention mechanism to effectively capture the importance of local and global neighbors and learn local and global representations for the nodes, respectively. We further exploit multi-layer perceptron (MLP) to aggregate the original features with the local and global representations and then derive the feature-specific representations. Third, to derive the final representations, we design feature-level attention to integrate feature-specific representations by taking the importance of different feature graphs into account. Extensive experimental results on three datasets under different settings demonstrated that our GCATSL model outperforms 14 state-of-the-art methods consistently. In addition, case studies further validated the effectiveness of our proposed model in identifying novel SL pairs.

AVAILABILITY

Python codes and dataset are freely available on GitHub (https://github.com/longyahui/GCATSL) and Zenodo (https://zenodo.org/record/4522679) under the MIT license.

Recording of elapsed time and temporal information about biological events using Cas9

DNA has not been utilized to record temporal information, although DNA has been used to record biological information and to compute mathematical problems. Here, we found that indel generation by Cas9 and guide RNA can occur at steady rates, in contrast to typical dynamic biological reactions, and the accumulated indel frequency can be a function of time. By measuring indel frequencies, we developed a method for recording and measuring absolute time periods over hours to weeks in mammalian cells. These time-recordings were conducted in several cell types, with different promoters and delivery vectors for Cas9, and in both cultured cells and cells of living mice. As applications, we recorded the duration of chemical exposure and the lengths of elapsed time since the onset of biological events (e.g., heat exposure and inflammation). We propose that our systems could serve as synthetic "DNA clocks."

Emergence and propagation of epistasis in metabolic networks

Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.

Predicting the efficiency of prime editing guide RNAs in human cells

Prime editing enables the introduction of virtually any small-sized genetic change without requiring donor DNA or double-strand breaks. However, evaluation of prime editing efficiency requires time-consuming experiments, and the factors that affect efficiency have not been extensively investigated. In this study, we performed high-throughput evaluation of prime editor 2 (PE2) activities in human cells using 54,836 pairs of prime editing guide RNAs (pegRNAs) and their target sequences. The resulting data sets allowed us to identify factors affecting PE2 efficiency and to develop three computational models to predict pegRNA efficiency. For a given target sequence, the computational models predict efficiencies of pegRNAs with different lengths of primer binding sites and reverse transcriptase templates for edits of various types and positions. Testing the accuracy of the predictions using test data sets that were not used for training, we found Spearman's correlations between 0.47 and 0.81. Our computational models and information about factors affecting PE2 efficiency will facilitate practical application of prime editing.

Exosome-mediated delivery of gene vectors for gene therapy

Gene vectors are nucleic acids that carry genetic materials or gene editing devices into cells to exert the sustained production of therapeutic proteins or to correct erroneous genes of the cells. However, the cell membrane sets a barrier for the entry of nucleic acid molecules, and nucleic acids are easily degraded or neutralized when they are externally administered into the body. Carriers to encapsulate, protect and deliver nucleic acid molecules therefore are essential for clinical applications of gene therapy. The secreted organelles, exosomes, which naturally mediate the communications between cells, have been engineered to encapsulate and deliver nucleic acids to the desired tissues and cells. The fusion of exosomes with liposomes can increase the loading capacity and also retain the targeting capability of exosomes. Altogether, this review summarizes the most recent designs of exosome-based applications for gene delivery and their future perspectives in gene therapy.

CRISPR/dCas system as the modulator of gene expression

CRISPR/Cas has been a very exciting field of research because of its multifaceted applications in biological science for editing genome. This tool can be programmed to target any region of DNA of choice by designing gRNA. The potential of gRNA to recruit a CRISPR-associated protein at a specific genomic site allowed scientists to engineer genome of diverse species for research and development. The application of Cas9 has been further expanded with a recently developed catalytically inactive protein (dead Cas9). CRISPR/dCas system is widely used as a programmable vector to deliver functional cargo (transcriptional effectors) to the desired sites at the genome for targeted transcriptional repression (CRISPR interference, CRISPRi) or activation (CRISPR activation, CRISPRa). It is now possible to regulate gene expression in cells without altering the DNA sequence. These CRISPRi/a toolboxes have explored many unsolved biological issues. Further research on CRISPR system could help diagnose and treat various human diseases.

Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses

Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes in thousands of crossed strains followed by statistical analysis to compare single and double mutants. The preferred computational approach is to use a multiplicative model that factors phenotype scores of single gene mutants to identify gene interactions in double mutants. Here we present how machine learning models that consider the characteristics of the phenotypic data improve on the classical multiplicative model. Importantly, machine learning improves the selection of cutoff values to identify gene interactions from phenotypic scores.

Genetic interaction mapping informs integrative structure determination of protein complexes

Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.

Master Regulators and Cofactors of Human Neuronal Cell Fate Specification Identified by CRISPR Gene Activation Screens

Technologies to reprogram cell-type specification have revolutionized the fields of regenerative medicine and disease modeling. Currently, the selection of fate-determining factors for cell reprogramming applications is typically a laborious and low-throughput process. Therefore, we use high-throughput pooled CRISPR activation (CRISPRa) screens to systematically map human neuronal cell fate regulators. We utilize deactivated Cas9 (dCas9)-based gene activation to target 1,496 putative transcription factors (TFs) in the human genome. Using a reporter of neuronal commitment, we profile the neurogenic activity of these factors in human pluripotent stem cells (PSCs), leading to a curated set of pro-neuronal factors. Activation of pairs of TFs reveals neuronal cofactors, including E2F7, RUNX3, and LHX8, that improve conversion efficiency, subtype specificity, and maturation of neuronal cell types. Finally, using multiplexed gene regulation with orthogonal CRISPR systems, we demonstrate improved neuronal differentiation with concurrent activation and repression of target genes, underscoring the power of CRISPR-based gene regulation for programming complex cellular phenotypes.

Prediction of the sequence-specific cleavage activity of Cas9 variants

Several Streptococcus pyogenes Cas9 (SpCas9) variants have been developed to improve an enzyme's specificity or to alter or broaden its protospacer-adjacent motif (PAM) compatibility, but selecting the optimal variant for a given target sequence and application remains difficult. To build computational models to predict the sequence-specific activity of 13 SpCas9 variants, we first assessed their cleavage efficiency at 26,891 target sequences. We found that, of the 256 possible four-nucleotide NNNN sequences, 156 can be used as a PAM by at least one of the SpCas9 variants. For the high-fidelity variants, overall activity could be ranked as SpCas9 ≥ Sniper-Cas9 > eSpCas9(1.1) > SpCas9-HF1 > HypaCas9 ≈ xCas9 >> evoCas9, whereas their overall specificities could be ranked as evoCas9 >> HypaCas9 ≥ SpCas9-HF1 ≈ eSpCas9(1.1) > xCas9 > Sniper-Cas9 > SpCas9. Using these data, we developed 16 deep-learning-based computational models that accurately predict the activity of these variants at any target sequence.

Time for remodeling: SNF2-family DNA translocases in replication fork metabolism and human disease

Over the course of DNA replication, DNA lesions, transcriptional intermediates and protein-DNA complexes can impair the progression of replication forks, thus resulting in replication stress. Failure to maintain replication fork integrity in response to replication stress leads to genomic instability and predisposes to the development of cancer and other genetic disorders. Multiple DNA damage and repair pathways have evolved to allow completion of DNA replication following replication stress, thus preserving genomic integrity. One of the processes commonly induced in response to replication stress is fork reversal, which consists in the remodeling of stalled replication forks into four-way DNA junctions. In normal conditions, fork reversal slows down replication fork progression to ensure accurate repair of DNA lesions and facilitates replication fork restart once the DNA lesions have been removed. However, in certain pathological situations, such as the deficiency of DNA repair factors that protect regressed forks from nuclease-mediated degradation, fork reversal can cause genomic instability. In this review, we describe the complex molecular mechanisms regulating fork reversal, with a focus on the role of the SNF2-family fork remodelers SMARCAL1, ZRANB3 and HLTF, and highlight the implications of fork reversal for tumorigenesis and cancer therapy.

A Three-Way Combinatorial CRISPR Screen for Analyzing Interactions among Druggable Targets

We present a CRISPR-based multi-gene knockout screening system and toolkits for extensible assembly of barcoded high-order combinatorial guide RNA libraries en masse. We apply this system for systematically identifying not only pairwise but also three-way synergistic therapeutic target combinations and successfully validate double- and triple-combination regimens for suppression of cancer cell growth and protection against Parkinson's disease-associated toxicity. This system overcomes the practical challenges of experimenting on a large number of high-order genetic and drug combinations and can be applied to uncover the rare synergistic interactions between druggable targets.