Genetic interaction mapping in mammalian cells using CRISPR interference

Computational Methods for Analysis of Large-Scale CRISPR Screens

Large-scale CRISPR-Cas pooled screens have shown great promise to investigate functional links between genotype and phenotype at the genome-wide scale. In addition to technological advancement, there is a need to develop computational methods to analyze the large datasets obtained from high-throughput CRISPR screens. Many computational methods have been developed to identify reliable gene hits from various screens. In this review, we provide an overview of the technology development of CRISPR screening platforms, with a focus on recent advances in computational methods to identify and model gene effects using CRISPR screen datasets. We also discuss existing challenges and opportunities for future computational methods development.

High-performance CRISPR-Cas12a genome editing for combinatorial genetic screening

CRISPR-based genetic screening has revolutionized cancer drug target discovery, yet reliable, multiplex gene editing to reveal synergies between gene targets remains a major challenge. Here, we present a simple and robust CRISPR-Cas12a-based approach for combinatorial genetic screening in cancer cells. By engineering the CRISPR-AsCas12a system with key modifications to the Cas protein and its CRISPR RNA (crRNA), we can achieve high efficiency combinatorial genetic screening. We demonstrate the performance of our optimized AsCas12a (opAsCas12a) through double knockout screening against epigenetic regulators. This screen reveals synthetic sick interactions between Brd9&Jmjd6, Kat6a&Jmjd6, and Brpf1&Jmjd6 in leukemia cells.

Technologies and Computational Analysis Strategies for CRISPR Applications

The CRISPR-Cas system offers a programmable platform for eukaryotic genome and epigenome editing. The ability to perform targeted genetic and epigenetic perturbations enables researchers to perform a variety of tasks, ranging from investigating questions in basic biology to potentially developing novel therapeutics for the treatment of disease. While CRISPR systems have been engineered to target DNA and RNA with increased precision, efficiency, and flexibility, assays to identify off-target editing are becoming more comprehensive and sensitive. Furthermore, techniques to perform high-throughput genome and epigenome editing can be paired with a variety of readouts and are uncovering important cellular functions and mechanisms. These technological advances drive and are driven by accompanying computational approaches. Here, we briefly present available CRISPR technologies and review key computational advances and considerations for various CRISPR applications. In particular, we focus on the analysis of on- and off-target editing and CRISPR pooled screen data.

CRISPR interference-mediated noggin knockdown promotes BMP2-induced osteogenesis and calvarial bone healing

Healing of large calvarial bone defects remains a challenging task in the clinical setting. Although BMP2 (bone morphogenetic protein 2) is a potent growth factor that can induce bone repair, BMP2 provokes the expression of antagonist Noggin that self-restricts its bioactivity. CRISPR interference (CRISPRi) is a technology for programmable gene suppression but its application in regenerative medicine is still in its infancy. We reasoned that Nog inhibition, concurrent with BMP2 overexpression, can promote the osteogenesis of adipose-derived stem cells (ASC) and improve calvarial bone healing. We designed and exploited a hybrid baculovirus (BV) system for the delivery of BMP2 gene and CRISPRi system targeting Nog. After BV-mediated co-delivery into ASC, the system conferred prolonged BMP2 expression and stimulated Nog expression while the CRISPRi system effectively repressed Nog upregulation for at least 14 days. The CRISPRi-mediated Nog knockdown, along with BMP2 overexpression, additively stimulated the osteogenic differentiation of ASC. Implantation of the CRISPRi-engineered ASC into the critical size defects at the calvaria significantly enhanced the calvarial bone healing and matrix mineralization. These data altogether implicate the potentials of CRISPRi-mediated gene knockdown for cell fate regulation and tissue regeneration.

Establishment of CRISPR interference in Methylorubrum extorquens and application of rapidly mining a new phytoene desaturase involved in carotenoid biosynthesis

The methylotrophic bacterium Methylorubrum extorquens AM1 holds a great potential of a microbial cell factory in producing high value chemicals with methanol as the sole carbon and energy source. However, many gene functions remain unknown, hampering further rewiring of metabolic networks. Clustered regularly interspaced short palindromic repeat interference (CRISPRi) has been demonstrated to be a robust tool for gene knockdown in diverse organisms. In this study, we developed an efficient CRISPRi system through optimizing the promoter strength of Streptococcus pyogenes-derived deactivated cas9 (dcas9). When the dcas9 and sgRNA were respectively controlled by medium PR/tetO and strong PmxaF-g promoters, dynamic repression efficacy of cell growth through disturbing a central metabolism gene glyA was achieved from 41.9 to 96.6% dependent on the sgRNA targeting sites. Furthermore, the optimized CRISPRi system was shown to effectively decrease the abundance of exogenous fluorescent protein gene mCherry over 50% and to reduce the expression of phytoene desaturase gene crtI by 97.7%. We then used CRISPRi technology combined with 26 sgRNAs pool to rapidly discover a new phytoene desaturase gene META1_3670 from 2470 recombinant mutants. The gene function was further verified through gene deletion and complementation as well as phylogenetic tree analysis. In addition, we applied CRISPRi to repress the transcriptional level of squalene-hopene cyclase gene shc involved in hopanoid biosynthesis by 64.9%, which resulted in enhancing 1.9-fold higher of carotenoid production without defective cell growth. Thus, the CRISPRi system developed here provides a useful tool in mining functional gene of M. extorquens as well as in biotechnology for producing high-valued chemicals from methanol. KEY POINTS: Developing an efficient CRISPRi to knockdown gene expression in C1-utilizing bacteria CRISPRi combined with sgRNAs pool to rapidly discover a new phytoene desaturase gene Improvement of carotenoid production by repressing a competitive pathway.

SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named SL² MF, which employs logistic matrix factorization to learn latent representations of genes from the observed SL data. The probability that two genes are likely to form SL is modeled by the linear combination of gene latent vectors. As known SL pairs are more trustworthy than unknown pairs, we design importance weighting schemes to assign higher importance weights for known SL pairs and lower importance weights for unknown pairs in SL² MF. Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO). In particular, we calculate the similarity between genes based on their GO annotations and topological properties in the PPI network. Extensive experiments on the SL interaction data from SynLethDB database have been conducted to demonstrate the effectiveness of SL² MF.

Genetic interaction profiles of regulatory kinases differ between environmental conditions and cellular states

Cell growth and quiescence in eukaryotic cells is controlled by an evolutionarily conserved network of signaling pathways. Signal transduction networks operate to modulate a wide range of cellular processes and physiological properties when cells exit proliferative growth and initiate a quiescent state. How signaling networks function to respond to diverse signals that result in cell cycle exit and establishment of a quiescent state is poorly understood. Here, we studied the function of signaling pathways in quiescent cells using global genetic interaction mapping in the model eukaryotic cell, Saccharomyces cerevisiae (budding yeast). We performed pooled analysis of genotypes using molecular barcode sequencing (Bar-seq) to test the role of ~4,000 gene deletion mutants and ~12,000 pairwise interactions between all non-essential genes and the protein kinase genes TOR1, RIM15, and PHO85 in three different nutrient-restricted conditions in both proliferative and quiescent cells. We detect up to 10-fold more genetic interactions in quiescent cells than proliferative cells. We find that both individual gene effects and genetic interaction profiles vary depending on the specific pro-quiescence signal. The master regulator of quiescence, RIM15, shows distinct genetic interaction profiles in response to different starvation signals. However, vacuole-related functions show consistent genetic interactions with RIM15 in response to different starvation signals, suggesting that RIM15 integrates diverse signals to maintain protein homeostasis in quiescent cells. Our study expands genome-wide genetic interaction profiling to additional conditions, and phenotypes, and highlights the conditional dependence of epistasis.

mSphere of Influence: Comprehensive Genetic Analysis

Jason M. Peters works in the fields of antibiotic resistance and biofuel production. In this mSphere of Influence article, he reflects on how the paper "A global genetic interaction network maps a wiring diagram of cellular function" by Costanzo et al. (Science 353:aaf1420, 2016, https://doi.org/10.1126/science.aaf1420) has impacted his work by highlighting the power of gene networks to uncover new biology.

A Quantitative Genetic Interaction Map of HIV Infection

We have developed a platform for quantitative genetic interaction mapping using viral infectivity as a functional readout and constructed a viral host-dependency epistasis map (vE-MAP) of 356 human genes linked to HIV function, comprising >63,000 pairwise genetic perturbations. The vE-MAP provides an expansive view of the genetic dependencies underlying HIV infection and can be used to identify drug targets and study viral mutations. We found that the RNA deadenylase complex, CNOT, is a central player in the vE-MAP and show that knockout of CNOT1, 10, and 11 suppressed HIV infection in primary T cells by upregulating innate immunity pathways. This phenotype was rescued by deletion of IRF7, a transcription factor regulating interferon-stimulated genes, revealing a previously unrecognized host signaling pathway involved in HIV infection. The vE-MAP represents a generic platform that can be used to study the global effects of how different pathogens hijack and rewire the host during infection.

Genetic screens in isogenic mammalian cell lines without single cell cloning

Isogenic pairs of cell lines, which differ by a single genetic modification, are powerful tools for understanding gene function. Generating such pairs of mammalian cells, however, is labor-intensive, time-consuming, and, in some cell types, essentially impossible. Here, we present an approach to create isogenic pairs of cells that avoids single cell cloning, and screen these pairs with genome-wide CRISPR-Cas9 libraries to generate genetic interaction maps. We query the anti-apoptotic genes BCL2L1 and MCL1, and the DNA damage repair gene PARP1, identifying both expected and uncharacterized buffering and synthetic lethal interactions. Additionally, we compare acute CRISPR-based knockout, single cell clones, and small-molecule inhibition. We observe that, while the approaches provide largely overlapping information, differences emerge, highlighting an important consideration when employing genetic screens to identify and characterize potential drug targets. We anticipate that this methodology will be broadly useful to comprehensively study gene function across many contexts.

An update on human astrocytes and their role in development and disease

Human astrocytes provide trophic as well as structural support to the surrounding brain cells. Furthermore, they have been implicated in many physiological processes important for central nervous system function. Traditionally astrocytes have been considered to be a homogeneous class of cells, however, it has increasingly become more evident that astrocytes can have very different characteristics in different regions of the brain, or even within the same region. In this review we will discuss the features of human astrocytes, their heterogeneity, and their generation during neurodevelopment and the extraordinary progress that has been made to model these fascinating cells in vitro, mainly from induced pluripotent stem cells. Astrocytes' role in disease will also be discussed with a particular focus on their role in neurodegenerative disorders. As outlined here, astrocytes are important for the homeostasis of the central nervous system and understanding their regional specificity is a priority to elucidate the complexity of the human brain.

Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization

BACKGROUND

Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments.

RESULTS

In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction.

CONCLUSIONS

In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.

Advancing CRISPR-Based Programmable Platforms beyond Genome Editing in Mammalian Cells

Human diseases are caused by dysregulation of cellular biological programs that are encoded in DNA. Unveiling the endogenous programs and encoding new programs into the genome are key to creating novel diagnostic and therapeutic strategies. CRISPR/Cas9, originally identified in bacteria, has revolutionized genome editing in mammalian cells. Recent advances in CRISPR technologies have provided new programmable platforms for modifying cell function and behavior. CRISPR-based transcriptional regulators and modified gRNAs have enabled multiplexed regulation and visualization of genome dynamics with spatiotemporal precision. Using these toolkits, genome-scale screening platforms can identify key genetic elements or combinations thereof that modulate phenotypes in mammalian cells. In addition, imaging platforms for multiplexed genomic labeling have been created to study the conformation and dynamics of chromatin in living cells, which are essential for genome function. Furthermore, CRISPR-based computation and memory platforms have been built in living mammalian cells by using DNA as a data processing and storage medium to regulate and monitor cellular behaviors. The conditional regulation of CRISPR-based parts has enabled the design of complex multilayered biological programs. CRISPR-based memory platforms can continuously record biological events as mutations in defined DNA loci. By making use of base editors, CRISPR-based computation and memory platforms have been interconnected to perform logic operations based on past events. These technologies open up new avenues for understanding biological phenomena and designing mammalian cells as living machines for biomedical applications.

SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance

We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA-encoding and target sequence pairs. Deep learning-based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity-predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas9.

Mapping genetic interactions in cancer: a road to rational combination therapies

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies.

A one-step tRNA-CRISPR system for genome-wide genetic interaction mapping in mammalian cells

Mapping genetic interactions in mammalian cells is limited due to technical obstacles. Here we describe a method called TCGI (tRNA-CRISPR for genetic interactions) to generate a high-efficient, barcode-free and scalable pairwise CRISPR libraries in mammalian cells for identifying genetic interactions. We have generated a genome- wide library to identify genes genetically interacting with TAZ in cell viability regulation. Validation of candidate synergistic genes reveals the screening accuracy of 85% and TAZ-MCL1 is characterized as combinational drug targets for non-small cell lung cancer treatments. TCGI has dramatically improved the current methods for mapping genetic interactions and screening drug targets for combinational therapies.

CRISPR technologies for stem cell engineering and regenerative medicine

CRISPR/Cas9 system exploits the concerted action of Cas9 nuclease and programmable single guide RNA (sgRNA), and has been widely used for genome editing. The Cas9 nuclease activity can be abolished by mutation to yield the catalytically deactivated Cas9 (dCas9). Coupling with the customizable sgRNA for targeting, dCas9 can be fused with transcription repressors to inhibit specific gene expression (CRISPR interference, CRISPRi) or fused with transcription activators to activate the expression of gene of interest (CRISPR activation, CRISPRa). Here we introduce the principles and recent advances of these CRISPR technologies, their delivery vectors and review their applications in stem cell engineering and regenerative medicine. In particular, we focus on in vitro stem cell fate manipulation and in vivo applications such as prevention of retinal and muscular degeneration, neural regeneration, bone regeneration, cartilage tissue engineering, as well as treatment of diseases in blood, skin and liver. Finally, the challenges to translate CRISPR to regenerative medicine and future perspectives are discussed and proposed.

A Gaussian process-based definition reveals new and bona fide genetic interactions compared to a multiplicative model in the Gram-negative Escherichia coli

MOTIVATION

A digenic genetic interaction (GI) is observed when mutations in two genes within the same organism yield a phenotype that is different from the expected, given each mutation's individual effects. While multiplicative scoring is widely applied to define GIs, revealing underlying gene functions, it remains unclear if it is the most suitable choice for scoring GIs in Escherichia coli. Here, we assess many different definitions, including the multiplicative model, for mapping functional links between genes and pathways in E.coli.

RESULTS

Using our published E.coli GI datasets, we show computationally that a machine learning Gaussian process (GP)-based definition better identifies functional associations among genes than a multiplicative model, which we have experimentally confirmed on a set of gene pairs. Overall, the GP definition improves the detection of GIs, biological reasoning of epistatic connectivity, as well as the quality of GI maps in E.coli, and, potentially, other microbes.

AVAILABILITY AND IMPLEMENTATION

The source code and parameters used to generate the machine learning models in WEKA software were provided in the Supplementary information.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PICKLES: the database of pooled in-vitro CRISPR knockout library essentiality screens

The adaptation of CRISPR/Cas9 systems for pooled library genetic knockout screens in mammalian cells has substantially advanced the state of the art in human functional genomics. Screening panels of cell lines for genes whose knockout imposes a significant fitness defect has dramatically expanded our catalog of high-confidence essential genes, and has already proven useful in identifying tumor-specific essential genes for the development of targeted therapies. However, nonexperts currently lack an easy to use way to access this data and to identify whether their genes of interest are essential across different genetic backgrounds. The volume of screening data is expected to grow massively, making the problem more intractable. Here we describe PICKLES, the database of Pooled In vitro CRISPR Knockout Library Essentiality Screens, where end users can display and download raw or normalized essentiality profiles for more that 18 000 protein-coding genes across more than 50 cell lines. An additional data set with 15,000 genes targeted by pooled library shRNA in over 100 cell lines is also included. Researchers can see at a glance the relative fitness defect and tissue specificity of their genes of interest, generate and save figures locally, and download all raw data. The database is available at http://pickles.hart-lab.org.

CRISPRai for simultaneous gene activation and inhibition to promote stem cell chondrogenesis and calvarial bone regeneration

Calvarial bone healing remains difficult but may be improved by stimulating chondrogenesis of implanted stem cells. To simultaneously promote chondrogenesis and repress adipogenesis of stem cells, we built a CRISPRai system that comprised inactive Cas9 (dCas9), two fusion proteins as activation/repression complexes and two single guide RNA (sgRNA) as scaffolds for recruiting activator (sgRNAa) or inhibitor (sgRNAi). By plasmid transfection and co-expression in CHO cells, we validated that dCas9 coordinated with sgRNAa to recruit the activator for mCherry activation and also orchestrated with sgRNAi to recruit the repressor for d2EGFP inhibition, without cross interference. After changing the sgRNA sequence to target endogenous Sox9/PPAR-γ, we packaged the entire CRISPRai system into an all-in-one baculovirus for efficient delivery into rat bone marrow-derived mesenchymal stem cells (rBMSC) and verified simultaneous Sox9 activation and PPAR-γ repression. The activation/inhibition effects were further enhanced/prolonged by using the Cre/loxP-based hybrid baculovirus. The CRISPRai system delivered by the hybrid baculovirus stimulated chondrogenesis and repressed adipogenesis of rBMSC in 2D culture and promoted the formation of engineered cartilage in 3D culture. Importantly, implantation of the rBMSC engineered by the CRISPRai improved calvarial bone healing. This study paves a new avenue to translate the CRISPRai technology to regenerative medicine.

CRISPR/Cas9 as a tool to dissect cancer mutations

The CRISPR/Cas9 system is transforming many biomedical disciplines, including cancer research. Through its flexible programmability and efficiency to induce DNA double strand breaks it has become straightforward to introduce cancer mutations into cells in vitro and/or in vivo. However, not all mutations contribute equally to tumorigenesis and distinguishing essential mutations for tumor growth and survival from biologically inert mutations is cumbersome. Here we present a method to screen for the functional relevance of mutations in high throughput in established cancer cell lines. We employ the CRISPR/Cas9 system to probe cancer vulnerabilities in a colorectal carcinoma cell line in an attempt to identify novel cancer driver mutations. We designed 100 high quality sgRNAs that are able to specifically cleave mutations present in the colorectal carcinoma cell line RKO. An all-in-one lentiviral library harboring these sgRNAs was then generated and used in a pooled screen to probe possible growth dependencies on these mutations. Genomic DNA at different time points were collected, the sgRNA cassettes were PCR amplified, purified and sgRNA counts were quantified by means of deep sequencing. The analysis revealed two sgRNAs targeting the same mutation (UTP14A: S99delS) to be depleted over time in RKO cells. Validation and characterization confirmed that the inactivation of this mutation impairs cell growth, nominating UTP14A: S99delS as a putative driver mutation in RKO cells. Overall, our approach demonstrates that the CRISPR/Cas9 system is a powerful tool to functionally dissect cancer mutations at large-scale.

Mapping the protein-protein and genetic interactions of cancer to guide precision medicine

Massive efforts to sequence cancer genomes have compiled an impressive catalogue of cancer mutations, revealing the recurrent exploitation of a handful of 'hallmark cancer pathways'. However, unraveling how sets of mutated proteins in these and other pathways hijack pro-proliferative signaling networks and dictate therapeutic responsiveness remains challenging. Here, we show that cancer driver protein-protein interactions are enriched for additional cancer drivers, highlighting the power of physical interaction maps to explain known, as well as uncover new, disease-promoting pathway interrelationships. We hypothesize that by systematically mapping the protein-protein and genetic interactions in cancer-thereby creating Cancer Cell Maps-we will create resources against which to contextualize a patient's mutations into perturbed pathways/complexes and thereby specify a matching targeted therapeutic cocktail.

Programmable RNA-Guided RNA Effector Proteins Built from Human Parts

Epitranscriptomic regulation controls information flow through the central dogma and provides unique opportunities for manipulating cells at the RNA level. However, both fundamental studies and potential translational applications are impeded by a lack of methods to target specific RNAs with effector proteins. Here, we present CRISPR-Cas-inspired RNA targeting system (CIRTS), a protein engineering strategy for constructing programmable RNA control elements. We show that CIRTS is a simple and generalizable approach to deliver a range of effector proteins, including nucleases, degradation machinery, translational activators, and base editors to target transcripts. We further demonstrate that CIRTS is not only smaller than naturally occurring CRISPR-Cas programmable RNA binding systems but can also be built entirely from human protein parts. CIRTS provides a platform to probe fundamental RNA regulatory processes, and the human-derived nature of CIRTS provides a potential strategy to avoid immune issues when applied to epitranscriptome-modulating therapies.

From parts to mechanisms: research heuristics for addressing heterogeneity in cancer genetics

A major approach to cancer research in the late twentieth century was to search for genes that, when altered, initiated the development of a cell into a cancerous state (oncogenes) or failed to stop this development (tumor suppressor genes). But as researchers acquired the capacity to sequence tumors and incorporated the resulting data into databases, it became apparent that for many tumors no genes were frequently altered and that the genes altered in different tumors in the same tissue type were often distinct. To address this heterogeneity problem, many researchers looked to a higher level of organization-to mechanisms in which gene products (proteins) participated. They proposed to reduce heterogeneity by recognizing that multiple gene alterations affect the same mechanism and that it is the altered mechanism that is responsible for the cell developing one or more hallmarks of cancer. I examine how mechanisms figure in this research and focus on two heuristics researchers use to integrate proteins into mechanisms, one focusing on pathways and one focusing on clusters in networks.

Large scale control and programming of gene expression using CRISPR

The control of gene expression in cells and organisms allows to unveil gene to function relationships and to reprogram biological responses. Several systems, such as Zinc fingers, TALE (Transcription activator-like effectors), and siRNAs (small-interfering RNAs), have been exploited to achieve this. However, recent advances in Clustered Regularly Interspaced Short Palindromic Repeats and Cas9 (CRISPR-Cas9) have overshadowed them due to high specificity, compatibility with many different organisms, and design flexibility. In this review we summarize state-of-the art for CRISPR-Cas9 technology for large scale gene perturbation studies, including single gene and multiple genes knock-out, knock-down, knock-up libraries, and their associated screening assays. We feature in particular the combination of these methods with single-cell transcriptomics approaches. Finally, we highlight the application of CRISPR-Cas9 systems in building synthetic circuits that can be interfaced with gene networks to control cellular states.

Context-dependent genetic interactions in cancer

Genetic co-dependencies have been found in many contexts, from processes during the development of organisms to many diseases in man, including cancer. Genetic interactions - and in particular synthetic lethal phenotypes - have provided fundamental insights into the genetic architecture of cells and identified potential new opportunities for therapeutic interventions. However, recent studies also demonstrated that genetic interactions are highly context dependent and synthetic lethal interactions in one tumor context might not be translatable to others. Therefore, to better define and understand contexts will be a key challenge for future studies to fully exploit genetic interaction networks for target identification and cancer therapy. In this review, we summarize recent developments in mapping context-specific genetic interaction networks with a particular focus on conceptual and experimental advances in the past years. We then discuss genetic and environmental contexts that influence genetic interaction networks. Finally, we outline challenges of putting genetic interaction networks into context and give an outlook on future directions.

A network of human functional gene interactions from knockout fitness screens in cancer cells

The function of human genes can be strongly inferred from their knockout fitness profiles across hundreds of CRISPR screens, illuminating the modular organization of the cell.

Genetic interactions mediate the emergence of phenotype from genotype. The systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Such assays of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains and similarly imply shared biological function. We constructed a network of genes with correlated fitness profiles across 276 high-quality CRISPR knockout screens in cancer cell lines into a “coessentiality network,” with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of gene function and highlighting the modular organization of the cell.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Functional Genomics for Cancer Research: Applications In Vivo and In Vitro

Functional genomics holds great promise for the dissection of cancer biology. The elucidation of genetic cooperation and molecular details that govern oncogenesis, metastasis, and response to therapy is made possible by robust technologies for perturbing gene function coupled to quantitative analysis of cancer phenotypes resulting from genetic or epigenetic perturbations. Multiplexed genetic perturbations enable the dissection of cooperative genetic lesions as well as the identification of synthetic lethal gene pairs that hold particular promise for constructing innovative cancer therapies. Lastly, functional genomics strategies enable the highly multiplexed in vivo analysis of genes that govern tumorigenesis as well as of the complex multicellular biology of a tumor, such as immune response and metastasis phenotypes. In this review, we discuss both historical and emerging functional genomics approaches and their impact on the cancer research landscape.

Improved discovery of genetic interactions using CRISPRiSeq across multiple environments

Large-scale genetic interaction (GI) screens in yeast have been invaluable for our understanding of molecular systems biology and for characterizing novel gene function. Owing in part to the high costs and long experiment times required, a preponderance of GI data has been generated in a single environmental condition. However, an unknown fraction of GIs may be specific to other conditions. Here, we developed a pooled-growth CRISPRi-based sequencing assay for GIs, CRISPRiSeq, which increases throughput such that GIs can be easily assayed across multiple growth conditions. We assayed the fitness of approximately 17,000 strains encompassing approximately 7700 pairwise interactions in five conditions and found that the additional conditions increased the number of GIs detected nearly threefold over the number detected in rich media alone. In addition, we found that condition-specific GIs are prevalent and improved the power to functionally classify genes. Finally, we found new links during respiratory growth between members of the Ras nutrient-sensing pathway and both the COG complex and a gene of unknown function. Our results highlight the potential of conditional GI screens to improve our understanding of cellular genetic networks.

Identification of Novel Regulatory Genes in APAP Induced Hepatocyte Toxicity by a Genome-Wide CRISPR-Cas9 Screen

Acetaminophen (APAP) is a commonly used analgesic responsible for more than half of acute liver failure cases. Identification of previously unknown genetic risk factors would provide mechanistic insights and novel therapeutic targets for APAP-induced liver injury. This study used a genome-wide CRISPR-Cas9 screen to evaluate genes that are protective against, or cause susceptibility to, APAP-induced liver injury. HuH7 human hepatocellular carcinoma cells containing CRISPR-Cas9 gene knockouts were treated with 15 mM APAP for 30 minutes to 4 days. A gene expression profile was developed based on the 1) top screening hits, 2) overlap of expression data from APAP overdose studies, and 3) predicted affected biological pathways. We further demonstrated the implementation of intermediate time points for the identification of early and late response genes. This study illustrated the power of a genome-wide CRISPR-Cas9 screen to systematically identify novel genes involved in APAP-induced hepatotoxicity and to provide potential targets to develop novel therapeutic modalities.

Genetic interaction networks in cancer cells

The genotype-to-phenotype relationship in health and disease is complex and influenced by both an individual's environment and their unique genome. Personal genetic variants can modulate gene function to generate a phenotype either through a single gene effect or through genetic interactions involving two or more genes. The relevance of genetic interactions to disease phenotypes has been particularly clear in cancer research, where an extreme genetic interaction, synthetic lethality, has been exploited as a therapeutic strategy. The obvious benefits of unmasking genetic background-specific vulnerabilities, coupled with the power of systematic genome editing, have fueled efforts to translate genetic interaction mapping from model organisms to human cells. Here, we review recent developments in genetic interaction mapping, with a focus on CRISPR-based genome editing technologies and cancer.

Multiplexed orthogonal genome editing and transcriptional activation by Cas12a

CRISPR-Cas9-based combinatorial perturbation approaches for orthogonal knockout and gene activation have been impeded by complex vector designs and co-delivery of multiple constructs. Here, we demonstrate that catalytically active CRISPR-Cas12a fused to a transcriptional-activator domain enables flexible switching between genome editing and transcriptional activation by altering guide length. By leveraging Cas12a-mediated CRISPR-RNA array processing, we illustrate that Cas12a-VPR enables simplified multiplexed knockout and transcriptional activation in vitro and in vivo.

Applications of CRISPR-Cas in Bioengineering, Biotechnology, and Translational Research

CRISPR technology is rapidly evolving, and the scope of CRISPR applications is constantly expanding. CRISPR was originally employed for genome editing. Its application was then extended to epigenome editing, karyotype engineering, chromatin imaging, transcriptome, and metabolic pathway engineering. Now, CRISPR technology is being harnessed for genetic circuits engineering, cell signaling sensing, cellular events recording, lineage information reconstruction, gene drive, DNA genotyping, miRNA quantification, in vivo cloning, site-directed mutagenesis, genomic diversification, and proteomic analysis in situ. It has also been implemented in the translational research of human diseases such as cancer immunotherapy, antiviral therapy, bacteriophage therapy, cancer diagnosis, pathogen screening, microbiota remodeling, stem-cell reprogramming, immunogenomic engineering, vaccine development, and antibody production. This review aims to summarize the key concepts of these CRISPR applications in order to capture the current state of play in this fast-moving field. The key mechanisms, strategies, and design principles for each technological advance are also highlighted.

Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies

Standard practice for phenotyping complex cell pools is to measure the fold enrichment of genotype-specific amplicons after a period of competitive growth. Here, we show that fold-enrichment measures cannot be compared across genotype pools with different fitness distributions. We develop a method to calculate an unbiased estimate of relative fitness by tracking abundances over several time points and show how to optimize experimental protocols to minimize fitness measurement error.

Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

Synthetic lethal interactions, in which the simultaneous loss of function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three Food and Drug Administration-approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic.

RNAi/CRISPR Screens: from a Pool to a Valid Hit

High-throughput genetic screens interfering with gene expression are invaluable tools to identify gene function and phenotype-to-genotype interactions. Implementing such screens in the laboratory is challenging, and the choice between currently available technologies based on RNAi and CRISPR/Cas9 (CRISPR-associated protein 9) is not trivial. Identifying reliable candidate hits requires a streamlined experimental setup adjusted to the specific biological question. Here, we provide a critical assessment of the various RNAi/CRISPR approaches to pooled screens and discuss their advantages and pitfalls. We specify a set of best practices for key parameters enabling a reproducible screen and provide a detailed overview of analysis methods and repositories for identifying the best candidate gene hits.

Integrating genetic and protein-protein interaction networks maps a functional wiring diagram of a cell

Systematic experimental approaches have led to construction of comprehensive genetic and protein-protein interaction networks for the budding yeast, Saccharomyces cerevisiae. Genetic interactions capture functional relationships between genes using phenotypic readouts, while protein-protein interactions identify physical connections between gene products. These complementary, and largely non-overlapping, networks provide a global view of the functional architecture of a cell, revealing general organizing principles, many of which appear to be evolutionarily conserved. Here, we focus on insights derived from the integration of large-scale genetic and protein-protein interaction networks, highlighting principles that apply to both unicellular and more complex systems, including human cells. Network integration reveals fundamental connections involving key functional modules of eukaryotic cells, defining a core network of cellular function, which could be elaborated to explore cell-type specificity in metazoans.

The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders

Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.

Mapping the Genetic Landscape of Human Cells

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

Synthetic far-red light-mediated CRISPR-dCas9 device for inducing functional neuronal differentiation

We have developed an optogenetic far-red light (FRL)-activated CRISPR-dCas9 system (FACE) that is orthogonal, fine-tunable, reversible, and has robust endogenous gene-activation profiles upon stimulation with FRL, with deep tissue penetration capacity, low brightness, short illumination time, and negligible phototoxicity. The FACE device is biocompatible and meets the criteria for safe medical application in humans, providing a robust differentiation strategy for mass production of functional neural cells from induced pluripotent stem cells simply by utilizing a beam of FRL. This optogenetic device has expanded the optogenetic toolkit for precise mammalian genome engineering in many areas of basic and translational research that require precise spatiotemporal control of cellular behavior, which may in turn boost the clinical progress of optogenetics-based precision therapy.

The ability to control the activity of CRISPR-dCas9 with precise spatiotemporal resolution will enable tight genome regulation of user-defined endogenous genes for studying the dynamics of transcriptional regulation. Optogenetic devices with minimal phototoxicity and the capacity for deep tissue penetration are extremely useful for precise spatiotemporal control of cellular behavior and for future clinic translational research. Therefore, capitalizing on synthetic biology and optogenetic design principles, we engineered a far-red light (FRL)-activated CRISPR-dCas9 effector (FACE) device that induces transcription of exogenous or endogenous genes in the presence of FRL stimulation. This versatile system provides a robust and convenient method for precise spatiotemporal control of endogenous gene expression and also has been demonstrated to mediate targeted epigenetic modulation, which can be utilized to efficiently promote differentiation of induced pluripotent stem cells into functional neurons by up-regulating a single neural transcription factor, NEUROG2. This FACE system might facilitate genetic/epigenetic reprogramming in basic biological research and regenerative medicine for future biomedical applications.

CRISPR/Cas9-Based Engineering of the Epigenome

Determining causal relationships between distinct chromatin features and gene expression, and ultimately cell behavior, remains a major challenge. Recent developments in targetable epigenome-editing tools enable us to assign direct transcriptional and functional consequences to locus-specific chromatin modifications. This Protocol Review discusses the unprecedented opportunity that CRISPR/Cas9 technology offers for investigating and manipulating the epigenome to facilitate further understanding of stem cell biology and engineering of stem cells for therapeutic applications. We also provide technical considerations for standardization and further improvement of the CRISPR/Cas9-based tools to engineer the epigenome.

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens

Protein complexes are assemblies of subunits that have co-evolved to execute one or many coordinated functions in the cellular environment. Functional annotation of mammalian protein complexes is critical to understanding biological processes, as well as disease mechanisms. Here, we used genetic co-essentiality derived from genome-scale RNAi- and CRISPR-Cas9-based fitness screens performed across hundreds of human cancer cell lines to assign measures of functional similarity. From these measures, we systematically built and characterized functional similarity networks that recapitulate known structural and functional features of well-studied protein complexes and resolve novel functional modules within complexes lacking structural resolution, such as the mammalian SWI/SNF complex. Finally, by integrating functional networks with large protein-protein interaction networks, we discovered novel protein complexes involving recently evolved genes of unknown function. Taken together, these findings demonstrate the utility of genetic perturbation screens alone, and in combination with large-scale biophysical data, to enhance our understanding of mammalian protein complexes in normal and disease states.