Identification Of Essential Genes Research Articles

Abstract 1878: CTPS1: An unexplored vulnerability in breast and ovarian cancer

Abstract Breast and ovarian cancer represent the most common cancer types in women worldwide and are among the top fatal cancers. Despite the use of cytotoxic chemotherapy and targeted agents, recurrence is frequent and survival rates for patients with advanced metastatic disease are dismal. These realities highlight the need to identify novel biomarkers and therapeutic vulnerabilities in such tumors and to develop alternative treatment strategies. We took an unbiased approach to tackle this problem in which we leveraged publically available (DepMap) and in-house generated genome-wide CRISPR screens conducted in over 1000 cancer cell lines. We initially focused on the identification of essential genes that were unique to triple-negative breast cancer (TNBC) cells and whose expression levels were associated with worse patient outcomes. This interrogation revealed six genes (CTPS1, RHOA, PRKRA, RAD9A, HUS1, and RAD1) meeting these initial criteria. The essentiality of these genes was validated using gene-specific siRNAs in a panel of TNBC cells and knockdown of CTPS1 was shown to elicit the most inhibitory effect. CTPS1 depletion resulted in a rapid S-phase cell cycle arrest followed by apoptosis, not only in TNBC cells but also in highly aggressive ovarian cancer cells which share many molecular features with TNBC. CTPS1 converts uridine triphosphate to cytidine triphosphate, forming an essential nucleic acid required for multiple cellular processes. CTPS1 mRNA expression was substantially elevated in tumor vs. normal tissue, for both breast and ovarian cancer. Using over 40 breast and ovarian cancer cell lines, RT-PCR analyses confirmed increased expression of CTPS1 in TNBC and most ovarian cancer cell lines compared to other disease sub-types and “normal” cell line/tissue controls. Interestingly, chemoresistant and PARP inhibitor-resistant models exhibited the highest levels of CTPS1 among all cells analyzed. We subsequently identified and acquired STP938, a first-in-class highly selective CTPS1 inhibitor being developed by Step-Pharma. STP938 was found to elicit potent anti-cancer effects at nM concentrations across many cell line and patient-derived models, in both 2D and 3D culture systems. RNAseq analysis in aggressive cell line models following STP938 treatment or CTPS1 knockdown revealed significant regulation of genes related to DNA replication and the cell cycle pathways suggesting that combinatorial/sequential treatments of STP938 with replication stress/DNA damage response related drugs may be synergistic. STP938 is currently being developed for the treatment of lymphoma, and our findings support the repurposing of STP938 for breast and ovarian cancer, an avenue that we are currently developing. Citation Format: Xiyin Wang, Michael Emch, Lauren Voll, Rebecca Russell, Esther Rodman, Nicole Pearson, Xiaonan Hou, Scott Kaufmann, Saravut Weroha, Philip Beer, John Hawse. CTPS1: An unexplored vulnerability in breast and ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1878.

Genome streamlining to improve performance of a fast-growing cyanobacterium Synechococcus elongatus UTEX 2973.

Cyanobacteria are photosynthetic organisms that have garnered significant recognition as potential hosts for sustainable bioproduction. However, their complex regulatory networks pose significant challenges to major metabolic engineering efforts, thereby limiting their feasibility as production hosts. Genome streamlining has been demonstrated to be a successful approach for improving productivity and fitness in heterotrophs but is yet to be explored to its full potential in phototrophs. Here, we present the systematic reduction of the genome of the cyanobacterium exhibiting the fastest exponential growth, Synechococcus elongatus UTEX 2973. This work, the first of its kind in a photoautotroph, involved an iterative process using state-of-the-art genome-editing technology guided by experimental analysis and computational tools. CRISPR-Cas3 enabled large, progressive deletions of predicted dispensable regions and aided in the identification of essential genes. The large deletions were combined to obtain a strain with 55-kb genome reduction. The strains with streamlined genome showed improvement in growth (up to 23%) and productivity (by 22.7%) as compared to the wild type (WT). This streamlining strategy not only has the potential to develop cyanobacterial strains with improved growth and productivity traits but can also facilitate a better understanding of their genome-to-phenome relationships.IMPORTANCEGenome streamlining is an evolutionary strategy used by natural living systems to dispense unnecessary genes from their genome as a mechanism to adapt and evolve. While this strategy has been successfully borrowed to develop synthetic heterotrophic microbial systems with desired phenotype, it has not been extensively explored in photoautotrophs. Genome streamlining strategy incorporates both computational predictions to identify the dispensable regions and experimental validation using genome-editing tool, and in this study, we have employed a modified strategy with the goal to minimize the genome size to an extent that allows optimal cellular fitness under specified conditions. Our strategy has explored a novel genome-editing tool in photoautotrophs, which, unlike other existing tools, enables large, spontaneous optimal deletions from the genome. Our findings demonstrate the effectiveness of this modified strategy in obtaining strains with streamlined genome, exhibiting improved fitness and productivity.

Computational analysis of genes with lethal knockout phenotype and prediction of essential genes in archaea.

The identification of microbial genes essential for survival as those with lethal knockout phenotype (LKP) is a common strategy for functional interrogation of genomes. However, interpretation of the LKP is complicated because a substantial fraction of the genes with this phenotype remains poorly functionally characterized. Furthermore, many genes can exhibit LKP not because their products perform essential cellular functions but because their knockout activates the toxicity of other genes (conditionally essential genes). We analyzed the sets of LKP genes for two archaea, Methanococcus maripaludis and Sulfolobus islandicus, using a variety of computational approaches aiming to differentiate between essential and conditionally essential genes and to predict at least a general function for as many of the proteins encoded by these genes as possible. This analysis allowed us to predict the functions of several LKP genes including previously uncharacterized subunit of the GINS protein complex with an essential function in genome replication and of the KEOPS complex that is responsible for an essential tRNA modification as well as GRP protease implicated in protein quality control. Additionally, several novel antitoxins (conditionally essential genes) were predicted, and this prediction was experimentally validated by showing that the deletion of these genes together with the adjacent genes apparently encoding the cognate toxins caused no growth defect. We applied principal component analysis based on sequence and comparative genomic features showing that this approach can separate essential genes from conditionally essential ones and used it to predict essential genes in other archaeal genomes.IMPORTANCEOnly a relatively small fraction of the genes in any bacterium or archaeon is essential for survival as demonstrated by the lethal effect of their disruption. The identification of essential genes and their functions is crucial for understanding fundamental cell biology. However, many of the genes with a lethal knockout phenotype remain poorly functionally characterized, and furthermore, many genes can exhibit this phenotype not because their products perform essential cellular functions but because their knockout activates the toxicity of other genes. We applied state-of-the-art computational methods to predict the functions of a number of uncharacterized genes with the lethal knockout phenotype in two archaeal species and developed a computational approach to predict genes involved in essential functions. These findings advance the current understanding of key functionalities of archaeal cells.

CRISPRi-Driven Genetic Screening for Designing Novel Microbial Phenotypes.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 system has enabled rapid advances in genomic engineering and transcriptional regulation. Specifically, CRISPR interference (CRISPRi) system has been used to systematically investigate the gene functions of microbial strains in a high-throughput manner. This method involves growth profiling using cells that have been transformed with the deactivated Cas9 (dCas9) and single-guide RNA (sgRNA) libraries that target individual genes. The fitness scores of each gene are calculated by measuring the abundance of individual sgRNAs during cell growth and represent gene essentiality. In this chapter, a process is described for functional genetic screening using CRISPRi at the whole-genome scale, starting from the synthesis of sgRNA libraries, construction of CRISPRi libraries, and identification of essential genes through growth profiling. The commensal bacterium Bacteroides thetaiotaomicron was used to implement the protocol. This method is expected to be applicable to a broader range of microorganisms to explore the novel phenotypic characteristics of microorganisms.

Identification of Essential Genes for the Establishment of Spray-Induced Gene Silencing-Based Disease Control in Fusarium graminearum.

As resistance to chemical fungicides continues to increase inFusarium graminearum, there is a growing need to develop novel disease control strategies. To discover essential genes that could serve as new disease control targets, we selected essential gene candidates that had failed to be deleted in previous studies. Thirteen genes were confirmed to be essential, either by constructing conditional promoter replacement mutants or by employing a clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9)-mediated editing strategy. We synthesized double-stranded RNAs (dsRNAs) targeting these essential genes and analyzed their protective effects in plants using a spray-induced gene silencing (SIGS) method. When dsRNAs targeting Fg10360, Fg13150, and Fg06123 were applied to detached barley leaves prior to fungal inoculation, disease lesions were greatly reduced. Our findings provide evidence of the potential of essential genes identified by a SIGS method to be effective targets for the control of fungal diseases.

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes.

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Genome-scale analysis of essential gene knockout mutants to identify an antibiotic target process.

We describe a genome-scale approach to identify the essential biological process targeted by a new antibiotic. The procedure is based on the identification of essential genes whose inactivation sensitizes a Gram-negative bacterium (Acinetobacter baylyi) to a drug and employs recently developed transposon mutant screening and single-mutant validation procedures. The approach, based on measuring the rates of loss of newly generated knockout mutants in the presence of antibiotic, provides an alternative to traditional procedures for studying essential functions using conditional expression or activity alleles. As a proof of principle study, we evaluated whether mutations enhancing sensitivity to the β-lactam antibiotic meropenem corresponded to the known essential target process of the antibiotic (septal peptidoglycan synthesis). We found that indeed mutations inactivating most genes needed for peptidoglycan synthesis and cell division strongly sensitized cells to meropenem. Additional classes of sensitizing mutations in essential genes were also identified, including those that inactivated capsule synthesis, DNA replication, or envelope stress response regulation. The essential capsule synthesis mutants appeared to enhance meropenem sensitivity by depleting a precursor needed for both capsule and peptidoglycan synthesis. The replication mutants may sensitize cells by impairing division. Nonessential gene mutations sensitizing cells to meropenem were also identified in the screen and largely corresponded to functions subordinately associated with the essential target process, such as in peptidoglycan recycling. Overall, these results help validate a new approach to identify the essential process targeted by an antibiotic and define the larger functional network determining sensitivity to it.

Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures. We propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19. Materials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis.

P13.04.A REPRODUCIBLE CRISPR-SEQ PIPELINE FOR NGS FUNCTIONAL GENOMICS SCREENINGS FOR GLIOMA CELL LINES

Abstract BACKGROUND The CRISPR/Cas9 genome editing technology has revolutionized the field of functional genomics by enabling precise editing of DNA. Over the past few years, CRISPR technology has been widely employed to identify genetic dependencies in a broad spectrum of applications paving the way for the discovery and validation of relevant molecular targets, for example potential drug targetsGlioblastoma are incurable primary tumors of the central nervous system. Functional genomics could enable the discovery of novel therapeutic targets or even combination therapies. MATERIAL AND METHODS Here, we present a bioinformatics pipeline that combines functional screens and target editing analysis to identify potential vulnerabilities and drug targets in glioblastoma or other diseases. The pipeline consists of two workflows, one performing functional screening and one performing base editing analysis. This developed CRISPR-seq pipeline is reproducible and containerized, allowing high-throughput processing. All processes are modularized in Nextflow DSL2 (Domain Specific Language) and versioned which facilitates maintainability and customization, as well as the addition of new tools. A quality control report is also automatically generated, allowing the user an overview of their ran experiments. The pipeline also helps us to keep the data FAIR (Findable, Accessible, Interoperable and Reusable) and optimized, making it easier to share with other researchers and collaborate on future studies. RESULTS This pipeline was applied to functional screens using CRISPR/Cas9 genome-wide knockout or activation libraries to detect genetic vulnerabilities that might enhance drug treatment efficacy, e.g. for ataxia telangiectasia and Rad3 related (ATR) inhibition in experimental glioma.With this pipeline, we were able to identify several genes that are involved in drug resistance in glioblastoma, by starting from raw data (fastq files) to genes presenting vulnerabilities.The results obtained through our pipeline provided important information on the disease's molecular mechanisms, which were subsequently confirmed through wet lab validation. CONCLUSION In conclusion, the crisprseq software described in this abstract is a powerful tool for identifying potential genetic dependencies. The pipeline aims at analyzing this output data in a reproducible and containerized way using Nextflow and respecting nf-core community standards. By combining functional screens and base editing analysis with bioinformatics tools, this pipeline allows for the identification of essential genes and pathways, as well as the characterization of specific mutations that contribute to the development and progression of glioblastoma.

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria.

The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein-protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under "persistent," inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under "shell." Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.

Identification of essential genes and immune cell infiltration in rheumatoid arthritis by bioinformatics analysis

Rheumatoid arthritis (RA) is a common autoimmune disease that can lead to severe joint damage and disability. And early diagnosis and treatment of RA can avert or substantially slow the progression of joint damage in up to 90% of patients, thereby preventing irreversible disability. Previous research indicated that 50% of the risk for the development of RA is attributable to genetic factors, but the pathogenesis is not well understood. Thus, it is urgent to identify biomarkers to arrest RA before joints are irreversibly damaged. Here, we first use the Robust Rank Aggregation method (RRA) to identify the differentially expressed genes (DEGs) between RA and normal samples by integrating four public RA patients’ mRNA expression data. Subsequently, these DEGs were used as the input for the weighted gene co-expression network analysis (WGCNA) approach to identify RA-related modules. The function enrichment analysis suggested that the RA-related modules were significantly enriched in immune-related actions. Then the hub genes were defined as the candidate genes. Our analysis showed that the expression levels of candidate genes were significantly associated with the RA immune microenvironment. And the results indicated that the expression of the candidate genes can use as predictors for RA. We hope that our method can provide a more convenient approach for the early diagnosis of RA.

Identification of essential genes in Coxiella burnetii.

Coxiella burnetii is an intracellular pathogen responsible for causing Q fever in humans, a disease with varied presentations ranging from a mild flu-like sickness to a debilitating illness that can result in endocarditis. The intracellular lifestyle of C. burnetii is unique, residing in an acidic phagolysosome-like compartment within host cells. An understanding of the core molecular biology of C. burnetii will greatly increase our understanding of C. burnetii growth, survival and pathogenesis. We used transposon-directed insertion site sequencing (TraDIS) to reveal C. burnetii Nine Mile Phase II genes fundamental for growth and in vitro survival. Screening a transposon library containing >10 000 unique transposon mutants revealed 512 predicted essential genes. Essential routes of synthesis were identified for the mevalonate pathway, as well as peptidoglycan and biotin synthesis. Some essential genes identified (e.g. predicted type IV secretion system effector genes) are typically considered to be associated with C. burnetii virulence, a caveat concerning the axenic media used in the study. Investigation into the conservation of the essential genes identified revealed that 78 % are conserved across all C. burnetii strains sequenced to date, which probably play critical functions. This is the first report of a whole genome transposon screen in C. burnetii that has been undertaken for the identification of essential genes.

Genetic and metabolic engineering of Methanococcus spp

Methanococcus is a genus of well-characterized mesophilic and hydrogenotrophic methanogens with a robust genetic system. Commonly found in tidal marshes, they compete with sulfate- and iron-reducing bacteria for hydrogen gas (H2) and formate, their two major energy sources. They are characterized by rapid growth in mineral medium and requirements for marine levels of salts for optimal growth. Genetic tools developed over the last twenty years include improvements in shuttle vectors for production of recombinant proteins and CRISPR systems for mutagenesis as well as a variety of marker proteins for studies of gene expression. These tools have enabled functional characterization of the genome, including regulation of the transcriptome and proteome, mapping of transcription start and termination sites, and identification of essential genes. Metabolic and gene regulatory network models have been created. This foundation in the basic biology of Methanococcus provides an opportunity for development of biotechnological applications of these hydrogenotrophic methanogens.

Identification of essential genes in Mycobacterium avium subsp. paratuberculosis genome for persistence in dairy calves.

To cause disease Mycobacterium avium subsp. paratuberculosis needs to enter mammalian cells, arrest phagosomal maturation and manipulate the host immune system. The genetic basis of the bacterial capacity to achieve these outcomes remains largely unknown. Identifying these genes would allow us to gain a deeper understanding of MAP's pathogenesis and potentially develop a live attenuated Johne's disease vaccine by knocking out these genes. MAP genes demonstrated to be essential for colonization in the natural host, ruminants, are unknown. Genome-wide transposon mutagenesis and high-throughput sequencing were combined to evaluate the essentiality of each coding region in the bacterial genome to survive in dairy calves. A saturated library of 3,852 MAP Tn mutants, with insertions in 56% of TA sites, interrupting 88% of genes, was created using a MycoMarT7 phagemid containing a mariner transposon. Six calves were inoculated with a high dose of a library of MAP mutants, 1011 CFUs, (input) at 2 weeks of age. Following 2 months of incubation, MAP cells were isolated from the ileum, jejunum, and their associated lymph nodes of calves, resulting in approximately 100,000 colonies grown on solid media across 6 animals (output). Targeted next-generation sequencing was used to identify the disrupted genes in all the mutants in the input pool and the output pool recovered from the tissues to identify in vivo essential genes. Statistical analysis for the determination of essential genes was performed by a Hidden Markov Model (HMM), categorizing genes into essential genes that are devoid of insertions and growth-defect genes whose disruption impairs the growth of the organism. Sequence analysis identified 430 in vivo essential and 260 in vivo growth-defect genes. Gene ontology enrichment analysis of the in vivo essential and growth-defect genes with the highest reduction in the tissues revealed a high representation of genes involved in metabolism and respiration, cell wall and cell processing, virulence, and information pathway processes. This study has systematically identified essential genes for the growth and persistence of MAP in the natural host body.

Exploration of Streptococcus core genome to reveal druggable targets and novel therapeutics against S. pneumoniae.

Streptococcus pneumoniae (S. pneumoniae), the major etiological agent of community-acquired pneumonia (CAP) contributes significantly to the global burden of infectious diseases which is getting resistant day by day. Nearly 30% of the S. pneumoniae genomes encode hypothetical proteins (HPs), and better understandings of these HPs in virulence and pathogenicity plausibly decipher new treatments. Some of the HPs are present across many Streptococcus species, systematic assessment of these unexplored HPs will disclose prospective drug targets. In this study, through a stringent bioinformatics analysis of the core genome and proteome of S. pneumoniae PCS8235, we identified and analyzed 28 HPs that are common in many Streptococcus species and might have a potential role in the virulence or pathogenesis of the bacteria. Functional annotations of the proteins were conducted based on the physicochemical properties, subcellular localization, virulence prediction, protein-protein interactions, and identification of essential genes, to find potentially druggable proteins among 28 HPs. The majority of the HPs are involved in bacterial transcription and translation. Besides, some of them were homologs of enzymes, binding proteins, transporters, and regulators. Protein-protein interactions revealed HP PCS8235_RS05845 made the highest interactions with other HPs and also has TRP structural motif along with virulent and pathogenic properties indicating it has critical cellular functions and might go under unconventional protein secretions. The second highest interacting protein HP PCS8235_RS02595 interacts with the Regulator of chromosomal segregation (RocS) which participates in chromosome segregation and nucleoid protection in S. pneumoniae. In this interacting network, 54% of protein members have virulent properties and 40% contain pathogenic properties. Among them, most of these proteins circulate in the cytoplasmic area and have hydrophilic properties. Finally, molecular docking and dynamics simulation demonstrated that the antimalarial drug Artenimol can act as a drug repurposing candidate against HP PCS8235_RS 04650 of S. pneumoniae. Hence, the present study could aid in drugs against S. pneumoniae.

Systematic Identification of Essential Genes Required for Yeast Cell Wall Integrity: Involvement of the RSC Remodelling Complex.

Conditions altering the yeast cell wall lead to the activation of an adaptive transcriptional response mainly governed by the cell wall integrity (CWI) mitogen-activated protein kinase (MAPK) pathway. Two high-throughput screenings were developed using the yTHC collection of yeast conditional mutant strains to systematically identify essential genes related to cell wall integrity, and those required for the transcriptional program elicited by cell wall stress. Depleted expression of 52 essential genes resulted in hypersensitivity to the dye Calcofluor white, with chromatin organization, Golgi vesicle transport, rRNA processing, and protein glycosylation processes, as the most highly representative functional groups. Via a flow cytometry-based quantitative assay using a CWI reporter plasmid, 97 strains exhibiting reduced gene-reporter expression levels upon stress were uncovered, highlighting genes associated with RNA metabolism, transcription/translation, protein degradation, and chromatin organization. This screening also led to the discovery of 41 strains displaying a basal increase in CWI-associated gene expression, including mainly putative cell wall-related genes. Interestingly, several members of the RSC chromatin remodelling complex were uncovered in both screenings. Notably, Rsc9 was necessary to regulate the gene expression of CWI-related genes both under stress and non-stress conditions, suggesting distinct requirements of the RSC complex for remodelling particular genes.

Abstract 3962: Mechanisms of tumor recurrence and drug resistance in rhabdomyosarcoma

Abstract Rhabdomyosarcoma (RMS) is an aggressive pediatric soft tissue sarcoma, which constitutes of two main histological subtypes. Alveolar Rhabdomyosarcoma (FP-RMS) is characterized by a fusion protein PAX3-FKHR, whereas embryonal Rhabdomyosarcoma (FN-RMS), which is the focus of this project, is more genetically heterogeneous. The prognosis for RMS has improved over the years, however the cure rates for recurrent or relapse disease remains dismal, warranting further identification of novel therapeutic interventions, which is the aim of this project. To this end, we performed two CRISPR knockout screens, using a kinome sgRNA library and a whole genome sgRNA library in combination with etoposide at a concentration of IC10, in order to identify genes that might confer resistance to chemotherapeutic drugs. In addition, the kinome screen was also designed to identify essential genes by using the treatment control samples as further time points. For treatment associated genes, we identified components of the JNK/P38 and Hippo signaling pathways among the top hits. We also found enrichment of a novel pathway involving HIFα stabilization via the genes LRRK2 and PTK6. Further and as expected, many DNA damage repair pathways were enriched. Finally in this category, players of the Wnt signaling pathway including JNK1, which is involved in non-canonical Wnt signaling were found to be enriched. As for essential gene identification, many cell cycle genes such as CDK6, PLK3 and PLK4 were among the top hits, as well as WEE1, which has been implicated in the context of RMS, were common between all the time points. Taken together, the CRISPR screens identified potential pathways that might be involved in resistance of FN-RMS cells towards etoposide. Selected candidates are being validated via an in vitro competition assay and drug screening is being performed to identify inhibitors that could be used to re-sensitize cells to treatment with standard chemotherapy. Citation Format: Devmini C. Moonamale, Marco Wachtel, Beat W. Schäfer. Mechanisms of tumor recurrence and drug resistance in rhabdomyosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3962.

Genome editing using preassembled CRISPR-Cas9 ribonucleoprotein complexes in Fusarium graminearum.

Genome editing using the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) system has greatly facilitated the genetic analysis of fungal pathogens. The head blight fungus, Fusarium graminearum, causes destructive losses of economically important cereal crops. The recent development of the CRISPR-Cas9 system for use with F. graminearum has enabled more efficient genome editing. In this study, we described a CRISPR-Cas9-based genome-editing tool for the direct delivery of preassembled Cas9 ribonucleoproteins (RNPs) into the protoplasts of F. graminearum. The use of RNPs significantly increased both the number of transformants and percentage of transformants in which the target gene was successfully replaced with a selectable marker. We showed that a single double-strand DNA break mediated by the Cas9 ribonucleoprotein was sufficient for gene deletion. In addition, short-homology recombination required only 50 base pair regions flanking the target gene. The high efficiency of Cas9 RNPs enables large-scale functional analysis, the identification of essential genes, and gene deletion that is difficult with conventional methods. We expect that our approach will accelerate genetic studies of F. graminearum.

An RNA Interference Tool to Silence Genes in Sarcoptes scabiei Eggs.

In a quest for new interventions against scabies—a highly significant skin disease of mammals, caused by a parasitic mite Sarcoptes scabiei—we are focusing on finding new intervention targets. RNA interference (RNAi) could be an efficient functional genomics approach to identify such targets. The RNAi pathway is present in S. scabiei and operational in the female adult mite, but other developmental stages have not been assessed. Identifying potential intervention targets in the egg stage is particularly important because current treatments do not kill this latter stage. Here, we established an RNAi tool to silence single-copy genes in S. scabiei eggs. Using sodium hypochlorite pre-treatment, we succeeded in rendering the eggshell permeable to dsRNA without affecting larval hatching. We optimised the treatment of eggs with gene-specific dsRNAs to three single-copy target genes (designated Ss-Cof, Ss-Ddp, and Ss-Nan) which significantly and repeatedly suppressed transcription by ~66.6%, 74.3%, and 84.1%, respectively. Although no phenotypic alterations were detected in dsRNA-treated eggs for Ss-Cof and Ss-Nan, the silencing of Ss-Ddp resulted in a 38% reduction of larval hatching. This RNAi method is expected to provide a useful tool for larger-scale functional genomic investigations for the identification of essential genes as potential drug targets.

Prediction of gene essentiality using machine learning and genome-scale metabolic models

The identification of essential genes, i.e. those that impair cell survival when deleted, requires large growth assays of knock-out strains. The complexity and cost of such experiments has triggered a growing interest in computational methods for prediction of gene essentiality. In the case of metabolic genes, Flux Balance Analysis (FBA) is widely employed to predict essentiality under the assumption that cells maximize their growth rate. However, this approach assumes that knock-out strains optimize the same objectives as the wild-type, which excludes cases in which deletions cause large physiological changes to meet other objectives for survival. Here, we resolve this limitation with a novel machine learning approach that predicts essentiality directly from wild-type flux distributions. We first project the wild-type FBA solution onto a mass flow graph, a digraph with reactions as nodes and edge weights proportional to the mass transfer between reactions, and then train binary classifiers on the connectivity of graph nodes. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli, achieving near state-of-the art prediction accuracy for essential genes. Our approach suggests that wild-type FBA solutions contain enough information to predict essentiality, without the need to assume optimality of deletion strains.