Cancer is a complicated disease that results from the interplay between specific changes in cellular genetics and diverse microenvironments. The application of high-performance and customizable clustered regularly interspaced palindromic repeats/associated protein (CRISPR/Cas) nuclease systems has significantly enhanced genome editing for accurate cancer modeling and facilitated simultaneous genetic modification for cancer therapy and mutation identification. Achieving an effective CRISPR/Cas platform for cancer treatment depends on the identification, selection, and optimization of specific mutated genes in targeted cancer tissues. However, overcoming the off-target effects, specificity, and immunogenicity are additional challenges that must be addressed while developing a gene editing system for cancer therapy. From this perspective, we briefly covered the pipeline of CRISPR/Cas cancer therapy, identified target genes to optimize gRNAs and sgRNAs, and explored alternative delivery modalities, including viral, non-viral, and extracellular vesicles. In addition, the list of patents and current clinical trials related to this unique cancer therapy method is discussed. In summary, we have discussed comprehensive start-to-end pipeline strategies for CRISPR/Cas development to advance the precision, effectiveness, and safety of clinical applications for cancer therapy.

Osimertinib, a third-generation EGFR tyrosine kinase inhibitor (EGFR-TKI), has dramatically transformed the treatment landscape for patients with EGFR-mutant NSCLC. However, the long-term success of this therapy is often compromised by the onset of acquired resistance, with non-genetic mechanisms increasingly recognized as pivotal contributors. Here, we exploit a multi-omics approach to profile genome-wide chromatin accessibility and transcriptional landscapes between drug sensitive and resistant EGFR-mutant cells. Our findings reveal a robust concordance between epigenetic regulome and transcriptomic changes that characterize the osimertinib resistant state. Through CRISPR-based functional genomics screen targeting epigenetic regulators and transcription factors, we uncover a critical regulatory network featuring key members of the NuRD and PRC2 complexes that mediate resistance. Most critically, our screen identifies FOSL1 and JUN, two subunits of the AP-1 transcription factor within this network, as the most significant hits. Mechanistically, we demonstrate that cis-regulatory elements exhibiting altered chromatin accessibility in the resistant state are enriched for cognate AP-1 binding motifs, enabling AP-1 to orchestrate a gene expression program that underpins the druggable MEK/ERK signaling axis, potentially enhancing cell viability and fitness of resistant cells. Importantly, genetic depletion or pharmacological inhibition of AP-1 reinstates cellular and molecular sensitivity, and reverts resistance-associated phenotypes, such as epithelial-to-mesenchymal transition, upon anti-EGFR rechallenge by repressing AKT and ERK signaling. These findings provide novel insights into the epigenetic and transcriptional control of osimertinib resistance in EGFR-mutant NSCLC, highlighting AP-1 as a targetable vulnerability of resistance-related hallmarks and offering a promising avenue for developing resistance reversal strategies.

Custom CRISPR screens are powerful tools for rapid, hypothesis-driven discovery, but their design is often complex and time-consuming. Green Listed v2.0 simplifies this process with an intuitive workflow for designing custom CRISPR spacer libraries and supports downstream analysis for all users, irrespective of their computational experience. The web application features a user-friendly graphical interface freely accessible at https://greenlisted.cmm.se. Version 2.0 includes significant upgrades to the original 2016 version that were implemented based on user feedback. This includes a new gene synonym tool, expanded library options, optimized output lists, performance improvements, and linked scripts for the rational design of custom CRISPR screen gene sets.

Cells are bathed in the extracellular fluids in which the extracellular pH (pHe) is maintained to a narrow range, and abnormal pHe is related to multiple diseases. However, the genes and signaling pathways underlying cell response to abnormal pHe remain unclear. Identification of genes responsive to extreme pHe challenge has great value in both basic research and medicine. Here, we performed genome-wide CRISPR screening to reveal genes and pathways related to insect cell response to abnormal pHe. Cells of the Bombyx mori embryonic cell line (BmE) genome-scale CRISPR screening cell library (BmEGCKLib) were cultured in different pHe (the physiological pH 6.3 as control; pH 5.0, 5.5, 6.6 and 6.95 as abnormal pHe). In the four extreme pH groups, we identified 44 overlapped fitness genes and 24 overlapped positive selected genes respectively. We also performed Kyoto Encyclopedia of Genes and Genomes pathways enrichment analysis for the selected genes. The "phosphatidylinositol signaling system", "mRNA surveillance pathway" and "spliceosome pathway" were significantly enriched in the negative selection, suggesting that cellular signal transduction and mRNA quality play essential roles for cells to resist to abnormal pHe. This is the first time to provide insight into insect cell response to abnormal pHe on a genome-scale.

Unbiased forward genetic screens have been extensively employed in biological research to elucidate functional genomics. In pooled clustered regularly interspaced short palindromic repeats (CRISPR) perturbation screens, various genetically encoded gain-of-function or loss-of-function mutations are introduced into a heterogeneous population of cells. Subsequently, these cells are screened for phenotypes, perturbation-associated genotypes are analyzed and a connection between genotype and phenotype is determined. CRISPR screening techniques enable the investigation of important biological questions, such as how bacterial toxins kill cells and cause disease. However, the broad spectrum of effects caused by diverse toxins presents a challenge when selecting appropriate screening strategies. Here, we provide a step-by-step protocol for a genome-wide pooled CRISPR perturbation screen to study bacterial toxins. We describe technical considerations, pilot experiments, library construction, screen execution, result analysis and validation of the top enriched hits. These screens are applicable for many different types of toxins and are anticipated to reveal a repertoire of host factors crucial in the intoxication pathway, such as receptors, trafficking/translocation factors and substrates. The entire protocol takes 21-27 weeks and does not require specialized knowledge beyond basic biology.

Boolean networks (BNs) are vital modeling tools in systems biology for biomolecular regulatory networks. After a transient phase, BNs converge to attractors that represent distinct cell types or conditions. Therefore, methods to control the long-term behavior of BNs have important implications for biological and genetic applications. In this paper, we propose a method to enforce convergence of a BN to a desired attractor from any initial state through a simple intervention: fixing a specific subset of network variables at definite values. We refer to this method as the global stabilization of a BN to a target attractor. Utilizing the algebraic state space representation of BNs, we introduce novel matrix tools to formulate this intervention method, as well as develop a foundation for analyzing the stabilizability of BNs. We derive necessary and sufficient conditions for the global stabilizability of BNs and utilize these criteria to identify a minimal subset of network variables-termed the global stabilizing kernel-whose regulation ensures that the BN converges to the desired attractor. Finally, we apply our proposed method to determine the stabilizing kernels of several biomolecular regulatory network models and demonstrate how they can be steered to their target attractors, showcasing the applicability of our approach. We also apply our method to identify the stabilizing kernels of 480 randomly generated BNs. Our experiments suggest that, on average, only a relatively small portion (approximately 25%) of the network nodes need to be manipulated for the networks to converge to their primary attractors.

In efforts to identify additional therapeutic targets for Acute Myeloid Leukemia (AML), we performed a high-throughput screen that includes 56 primary specimens tested with 10,000 structurally diverse small molecules. One specific hit, called S656 acts as a molecular glue degrader (MGD), that mediates the CRL4-dependent proteolysis of cyclin K. Structurally, S656 features a moiety that binds to the ATP binding site of cyclin-dependent kinases (CDKs), allowing the recruitment of the CDK12-cyclin K complex, along with a binding site for DDB1 bridging the CRL4 complex. Structure activity relationship studies reveal that minimal modifications to the dimethylaniline moiety of S656 improve its cyclin K MGD function over CDK inhibition by promoting DDB1 engagement. This includes full occupation of the DDB1 pocket, preferably with hydrophobic terminal groups, and cation-π interaction with Arg928. Additionally, we demonstrate that despite structural diversity, cyclin K degraders exhibit similar functional activity in AML which is distinct from direct CDK12 inhibition.

Controlling the outcomes of CRISPR editing is crucial for the success of gene therapy. Since donor template-based editing is often inefficient, alternative strategies have emerged that leverage mutagenic end-joining repair instead. Existing machine learning models can accurately predict end-joining repair outcomes, however: generalisability beyond the specific cell line used for training remains a challenge, and interpretability is typically limited by suboptimal feature representation and model architecture.

We propose X-CRISP, a flexible and interpretable neural network for predicting repair outcome frequencies based on a minimal set of outcome and sequence features, including microhomologies (MH). Outperforming prior models on detailed and aggregate outcome predictions, X-CRISP prioritised MH location over MH sequence properties such as GC content for deletion outcomes. Through transfer learning, we adapted X-CRISP pre-trained on wild-type mESC data to target human cell lines K562, HAP1, U2OS, and mESC lines with altered DNA repair function. Adapted X-CRISP models improved over direct training on target data from as few as 50 samples, suggesting that this strategy could be leveraged to build models for new domains using a fraction of the data required to train models from scratch.

An implementation of X-CRISP is available at github.com/joanagoncalveslab/xcrisp .

Dysregulated lipid metabolism plays an important role in prostate cancer, although the understanding of the essential regulatory processes in tumorigenesis is incomplete. We employ a CRISPR-Cas9 screen using a custom human lipid metabolism knockout library to identify essential genes for prostate cancer survival. Screening in three prostate cancer cell lines reveals 63 shared dependencies, with enrichment in terpenoid backbone synthesis and N-glycan biosynthesis. Independent knockout of key genes of the mevalonate pathway reduces cell proliferation. Further investigation focuses on NUS1, a subunit of cis-prenyltransferase required for dolichol synthesis. NUS1 knockout decreases tumor growth in vivo and viability in patient-derived xenograft (PDX)-derived organoids. Mechanistic studies reveal that loss of NUS1 promotes oxidative stress, lipid peroxidation and ferroptosis sensitivity, endoplasmic reticulum (ER) stress, and G1 cell-cycle arrest, and it dampens androgen receptor (AR) signaling, collectively leading to growth arrest. This study highlights the critical role of the mevalonate-dolichol-N-glycan biosynthesis pathway, particularly NUS1, in prostate cancer survival and growth.

Metabolic engineering has a wide range of applications, spanning key sectors such as energy, pharmaceuticals, agriculture, chemicals, and environmental sustainability. Its core focus is on precisely modulating metabolic pathways to achieve efficient, sustainable, and environmentally friendly biomanufacturing processes, offering new possibilities for societal sustainable development. Gene attenuation is a critical technique within metabolic engineering, pivotal in optimizing metabolic fluxes and improving target metabolite yields. This review article discusses gene attenuation mechanisms, the applications across various biological systems, and implementation strategies. Additionally, we address potential future challenges and explore its potential to drive further advancements in the field.

Mitochondria serve as central hubs in cellular metabolism by sensing, integrating, and responding to metabolic demands. This integrative function is achieved through inter-organellar communication, involving the exchange of metabolites, lipids, and signaling molecules. The functional diversity of metabolite exchange and pathway interactions is enabled by compartmentalization within organelle membranes. Membrane contact sites (MCSs) are critical for facilitating mitochondria-organelle communication, creating specialized microdomains that enhance the efficiency of metabolite and lipid exchange. MCS dynamics, regulated by tethering proteins, adapt to changing cellular conditions. Dysregulation of mitochondrial-organelle interactions at MCSs is increasingly recognized as a contributing factor in the pathogenesis of multiple diseases. Emerging technologies, such as advanced microscopy, biosensors, chemical-biology tools, and functional genomics, are revolutionizing our understanding of inter-organellar communication. These approaches provide novel insights into the role of these interactions in both normal cellular physiology and disease states. This review will highlight the roles of metabolite transporters, lipid-transfer proteins, and mitochondria-organelle interfaces in the coordination of metabolism and transport.

Pooled CRISPR screens are vital in the unbiased interrogation of gene function and are instrumental in uncovering therapeutic targets and biological processes. However, follow-up hit validation is critical to confirm observed results. Researchers need a simple and robust approach to rapidly verify putative hits and test resulting observations. Thus, we developed a CRISPR-based method for hit validation that tests the effect of a genetic perturbation on cell fitness. By editing target loci and monitoring the indel profiles over time, we have created a Cellular Fitness (CelFi) assay that can elucidate cellular vulnerabilities and verify hits from pooled CRISPR knockout screens. Unlike traditional cellular fitness assays that evaluate viability over time, the CelFi assay correlates changes in the indel profile at the target gene with a selective growth advantage or disadvantage in individual cells over time. Moreover, the CelFi assay can be utilized to evaluate gene dependencies and test new hypotheses, regardless of variations in single guide RNA optimization, ribonucleoprotein concentration, and gene copy number.

Oral cancer poses significant health risks with an increasing incidence annually. Despite advancements in treatment methods, their efficacy is frequently constrained by cancer heterogeneity and drug resistance, leading to minimal improvement in the 5-year survival rate. Therefore, there is a critical need for new treatment methods leaded by representative preclinical research models. Compared to other models, organoids can more precisely simulate the tissue structure, genetic characteristics, and tumor microenvironment (TME) of in vivo tumors, exhibiting high tumor specificity. This makes organoid technology a valuable tool in investigating tumor development, mechanisms of metastasis, drug screening, prediction of clinical responses, and personalized patient treatment. Moreover, integrating organoid technology with other biotechnologies could expand its applications in tissue regeneration. Although organoid technology is increasingly utilized in oral cancer research, a systematic review in this field is absent. This paper is to bridge the gap by reviewing the development and current status of organoid research, highlighting its applications, future prospects, and challenges in oral cancer. It aims to provide novel insights into the role of organoids in precision treatment and regenerative medicine for oral cancer.

The CRISPR system has transformed many research areas, including cancer and immunology, by providing a simple yet effective genome editing system. Its simplicity has facilitated large-scale experiments to assess gene functionality across diverse biological contexts, generating extensive datasets that boosted the development of computational methods and machine learning/artificial intelligence applications. Integrating CRISPR with single-cell technologies has further advanced our understanding of genome function and its role in many biological processes, providing unprecedented insights into human biology and disease mechanisms. This powerful combination has accelerated AI-driven analyses, enhancing disease diagnostics, risk prediction, and therapeutic innovations. This review provides a comprehensive overview of CRISPR-based genome editing systems, highlighting their advancements, current progress, challenges, and future opportunities, especially in cancer and immunology.

The TMPRSS2 cell surface protease is used by a broad range of respiratory viruses to facilitate entry into target cells. Together with ACE2, TMPRSS2 represents a key factor for SARS-CoV-2 infection, as TMPRSS2 mediates cleavage of viral spike protein, enabling direct fusion of the viral envelope with the host cell membrane. Since the start of the COVID-19 pandemic, TMPRSS2 has gained attention as a therapeutic target for protease inhibitors which would inhibit SARS-CoV-2 infection, but little is known about TMPRSS2 regulation, particularly in cell types physiologically relevant for SARS-CoV-2 infection. Here, we performed an unbiased genome-wide CRISPR-Cas9 library screen, together with a library targeted at epigenetic modifiers and transcriptional regulators, to identify cellular factors that modulate cell surface expression of TMPRSS2 in human colon epithelial cells. We find that endogenous TMPRSS2 is regulated by the Elongin BC-VHL complex and HIF transcription factors. Depletion of Elongin B or treatment of cells with PHD inhibitors resulted in downregulation of TMPRSS2 and inhibition of SARS-CoV-2 infection. We show that TMPRSS2 is still utilised by SARS-CoV-2 Omicron variants for entry into colonic epithelial cells. Our study enhances our understanding of the regulation of endogenous surface TMPRSS2 in cells physiologically relevant to SARS-CoV-2 infection.

Design of guide RNA (gRNA) with high efficiency and specificity is vital for successful application of the CRISPR gene editing technology. Although many machine learning (ML) and deep learning (DL)-based tools have been developed to predict gRNA activities, a systematic and unbiased evaluation of their predictive performance is still needed. Here, we provide a brief overview of in silico tools for CRISPR design and assess the CRISPR datasets and statistical metrics used for evaluating model performance. We benchmark seven ML and DL-based CRISPR-Cas9 editing efficiency prediction tools across nine CRISPR datasets covering six cell types and three species. The DL models CRISPRon and DeepHF outperform the other models exhibiting greater accuracy and higher Spearman correlation coefficient across multiple datasets. We compile all CRISPR datasets and in silico prediction tools into a GuideNet resource web portal, aiming to facilitate and streamline the sharing of CRISPR datasets. Furthermore, we summarize features affecting CRISPR gene editing activity, providing important insights into model performance and the further development of more accurate CRISPR prediction models.

Babesiosis is a tick-borne parasitic disease that poses a significant risk to both animal and human health. A comprehensive understanding of Babesia biology necessitates the application of advanced laboratory techniques. This review explores recent advancements in gene editing technologies of Babesia, emphasizing the foundational importance of in vitro culture systems. We highlight the historical challenges encountered in establishing effective in vitro culture and discuss the need for optimizing these methods to enhance gene editing efficiency. Here, we describe recent progress in Babesia transfection, different gene manipulation systems, and the applications of gene editing. This review aims to provide essential insights and technical guidance for future studies in Babesia genetics, highlighting the transformative potential of gene manipulation in combating this important parasitic disease.

GPER is broadly expressed in human tissues and has tumor-suppressive activity. No FDA-approved agents selectively target GPER. LNS8801 is a synthetic, orally bioavailable, enantiomerically pure, GPER agonist with potent anticancer activity in vivo. LNS8801 response is attenuated by a common germline coding variant present in roughly half of humans.

Organoids represent a significant advancement in disease modeling, demonstrated by their capacity to mimic the physiological/pathological structure and functional characteristics of the native tissue. Recently CRISPR/Cas9 technology has emerged as a powerful tool in combination with organoids for the development of novel therapies in preclinical settings. This review explores the current literature on applications of pooled CRISPR screening in organoids and the emerging role of these models in understanding cancer. We highlight the evolution of genome-wide CRISPR gRNA library screens in organoids, noting their increasing adoption in the field over the past decade. Noteworthy studies utilizing these screens to investigate oncogenic vulnerabilities and developmental pathways in various organoid systems are discussed. Despite the promise organoids hold, challenges such as standardization, reproducibility, and the complexity of data interpretation remain. The review also addresses the ideas of assessing tumor organoids (tumoroids) against established cancer hallmarks and the potential of studying intercellular cooperation within these models. Ultimately, we propose that organoids, particularly when personalized for patient-specific applications, could revolutionize drug screening and therapeutic approaches, minimizing the reliance on traditional animal models and enhancing the precision of clinical interventions.

CRISPR-Cas9 gene editing has become an important tool for the study of plant pathogens, allowing researchers to functionally characterize specific genes involved in phytopathogenicity, virulence, and fungicide resistance. Protocols for CRISPR-Cas9 gene editing have already been developed for Phytophthoras, an important group of oomycete plant pathogens; however, these efforts have exclusively focused on agricultural pathosystems, with research lacking for forest pathosystems. We sought to develop CRISPR-Cas9 gene editing in two forest pathogenic Phytophthoras, Phytophthora cactorum and P. ramorum, using a plasmid-ribonucleoprotein (RNP) co-transformation approach. Our gene target in both species was the ortholog of PcORP1, which encodes an oxysterol-binding protein that is the target of the fungicide oxathiapiprolin in the agricultural pathogen P. capsici. We delivered liposome complexes, each containing plasmid DNA and CRISPR-Cas9 RNPs, to Phytophthora protoplasts using a polyethylene glycol-mediated transformation protocol. We obtained two ORP1 mutants in P. cactorum but were unable to obtain any mutants in P. ramorum. The two P. cactorum mutants exhibited decreased resistance to oxathiapiprolin, as measured by their radial growth relative to wild-type cultures on oxathiapiprolin-supplemented medium. Our results demonstrate the potential for RNP-mediated CRISPR-Cas9 gene editing in P. cactorum and provide a foundation for future optimization of our protocol in other forest pathogenic Phytophthora species.IMPORTANCECRISPR-Cas9 gene editing has become a valuable tool for characterizing the genetics driving virulence and pathogenicity in plant pathogens. CRISPR-Cas9 protocols are now well-established in several Phytophthora species, an oomycete genus with significant economic and ecological impact globally. These protocols, however, have been developed for agricultural Phytophthora pathogens only; CRISPR-Cas9 systems have not yet been developed for any forest pathogenic Phytophthoras. In this study, we sought to establish CRISPR-Cas9 gene editing in two forest Phytophthora pathogens that cause widespread tree mortality: P. cactorum and P. ramorum. We successfully obtained gene mutations in P. cactorum and demonstrated a decrease in fungicide resistance, a trait that could impact the pathogen's ability to cause disease. However, the same protocol did not yield any mutants in P. ramorum. The results of our study will serve as a baseline for the development of CRISPR-Cas9 gene editing in forest Phytophthoras and other oomycetes.

The CRISPR/Cas9 gene-editing technology, derived from the adaptive immune mechanisms of bacteria, has demonstrated remarkable advantages in fields such as gene function research and the treatment of genetic diseases due to its simplicity in design, precise targeting, and ease of use. Despite challenges such as off-target effects and cytotoxicity, effective spatiotemporal control strategies have been achieved for the CRISPR/Cas9 system through precise regulation of Cas9 protein activity as well as engineering of guide RNAs (gRNAs). This review provides a comprehensive analysis of the core components and functional mechanisms underlying the CRISPR/Cas9 system, highlights recent advancements in spatiotemporal control strategies, and discusses future directions for development.

Tyrosine kinase inhibitors (TKIs) targeting the BCR-ABL1 fusion tyrosine kinase have revolutionized the treatment of chronic myeloid leukaemia (CML). However, the development of TKI resistance and the subsequent transition from the chronic phase (CP) to blast crisis (BC) threaten patients with CML. Accumulating evidence suggests that translational control is crucial for cancer progression. Our high-throughput CRISPR-Cas9 screening identified poly(A) binding protein cytoplasmic 1 (PABPC1) as a driver for CML progression in the BC stage. PABPC1 preferentially improved the translation efficiency of multiple leukaemogenic mRNAs with long and highly structured 5' untranslated regions by forming biomolecular condensates. Inhibiting PABPC1 significantly suppressed CML cell proliferation and attenuated disease progression, with minimal effects on normal haematopoiesis. Moreover, we identified two PABPC1 inhibitors that inhibited BC progression and overcame TKI resistance in murine and human CML. Overall, our work identifies PABPC1 as a selective translation enhancing factor in CML-BC, with its genetic or pharmacological inhibition overcoming TKI resistance and suppressed BC progression.

CRISPR-Cas technology has revolutionized biology by enabling precise DNA and RNA edits with ease. However, significant challenges remain for translating this technology into clinical applications. Traditional protein engineering methods, such as rational design, mutagenesis screens, and directed evolution, have been used to address issues like low efficacy, specificity, and high immunogenicity. These methods are labor-intensive, time-consuming, and resource-intensive and often require detailed structural knowledge. Recently, computational strategies have emerged as powerful solutions to these limitations. Using artificial intelligence (AI) and machine learning (ML), the discovery and design of novel gene-editing enzymes can be streamlined. AI/ML models predict activity, specificity, and immunogenicity while also enhancing mutagenesis screens and directed evolution. These approaches not only accelerate rational design but also create new opportunities for developing safer and more efficient genome-editing tools, which could eventually be translated into the clinic.

The specification of primordial germ cells (PGCs) during early embryogenesis initiates the development of the germ cell lineage that ensures the perpetuation of genetic and epigenetic information from parents to offspring. Defects in germ cell development may lead to infertility or birth defects. Historically, our understanding of human PGCs (hPGCs) regulation has primarily been derived from studies in mice, given the ethical restrictions and practical limitations of human embryos at the stage of PGC specification. However, recent studies have increasingly highlighted significant mechanistic differences for PGC development in humans and mice. The past decade has witnessed the establishment of human pluripotent stem cell (hPSC)-derived hPGC-like cells (hPGCLCs) as new models for studying hPGC fate specification and differentiation. In this review, we systematically summarize the current hPSC-derived models for hPGCLC induction, and how these studies uncover the regulatory machinery for human germ cell fate specification and differentiation, forming the basis for reconstituting gametogenesis in vitro from hPSCs for clinical applications and disease modeling.

Genome-wide CRISPR screening in the organism has tremendous potential to answer long-standing questions of mammalian physiology and disease. However, bringing this powerful technology in vivo presents unique challenges, including delivering a genome-wide sgRNA library to the appropriate cell type, achieving sufficient coverage of the library, and selecting for the phenotype of interest. In this review, we highlight recent advances in sgRNA delivery, library design, and phenotypic readout that can help overcome these technical challenges and thereby bring high-throughput genetic dissection to an increasing number of tissues and questions. We are excited about the potential for ongoing innovation in these areas to ultimately enable genome-wide CRISPR screening in any cell type of interest in the organism, allowing for unprecedented investigation into diverse questions of mammalian physiology and disease.

Background: Clear cell renal cell carcinoma (ccRCC) is a prevalent and aggressive subtype of kidney cancer, often associated with metastasis and recurrence. Identifying key genes involved in ccRCC progression is critical for improving treatment strategies and patient outcomes. Methods: We performed a large-scale genome-wide CRISPR screening to identify genes crucial to ccRCC progression using the DepMap database. For discovery and validation, we integrated multi-omics data from The Cancer Genome Atlas (TCGA), GEO, and the NJMU-ccRCC clinical cohort. Bioinformatics analyses, including differential expression, pathway enrichment, and protein-protein interaction network analysis, were conducted to elucidate the biological functions. To validate our findings, we employed immunohistochemistry, qRT-PCR, and various cellular assays to investigate the role of PRC1 in ccRCC. Results: CRISPR screening identified PRC1 as a key gene significantly overexpressed in ccRCC tissues from the DepMap database. Elevated PRC1 expression was associated with poor overall survival, disease-specific survival, and progression-free interval. Silencing PRC1 in ccRCC cell lines inhibited cell proliferation, migration, and colony formation. Functional enrichment analyses revealed that PRC1 is involved in essential processes such as cell cycle regulation, mitosis, and cytokinesis. Additionally, PRC1 expression was correlated with the activation of the Wnt/β-catenin pathway, suggesting that PRC1 plays a pivotal role in tumor progression. Conclusion: PRC1 emerges as a promising biomarker and therapeutic target for ccRCC. Elevated PRC1 expression is associated with poor prognosis, and its inhibition suppresses ccRCC cell proliferation and migration. Our findings underscore the crucial role of PRC1 in ccRCC progression and highlight the need for further investigation into its molecular mechanisms and therapeutic potential.

Recent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities, but predicting causal regulatory relationships from observational data remains challenging. Here, we leverage pooled genetic screens and single-cell sequencing (Perturb-seq) to systematically identify the targets of signalling regulators in diverse biological contexts. We demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial indexing and next-generation sequencing, and perform more than 1,500 perturbations split across six cell lines and five biological signalling contexts. We introduce an improved computational framework (Mixscale) to address cellular variation in perturbation efficiency, alongside optimized statistical methods to learn differentially expressed gene lists and conserved molecular signatures. Finally, we demonstrate how our Perturb-seq derived gene lists can be used to precisely infer changes in signalling pathway activation for in vivo and in situ samples. Our work enhances our understanding of signalling regulators and their targets, and lays a computational framework towards the data-driven inference of an 'atlas' of perturbation signatures.

T cell-based immunotherapies hold promise in treating cancer by leveraging the immune system's recognition of cancer-specific antigens1. However, their efficacy is limited in tumours with few somatic mutations and substantial intratumoural heterogeneity2-4. Here we introduce a previously uncharacterized class of tumour-wide public neoantigens originating from RNA splicing aberrations in diverse cancer types. We identified T cell receptor clones capable of recognizing and targeting neoantigens derived from aberrant splicing in GNAS and RPL22. In cases with multi-site biopsies, we detected the tumour-wide expression of the GNAS neojunction in glioma, mesothelioma, prostate cancer and liver cancer. These neoantigens are endogenously generated and presented by tumour cells under physiologic conditions and are sufficient to trigger cancer cell eradication by neoantigen-specific CD8+ T cells. Moreover, our study highlights a role for dysregulated splicing factor expression in specific cancer types, leading to recurrent patterns of neojunction upregulation. These findings establish a molecular basis for T cell-based immunotherapies addressing the challenges of intratumoural heterogeneity.

Genes and genetic variants associated with human disease are continually being discovered, but validating their causative roles and mechanisms remains a significant challenge. CRISPR/Cas9 genome editing in model organisms like zebrafish can enable phenotypic characterization of founder generation (F0) knockouts (Crispants), but existing approaches are not amenable to high-throughput genetic screening due to high variability, cost, and low phenotype penetrance. To overcome these challenges, here we provide guide RNA (gRNA) selection rules that enable high phenotypic penetrance of up to three simultaneous knockouts in F0 animals following injection of 1-2 gRNAs per gene. We demonstrate a strong transcriptomic overlap in our F0 knockouts and stable knockout lines that take several months to generate. We systematically evaluated this approach across 324 gRNAs targeting 125 genes and demonstrated its utility in studying epistasis, characterizing paralogous genes, and validating human disease gene phenotypes across multiple tissues. Applying our approach in a high-throughput manner, we screened and identified 10 novel neurodevelopmental disorders and 50 hearing genes not previously studied in zebrafish. Altogether, our approach achieves high phenotypic penetrance using low numbers of gRNAs per gene in F0 zebrafish, offering a robust pipeline for rapidly characterizing candidate human disease genes.

Suppressor screens can identify genetic modifiers of biochemical pathways but generally require that the suppressed mutant be viable and fertile. We developed a screening method that obviated this requirement and enabled the identification of mutations that partially suppressed the early developmental arrest and lethality caused by loss of the epidermal growth factor (EGF) receptor ortholog LET-23 in Caenorhabditis elegans. We chemically mutagenized animals carrying the loss-of-function allele let-23(sy15), recovered let-23(sy15) homozygotes that escaped early developmental arrest but were nevertheless inviable, and sequenced their genomes. Testing of candidate causal mutations identified 11 genes that, when mutated, mitigated the early lethality caused by loss of EGF signaling. These included genes encoding homologs of the small guanosine triphosphatase (GTPase) Ras (let-60), which is a downstream effector of LET-23, and of regulators of the small GTPase Rho, including the homolog of the phosphotyrosine-binding protein TENSIN (tns-1). We also recovered suppressing mutations in genes encoding nuclear proteins that protect against DNA damage, including the homolog of MutS homolog 4 (him-14). Genetic experiments were consistent with the repression of Rho activity or the activation of the DNA damage response compensating for the loss of EGF signaling. This sequencing-based, whole-animal screening method may be adapted to other organisms to enable the identification of mutations for which the phenotype does not allow the recovery of viable animals.

The process of drug discovery and development is both lengthy and intricate, demanding a substantial investment of time and financial resources. Bioinformatics techniques and tools can not only accelerate the identification of drug targets and the screening and refinement of drug candidates, but also facilitate the characterization of side effects and the prediction of drug resistance. High-throughput data from genomics, transcriptomics, proteomics, and metabolomics make significant contributions to mechanics-based drug discovery and drug reuse. This paper summarizes bioinformatics technologies and tools in drug research and development and their roles and applications in drug research and development, aiming to provide references for the development of new drugs and the realization of precision medicine.

Cis-regulatory elements (CREs), such as enhancers and promoters, are fundamental regulators of gene expression and, across different cell types, the MYC locus utilizes a diverse regulatory architecture driven by multiple CREs. To better understand differences in CRE function, we perform pooled CRISPR inhibition (CRISPRi) screens to comprehensively probe the 2.8 Mb topologically-associated domain containing MYC in 6 human cancer cell lines with nucleotide resolution. We map 32 CREs where inhibition leads to changes in cell growth, including 8 that overlap previously identified enhancers. Targeting specific CREs decreases MYC expression by as much as 60%, and cell growth by as much as 50%. Using 3-D enhancer contact mapping, we find that these CREs almost always contact MYC but less than 10% of total MYC contacts impact growth when silenced, highlighting the utility of our approach to identify phenotypically-relevant CREs. We also detect an enrichment of lineage-specific transcription factors (TFs) at MYC CREs and, for some of these TFs, find a strong, tumor-specific correlation between TF and MYC expression not found in normal tissue. Taken together, these CREs represent systematically identified, functional regulatory regions and demonstrate how the same region of the human genome can give rise to complex, tissue-specific gene regulation.

Precision medicine relies on identifying reliable biomarkers for gene dependencies to tailor individualized therapeutic strategies. The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. Traditional statistical methods often fall short in this context, necessitating novel computational approaches that harness the full potential of big data in bioinformatics. Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. This bio-primed approach prioritizes variables that are both statistically significant and biologically relevant. Applying our method to multiple dependency datasets, we identified biomarkers which traditional methods overlooked. Our biologically informed LASSO method effectively identifies relevant biomarkers from high-dimensional collinear data, bridging the gap between statistical rigor and biological insight. This method holds promise for advancing personalized medicine by uncovering novel therapeutic targets and understanding the complex interplay of genetic and molecular factors in disease.

Based on their ability to canvas vast genetic or chemical space at low cost and high speed, DNA-encoded libraries (DEL) have served to enable both genomic and small molecule discovery. Current DEL chemical library screening approaches focus primarily on in vitro target-based affinity or activity. Here we describe an approach to record the phenotype-based activity of DNA-encoded small molecules on their cognate barcode in living cells. We transfected chloroalkane-derivatized DNA barcodes carrying photoreleasable small molecules into cells. Following photorelease, bioactive compounds induced expression of a reporter gene cassette containing self-labeling HaloTag protein that becomes covalently modified by encoding barcodes. We demonstrate that we can recover activity information from cells that received active compound following immunoprecipitation-based enrichment. This generalizable approach should enable future strategies that facilitate phenotype-based screens of DNA-encoded chemical libraries in complex cellular or organism level systems.

The revolutionary CRISPR-Cas9 system leverages a programmable guide RNA (gRNA) and Cas9 proteins to precisely cleave problematic regions within DNA sequences. This groundbreaking technology holds immense potential for the development of targeted therapies for a wide range of diseases, including cancers, genetic disorders, and hereditary diseases. CRISPR-Cas9 based genome editing is a multi-step process such as designing a precise gRNA, selecting the appropriate Cas protein, and thoroughly evaluating both on-target and off-target activity of the Cas9-gRNA complex. To ensure the accuracy and effectiveness of CRISPR-Cas9 system, after the targeted DNA cleavage, the process requires careful analysis of the resultant outcomes such as indels and deletions. Following the success of artificial intelligence (AI) in various fields, researchers are now leveraging AI algorithms to catalyze and optimize the multi-step process of CRISPR-Cas9 system. To achieve this goal AI-driven applications are being integrated into each step, but existing AI predictors have limited performance and many steps still rely on expensive and time-consuming wet-lab experiments. The primary reason behind low performance of AI predictors is the gap between CRISPR and AI fields. Effective integration of AI into multi-step CRISPR-Cas9 system demands comprehensive knowledge of both domains. This paper bridges the knowledge gap between AI and CRISPR-Cas9 research. It offers a unique platform for AI researchers to grasp deep understanding of the biological foundations behind each step in the CRISPR-Cas9 multi-step process. Furthermore, it provides details of 80 available CRISPR-Cas9 system-related datasets that can be utilized to develop AI-driven applications. Within the landscape of AI predictors in CRISPR-Cas9 multi-step process, it provides insights of representation learning methods, machine and deep learning methods trends, and performance values of existing 50 predictive pipelines. In the context of representation learning methods and classifiers/regressors, a thorough analysis of existing predictive pipelines is utilized for recommendations to develop more robust and precise predictive pipelines.

Microtubules play essential roles in diverse cellular processes and are important pharmacological targets for treating human disease. Here, we sought to identify cellular factors that modulate the sensitivity of cells to antimicrotubule drugs. We conducted a genome-wide CRISPR/Cas9-based functional genetics screen in human cells treated with the microtubule-destabilizing drug nocodazole or the microtubule-stabilizing drug paclitaxel. We further conducted a focused secondary screen to test drug sensitivity for ∼1,400 gene targets across two distinct human cell lines and to additionally test sensitivity to the KIF11 inhibitor, STLC. These screens defined gene targets whose loss enhances or suppresses sensitivity to antimicrotubule drugs. In addition to gene targets whose loss sensitized cells to multiple compounds, we observed cases of differential sensitivity to specific compounds and differing requirements between cell lines. Our downstream molecular analysis further revealed additional roles for established microtubule-associated proteins and identified new players in microtubule function.

Cell signaling pathways are enriched for biological processes crucial for cellular communication, response to external stimuli, and metabolism. Here, a cell signaling-focused CRISPR screen identified cytochrome c oxidase subunit 4 isoform 1 (COX4I1) as a novel vulnerability in acute myeloid leukemia (AML). Depletion of COX4I1 hindered leukemia cell proliferation and impacted in vivo AML progression. Mechanistically, loss of COX4I1 induced mitochondrial stress and ferroptosis, disrupting mitochondrial ultrastructure and oxidative phosphorylation. CRISPR gene tiling scans, coupled with mitochondrial proteomics, dissected critical regions within COX4I1 essential for leukemia cell survival, providing detailed insights into the mitochondrial Complex IV assembly network. Furthermore, COX4I1 depletion or pharmacological inhibition of Complex IV (using chlorpromazine) synergized with venetoclax, providing a promising avenue for improved leukemia therapy. This study highlights COX4I1, a nuclear encoded mitochondrial protein, as a critical mitochondrial checkpoint, offering insights into its functional significance and potential clinical implications in AML.