High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities

p53 controls choice between apoptotic and non-apoptotic death following DNA damage

DNA damage can activate apoptotic and non-apoptotic forms of cell death; however, it remains unclear what features dictate which type of cell death is activated. We report that p53 controls the choice between apoptotic and non-apoptotic death following exposure to DNA damage. In contrast to the conventional model, which suggests that p53-deficient cells should be resistant to DNA damage-induced cell death, we find that p53-deficient cells die at high rates following DNA damage, but exclusively using non-apoptotic mechanisms. Our experimental data and computational modeling reveal that non-apoptotic death in p53-deficient cells has not been observed due to use of assays that are either insensitive to cell death, or that specifically score apoptotic cells. Using functional genetic screening - with an analysis that enables computational inference of the drug-induced death rate - we find in p53-deficient cells that DNA damage activates a mitochondrial respiration-dependent form of cell death, called MPT-driven necrosis. Cells deficient for p53 have high basal respiration, which primes MPT-driven necrosis. Finally, using metabolite profiling, we identified mitochondrial activity-dependent metabolic vulnerabilities that can be targeted to potentiate the lethality of DNA damage specifically in p53-deficient cells. Our findings reveal how the dual functions of p53 in regulating mitochondrial activity and the DNA damage response combine to facilitate the choice between apoptotic and non-apoptotic death.

Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities

Life science studies involving clustered regularly interspaced short palindromic repeat (CRISPR) editing generally apply the best-performing guide RNA (gRNA) for a gene of interest. Computational models are combined with massive experimental quantification on synthetic gRNA-target libraries to accurately predict gRNA activity and mutational patterns. However, the measurements are inconsistent between studies due to differences in the designs of the gRNA-target pair constructs, and there has not yet been an integrated investigation that concurrently focuses on multiple facets of gRNA capacity. In this study, we analyzed the DNA double-strand break (DSB)-induced repair outcomes and measured SpCas9/gRNA activities at both matched and mismatched locations using 926,476 gRNAs covering 19,111 protein-coding genes and 20,268 non-coding genes. We developed machine learning models to forecast the on-target cleavage efficiency (AIdit_ON), off-target cleavage specificity (AIdit_OFF), and mutational profiles (AIdit_DSB) of SpCas9/gRNA from a uniformly collected and processed dataset by deep sampling and massively quantifying gRNA capabilities in K562 cells. Each of these models exhibited superlative performance in predicting SpCas9/gRNA activities on independent datasets when benchmarked with previous models. A previous unknown parameter was also empirically determined regarding the "sweet spot" in the size of datasets used to establish an effective model to predict gRNA capabilities at a manageable experimental scale. In addition, we observed cell type-specific mutational profiles and were able to link nucleotidylexotransferase as the key factor driving these outcomes. These massive datasets and deep learning algorithms have been implemented into the user-friendly web service http://crispr-aidit.com to evaluate and rank gRNAs for life science studies.

CRISPR-Cas9 knockout screen identifies novel treatment targets in childhood high-grade glioma

BACKGROUND

Brain tumours are the leading cause of cancer-related death in children, and there is no effective treatment. A growing body of evidence points to deregulated epigenetics as a tumour driver, particularly in paediatric cancers as they have relatively few genomic alterations, and key driver mutations have been identified in histone 3 (H3). Cancer stem cells (CSC) are implicated in tumour development, relapse and therapy resistance and thus particularly important to target. We therefore aimed to identify novel epigenetic treatment targets in CSC derived from H3-mutated high-grade glioma (HGG) through a CRISPR-Cas9 knockout screen.

RESULTS

The knockout screen identified more than 100 novel genes essential for the growth of CSC derived from paediatric HGG with H3K27M mutation. We successfully validated 12 of the 13 selected hits by individual knockout in the same two CSC lines, and for the top six hits we included two additional CSC lines derived from H3 wild-type paediatric HGG. Knockout of these genes led to a significant decrease in CSC growth, and altered stem cell and differentiation markers.

CONCLUSIONS

The screen robustly identified essential genes known in the literature, but also many novel genes essential for CSC growth in paediatric HGG. Six of the novel genes (UBE2N, CHD4, LSM11, KANSL1, KANSL3 and EED) were validated individually thus demonstrating their importance for CSC growth in H3-mutated and wild-type HGG. These genes should be further studied and evaluated as novel treatment targets in paediatric HGG.

BAP1 Loss Is Associated with Higher ASS1 Expression in Epithelioid Mesothelioma: Implications for Therapeutic Stratification

The nuclear deubiquitylase BRCA1-associated protein 1 (BAP1) is frequently inactivated in malignant pleural mesothelioma (MPM) and germline BAP1 mutation predisposes to cancers including MPM. To explore the influence on cell physiology and drug sensitivity, we sequentially edited a predisposition mutation (w-) and a promoter trap (KO) into human mesothelial cells. BAP1w-/KO MeT5A cells express less BAP1 protein and phenocopy key aspects of BAP1 loss in MPM. Stable isotope labeling with amino acids in cell culture-mass spectrometry revealed evidence of metabolic adaptation, with concomitant alteration of cellular metabolites. In MeT5A, BAP1 deficiency reduces glycolytic enzyme levels but increases enzymes involved in the tricarboxylic acid cycle and anaplerotic pathways. Notably both argininosuccinate synthase 1 (ASS1), essential for cellular synthesis of arginine, and its substrate aspartate, are elevated in BAP1w-/KO MeT5A cells. Likewise, ASS1 expression is higher in BAP1-altered MPM cell lines, and inversely correlates with BAP1 in The Cancer Genome Atlas MESO dataset. Elevated ASS1 is also evident by IHC staining in epithelioid MPM lacking nuclear BAP1 expression, with improved survival among patients with BAP1-negative/ASS1-expressing tumors. Alterations in arginine metabolism may sensitize cells to metabolic drugs and we find that BAP1-negative/ASS1-expressing MPM cell lines are more sensitive to ASS1 inhibition, although not to inhibition of purine synthesis by mizoribine. Importantly, BAP1w-/KO MeT5A become desensitized to arginine deprivation by pegylated arginine deiminase (ADI-PEG20), phenocopying BAP1-negative/ASS1-expressing MPM cell lines.

IMPLICATIONS

Our data reveal an interrelationship between BAP1 and arginine metabolism, providing a potential means of identifying patients with epithelioid MPM likely to benefit from ADI-PEG20.

The genome-wide mutational consequences of DNA hypomethylation

DNA methylation is important for establishing and maintaining cell identity and for genomic stability. This is achieved by regulating the accessibility of regulatory and transcriptional elements and the compaction of subtelomeric, centromeric, and other inactive genomic regions. Carcinogenesis is accompanied by a global loss in DNA methylation, which facilitates the transformation of cells. Cancer hypomethylation may also cause genomic instability, for example through interference with the protective function of telomeres and centromeres. However, understanding the role(s) of hypomethylation in tumor evolution is incomplete because the precise mutational consequences of global hypomethylation have thus far not been systematically assessed. Here we made genome-wide inventories of all possible genetic variation that accumulates in single cells upon the long-term global hypomethylation by CRISPR interference-mediated conditional knockdown of DNMT1. Depletion of DNMT1 resulted in a genomewide reduction in DNA methylation. The degree of DNA methylation loss was similar to that observed in many cancer types. Hypomethylated cells showed reduced proliferation rates, increased transcription of genes, reactivation of the inactive X-chromosome and abnormal nuclear morphologies. Prolonged hypomethylation was accompanied by increased chromosomal instability. However, there was no increase in mutational burden, enrichment for certain mutational signatures or accumulation of structural variation to the genome. In conclusion, the primary consequence of hypomethylation is genomic instability, which in cancer leads to increased tumor heterogeneity and thereby fuels cancer evolution.

The mitochondrial protease OMA1 acts as a metabolic safeguard upon nuclear DNA damage

The metabolic plasticity of mitochondria ensures cell development, differentiation, and survival. The peptidase OMA1 regulates mitochondrial morphology via OPA1 and stress signaling via DELE1 and orchestrates tumorigenesis and cell survival in a cell- and tissue-specific manner. Here, we use unbiased systems-based approaches to show that OMA1-dependent cell survival depends on metabolic cues. A metabolism-focused CRISPR screen combined with an integrated analysis of human gene expression data found that OMA1 protects against DNA damage. Nucleotide deficiencies induced by chemotherapeutic agents promote p53-dependent apoptosis of cells lacking OMA1. The protective effect of OMA1 does not depend on OMA1 activation or OMA1-mediated OPA1 and DELE1 processing. OMA1-deficient cells show reduced glycolysis and accumulate oxidative phosphorylation (OXPHOS) proteins upon DNA damage. OXPHOS inhibition restores glycolysis and confers resistance against DNA damage. Thus, OMA1 dictates the balance between cell death and survival through the control of glucose metabolism, shedding light on its role in cancerogenesis.

Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review

CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method of gene and genome editing, which benefits from a large variety of practical applications. For example, in biomedicine, it has been used in research related to cancer, virus infections, pathogen detection, and genetic diseases. Current CRISPR/Cas9 research is based on data-driven models for on- and off-target prediction as a cleavage may occur at non-target sequence locations. Nowadays, conventional machine learning and deep learning methods are applied on a regular basis to accurately predict on-target knockout efficacy and off-target profile of given single-guide RNAs (sgRNAs). In this paper, we present an overview and a comparative analysis of traditional machine learning and deep learning models used in CRISPR/Cas9. We highlight the key research challenges and directions associated with target activity prediction. We discuss recent advances in the sgRNA-DNA sequence encoding used in state-of-the-art on- and off-target prediction models. Furthermore, we present the most popular deep learning neural network architectures used in CRISPR/Cas9 prediction models. Finally, we summarize the existing challenges and discuss possible future investigations in the field of on- and off-target prediction. Our paper provides valuable support for academic and industrial researchers interested in the application of machine learning methods in the field of CRISPR/Cas9 genome editing.

A kinome-wide CRISPR screen identifies CK1α as a target to overcome enzalutamide resistance of prostate cancer

Enzalutamide (ENZA), a second-generation androgen receptor antagonist, has significantly increased progression-free and overall survival of patients with metastatic prostate cancer (PCa). However, resistance remains a prominent obstacle in treatment. Utilizing a kinome-wide CRISPR-Cas9 knockout screen, we identified casein kinase 1α (CK1α) as a therapeutic target to overcome ENZA resistance. Depletion or pharmacologic inhibition of CK1α enhanced ENZA efficacy in ENZA-resistant cells and patient-derived xenografts. Mechanistically, CK1α phosphorylates the serine residue S1270 and modulates the protein abundance of ataxia telangiectasia mutated (ATM), a primary initiator of DNA double-strand break (DSB)-response signaling, which is compromised in ENZA-resistant cells and patients. Inhibition of CK1α stabilizes ATM, resulting in the restoration of DSB signaling, and thus increases ENZA-induced cell death and growth arrest. Our study details a therapeutic approach for ENZA-resistant PCa and characterizes a particular perspective for the function of CK1α in the regulation of DNA-damage response.

Generation and multi-dimensional profiling of a childhood cancer cell line atlas defines new therapeutic opportunities

Pediatric solid and central nervous system tumors are the leading cause of cancer-related death among children. Identifying new targeted therapies necessitates the use of pediatric cancer models that faithfully recapitulate the patient's disease. However, the generation and characterization of pediatric cancer models has significantly lagged behind adult cancers, underscoring the urgent need to develop pediatric-focused cell line resources. Herein, we establish a single-site collection of 261 cell lines, including 224 pediatric cell lines representing 18 distinct extracranial and brain childhood tumor types. We subjected 182 cell lines to multi-omics analyses (DNA sequencing, RNA sequencing, DNA methylation), and in parallel performed pharmacological and genetic CRISPR-Cas9 loss-of-function screens to identify pediatric-specific treatment opportunities and biomarkers. Our work provides insight into specific pathway vulnerabilities in molecularly defined pediatric tumor classes and uncovers biomarker-linked therapeutic opportunities of clinical relevance. Cell line data and resources are provided in an open access portal.

E3 ubiquitin ligase ASB8 promotes selinexor-induced proteasomal degradation of XPO1

Selinexor (KPT-330), a small-molecule inhibitor of exportin-1 (XPO1, CRM1) with potent anticancer activity, has recently been granted FDA approval for treatment of relapsed/refractory multiple myeloma and diffuse large B-cell lymphoma (DLBCL), with a number of additional indications currently under clinical investigation. Since selinexor has often demonstrated synergy when used in combination with other drugs, notably bortezomib and dexamethasone, a more comprehensive approach to uncover new beneficial interactions would be of great value. Moreover, stratifying patients, personalizing therapeutics and improving clinical outcomes requires a better understanding of the genetic vulnerabilities and resistance mechanisms underlying drug response. Here, we used CRISPR-Cas9 loss-of-function chemogenetic screening to identify drug-gene interactions with selinexor in chronic myeloid leukemia, multiple myeloma and DLBCL cell lines. We identified the TGFβ-SMAD4 pathway as an important mediator of resistance to selinexor in multiple myeloma cells. Moreover, higher activity of this pathway correlated with prolonged progression-free survival in multiple myeloma patients treated with selinexor, indicating that the TGFβ-SMAD4 pathway is a potential biomarker predictive of therapeutic outcome. In addition, we identified ASB8 (ankyrin repeat and SOCS box containing 8) as a shared modulator of selinexor sensitivity across all tested cancer types, with both ASB8 knockout and overexpression resulting in selinexor hypersensitivity. Mechanistically, we showed that ASB8 promotes selinexor-induced proteasomal degradation of XPO1. This study provides insight into the genetic factors that influence response to selinexor treatment and could support both the development of predictive biomarkers as well as new drug combinations.

Optimized whole-genome CRISPR interference screens identify ARID1A-dependent growth regulators in human induced pluripotent stem cells

Perturbing expression is a powerful way to understand the role of individual genes, but can be challenging in important models. CRISPR-Cas screens in human induced pluripotent stem cells (iPSCs) are of limited efficiency due to DNA break-induced stress, while the less stressful silencing with an inactive Cas9 has been considered less effective so far. Here, we developed the dCas9-KRAB-MeCP2 fusion protein for screening in iPSCs from multiple donors. We found silencing in a 200 bp window around the transcription start site in polyclonal pools to be as effective as using wild-type Cas9 for identifying essential genes, but with much reduced cell numbers. Whole-genome screens to identify ARID1A-dependent dosage sensitivity revealed the PSMB2 gene, and enrichment of proteasome genes among the hits. This selective dependency was replicated with a proteasome inhibitor, indicating a targetable drug-gene interaction. Many more plausible targets in challenging cell models can be efficiently identified with our approach.

Network Analysis Reveals the Molecular Bases of Statin Pleiotropy That Vary with Genetic Background

Many approved drugs are pleiotropic: for example, statins, whose main cholesterol-lowering activity is complemented by anticancer and prodiabetogenic mechanisms involving poorly characterized genetic interaction networks. We investigated these using the Saccharomyces cerevisiae genetic model, where most genetic interactions known are limited to the statin-sensitive S288C genetic background. We therefore broadened our approach by investigating gene interactions to include two statin-resistant genetic backgrounds: UWOPS87-2421 and Y55. Networks were functionally focused by selection of HMG1 and BTS1 mevalonate pathway genes for detection of genetic interactions. Networks, multilayered by genetic background, were analyzed for key genes using network centrality (degree, betweenness, and closeness), pathway enrichment, functional community modules, and Gene Ontology. Specifically, we found modification genes related to dysregulated endocytosis and autophagic cell death. To translate results to human cells, human orthologues were searched for other drug targets, thus identifying candidates for synergistic anticancer bioactivity. IMPORTANCE Atorvastatin is a highly successful drug prescribed to lower cholesterol and prevent cardiovascular disease in millions of people. Though much of its effect comes from inhibiting a key enzyme in the cholesterol biosynthetic pathway, genes in this pathway interact with genes in other pathways, resulting in 15% of patients suffering painful muscular side effects and 50% having inadequate responses. Such multigenic complexity may be unraveled using gene networks assembled from overlapping pairs of genes that complement each other. We used the unique power of yeast genetics to construct genome-wide networks specific to atorvastatin bioactivity in three genetic backgrounds to represent the genetic variation and varying response to atorvastatin in human individuals. We then used algorithms to identify key genes and their associated FDA-approved drugs in the networks, which resulted in the distinction of drugs that may synergistically enhance the known anticancer activity of atorvastatin.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

DROEG: a method for cancer drug response prediction based on omics and essential genes integration

Predicting therapeutic responses in cancer patients is a major challenge in the field of precision medicine due to high inter- and intra-tumor heterogeneity. Most drug response models need to be improved in terms of accuracy, and there is limited research to assess therapeutic responses of particular tumor types. Here, we developed a novel method DROEG (Drug Response based on Omics and Essential Genes) for prediction of drug response in tumor cell lines by integrating genomic, transcriptomic and methylomic data along with CRISPR essential genes, and revealed that the incorporation of tumor proliferation essential genes can improve drug sensitivity prediction. Concisely, DROEG integrates literature-based and statistics-based methods to select features and uses Support Vector Regression for model construction. We demonstrate that DROEG outperforms most state-of-the-art algorithms by both qualitative (prediction accuracy for drug-sensitive/resistant) and quantitative (Pearson correlation coefficient between the predicted and actual IC50) evaluation in Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia datasets. In addition, DROEG is further applied to the pan-gastrointestinal tumor with high prevalence and mortality as a case study at both cell line and clinical levels to evaluate the model efficacy and discover potential prognostic biomarkers in Cisplatin and Epirubicin treatment. Interestingly, the CRISPR essential gene information is found to be the most important contributor to enhance the accuracy of the DROEG model. To our knowledge, this is the first study to integrate essential genes with multi-omics data to improve cancer drug response prediction and provide insights into personalized precision treatment.

HAP1, a new revolutionary cell model for gene editing using CRISPR-Cas9

The use of next-generation sequencing (NGS) technologies has been instrumental in the characterization of the mutational landscape of complex human diseases like cancer. But despite the enormous rise in the identification of disease candidate genetic variants, their functionality is yet to be fully elucidated in order to have a clear implication in patient care. Haploid human cell models have become the tool of choice for functional gene studies, since they only contain one copy of the genome and can therefore show the unmasked phenotype of genetic variants. Over the past few years, the human near-haploid cell line HAP1 has widely been consolidated as one of the favorite cell line models for functional genetic studies. Its rapid turnover coupled with the fact that only one allele needs to be modified in order to express the subsequent desired phenotype has made this human cell line a valuable tool for gene editing by CRISPR-Cas9 technologies. This review examines the recent uses of the HAP1 cell line model in functional genetic studies and high-throughput genetic screens using the CRISPR-Cas9 system. It covers its use in an attempt to develop new and relevant disease models to further elucidate gene function, and create new ways to understand the genetic basis of human diseases. We will cover the advantages and potential of the use of CRISPR-Cas9 technology on HAP1 to easily and efficiently study the functional interpretation of gene function and human single-nucleotide genetic variants of unknown significance identified through NGS technologies, and its implications for changes in clinical practice and patient care.

Interactomes of Glycogen Synthase Kinase-3 Isoforms

Functional differentiation of the two isoforms of the protein-serine/threonine kinase, glycogen synthase kinase-3 (GSK-3), is an unsettled area of research. The isoforms are highly similar in structure and are largely redundant, though there is also evidence for specific roles. Identification of isoform-specific protein interactors may elucidate the differences in function and provide insight into isoform-selective regulation. We therefore sought to identify novel GSK-3 interaction partners and to examine differences in the interactomes of the two isoforms using both affinity purification and proximity-dependent biotinylation (BioID) mass spectrometry methods. While the interactomes of the two isomers are highly similar in HEK293 cells, BioID in HeLa cells yielded a variety of preys that are preferentially associated with one of the two isoforms. DCP1B, which favored GSK-3α, and MISP, which favored GSK-3β, were evaluated for reciprocal interactions. The differences in interactions between isoforms may help in understanding the distinct functions and regulation of the two isoforms as well as offer avenues for the development of isoform-specific strategies.

Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages

Human genome variation contributes to diversity in neurodevelopmental outcomes and vulnerabilities; recognizing the underlying molecular and cellular mechanisms will require scalable approaches. Here, we describe a "cell village" experimental platform we used to analyze genetic, molecular, and phenotypic heterogeneity across neural progenitor cells from 44 human donors cultured in a shared in vitro environment using algorithms (Dropulation and Census-seq) to assign cells and phenotypes to individual donors. Through rapid induction of human stem cell-derived neural progenitor cells, measurements of natural genetic variation, and CRISPR-Cas9 genetic perturbations, we identified a common variant that regulates antiviral IFITM3 expression and explains most inter-individual variation in susceptibility to the Zika virus. We also detected expression QTLs corresponding to GWAS loci for brain traits and discovered novel disease-relevant regulators of progenitor proliferation and differentiation such as CACHD1. This approach provides scalable ways to elucidate the effects of genes and genetic variation on cellular phenotypes.

Molecular mechanisms of adaptive evolution in wild animals and plants

Wild animals and plants have developed a variety of adaptive traits driven by adaptive evolution, an important strategy for species survival and persistence. Uncovering the molecular mechanisms of adaptive evolution is the key to understanding species diversification, phenotypic convergence, and inter-species interaction. As the genome sequences of more and more non-model organisms are becoming available, the focus of studies on molecular mechanisms of adaptive evolution has shifted from the candidate gene method to genetic mapping based on genome-wide scanning. In this study, we reviewed the latest research advances in wild animals and plants, focusing on adaptive traits, convergent evolution, and coevolution. Firstly, we focused on the adaptive evolution of morphological, behavioral, and physiological traits. Secondly, we reviewed the phenotypic convergences of life history traits and responding to environmental pressures, and the underlying molecular convergence mechanisms. Thirdly, we summarized the advances of coevolution, including the four main types: mutualism, parasitism, predation and competition. Overall, these latest advances greatly increase our understanding of the underlying molecular mechanisms for diverse adaptive traits and species interaction, demonstrating that the development of evolutionary biology has been greatly accelerated by multi-omics technologies. Finally, we highlighted the emerging trends and future prospects around the above three aspects of adaptive evolution.

Systematic analysis of cancer-specific synthetic lethal interactions provides insight into personalized anticancer therapy

The concept of synthetic lethality has great potential for anticancer therapy as a new strategy to specifically kill cancer cells while sparing normal cells. To further understand the potential molecular interactions and gene characteristics involved in synthetic lethality, we performed a comprehensive analysis of predicted cancer-specific genetic interactions. Many genes were identified as cancer-associated genes that contributed to multiple biological processes and pathways, and the gene features were not random, indicating their potential roles in human carcinogenesis. Some relevant genes detected in multiple cancers were prone to be enriched in specific biological progresses and pathways, especially processes associated with DNA damage, chromosome-related functions and cancer pathways. These findings strongly implicated potential roles for these genes in cancer pathophysiology and functional relationships, as well as applications for future anticancer drug discovery. Further experimental validation indicated that the synthetic lethal interaction of APC and GFER may provide a potential anticancer strategy for patients with APC-mutant colon cancer. These results will contribute to further exploration of synthetic lethal interactions and broader application of the concept of synthetic lethality in anticancer therapeutics.

LRP6 Is a Functional Receptor for Attenuated Canine Distemper Virus

Wild-type canine distemper virus (CDV) is an important pathogen of dogs as well as wildlife that can infect immune and epithelial cells through two known receptors: the signaling lymphocytic activation molecule (SLAM) and nectin-4, respectively. Conversely, the ferret and egg-adapted CDV-Onderstepoort strain (CDV-OP) is employed as an effective vaccine for dogs. CDV-OP also exhibits promising oncolytic properties, such as its abilities to infect and kill multiple cancer cells in vitro. Interestingly, several cancer cells do not express SLAM or nectin-4, suggesting the presence of a yet unknown entry factor for CDV-OP. By conducting a genome-wide CRISPR/Cas9 knockout (KO) screen in CDV-OP-susceptible canine mammary carcinoma P114 cells, which neither express SLAM nor nectin-4, we identified low-density lipoprotein receptor-related protein 6 (LRP6) as a host factor that promotes CDV-OP infectivity. Whereas the genetic ablation of LRP6 rendered cells resistant to infection, ectopic expression in resistant LRP6KO cells restored susceptibility. Furthermore, multiple functional studies revealed that (i) the overexpression of LRP6 leads to increased cell-cell fusion, (ii) a soluble construct of the viral receptor-binding protein (solHOP) interacts with a soluble form of LRP6 (solLRP6), (iii) an H-OP point mutant that prevents interaction with solLRP6 abrogates cell entry in multiple cell lines once transferred into recombinant viral particles, and (iv) vesicular stomatitis virus (VSV) pseudotyped with CDV-OP envelope glycoproteins loses its infectivity in LRP6KO cells. Collectively, our study identified LRP6 as the long sought-after cell entry receptor of CDV-OP in multiple cell lines, which set the molecular bases to refine our understanding of viral-cell adaptation and to further investigate its oncolytic properties. IMPORTANCE Oncolytic viruses (OV) have gathered increasing interest in recent years as an alternative option to treat cancers. The Onderstepoort strain of canine distemper virus (CDV-OP), an enveloped RNA virus belonging to the genus Morbillivirus, is employed as a safe and efficient vaccine for dogs against distemper disease. Importantly, although CDV-OP can infect and kill multiple cancer cell lines, the basic mechanisms of entry remain to be elucidated, as most of those transformed cells do not express natural receptors (i.e., SLAM and nectin-4). In this study, using a genome-wide CRISPR/Cas9 knockout screen, we describe the discovery of LRP6 as a novel functional entry receptor for CDV-OP in various cancer cell lines and thereby uncover a basic mechanism of cell culture adaptation. Since LRP6 is upregulated in various cancer types, our data provide important insights in order to further investigate the oncolytic properties of CDV-OP.

Recovering false negatives in CRISPR fitness screens with JLOE

It is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a ∼20% false negative rate, in addition to library-specific false negatives. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses across tissues improves our understanding of core essential genes and suggest only a small number of lineage-specific essential genes, enriched for transcription factors that define pathways of tissue differentiation. To recover false negatives, we introduce a method, Joint Log Odds of Essentiality (JLOE), which builds on our prior work with BAGEL to selectively rescue the false negatives without an increased false discovery rate.

Efficient prioritization of CRISPR screen hits by accounting for targeting efficiency of guide RNA

BACKGROUND

CRISPR-based screens are revolutionizing drug discovery as tools to identify genes whose ablation induces a phenotype of interest. For instance, CRISPR-Cas9 screening has been successfully used to identify novel therapeutic targets in cancer where disruption of genes leads to decreased viability of malignant cells. However, low-activity guide RNAs may give rise to variable changes in phenotype, preventing easy identification of hits and leading to false negative results. Therefore, correcting the effects of bias due to differences in guide RNA efficiency in CRISPR screening data can improve the efficiency of prioritizing hits for further validation. Here, we developed an approach to identify hits from negative CRISPR screens by correcting the fold changes (FC) in gRNA frequency by the actual, observed frequency of indel mutations generated by gRNA.

RESULTS

Each gRNA was coupled with the "reporter sequence" that can be targeted by the same gRNA so that the frequency of mutations in the reporter sequence can be used as a proxy for the endogenous target gene. The measured gRNA activity was used to correct the FC. We identified indel generation efficiency as the dominant factor contributing significant bias to screening results, and our method significantly removed such bias and was better at identifying essential genes when compared to conventional fold change analysis. We successfully applied our gRNA activity data to previously published gRNA screening data, and identified novel genes whose ablation could synergize with vemurafenib in the A375 melanoma cell line. Our method identified nicotinamide N-methyltransferase, lactate dehydrogenase B, and polypyrimidine tract-binding protein 1 as synergistic targets whose ablation sensitized A375 cells to vemurafenib.

CONCLUSIONS

We identified the variations in target cleavage efficiency, even in optimized sgRNA libraries, that pose a strong bias in phenotype and developed an analysis method that corrects phenotype score by the measured differences in the targeting efficiency among sgRNAs. Collectively, we expect that our new analysis method will more accurately identify genes that confer the phenotype of interest.

LINE-associated cryptic splicing induces dsRNA-mediated interferon response and tumor immunity

RNA splicing plays a critical role in post-transcriptional gene regulation. Exponential expansion of intron length poses a challenge for accurate splicing. Little is known about how cells prevent inadvertent and often deleterious expression of intronic elements due to cryptic splicing. In this study, we identify hnRNPM as an essential RNA binding protein that suppresses cryptic splicing through binding to deep introns, preserving transcriptome integrity. Long interspersed nuclear elements (LINEs) harbor large amounts of pseudo splice sites in introns. hnRNPM preferentially binds at intronic LINEs and represses LINE-containing pseudo splice site usage for cryptic splicing. Remarkably, a subgroup of the cryptic exons can form long dsRNAs through base-pairing of inverted Alu transposable elements scattered in between LINEs and trigger interferon immune response, a well-known antiviral defense mechanism. Notably, these interferon-associated pathways are found to be upregulated in hnRNPM-deficient tumors, which also exhibit elevated immune cell infiltration. These findings unveil hnRNPM as a guardian of transcriptome integrity. Targeting hnRNPM in tumors may be used to trigger an inflammatory immune response thereby boosting cancer surveillance.

Base editing screens map mutations affecting interferon-γ signaling in cancer

Interferon-γ (IFN-γ) signaling mediates host responses to infection, inflammation and anti-tumor immunity. Mutations in the IFN-γ signaling pathway cause immunological disorders, hematological malignancies, and resistance to immune checkpoint blockade (ICB) in cancer; however, the function of most clinically observed variants remains unknown. Here, we systematically investigate the genetic determinants of IFN-γ response in colorectal cancer cells using CRISPR-Cas9 screens and base editing mutagenesis. Deep mutagenesis of JAK1 with cytidine and adenine base editors, combined with pathway-wide screens, reveal loss-of-function and gain-of-function mutations, including causal variants in hematological malignancies and mutations detected in patients refractory to ICB. We functionally validate variants of uncertain significance in primary tumor organoids, where engineering missense mutations in JAK1 enhanced or reduced sensitivity to autologous tumor-reactive T cells. We identify more than 300 predicted missense mutations altering IFN-γ pathway activity, generating a valuable resource for interpreting gene variant function.

Utilizing Large Functional and Population Genomics Resources for CRISPR/Cas Perturbation Experiment Design

Genome sequencing technologies have rapidly evolved in the past decades, enabling us to interpret the human genome through multiple perspectives, ranging from cross-species comparisons, naturally occurring variation in health and disease state to regulatory mechanisms.Although such perspectives are all informative to narrow down the list of genes or variants for perturbation experiments based on specific biological aims, utilizing multiple sources of information is often challenging in practice.In this chapter, we provide an overview of major large-scale functional and population genomics resources, followed by a practical example of selecting target variants for genetic perturbation experiments involving genome engineering techniques such as CRISPR/Cas.

ShrinkCRISPR: a flexible method for differential fitness analysis of CRISPR-Cas9 screen data

BACKGROUND

CRISPR screens provide large-scale assessment of cellular gene functions. Pooled libraries typically consist of several single guide RNAs (sgRNAs) per gene, for a large number of genes, which are transduced in such a way that every cell receives at most one sgRNA, resulting in the disruption of a single gene in that cell. This approach is often used to investigate effects on cellular fitness, by measuring sgRNA abundance at different time points. Comparing gene knockout effects between different cell populations is challenging due to variable cell-type specific parameters and between replicates variation. Failure to take those into account can lead to inflated or false discoveries.

RESULTS

We propose a new, flexible approach called ShrinkCRISPR that can take into account multiple sources of variation. Impact on cellular fitness between conditions is inferred by using a mixed-effects model, which allows to test for gene-knockout effects while taking into account sgRNA-specific variation. Estimates are obtained using an empirical Bayesian approach. ShrinkCRISPR can be applied to a variety of experimental designs, including multiple factors. In simulation studies, we compared ShrinkCRISPR results with those of drugZ and MAGeCK, common methods used to detect differential effect on cell fitness. ShrinkCRISPR yielded as many true discoveries as drugZ using a paired screen design, and outperformed both drugZ and MAGeCK for an independent screen design. Although conservative, ShrinkCRISPR was the only approach that kept false discoveries under control at the desired level, for both designs. Using data from several publicly available screens, we showed that ShrinkCRISPR can take data for several time points into account simultaneously, helping to detect early and late differential effects.

CONCLUSIONS

ShrinkCRISPR is a robust and flexible approach, able to incorporate different sources of variations and to test for differential effect on cell fitness at the gene level. These improve power to find effects on cell fitness, while keeping multiple testing under the correct control level and helping to improve reproducibility. ShrinkCrispr can be applied to different study designs and incorporate multiple time points, making it a complete and reliable tool to analyze CRISPR screen data.

CRISPR activation screening in a mouse model for drivers of hepatocellular carcinoma growth and metastasis

Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide. Here we described a genome-wide screen by CRISPR activation (CRISPRa) library in vivo for drivers of HCC growth and metastasis. Pathological results showed the cell population formed highly metastatic tumors in lung after being mutagenized with CRISPRa. In vitro validation indicated overexpression of XAGE1B, PLK4, LMO1 and MYADML2 promoted cells proliferation and invasion, and the inhibition suppressed HCC progress. In addition, we reported high MYADML2 protein level exhibited worse overall survival in HCC, which increased significantly in patients over 60 years. Moreover, high MYADML2 reduced the sensitivity to chemotherapeutic drugs. Interestingly, immune cell infiltration analysis showed that the dendritic cells, macrophages, and so forth might play important role in HCC progress. In brief, we provides a roadmap for screening functional genes related to HCC invasion and metastasis in vivo, which may provide new potential targets for the treatment of HCC.

Protein biomarkers for response to XPO1 inhibition in haematologic malignancies

XPO1 (Exportin-1) is the nuclear export protein responsible for the normal shuttling of several proteins and RNA species between the nucleocytoplasmic compartment of eukaryotic cells. XPO1 recognizes the nuclear export signal (NES) of its cargo proteins to facilitate its export. Alterations of nuclear export have been shown to play a role in oncogenesis in several types of solid tumour and haematologic cancers. Over more than a decade, there has been substantial progress in targeting nuclear export in cancer using selective XPO1 inhibitors. This has resulted in recent approval for the first-in-class drug selinexor for use in relapsed, refractory multiple myeloma and diffuse large B-cell lymphoma (DLBCL). Despite these successes, not all patients respond effectively to XPO1 inhibition and there has been lack of biomarkers for response to XPO1 inhibitors in the clinic. Using haematologic malignancy cell lines and samples from patients with myelodysplastic neoplasms treated with selinexor, we have identified XPO1, NF-κB(p65), MCL-1 and p53 protein levels as protein markers of response to XPO1 inhibitor therapy. These markers could lead to the identification of response upon XPO1 inhibition for more accurate decision-making in the personalized treatment of cancer patients undergoing treatment with selinexor.

Optimization of Genomewide CRISPR Screens Using AsCas12a and Multi-Guide Arrays

Genomewide loss-of-function (LOF) screening using clustered regularly interspaced short palindromic repeats (CRISPR) has facilitated the discovery of novel gene functions across diverse physiological and pathophysiological systems. A challenge with conventional genomewide CRISPR-Cas9 libraries is the unwieldy size (60,000-120,000 constructs), which is resource intensive and prohibitive in some experimental contexts. One solution to streamlining CRISPR screening is by multiplexing two or more guides per gene on a single construct, which enables functional redundancy to compensate for suboptimal gene knockout by individual guides. In this regard, AsCas12a (Cpf1) and its derivatives, for example, enhanced AsCas12a (enAsCas12a), have enabled multiplexed guide arrays to be specifically and efficiently processed for genome editing. Prior studies have established that multiplexed CRISPR-Cas12a libraries perform comparably to the larger equivalent CRISPR-Cas9 libraries, yet the most efficient CRISPR-Cas12a library design remains unresolved. In this study, we demonstrate that CRISPR-Cas12a genomewide LOF screening performed optimally with three guides arrayed per gene construct and could be adapted to robotic cell culture without noticeable differences in screen performance. Thus, the conclusions from this study provide novel insight to streamlining genomewide LOF screening using CRISPR-Cas12a and robotic cell culture.

Chromatin complex dependencies reveal targeting opportunities in leukemia

Chromatin regulators are frequently mutated in human cancer and are attractive drug targets. They include diverse proteins that share functional domains and assemble into related multi-subunit complexes. To investigate functional relationships among these regulators, here we apply combinatorial CRISPR knockouts (KOs) to test over 35,000 gene-gene pairings in leukemia cells, using a library of over 300,000 constructs. Top pairs that demonstrate either compensatory non-lethal interactions or synergistic lethality enrich for paralogs and targets that occupy the same protein complex. The screen highlights protein complex dependencies not apparent in single KO screens, for example MCM histone exchange, the nucleosome remodeling and deacetylase (NuRD) complex, and HBO1 (KAT7) complex. We explore two approaches to NuRD complex inactivation. Paralog and non-paralog combinations of the KAT7 complex emerge as synergistic lethal and specifically nominate the ING5 PHD domain as a potential therapeutic target when paired with other KAT7 complex member losses. These findings highlight the power of combinatorial screening to provide mechanistic insight and identify therapeutic targets within redundant networks.

Rapid PTEFb-dependent transcriptional reorganization underpins the glioma adaptive response to radiotherapy

Dynamic regulation of gene expression is fundamental for cellular adaptation to exogenous stressors. PTEFb-mediated pause-release of RNA polymerase II (Pol II) is a conserved regulatory mechanism for synchronous transcriptional induction in response to heat shock, but this pro-survival role has not been examined in the applied context of cancer therapy. Using model systems of pediatric high-grade glioma, we show that rapid genome-wide reorganization of active chromatin facilitates PTEFb-mediated nascent transcriptional induction within hours of exposure to therapeutic ionizing radiation. Concurrent inhibition of PTEFb disrupts this chromatin reorganization and blunts transcriptional induction, abrogating key adaptive programs such as DNA damage repair and cell cycle regulation. This combination demonstrates a potent, synergistic therapeutic potential agnostic of glioma subtype, leading to a marked induction of tumor cell apoptosis and prolongation of xenograft survival. These studies reveal a central role for PTEFb underpinning the early adaptive response to radiotherapy, opening new avenues for combinatorial treatment in these lethal malignancies.

An interactive web application for processing, correcting, and visualizing genome-wide pooled CRISPR-Cas9 screens

A limitation of pooled CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes arising from copy-number-amplified genomics regions. To solve this issue, we previously developed CRISPRcleanR: a computational method implemented as R/python package and in a dockerized version. CRISPRcleanR detects and corrects biased responses to CRISPR-Cas9 targeting in an unsupervised fashion, accurately reducing false-positive signals while maintaining sensitivity in identifying relevant genetic dependencies. Here, we present CRISPRcleanR WebApp , a web application enabling access to CRISPRcleanR through an intuitive interface. CRISPRcleanR WebApp removes the complexity of R/python language user interactions; provides user-friendly access to a complete analytical pipeline, not requiring any data pre-processing and generating gene-level summaries of essentiality with associated statistical scores; and offers a range of interactively explorable plots while supporting a more comprehensive range of CRISPR guide RNAs' libraries than the original package. CRISPRcleanR WebApp is available at https://crisprcleanr-webapp.fht.org/.

Insulin secretion assays in an engineered MIN6 cell line

Insulin secretion from pancreatic beta cells is crucial for maintaining glucose homeostasis. The murine insulinoma derived MIN6 cell line is commonly used as a model for insulin secretion studies. However, its glucose responsiveness wanes with passaging, and insulin secretion is traditionally measured by expensive and time-consuming RIA or ELISA. We have developed a MIN6 subclone (MIN6-6) that allows for high throughput assay of insulin secretion in both population and single cells. In addition, MIN6-6 also expresses Cas9, permitting genome wide CRISPR screen of insulin secretion using a pooled sgRNA library. Here we provide methods for assaying insulin secretion both in bulk and in single cells in MIN6-6 cells, as well as for CRISPR screen of insulin secretion.•A highly glucose responsive beta cell reporter line (MIN6-6) with multiple engineered functionalities.•Allows for CRISPR/Cas9 mutagenesis, quantification of bulk insulin secretion by a straightforward nanoLuc assay and visualization of intracellular insulin granules.•Allows for en masse quantification of insulin granule exocytosis in individual cells under multiple conditions.

Entry receptors - the gateway to alphavirus infection

Alphaviruses are enveloped, insect-transmitted, positive-sense RNA viruses that infect humans and other animals and cause a range of clinical manifestations, including arthritis, musculoskeletal disease, meningitis, encephalitis, and death. Over the past four years, aided by CRISPR/Cas9-based genetic screening approaches, intensive research efforts have focused on identifying entry receptors for alphaviruses to better understand the basis for cellular and species tropism. Herein, we review approaches to alphavirus receptor identification and how these were used for discovery. The identification of new receptors advances our understanding of viral pathogenesis, tropism, and evolution and is expected to contribute to the development of novel strategies for prevention and treatment of alphavirus infection.

Glioblastoma and the search for non-hypothesis driven combination therapeutics in academia

Glioblastoma (GBM) remains a cancer of high unmet clinical need. Current standard of care for GBM, consisting of maximal surgical resection, followed by ionisation radiation (IR) plus concomitant and adjuvant temozolomide (TMZ), provides less than 15-month survival benefit. Efforts by conventional drug discovery to improve overall survival have failed to overcome challenges presented by inherent tumor heterogeneity, therapeutic resistance attributed to GBM stem cells, and tumor niches supporting self-renewal. In this review we describe the steps academic researchers are taking to address these limitations in high throughput screening programs to identify novel GBM combinatorial targets. We detail how they are implementing more physiologically relevant phenotypic assays which better recapitulate key areas of disease biology coupled with more focussed libraries of small compounds, such as drug repurposing, target discovery, pharmacologically active and novel, more comprehensive anti-cancer target-annotated compound libraries. Herein, we discuss the rationale for current GBM combination trials and the need for more systematic and transparent strategies for identification, validation and prioritisation of combinations that lead to clinical trials. Finally, we make specific recommendations to the preclinical, small compound screening paradigm that could increase the likelihood of identifying tractable, combinatorial, small molecule inhibitors and better drug targets specific to GBM.

Deep mutational scanning of essential bacterial proteins can guide antibiotic development

Deep mutational scanning is a powerful approach to investigate a wide variety of research questions including protein function and stability. Here, we perform deep mutational scanning on three essential E. coli proteins (FabZ, LpxC and MurA) involved in cell envelope synthesis using high-throughput CRISPR genome editing, and study the effect of the mutations in their original genomic context. We use more than 17,000 variants of the proteins to interrogate protein function and the importance of individual amino acids in supporting viability. Additionally, we exploit these libraries to study resistance development against antimicrobial compounds that target the selected proteins. Among the three proteins studied, MurA seems to be the superior antimicrobial target due to its low mutational flexibility, which decreases the chance of acquiring resistance-conferring mutations that simultaneously preserve MurA function. Additionally, we rank anti-LpxC lead compounds for further development, guided by the number of resistance-conferring mutations against each compound. Our results show that deep mutational scanning studies can be used to guide drug development, which we hope will contribute towards the development of novel antimicrobial therapies.

Genome-wide siRNA screens identify RBBP9 function as a potential target in Fanconi anaemia-deficient head-and-neck squamous cell carcinoma

Fanconi anaemia (FA) is a rare chromosomal-instability syndrome caused by mutations of any of the 22 known FA DNA-repair genes. FA individuals have an increased risk of head-and-neck squamous-cell-carcinomas (HNSCC), often fatal. Systemic intolerance to standard cisplatin-based protocols due to somatic-cell hypersensitivity underscores the urgent need to develop novel therapies. Here, we performed unbiased siRNA screens to unveil genetic interactions synthetic-lethal with FA-pathway deficiency in FA-patient HNSCC cell lines. We identified based on differential-lethality scores between FA-deficient and FA-proficient cells, next to common-essential genes such as PSMC1, PSMB2, and LAMTOR2, the otherwise non-essential RBBP9 gene. Accordingly, low dose of the FDA-approved RBBP9-targeting drug Emetine kills FA-HNSCC. Importantly both RBBP9-silencing as well as Emetine spared non-tumour FA cells. This study provides a minable genome-wide analyses of vulnerabilities to address treatment challenges in FA-HNSCC. Our investigation divulges a DNA-cross-link-repair independent lead, RBBP9, for targeted treatment of FA-HNSCCs without systemic toxicity.

Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.

Molecular Landscape of Tourette's Disorder

Tourette's disorder (TD) is a highly heritable childhood-onset neurodevelopmental disorder and is caused by a complex interplay of multiple genetic and environmental factors. Yet, the molecular mechanisms underlying the disorder remain largely elusive. In this study, we used the available omics data to compile a list of TD candidate genes, and we subsequently conducted tissue/cell type specificity and functional enrichment analyses of this list. Using genomic data, we also investigated genetic sharing between TD and blood and cerebrospinal fluid (CSF) metabolite levels. Lastly, we built a molecular landscape of TD through integrating the results from these analyses with an extensive literature search to identify the interactions between the TD candidate genes/proteins and metabolites. We found evidence for an enriched expression of the TD candidate genes in four brain regions and the pituitary. The functional enrichment analyses implicated two pathways ('cAMP-mediated signaling' and 'Endocannabinoid Neuronal Synapse Pathway') and multiple biological functions related to brain development and synaptic transmission in TD etiology. Furthermore, we found genetic sharing between TD and the blood and CSF levels of 39 metabolites. The landscape of TD not only provides insights into the (altered) molecular processes that underlie the disease but, through the identification of potential drug targets (such as FLT3, NAALAD2, CX3CL1-CX3CR1, OPRM1, and HRH2), it also yields clues for developing novel TD treatments.

siRNA-Mediated MELK Knockdown Induces Accelerated Wound Healing with Increased Collagen Deposition

Skin wounds remain a significant problem for the healthcare system, affecting the clinical outcome, patients' quality of life, and financial costs. Reduced wound healing times would improve clinical, economic, and social aspects for both patients and the healthcare system. Skin wound healing has been studied for years, but effective therapy that leads to accelerated wound healing remains to be discovered. This study aimed to evaluate the potential of MELK silencing to accelerate wound healing. A vectorless, transient knockdown of the MELK gene using siRNA was performed in a murine skin wound model. The wound size, total collagen, type 3 collagen, vessel size, vessel number, cell proliferation, cell apoptosis, number of mast cells, and immune infiltration by CD45, CD11b, CD45, and CD8a cells were evaluated. We observed that treatment with MELK siRNA leads to significantly faster wound closing associated with increased collagen deposition.

Modulation of RNA splicing enhances response to BCL2 inhibition in leukemia

Therapy resistance is a major challenge in the treatment of cancer. Here, we performed CRISPR-Cas9 screens across a broad range of therapies used in acute myeloid leukemia to identify genomic determinants of drug response. Our screens uncover a selective dependency on RNA splicing factors whose loss preferentially enhances response to the BCL2 inhibitor venetoclax. Loss of the splicing factor RBM10 augments response to venetoclax in leukemia yet is completely dispensable for normal hematopoiesis. Combined RBM10 and BCL2 inhibition leads to mis-splicing and inactivation of the inhibitor of apoptosis XIAP and downregulation of BCL2A1, an anti-apoptotic protein implicated in venetoclax resistance. Inhibition of splicing kinase families CLKs (CDC-like kinases) and DYRKs (dual-specificity tyrosine-regulated kinases) leads to aberrant splicing of key splicing and apoptotic factors that synergize with venetoclax, and overcomes resistance to BCL2 inhibition. Our findings underscore the importance of splicing in modulating response to therapies and provide a strategy to improve venetoclax-based treatments.

Functional genomic analysis of adult and pediatric brain tumor isolates

Background

Adult and pediatric tumors display stark differences in their mutation spectra and chromosome alterations. Here, we attempted to identify common and unique gene dependencies and their associated biomarkers among adult and pediatric tumor isolates using functional genetic lethal screens and computational modeling.

Methods

We performed CRISRP-Cas9 lethality screens in two adult glioblastoma (GBM) tumor isolates and five pediatric brain tumor isolates representing atypical teratoid rhabdoid tumors (ATRT), diffuse intrinsic pontine glioma, GBM, and medulloblastoma. We then integrated the screen results with machine learning-based gene-dependency models generated from data from >900 cancer cell lines.

Results

We found that >50% of candidate dependencies of 280 identified were shared between adult GBM tumors and individual pediatric tumor isolates. 68% of screen hits were found as nodes in our network models, along with shared and tumor-specific predictors of gene dependencies. We investigated network predictors associated with ADAR, EFR3A, FGFR1 (pediatric-specific), and SMARCC2 (ATRT-specific) gene dependency among our tumor isolates.

Conclusions

The results suggest that, despite harboring disparate genomic signatures, adult and pediatric tumor isolates share a preponderance of genetic dependences. Further, combining data from primary brain tumor lethality screens with large cancer cell line datasets produced valuable insights into biomarkers of gene dependency, even for rare cancers.

Importance of the Study

Our results demonstrate that large cancer cell lines data sets can be computationally mined to identify known and novel gene dependency relationships in adult and pediatric human brain tumor isolates. Gene dependency networks and lethality screen results represent a key resource for neuro-oncology and cancer research communities. We also highlight some of the challenges and limitations of this approach.

Combination Therapies with CDK4/6 Inhibitors to Treat KRAS-Mutant Pancreatic Cancer

Mutational loss of CDKN2A (encoding p16INK4A) tumor-suppressor function is a key genetic step that complements activation of KRAS in promoting the development and malignant growth of pancreatic ductal adenocarcinoma (PDAC). However, pharmacologic restoration of p16INK4A function with inhibitors of CDK4 and CDK6 (CDK4/6) has shown limited clinical efficacy in PDAC. Here, we found that concurrent treatment with both a CDK4/6 inhibitor (CDK4/6i) and an ERK-MAPK inhibitor (ERKi) synergistically suppresses the growth of PDAC cell lines and organoids by cooperatively blocking CDK4/6i-induced compensatory upregulation of ERK, PI3K, antiapoptotic signaling, and MYC expression. On the basis of these findings, a Phase I clinical trial was initiated to evaluate the ERKi ulixertinib in combination with the CDK4/6i palbociclib in patients with advanced PDAC (NCT03454035). As inhibition of other proteins might also counter CDK4/6i-mediated signaling changes to increase cellular CDK4/6i sensitivity, a CRISPR-Cas9 loss-of-function screen was conducted that revealed a spectrum of functionally diverse genes whose loss enhanced CDK4/6i growth inhibitory activity. These genes were enriched around diverse signaling nodes, including cell-cycle regulatory proteins centered on CDK2 activation, PI3K-AKT-mTOR signaling, SRC family kinases, HDAC proteins, autophagy-activating pathways, chromosome regulation and maintenance, and DNA damage and repair pathways. Novel therapeutic combinations were validated using siRNA and small-molecule inhibitor-based approaches. In addition, genes whose loss imparts a survival advantage were identified (e.g., RB1, PTEN, FBXW7), suggesting possible resistance mechanisms to CDK4/6 inhibition. In summary, this study has identified novel combinations with CDK4/6i that may have clinical benefit to patients with PDAC.

SIGNIFICANCE

CRISPR-Cas9 screening and protein activity mapping reveal combinations that increase potency of CDK4/6 inhibitors and overcome drug-induced compensations in pancreatic cancer.

In Vivo Cross-Linking and Co-Immunoprecipitation Procedure to Analyze Nuclear tRNA Export Complexes in Yeast Cells

tRNAs are small noncoding RNAs that are predominantly known for their roles in protein synthesis and also participate in numerous other functions ranging from retroviral replication to apoptosis. In eukaryotic cells, all tRNAs move bidirectionally, shuttling between the nucleus and the cytoplasm. Bidirectional nuclear-cytoplasmic tRNA trafficking requires a complex set of conserved proteins. Here, we describe an in vivo biochemical methodology in Saccharomyces cerevisiae to assess the ability of proteins implicated in tRNA nuclear export to form nuclear export complexes with tRNAs. This method employs tagged putative tRNA nuclear exporter proteins and co-immunoprecipitation of tRNA-exporter complexes using antibody-conjugated magnetic beads. Because the interaction between nuclear exporters and tRNAs may be transient, this methodology employs strategies to effectively trap tRNA-protein complexes in vivo. This pull-down method can be used to verify and characterize candidate proteins and their potential interactors implicated in tRNA nuclear-cytoplasmic trafficking.

Understanding neural development and diseases using CRISPR screens in human pluripotent stem cell-derived cultures

The brain is arguably the most complex part of the human body in form and function. Much remains unclear about the molecular mechanisms that regulate its normal and pathological physiology. This lack of knowledge largely stems from the inaccessible nature of the human brain, and the limitation of animal models. As a result, brain disorders are difficult to understand and even more difficult to treat. Recent advances in generating human pluripotent stem cells (hPSCs)-derived 2-dimensional (2D) and 3-dimensional (3D) neural cultures have provided an accessible system to model the human brain. Breakthroughs in gene editing technologies such as CRISPR/Cas9 further elevate the hPSCs into a genetically tractable experimental system. Powerful genetic screens, previously reserved for model organisms and transformed cell lines, can now be performed in human neural cells. Combined with the rapidly expanding single-cell genomics toolkit, these technological advances culminate to create an unprecedented opportunity to study the human brain using functional genomics. This review will summarize the current progress of applying CRISPR-based genetic screens in hPSCs-derived 2D neural cultures and 3D brain organoids. We will also evaluate the key technologies involved and discuss their related experimental considerations and future applications.

CRISPRi screening identifies CASP8AP2 as an essential viability factor in lung cancer controlling tumor cell death via the AP-1 pathway

Since lung cancer remains the leading cause of cancer death globally, there is an urgent demand for novel therapeutic targets. We carried out a CRISPR interference (CRISPRi) loss-of-function screen for human lung adenocarcinoma (LUAD) targeting 2098 deregulated genes using a customized algorithm to comprehensively probe the functionality of every resolvable transcriptional start site (TSS). CASP8AP2 was identified as the only hit that significantly affected the viability of all eight screened LUAD cell lines while the viability of non-transformed lung cells was only moderately impacted. Knockdown (KD) of CASP8AP2 induced both autophagy and apoptotic cell death pathways. Systematic expression profiling linked the AP-1 transcription factor to the CASP8AP2 KD-induced cancer cell death. Furthermore, inhibition of AP-1 reverted the CASP8AP2 silencing-induced phenotype. Overall, the tailored CRISPRi screen profiled the impact of over 2000 genes on the survival of eight LUAD cell lines and identified the CASP8AP2 - AP-1 axis mediating lung cancer viability.

CRISPR Libraries and Whole-Genome Screening to Identify Essential Factors for Viral Infections

The CRISPR-Cas9 system has revolutionized genetics and offers a simple and inexpensive way of generating perturbation that results in gene repression, activation, or editing. The advances in this technique make possible the development of CRISPR libraries which consist of a set of sgRNAs to cause perturbations in several genes in the same cell population. The use of libraries raised the CRISPR-Cas9 technique to a genomic scale and provides a powerful approach for identifying previously unknown molecular mechanisms and pathways involved in a specific phenotype or biological process. More specifically, the CRISPRko libraries (set of sgRNAs for gene knockout) and their high-throughput screenings are widely used in research with viral agents, and it was enlarged even more with the COVID-19 pandemic. With this chapter, we aim to point out how this tool helps in understanding virus-host relationships, such as the mechanisms of virus entry into the cell, the essential factors for its replication, and the cellular pathways involved in the response against the pathogen. The chapter also provided some practical considerations for each step of an experimentation using these tools that include choosing the library and screening type, the target cell, the viral strain, the library amplification and guaranteeing its coverage, the strategies for the gene screening pipeline by bioinformatics, and finally, target validation. To conclude, it was presented a table reviewing the last updates in the research for antiviral therapies using CRISPR libraries.

Cancer fitness genes: emerging therapeutic targets for metastasis

Development of cancer therapeutics has traditionally focused on targeting driver oncogenes. Such an approach is limited by toxicity to normal tissues and treatment resistance. A class of 'cancer fitness genes' with crucial roles in metastasis have been identified. Elevated or altered activities of these genes do not directly cause cancer; instead, they relieve the stresses that tumor cells encounter and help them adapt to a changing microenvironment, thus facilitating tumor progression and metastasis. Importantly, as normal cells do not experience high levels of stress under physiological conditions, targeting cancer fitness genes is less likely to cause toxicity to noncancerous tissues. Here, we summarize the key features and function of cancer fitness genes and discuss their therapeutic potential.

Survival-based CRISPR genetic screens across a panel of permissive cell lines identify common and cell-specific SARS-CoV-2 host factors

SARS-CoV-2 depends on host cell components for infection and replication. Identification of virus-host dependencies offers an effective way to elucidate mechanisms involved in viral infection and replication. If druggable, host factor dependencies may present an attractive strategy for anti-viral therapy. In this study, we performed genome wide CRISPR knockout screens in Vero E6 cells and four human cell lines including Calu-3, UM-UC-4, HEK-293 and HuH-7 to identify genetic regulators of SARS-CoV-2 infection. Our findings identified only ACE2, the cognate SARS-CoV-2 entry receptor, as a common host dependency factor across all cell lines, while other host genes identified were largely cell line specific, including known factors TMPRSS2 and CTSL. Several of the discovered host-dependency factors converged on pathways involved in cell signalling, immune-related pathways, and chromatin modification. Notably, the chromatin modifier gene KMT2C in Calu-3 cells had the strongest impact in preventing SARS-CoV-2 infection when perturbed.