CRISPRcloud: a secure cloud-based pipeline for CRISPR pooled screen deconvolution

Genome-Wide CRISPR Screen Identifies an NF2-Adherens Junction Mechanistic Dependency for Cardiac Lineage

BACKGROUND

Cardiomyocyte differentiation involves a stepwise clearance of repressors and fate-restricting regulators through the modulation of BMP (bone morphogenic protein)/Wnt-signaling pathways. However, the mechanisms and how regulatory roadblocks are removed with specific developmental signaling pathways remain unclear.

METHODS

We conducted a genome-wide CRISPR screen to uncover essential regulators of cardiomyocyte specification in human embryonic stem cells using a myosin heavy chain 6 (MYH6)-GFP (green fluorescence protein) reporter system. After an independent secondary single guide ribonucleic acid validation of 25 candidates, we identified NF2 (neurofibromin 2), a moesin-ezrin-radixin like (MERLIN) tumor suppressor, as an upstream driver of early cardiomyocyte lineage specification. Independent monoclonal NF2 knockouts were generated using CRISPR-Cas9, and cell states were inferred through bulk RNA sequencing and protein expression analysis across differentiation time points. Terminal lineage differentiation was assessed by using an in vitro 2-dimensional-micropatterned gastruloid model, trilineage differentiation, and cardiomyocyte differentiation. Protein interaction and post-translation modification of NF2 with its interacting partners were assessed using site-directed mutagenesis, coimmunoprecipitation, and proximity ligation assays.

RESULTS

Transcriptional regulation and trajectory inference from NF2-null cells reveal the loss of cardiomyocyte identity and the acquisition of nonmesodermal identity. Sustained elevation of early mesoderm lineage repressor SOX2 and upregulation of late anticardiac regulators CDX2 and MSX1 in NF2 knockout cells reflect a necessary role for NF2 in removing regulatory roadblocks. Furthermore, we found that NF2 and AMOT (angiomotin) cooperatively bind to YAP (yes-associated protein) during mesendoderm formation, thereby preventing YAP activation, independent of canonical MST (mammalian sterile 20-like serine-threonine protein kinase)-LATS (large tumor suppressor serine-threonine protein kinase) signaling. Mechanistically, cardiomyocyte lineage identity was rescued by wild-type and NF2 serine-518 phosphomutants, but not NF2 FERM (ezrin-radixin-meosin homology protein) domain blue-box mutants, demonstrating that the critical FERM domain-dependent formation of the AMOT-NF2-YAP scaffold complex at the adherens junction is required for early cardiomyocyte lineage differentiation.

CONCLUSIONS

These results provide mechanistic insight into the essential role of NF2 during early epithelial-mesenchymal transition by sequestering the repressive effect of YAP and relieving regulatory roadblocks en route to cardiomyocytes.

Evolutionarily conserved regulators of tau identify targets for new therapies

Tauopathies are neurodegenerative diseases that involve the pathological accumulation of tau proteins; in this family are Alzheimer disease, corticobasal degeneration, and chronic traumatic encephalopathy, among others. Hypothesizing that reducing this accumulation could mitigate pathogenesis, we performed a cross-species genetic screen targeting 6,600 potentially druggable genes in human cells and Drosophila. We found and validated 83 hits in cells and further validated 11 hits in the mouse brain. Three of these hits (USP7, RNF130, and RNF149) converge on the C terminus of Hsc70-interacting protein (CHIP) to regulate tau levels, highlighting the role of CHIP in maintaining tau proteostasis in the brain. Knockdown of each of these three genes in adult tauopathy mice reduced tau levels and rescued the disease phenotypes. This study thus identifies several points of intervention to reduce tau levels and demonstrates that reduction of tau levels via regulation of this pathway is a viable therapeutic strategy for Alzheimer disease and other tauopathies.

CRISPR genome editing using computational approaches: A survey

Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has been widely used in various cell types and organisms. To make genome editing with Clustered regularly interspaced short palindromic repeats far more precise and practical, we must concentrate on the design of optimal gRNA and the selection of appropriate Cas enzymes. Numerous computational tools have been created in recent years to help researchers design the best gRNA for Clustered regularly interspaced short palindromic repeats researches. There are two approaches for designing an appropriate gRNA sequence (which targets our desired sites with high precision): experimental and predicting-based approaches. It is essential to reduce off-target sites when designing an optimal gRNA. Here we review both traditional and machine learning-based approaches for designing an appropriate gRNA sequence and predicting off-target sites. In this review, we summarize the key characteristics of all available tools (as far as possible) and compare them together. Machine learning-based tools and web servers are believed to become the most effective and reliable methods for predicting on-target and off-target activities of Clustered regularly interspaced short palindromic repeats in the future. However, these predictions are not so precise now and the performance of these algorithms -especially deep learning one's-depends on the amount of data used during training phase. So, as more features are discovered and incorporated into these models, predictions become more in line with experimental observations. We must concentrate on the creation of ideal gRNA and the choice of suitable Cas enzymes in order to make genome editing with Clustered regularly interspaced short palindromic repeats far more accurate and feasible.

Identifying Genetic Regulators of Protein-Glycan Interactions with Genome-Wide CRISPR Screening

Glycans are carbohydrate molecules appended to proteins and lipids on the surface of all living cells. Glycans play key roles in a wide array of biological processes, and structural changes in cell-surface glycosylation patterns have been connected to pathogenesis of several diseases. In particular, cancer cells frequently upregulate expression of glycans that bind to inhibitory receptors (lectins) on immune cells. These glycosylated antigens systematically inhibit immune activity and protect cancer cells from immune surveillance. Understanding how cancer cells generate these glycan ligands can thus lead to identification of novel druggable targets for therapeutic intervention. However, glycan ligand biosynthesis is subject to extremely complex genetic regulation, making it difficult to identify the key genes involved in production of immune-regulatory glycan antigens. In a recent publication, we described a CRISPR/Cas9 screening approach to identify genes that drive synthesis of ligands for glycan-binding immune receptors. Here, we outline a detailed, step-by-step protocol for completing this type of genome-wide screen. Our protocol produces a genome-wide atlas of all genes whose expression is required for cell-surface binding of a recombinant immune lectin. This dataset can be used both to identify novel ligands for immune lectins and annotate regulatory genes that drive changes in cancer-associated glycosylation. Our protocol serves as a general resource for researchers interested in the detailed study of cancer glyco-immunology. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Generation of a genome-wide CRISPR library using lentiviral transduction Support Protocol: Generation of dCas9KRAB-expressing K-562 cells Basic Protocol 2: Staining of genome-wide CRISPR libraries with Siglec-Fc reagents and fluorescence-activated cell sorting Basic Protocol 3: Library amplification and sequencing Basic Protocol 4: Data analysis and hit identification.

Base editor enables rational genome-scale functional screening for enhanced industrial phenotypes in Corynebacterium glutamicum

Genome-scale functional screening accelerates comprehensive assessment of gene function in cells. Here, we have established a genome-scale loss-of-function screening strategy that combined a cytosine base editor with approximately 12,000 parallel sgRNAs targeting 98.1% of total genes in Corynebacterium glutamicum ATCC 13032. Unlike previous data processing methods developed in yeast or mammalian cells, we developed a new data processing procedure to locate candidate genes by statistical sgRNA enrichment analysis. Known and novel functional genes related to 5-fluorouracil resistance, 5-fluoroorotate resistance, oxidative stress tolerance, or furfural tolerance have been identified. In particular, purU and serA were proven to be related to the furfural tolerance in C. glutamicum. A cloud platform named FSsgRNA-Analyzer was provided to accelerate sequencing data processing for CRISPR-based functional screening. Our method would be broadly useful to functional genomics study and strain engineering in other microorganisms.

Cross-species genetic screens identify transglutaminase 5 as a regulator of polyglutamine-expanded ataxin-1

Many neurodegenerative disorders are caused by abnormal accumulation of misfolded proteins. In spinocerebellar ataxia type 1 (SCA1), accumulation of polyglutamine-expanded (polyQ-expanded) ataxin-1 (ATXN1) causes neuronal toxicity. Lowering total ATXN1, especially the polyQ-expanded form, alleviates disease phenotypes in mice, but the molecular mechanism by which the mutant ATXN1 is specifically modulated is not understood. Here, we identified 22 mutant ATXN1 regulators by performing a cross-species screen of 7787 and 2144 genes in human cells and Drosophila eyes, respectively. Among them, transglutaminase 5 (TG5) preferentially regulated mutant ATXN1 over the WT protein. TG enzymes catalyzed cross-linking of ATXN1 in a polyQ-length-dependent manner, thereby preferentially modulating mutant ATXN1 stability and oligomerization. Perturbing Tg in Drosophila SCA1 models modulated mutant ATXN1 toxicity. Moreover, TG5 was enriched in the nuclei of SCA1-affected neurons and colocalized with nuclear ATXN1 inclusions in brain tissue from patients with SCA1. Our work provides a molecular insight into SCA1 pathogenesis and an opportunity for allele-specific targeting for neurodegenerative disorders.

Intermedilysin cytolytic activity depends on heparan sulfates and membrane composition

Cholesterol-dependent cytolysins (CDCs), of which intermedilysin (ILY) is an archetypal member, are a group of pore-forming toxins secreted by a large variety of pathogenic bacteria. These toxins, secreted as soluble monomers, oligomerize upon interaction with cholesterol in the target membrane and transect it as pores of diameters of up to 100 to 300 Å. These pores disrupt cell membranes and result in cell lysis. The immune receptor CD59 is a well-established cellular factor required for intermedilysin pore formation. In this study, we applied genome-wide CRISPR-Cas9 knock-out screening to reveal additional cellular co-factors essential for ILY-mediated cell lysis. We discovered a plethora of genes previously not associated with ILY, many of which are important for membrane constitution. We show that heparan sulfates facilitate ILY activity, which can be inhibited by heparin. Furthermore, we identified hits in both protein and lipid glycosylation pathways and show a role for glucosylceramide, demonstrating that membrane organization is important for ILY activity. We also cross-validated identified genes with vaginolysin and pneumolysin and found that pneumolysin's cytolytic activity strongly depends on the asymmetric distribution of membrane phospholipids. This study shows that membrane-targeting toxins combined with genetic screening can identify genes involved in biological membrane composition and metabolism.

Trends in CRISPR-Cas9 technology application in cancer

The evolution of the CRISPR-Cas9 technology in cancer research has tremendous potential to shape the future of oncology. Although this gene-editing tool's pre-clinical progress is into its nascent stage, there are many unanswered questions regarding health benefits and therapy precision using CRISPR. The application of CRISPR is highly specific, economically sustainable, and is a high throughput technique, but on the other hand, its application involves measured risk of countering the toxic immune response of Cas protein, off-target effects, limitation of delivering the edited cells back into cancer patients. The current chapter highlights the possibilities and perils of the present-day CRISPR engineering in cancer that should highlight CRISPR translation to therapy.

Computational Methods for Analysis of Large-Scale CRISPR Screens

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

Technologies and Computational Analysis Strategies for CRISPR Applications

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

Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study

BACKGROUND

Cloud computing for microbiome data sets can significantly increase working efficiencies and expedite the translation of research findings into clinical practice. The Amazon Web Services (AWS) cloud provides an invaluable option for microbiome data storage, computation, and analysis.

OBJECTIVE

The goals of this study were to develop a microbiome data analysis pipeline by using AWS cloud and to conduct a proof-of-concept test for microbiome data storage, processing, and analysis.

METHODS

A multidisciplinary team was formed to develop and test a reproducible microbiome data analysis pipeline with multiple AWS cloud services that could be used for storage, computation, and data analysis. The microbiome data analysis pipeline developed in AWS was tested by using two data sets: 19 vaginal microbiome samples and 50 gut microbiome samples.

RESULTS

Using AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances (ie, servers) for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline. Bioinformatics and statistical tools (ie, Quantitative Insights Into Microbial Ecology 2 and RStudio) were installed within the Linux EC2 instances to run microbiome statistical analysis. The microbiome data analysis pipeline was performed through command-line interfaces within the Linux operating system or in the Mac operating system. Using this new pipeline, we were able to successfully process and analyze 50 gut microbiome samples within 4 hours at a very low cost (a c4.4xlarge EC2 instance costs $0.80 per hour). Gut microbiome findings regarding diversity, taxonomy, and abundance analyses were easily shared within our research team.

CONCLUSIONS

Building a microbiome data analysis pipeline with AWS cloud is feasible. This pipeline is highly reliable, computationally powerful, and cost effective. Our AWS-based microbiome analysis pipeline provides an efficient tool to conduct microbiome data analysis.

Beta-binomial modeling of CRISPR pooled screen data identifies target genes with greater sensitivity and fewer false negatives

The simplicity and cost-effectiveness of CRISPR technology have made high-throughput pooled screening approaches accessible to virtually any laboratory. Analyzing the large sequencing data derived from these studies, however, still demands considerable bioinformatics expertise. Various methods have been developed to lessen this requirement, but there are still three tasks for accurate CRISPR screen analysis that involve bioinformatic know-how, if not prowess: designing a proper statistical hypothesis test for robust target identification, developing an accurate mapping algorithm to quantify sgRNA levels, and minimizing the parameters that need to be fine-tuned. To make CRISPR screen analysis more reliable as well as more readily accessible, we have developed a new algorithm, called CRISPRBetaBinomial or CB2 Based on the beta-binomial distribution, which is better suited to sgRNA data, CB2 outperforms the eight most commonly used methods (HiTSelect, MAGeCK, PBNPA, PinAPL-Py, RIGER, RSA, ScreenBEAM, and sgRSEA) in both accurately quantifying sgRNAs and identifying target genes, with greater sensitivity and a much lower false discovery rate. It also accommodates staggered sgRNA sequences. In conjunction with CRISPRcloud, CB2 brings CRISPR screen analysis within reach for a wider community of researchers.

Functional Genomics via CRISPR-Cas

RNA-guided CRISPR (clustered regularly interspaced short palindromic repeat)-associated Cas proteins have recently emerged as versatile tools to investigate and engineer the genome. The programmability of CRISPR-Cas has proven especially useful for probing genomic function in high-throughput. Facile single-guide RNA library synthesis allows CRISPR-Cas screening to rapidly investigate the functional consequences of genomic, transcriptomic, and epigenomic perturbations. Furthermore, by combining CRISPR-Cas perturbations with downstream single-cell analyses (flow cytometry, expression profiling, etc.), forward screens can generate robust data sets linking genotypes to complex cellular phenotypes. In the following review, we highlight recent advances in CRISPR-Cas genomic screening while outlining protocols and pitfalls associated with screen implementation. Finally, we describe current challenges limiting the utility of CRISPR-Cas screening as well as future research needed to resolve these impediments. As CRISPR-Cas technologies develop, so too will their clinical applications. Looking ahead, patient centric functional screening in primary cells will likely play a greater role in disease management and therapeutic development.

A Druggable Genome Screen Identifies Modifiers of α-Synuclein Levels via a Tiered Cross-Species Validation Approach

Accumulation of α-Synuclein (α-Syn) causes Parkinson's disease (PD) as well as other synucleopathies. α-Syn is the major component of Lewy bodies and Lewy neurites, the proteinaceous aggregates that are a hallmark of sporadic PD. In familial forms of PD, mutations or copy number variations in SNCA (the α-Syn gene) result in a net increase of its protein levels. Furthermore, common risk variants tied to PD are associated with small increases of wild-type α-Syn levels. These findings are further bolstered by animal studies which show that overexpression of α-Syn is sufficient to cause PD-like features. Thus, increased α-Syn levels are intrinsically tied to PD pathogenesis and underscore the importance of identifying the factors that regulate its levels. In this study, we establish a pooled RNAi screening approach and validation pipeline to probe the druggable genome for modifiers of α-Syn levels and identify 60 promising targets. Using a cross-species, tiered validation approach, we validate six strong candidates that modulate α-Syn levels and toxicity in cell lines, Drosophila, human neurons, and mouse brain of both sexes. More broadly, this genetic strategy and validation pipeline can be applied for the identification of therapeutic targets for disorders driven by dosage-sensitive proteins.SIGNIFICANCE STATEMENT We present a research strategy for the systematic identification and validation of genes modulating the levels of α-Synuclein, a protein involved in Parkinson's disease. A cell-based screen of the druggable genome (>7,500 genes that are potential therapeutic targets) yielded many modulators of α-Synuclein that were subsequently confirmed and validated in Drosophila, human neurons, and mouse brain. This approach has broad applicability to the multitude of neurological diseases that are caused by mutations in genes whose dosage is critical for brain function.

CRISPRMatch: An Automatic Calculation and Visualization Tool for High-throughput CRISPR Genome-editing Data Analysis

Custom-designed nucleases, including CRISPR-Cas9 and CRISPR-Cpf1, are widely used to realize the precise genome editing. The high-coverage, low-cost and quantifiability make high-throughput sequencing (NGS) to be an effective method to assess the efficiency of custom-designed nucleases. However, contrast to standardized transcriptome protocol, the NGS data lacks a user-friendly pipeline connecting different tools that can automatically calculate mutation, evaluate editing efficiency and realize in a more comprehensive dataset that can be visualized. Here, we have developed an automatic stand-alone toolkit based on python script, namely CRISPRMatch, to process the high-throughput genome-editing data of CRISPR nuclease transformed protoplasts by integrating analysis steps like mapping reads and normalizing reads count, calculating mutation frequency (deletion and insertion), evaluating efficiency and accuracy of genome-editing, and visualizing the results (tables and figures). Both of CRISPR-Cas9 and CRISPR-Cpf1 nucleases are supported by CRISPRMatch toolkit and the integrated code has been released on GitHub (https://github.com/zhangtaolab/CRISPRMatch).

PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens

Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise. To make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots. PinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions and test datasets.

High-Throughput Approaches to Pinpoint Function within the Noncoding Genome

The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas nuclease system is a powerful tool for genome editing, and its simple programmability has enabled high-throughput genetic and epigenetic studies. These high-throughput approaches offer investigators a toolkit for functional interrogation of not only protein-coding genes but also noncoding DNA. Historically, noncoding DNA has lacked the detailed characterization that has been applied to protein-coding genes in large part because there has not been a robust set of methodologies for perturbing these regions. Although the majority of high-throughput CRISPR screens have focused on the coding genome to date, an increasing number of CRISPR screens targeting noncoding genomic regions continue to emerge. Here, we review high-throughput CRISPR-based approaches to uncover and understand functional elements within the noncoding genome and discuss practical aspects of noncoding library design and screen analysis.