Probing Differences in Gene Essentiality Between the Human and Animal Adapted Lineages of the Mycobacterium tuberculosis Complex Using TnSeq

Members of the Mycobacterium tuberculosis complex (MTBC) show distinct host adaptations, preferences and phenotypes despite being >99% identical at the nucleic acid level. Previous studies have explored gene expression changes between the members, however few studies have probed differences in gene essentiality. To better understand the functional impacts of the nucleic acid differences between Mycobacterium bovis and Mycobacterium tuberculosis, we used the Mycomar T7 phagemid delivery system to generate whole genome transposon libraries in laboratory strains of both species and compared the essentiality status of genes during growth under identical in vitro conditions. Libraries contained insertions in 54% of possible TA sites in M. bovis and 40% of those present in M. tuberculosis, achieving similar saturation levels to those previously reported for the MTBC. The distributions of essentiality across the functional categories were similar in both species. 527 genes were found to be essential in M. bovis whereas 477 genes were essential in M. tuberculosis and 370 essential genes were common in both species. CRISPRi was successfully utilised in both species to determine the impacts of silencing genes including wag31, a gene involved in peptidoglycan synthesis and Rv2182c/Mb2204c, a gene involved in glycerophospholipid metabolism. We observed species specific differences in the response to gene silencing, with the inhibition of expression of Mb2204c in M. bovis showing significantly less growth impact than silencing its orthologue (Rv2182c) in M. tuberculosis. Given that glycerophospholipid metabolism is a validated pathway for antimicrobials, our observations suggest that target vulnerability in the animal adapted lineages cannot be assumed to be the same as the human counterpart. This is of relevance for zoonotic tuberculosis as it implies that the development of antimicrobials targeting the human adapted lineage might not necessarily be effective against the animal adapted lineage. The generation of a transposon library and the first reported utilisation of CRISPRi in M. bovis will enable the use of these tools to further probe the genetic basis of survival under disease relevant conditions.

Gene essentiality in cancer cell lines is modified by the sex chromosomes

Human sex differences are thought to arise from gonadal hormones and genes on the sex chromosomes. Here we studied how sex and the sex chromosomes can modulate the outcome of mutations across the genome. We used the results of genome-wide CRISPR-based screens on 306 female and 396 male cancer cell lines to detect differences in gene essentiality between the sexes. By exploiting the tendency of cancer cells to lose or gain sex chromosomes, we were able to dissect the contribution of the Y and X chromosomes to variable gene essentiality. Using this approach, we identified 178 differentially essential genes that depend on the biological sex or the sex chromosomes. Integration with sex bias in gene expression and the rate of somatic mutations in human tumors highlighted genes that escape from X-inactivation, cancer-testis antigens, and Y-linked paralogs as central to the functional genetic differences between males and females.

Computational correction of cell-specific gene-independent effects in CRISPR-Cas9 essentiality screens: REStricted CUbic SplinEs with Mixed Models (RESCUE-MM)

Increased gene copy number has been associated with a greater antiproliferative response upon genome editing, regardless of the true essentiality profile of the targeted gene. Many methods have been developed to adjust for genomic copy number technical artifacts. Existing methods use a two-step correction by pre-processing the data prior to the final analysis. It has been shown that two-step corrections can produce unreliable results, due to the creation of a correlation structure in the corrected data. If this structure is unaccounted for, gene-essentiality levels can be inflated or underestimated, affecting the False Discovery Rate (FDR). We propose a one-step correction using restricted cubic splines (RCS) to be a simpler alternative which reduces the bias in downstream analyses. Moreover, most existing methods combine guide-level results to yield gene-level estimates which can misrepresent the true gene essentiality profile depending on the guide-averaging method. Our model-based approach (RESCUE-MM) for copy number correction provides a more flexible framework that allows for guide-level essentiality estimation while accommodating more complex designs with grouped data. We provide comparisons to existing copy number correction methods and suggest how to include copy number adjustment in a one-step correction fashion in multiple experimental designs.

Probing differences in gene essentiality between the human and animal adapted lineages of the Mycobacterium tuberculosis complex using TnSeq

Members of the Mycobacterium tuberculosis complex (MTBC) show distinct host adaptations, preferences and phenotypes despite being >99% identical at the nucleic acid level. Previous studies have explored gene expression changes between the members, however few studies have probed differences in gene essentiality. To better understand the functional impacts of the nucleic acid differences between Mycobacterium bovis and Mycobacterium tuberculosis we used the Mycomar T7 phagemid delivery system to generate whole genome transposon libraries in laboratory strains of both species and compared the essentiality status of genes during growth under identical in vitro conditions. Libraries contained insertions in 54% of possible TA sites in M. bovis and 40% of those present in M. tuberculosis, achieving similar saturation levels to those previously reported for the MTBC. The distributions of essentiality across the functional categories were similar in both species. 527 genes were found to be essential in M. bovis whereas 477 genes were essential in M. tuberculosis and 370 essential genes were common in both species. CRISPRi was successfully utilised in both species to determine the impacts of silencing genes including wag31, a gene involved in peptidoglycan synthesis and Rv2182c/Mb2204c, a gene involved in glycerophospholipid metabolism. We observed species specific differences in the response to gene silencing, with the inhibition of expression of Mb2204c in M. bovis showing significantly less growth impact than silencing its ortholog (Rv2182c) in M. tuberculosis. Given that glycerophospholipid metabolism is a validated pathway for antimicrobials, our observations suggest that target vulnerability in the animal adapted lineages cannot be assumed to be the same as the human counterpart. This is of relevance for zoonotic tuberculosis as it implies that the development of antimicrobials targeting the human adapted lineage might not necessarily be effective against the animal adapted lineage. The generation of a transposon library and the first reported utilisation of CRISPRi in M. bovis will enable the use of these tools to further probe the genetic basis of survival under disease relevant conditions.

DELEAT: gene essentiality prediction and deletion design for bacterial genome reduction

Background The study of gene essentiality is fundamental to understand the basic principles of life, as well as for applications in many fields. In recent decades, dozens of sets of essential genes have been determined using different experimental and bioinformatics approaches, and this information has been useful for genome reduction of model organisms. Multiple in silico strategies have been developed to predict gene essentiality, but no optimal algorithm or set of gene features has been found yet, especially for non-model organisms with incomplete functional annotation. Results We have developed DELEAT v0.1 (DELetion design by Essentiality Analysis Tool), an easy-to-use bioinformatic tool which integrates an in silico gene essentiality classifier in a pipeline allowing automatic design of large-scale deletions in any bacterial genome. The essentiality classifier consists of a novel logistic regression model based on only six gene features which are not dependent on experimental data or functional annotation. As a proof of concept, we have applied this pipeline to the determination of dispensable regions in the genome of Bartonella quintana str. Toulouse. In this already reduced genome, 35 possible deletions have been delimited, spanning 29% of the genome. Conclusions Built on in silico gene essentiality predictions, we have developed an analysis pipeline which assists researchers throughout multiple stages of bacterial genome reduction projects, and created a novel classifier which is simple, fast, and universally applicable to any bacterial organism with a GenBank annotation file.

ELIMINATOR: Essentiality anaLysIs using MultIsystem Networks And inTeger prOgRamming

A gene is considered as essential when it is indispensable for cells to grow and replicate under a certain environment. However, gene essentiality is not a structural property but rather a contextual one, which depends on the specific biological conditions affecting the cell. This circumstantial essentiality of genes is what brings the attention of scientist since we can identify genes essential for cancer cells but not essential for healthy cells. This same contextuality makes their identification extremely challenging. Huge experimental efforts such as Project Achilles where the essentiality of thousands of genes is measured in over one thousand cell lines together with a plethora of molecular data (transcriptomics, copy number, mutations, etc.) can shed light on the causality behind the essentiality of a gene in a given environment by associating the measured essentiality to molecular features of the cell line. Here, we present an in-silico method for the identification of patient-specific essential genes using constraint-based modelling (CBM). Our method expands the ideas behind traditional CBM to accommodate multisystem networks, that is a biological network that focuses on complex interactions within several biological systems. In essence, it first calculates the minimum number of non-expressed genes required to be active by the cell to sustain life as defined by a set of requirements; and second, it performs an exhaustive in-silico gene knockout to find those that lead to the need of activating extra non-expressed genes. We validated the proposed methodology using a set of 452 cancer cell lines derived from the Cancer Cell Line Encyclopedia where an exhaustive experimental large-scale gene knockout study using CRISPR (Achilles Project) evaluates the impact of each removal. We also show that the integration of different essentiality predictions per gene, what we called Essentiality Congruity Score, (derived from multiple pathways) reduces the number of false positives. Finally, we explored the gene essentiality predictions for a breast cancer patient dataset, and our results showed high concordance with previous publications. These findings suggest that identifying genes whose activity are fundamental to sustain cellular life in a patient-specific manner is feasible using in-silico methods. The patient-level gene essentiality predictions can pave the way for precision medicine by identifying potential drug targets whose deletion can induce death in tumour cells.

In vivo and in vitro human gene essentiality estimations capture contrasting functional constraints

Gene essentiality estimation is a popular empirical approach to link genotypes to phenotypes. In humans, essentiality is estimated based on loss-of-function (LoF) mutation intolerance, either from population exome sequencing (in vivo) data or CRISPR-based in vitro perturbation experiments. Both approaches identify genes presumed to have detrimental consequences on the organism upon mutation. Are these genes constrained by having key cellular/organismal roles? Do in vivo and in vitro estimations equally recover these constraints? Insights into these questions have important implications in generalizing observations from cell models and interpreting disease risk genes. To empirically address these questions, we integrate genome-scale datasets and compare structural, functional and evolutionary features of essential genes versus genes with extremely high mutational tolerance. We found that essentiality estimates do recover functional constraints. However, the organismal or cellular context of estimation leads to functionally contrasting properties underlying the constraint. Our results suggest that depletion of LoF mutations in human populations effectively captures organismal-level functional constraints not experimentally accessible through CRISPR-based screens. Finally, we identify a set of genes (OrgEssential), which are mutationally intolerant in vivo but highly tolerant in vitro. These genes drive observed functional constraint differences and have an unexpected preference for nervous system expression.

Genome-wide CRISPR/Cas9 deletion screen defines mitochondrial gene essentiality and identifies routes for tumour cell viability in hypoxia

Mitochondria are typically essential for the viability of eukaryotic cells, and utilize oxygen and nutrients (e.g. glucose) to perform key metabolic functions that maintain energetic homeostasis and support proliferation. Here we provide a comprehensive functional annotation of mitochondrial genes that are essential for the viability of a large panel (625) of tumour cell lines. We perform genome-wide CRISPR/Cas9 deletion screening in normoxia-glucose, hypoxia-glucose and normoxia-galactose conditions, and identify both unique and overlapping genes whose loss influences tumour cell viability under these different metabolic conditions. We discover that loss of certain oxidative phosphorylation (OXPHOS) genes (e.g. SDHC) improves tumour cell growth in hypoxia-glucose, but reduces growth in normoxia, indicating a metabolic switch in OXPHOS gene function. Moreover, compared to normoxia-glucose, loss of genes involved in energy-consuming processes that are energetically demanding, such as translation and actin polymerization, improve cell viability under both hypoxia-glucose and normoxia-galactose. Collectively, our study defines mitochondrial gene essentiality in tumour cells, highlighting that essentiality is dependent on the metabolic environment, and identifies routes for regulating tumour cell viability in hypoxia.

Semi-Supervised Learning Using Hierarchical Mixture Models: Gene Essentiality Case Study

Integrating gene-level data is useful for predicting the role of genes in biological processes. This problem has typically focused on supervised classification, which requires large training sets of positive and negative examples. However, training data sets that are too small for supervised approaches can still provide valuable information. We describe a hierarchical mixture model that uses limited positively labeled gene training data for semi-supervised learning. We focus on the problem of predicting essential genes, where a gene is required for the survival of an organism under particular conditions. We applied cross-validation and found that the inclusion of positively labeled samples in a semi-supervised learning framework with the hierarchical mixture model improves the detection of essential genes compared to unsupervised, supervised, and other semi-supervised approaches. There was also improved prediction performance when genes are incorrectly assumed to be non-essential. Our comparisons indicate that the incorporation of even small amounts of existing knowledge improves the accuracy of prediction and decreases variability in predictions. Although we focused on gene essentiality, the hierarchical mixture model and semi-supervised framework is standard for problems focused on prediction of genes or other features, with multiple data types characterizing the feature, and a small set of positive labels.