Over the years, comprehensive explorations of the model organisms Caenorhabditis elegans (elegant worm) and Drosophila melanogaster (vinegar fly) have contributed substantially to our understanding of complex biological processes and pathways in multicellular organisms generally. Extensive functional genomic-phenomic, genomic, transcriptomic, and proteomic data sets have enabled the discovery and characterisation of genes that are crucial for life, called \'essential genes\'. Recently, we investigated the feasibility of inferring essential genes from such data sets using advanced bioinformatics and showed that a machine learning (ML)-based workflow could be used to extract or engineer features from DNA, RNA, protein, and/or cellular data/information to underpin the reliable prediction of essential genes both within and between C. elegans and D. melanogaster. As these are two distantly related species within the Ecdysozoa, we proposed that this ML approach would be particularly well suited for species that are within the same phylum or evolutionary clade. In the present study, we cross-predicted essential genes within the phylum Nematoda (evolutionary clade V)-between C. elegans and the pathogenic parasitic nematode H. contortus-and then ranked and prioritised H. contortus proteins encoded by these genes as intervention (e.g., drug) target candidates. Using strong, validated predictors, we inferred essential genes of H. contortus that are involved predominantly in crucial biological processes/pathways including ribosome biogenesis, translation, RNA binding/processing, and signalling and which are highly transcribed in the germline, somatic gonad precursors, sex myoblasts, vulva cell precursors, various nerve cells, glia, or hypodermis. The findings indicate that this in silico workflow provides a promising avenue to identify and prioritise panels/groups of drug target candidates in parasitic nematodes for experimental validation in vitro and/or in vivo.

The ability to sense and respond to osmotic fluctuations is critical for the maintenance of cellular integrity. We used gene co-essentiality analysis to identify an unappreciated relationship between TSC22D2, WNK1, and NRBP1 in regulating cell volume homeostasis. All of these genes have paralogs and are functionally buffered for osmo-sensing and cell volume control. Within seconds of hyperosmotic stress, TSC22D, WNK, and NRBP family members physically associate into biomolecular condensates, a process that is dependent on intrinsically disordered regions (IDRs). A close examination of these protein families across metazoans revealed that TSC22D genes evolved alongside a domain in NRBPs that specifically binds to TSC22D proteins, which we have termed NbrT (NRBP binding region with TSC22D), and this co-evolution is accompanied by rapid IDR length expansion in WNK-family kinases. Our study reveals that TSC22D, WNK, and NRBP genes evolved in metazoans to co-regulate rapid cell volume changes in response to osmolarity.

The baculovirus expression vector system (BEVS) has now found acceptance in both research laboratories and industry, which can be attributed to many of its key features including the limited host range of the vectors, their non-pathogenicity to humans, and the mammalian-like post-translational modification (PTMs) that can be achieved in insect cells. In fact, this system acts as a middle ground between prokaryotes and higher eukaryotes to produce complex biologics. Still, industrial use of the BEVS lags compared to other platforms. We have postulated that one reason for this has been a lack of genetic tools that can complement the study of baculovirus vectors, while a second reason is the co-production of the baculovirus vector with the desired product. While some genetic enhancements have been made to improve the BEVS as a production platform, the genome remains under-scrutinized. This chapter outlines the methodology for a CRISPR-Cas9-based transfection-infection assay to probe the baculovirus genome for essential/nonessential genes that can potentially maximize foreign gene expression under a promoter of choice.

Mycoplasma bovis is an important emerging pathogen of cattle and bison, but our understanding of the genetic basis of its interactions with its host is limited. The aim of this study was to identify genes of M. bovis required for interaction and survival in association with host cells. One hundred transposon-induced mutants of the type strain PG45 were assessed for their capacity to survive and proliferate in Madin-Darby bovine kidney cell cultures. The growth of 19 mutants was completely abrogated, and 47 mutants had a prolonged doubling time compared to the parent strain. All these mutants had a similar growth pattern to the parent strain PG45 in the axenic media. Thirteen genes previously classified as dispensable for the axenic growth of M. bovis were found to be essential for the growth of M. bovis in association with host cells. In most of the mutants with a growth-deficient phenotype, the transposon was inserted into a gene involved in transportation or metabolism. This included genes coding for ABC transporters, proteins related to carbohydrate, nucleotide and protein metabolism, and membrane proteins essential for attachment. It is likely that these genes are essential not only in vitro but also for the survival of M. bovis in infected animals.
OBJECTIVE: Mycoplasma bovis causes chronic bronchopneumonia, mastitis, arthritis, keratoconjunctivitis, and reproductive tract disease in cattle around the globe and is an emerging pathogen in bison. Control of mycoplasma infections is difficult in the absence of appropriate antimicrobial treatment or effective vaccines. A comprehensive understanding of host-pathogen interactions and virulence factors is important to implement more effective control methods against M. bovis. Recent studies of other mycoplasmas with in vitro cell culture models have identified essential virulence genes of mycoplasmas. Our study has identified genes of M. bovis required for survival in association with host cells, which will pave the way to a better understanding of host-pathogen interactions and the role of specific genes in the pathogenesis of disease caused by M. bovis.

Humans benefit from a vast community of microorganisms in their gastrointestinal tract, known as the gut microbiota, numbering in the tens of trillions. An imbalance in the gut microbiota known as dysbiosis, can lead to changes in the metabolite profile, elevating the levels of toxins like Bacteroides fragilis toxin (BFT), colibactin, and cytolethal distending toxin. These toxins are implicated in the process of oncogenesis. However, a significant portion of the Bacteroides fragilis genome consists of functionally uncharacterized and hypothetical proteins. This study delves into the functional characterization of hypothetical proteins (HPs) encoded by the Bacteroides fragilis genome, employing a systematic in silico approach. A total of 379 HPs were subjected to a BlastP homology search against the NCBI non-redundant protein sequence database, resulting in 162 HPs devoid of identity to known proteins. CDD-Blast identified 106 HPs with functional domains, which were then annotated using Pfam, InterPro, SUPERFAMILY, SCANPROSITE, SMART, and CATH. Physicochemical properties, such as molecular weight, isoelectric point, and stability indices, were assessed for 60 HPs whose functional domains were identified by at least three of the aforementioned bioinformatic tools. Subsequently, subcellular localization analysis was examined and the gene ontology analysis revealed diverse biological processes, cellular components, and molecular functions. Remarkably, E1WPR3 was identified as a virulent and essential gene among the HPs. This study presents a comprehensive exploration of B. fragilis HPs, shedding light on their potential roles and contributing to a deeper understanding of this organism\'s functional landscape.

OBJECTIVE: Existing resources that characterize the essentiality status of genes are based on either proliferation assessment in human cell lines, viability evaluation in mouse knockouts, or constraint metrics derived from human population sequencing studies. Several repositories document phenotypic annotations for rare disorders; however, there is a lack of comprehensive reporting on lethal phenotypes.
METHODS: We queried Online Mendelian Inheritance in Man for terms related to lethality and classified all Mendelian genes according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. We characterized the genes across these lethality categories, examined the evidence on viability from mouse models and explored how this information could be used for novel gene discovery.
RESULTS: We developed the Lethal Phenotypes Portal to showcase this curated catalog of human essential genes. Differences in the mode of inheritance, physiological systems affected, and disease class were found for genes in different lethality categories, as well as discrepancies between the lethal phenotypes observed in mouse and human.
CONCLUSIONS: We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.

Sinorhizobium fredii CCBAU45436 is an excellent rhizobium that plays an important role in agricultural production. However, there still needs more comprehensive understanding of the metabolic system of S. fredii CCBAU45436, which hinders its application in agriculture. Therefore, based on the first-generation metabolic model iCC541 we developed a new genome-scale metabolic model iAQY970, which contains 970 genes, 1,052 reactions, 942 metabolites and is scored 89% in the MEMOTE test. Cell growth phenotype predicted by iAQY970 is 81.7% consistent with the experimental data. The results of mapping the proteome data under free-living and symbiosis conditions to the model showed that the biomass production rate in the logarithmic phase was faster than that in the stable phase, and the nitrogen fixation efficiency of rhizobia parasitized in cultivated soybean was higher than that in wild-type soybean, which was consistent with the actual situation. In the symbiotic condition, there are 184 genes that would affect growth, of which 94 are essential; In the free-living condition, there are 143 genes that influence growth, of which 78 are essential. Among them, 86 of the 94 essential genes in the symbiotic condition were consistent with the prediction of iCC541, and 44 essential genes were confirmed by literature information; meanwhile, 30 genes were identified by DEG and 33 genes were identified by Geptop. In addition, we extracted four key nitrogen fixation modules from the model and predicted that sulfite reductase (EC 1.8.7.1) and nitrogenase (EC 1.18.6.1) as the target enzymes to enhance nitrogen fixation by MOMA, which provided a potential focus for strain optimization. Through the comprehensive metabolic model, we can better understand the metabolic capabilities of S. fredii CCBAU45436 and make full use of it in the future.

The spread of multi-drug-resistant (MDR) pathogens has rapidly outpaced the development of effective treatments. Diverse resistance mechanisms further limit the effectiveness of our best treatments, including multi-drug regimens and last line-of-defense antimicrobials. Biofilm formation is a powerful component of microbial pathogenesis, providing a scaffold for efficient colonization and shielding against anti-microbials, which further complicates drug resistance studies. Early genetic knockout tools didn\'t allow the study of essential genes, but clustered regularly interspaced palindromic repeat inference (CRISPRi) technologies have overcome this challenge via genetic silencing. These tools rapidly evolved to meet new demands and exploit native CRISPR systems. Modern tools range from the creation of massive CRISPRi libraries to tunable modulation of gene expression with CRISPR activation (CRISPRa). This review discusses the rapid expansion of CRISPRi/a-based technologies, their use in investigating MDR and biofilm formation, and how this drives further development of a potent tool to comprehensively examine multi-drug resistance.

Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper\'s transparent peer review process is included in the supplemental information.

Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.