The conditional nature of gene essentiality

Expanding the capabilities of MuGENT for large-scale genetic engineering of the fastest-replicating species, Vibrio natriegens

The fastest replicating bacterium Vibrio natriegens is a rising workhorse for molecular and biotechnological research with established tools for efficient genetic manipulation. Here, we expand on the capabilities of multiplex genome editing by natural transformation (MuGENT) by identifying a neutral insertion site and showing how two selectable markers can be swapped at this site for sequential rounds of natural transformation. Second, we demonstrated that MuGENT can be used for complementation by gene insertion at an ectopic chromosomal locus. Additionally, we developed a robust method to cure the competence plasmid required to induce natural transformation. Finally, we demonstrated the ability of MuGENT to create massive deletions; the 280 kb deletion created in this study is one of the largest artificial deletions constructed in a single round of targeted mutagenesis of a bacterium. These methods each advance the genetic potential of V. natriegens and collectively expand upon its utility as an emerging model organism for synthetic biology.

IMPORTANCE

Vibrio natriegens is an emerging model organism for molecular and biotechnological applications. Its fast growth, metabolic versatility, and ease of genetic manipulation provide an ideal platform for synthetic biology. Here, we develop and apply novel methods that expand the genetic capabilities of the V. natriegens model system. Prior studies developed a method to manipulate multiple regions of the chromosome in a single step. Here, we provide new resources that diversify the utility of this method. We also provide a technique to remove the required genetic tools from the cell once the manipulation is performed, thus establishing "clean" derivative cells. Finally, we show the full extent of this technique's capability by generating one of the largest chromosomal deletions reported in the literature. Collectively, these new tools will be beneficial broadly to the Vibrio community and specifically to the advancement of V. natriegens as a model system.

Translation variation across genetic backgrounds reveals a post-transcriptional buffering signature in yeast

Gene expression is known to vary among individuals, and this variability can impact the phenotypic diversity observed in natural populations. While the transcriptome and proteome have been extensively studied, little is known about the translation process itself. Here, we therefore performed ribosome and transcriptomic profiling on a genetically and ecologically diverse set of natural isolates of the Saccharomyces cerevisiae yeast. Interestingly, we found that the Euclidean distances between each profile and the expression fold changes in each pairwise isolate comparison were higher at the transcriptomic level. This observation clearly indicates that the transcriptional variation observed in the different isolates is buffered through a phenomenon known as post-transcriptional buffering at the translation level. Furthermore, this phenomenon seemed to have a specific signature by preferentially affecting essential genes as well as genes involved in complex-forming proteins, and low transcribed genes. We also explored the translation of the S. cerevisiae pangenome and found that the accessory genes related to introgression events displayed similar transcription and translation levels as the core genome. By contrast, genes acquired through horizontal gene transfer events tended to be less efficiently translated. Together, our results highlight both the extent and signature of the post-transcriptional buffering.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience

Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.

Construction and application of the conditionally essential gene knockdown library in Klebsiella pneumoniae to screen potential antimicrobial targets and virulence genes via Mobile-CRISPRi-seq

Klebsiella pneumoniae is a ubiquitous human pathogen, and its clinical treatment faces two major challenges: multidrug resistance and the pathogenesis of hypervirulent K. pneumoniae. The discovery and study of conditionally essential (CE) genes that can function as potential antimicrobial targets has always been a research concern due to their restriction in the development of novel antibiotics. However, the lack of essential functional genomic data has hampered the study of the mechanisms of essential genes related to antimicrobial susceptibility. In this study, we developed a pooled CE genes mobile clustered regularly interspaced short palindromic repeat (CRISPR) interference screening method (Mobile-CRISPRi-seq) for K. pneumoniae to identify genes that play critical roles in antimicrobial fitness in vitro and host immunity in vivo. Targeting 870 predicted CE genes in K. pneumoniae, Mobile-CRISPRi-seq uncovered the depletion of tetrahydrofolate synthesis pathway genes folB and folP under trimethoprim pressure. Our screening also identified genes waaE and fldA related to polymyxin and β-lactam susceptibility by applying a screening strategy based on Mobile-CRISPRi-seq and comparative genomics. Furthermore, using a mouse infection model and Mobile-CRISPRi-seq, multiple virulence genes were identified, and among these genes, pal, yciS, and ribB were demonstrated to contribute to the pathogenesis of K. pneumoniae. This study provides a simple, rapid, and effective platform for screening potential antimicrobial targets and virulence genes in K. pneumoniae, and this broadly applicable system can be expanded for high-throughput functional gene study in multiple pathogenic bacteria, especially in gram-negative bacteria. IMPORTANCE The discovery and investigation of conditionally essential (CE) genes that can function as potential antimicrobial targets has always been a research concern because of the restriction of antimicrobial targets in the development of novel antibiotics. In this study, we developed a pooled CE gene-wide mobile clustered regularly interspaced short palindromic repeat (CRISPR) interference sequencing (Mobile-CRISPRi-seq) strategy in Klebsiella pneumoniae to identify genes that play critical roles in the fitness of antimicrobials in vitro and host immunity in vivo. The data suggest a robust tool to screen for loss-of-function phenotypes in a pooled gene knockdown library in K. pneumoniae, and Mobile-CRISPRi-seq may be expanded to multiple bacteria for screening and identification of genes with crucial roles in the fitness of antimicrobials and hosts.

Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster

Computational prediction of absolute essential genes using machine learning has gained wide attention in recent years. However, essential genes are mostly conditional and not absolute. Experimental techniques provide a reliable approach of identifying conditionally essential genes; however, experimental methods are laborious, time and resource consuming, hence computational techniques have been used to complement the experimental methods. Computational techniques such as supervised machine learning, or flux balance analysis are grossly limited due to the unavailability of required data for training the model or simulating the conditions for gene essentiality. This study developed a heuristic-enabled active machine learning method based on a light gradient boosting model to predict essential immune response and embryonic developmental genes in Drosophila melanogaster. We proposed a new sampling selection technique and introduced a heuristic function which replaces the human component in traditional active learning models. The heuristic function dynamically selects the unlabelled samples to improve the performance of the classifier in the next iteration. Testing the proposed model with four benchmark datasets, the proposed model showed superior performance when compared to traditional active learning models (random sampling and uncertainty sampling). Applying the model to identify conditionally essential genes, four novel essential immune response genes and a list of 48 novel genes that are essential in embryonic developmental condition were identified. We performed functional enrichment analysis of the predicted genes to elucidate their biological processes and the result evidence our predictions. Immune response and embryonic development related processes were significantly enriched in the essential immune response and embryonic developmental genes, respectively. Finally, we propose the predicted essential genes for future experimental studies and use of the developed tool accessible at http://heal.covenantuniversity.edu.ng for conditional essentiality predictions.

A bacterial pan-genome makes gene essentiality strain-dependent and evolvable

Many bacterial species are represented by a pan-genome, whose genetic repertoire far outstrips that of any single bacterial genome. Here we investigate how a bacterial pan-genome might influence gene essentiality and whether essential genes that are initially critical for the survival of an organism can evolve to become non-essential. By using Transposon insertion sequencing (Tn-seq), whole-genome sequencing and RNA-seq on a set of 36 clinical Streptococcus pneumoniae strains representative of >68% of the species' pan-genome, we identify a species-wide 'essentialome' that can be subdivided into universal, core strain-specific and accessory essential genes. By employing 'forced-evolution experiments', we show that specific genetic changes allow bacteria to bypass essentiality. Moreover, by untangling several genetic mechanisms, we show that gene essentiality can be highly influenced by and/or be dependent on: (1) the composition of the accessory genome, (2) the accumulation of toxic intermediates, (3) functional redundancy, (4) efficient recycling of critical metabolites and (5) pathway rewiring. While this functional characterization underscores the evolvability potential of many essential genes, we also show that genes with differential essentiality remain important antimicrobial drug target candidates, as their inactivation almost always has a severe fitness cost in vivo.

Rapid intraspecies evolution of fitness effects of yeast genes

Organisms within species have numerous genetic and phenotypic variations. Growing evidences show intraspecies variation of mutant phenotypes may be more complicated than expected. Current studies on intraspecies variations of mutant phenotypes are limited to just a few strains. This study investigated the intraspecies variation of fitness effects of 5,630 gene mutants in ten Saccharomyces cerevisiae strains using CRISPR–Cas9 screening. We found that the variability of fitness effects induced by gene disruptions is very large across different strains. Over 75% of genes affected cell fitness in a strain-specific manner to varying degrees. The strain specificity of the fitness effect of a gene is related to its evolutionary and functional properties. Subsequent analysis revealed that younger genes, especially those newly acquired in S. cerevisiae species, are more likely to be strongly strain-specific. Intriguingly, there seems to exist a ceiling of fitness effect size for strong strain-specific genes, and among them, the newly acquired genes are still evolving and have yet to reach this ceiling. Additionally, for a large proportion of protein complexes, the strain specificity profile is inconsistent among genes encoding the same complex. Taken together, these results offer a genome-wide map of intraspecies variation for fitness effect as a mutant phenotype and provide an updated insight on intraspecies phenotypic evolution.

XGEM: Predicting Essential miRNAs by the Ensembles of Various Sequence-Based Classifiers With XGBoost Algorithm

MicroRNAs (miRNAs) play vital roles in gene expression regulations. Identification of essential miRNAs is of fundamental importance in understanding their cellular functions. Experimental methods for identifying essential miRNAs are always costly and time-consuming. Therefore, computational methods are considered as alternative approaches. Currently, only a handful of studies are focused on predicting essential miRNAs. In this work, we proposed to predict essential miRNAs using the XGBoost framework with CART (Classification and Regression Trees) on various types of sequence-based features. We named this method as XGEM (XGBoost for essential miRNAs). The prediction performance of XGEM is promising. In comparison with other state-of-the-art methods, XGEM performed the best, indicating its potential in identifying essential miRNAs.

CRISPR/Cas9 Screening to Identify Conditionally Essential Genes in Human Cell Lines

Forward genetic screens across hundreds of cancer cell lines have started to define the genetic dependencies of proliferating human cells. However, most such screens have been performed in vitro with little consideration into how medium composition might affect gene essentiality. This protocol describes a method to use CRISPR/Cas9-based loss-of-function screens to ask how gene essentiality in human cell lines varies with medium composition. First, a single-guide RNA (sgRNA) library is packaged into lentivirus, and an optimal infection titer is determined for the target cells. Following selection, genomic DNA (gDNA) is extracted from an aliquot of the transduced cells. The remaining transduced cells are then screened in at least two distinct cell culture media. At the conclusion of the screening period, gDNA is collected from each cell population. Next, high-throughput sequencing is used to determine sgRNA barcode abundances from the initial and each of the final populations. Finally, an analytical pipeline is used to identify medium-essential candidate genes from these screen results.

A Method to Map Gene Essentiality of Human Pluripotent Stem Cells by Genome-Scale CRISPR Screens with Inducible Cas9

Human pluripotent stem cells (hPSCs) have the capacity for self-renewal and differentiation into most cell types and, in contrast to widely used cell lines, are karyotypically normal and non-transformed. Hence, hPSCs are considered the gold-standard system for modelling diseases, especially in the field of regenerative medicine. Despite widespread research use of hPSCs and induced pluripotent stem cells (iPSCs), the systematic understanding of pluripotency and lineage differentiation mechanisms are still incomplete. Before tackling the complexities of lineage differentiation with genetic screens, it is critical to catalogue the general genetic requirements for cell fitness and proliferation in the pluripotent state and assess their plasticity under commonly used culture conditions.We describe a method to map essential genetic determinants of hPSC fitness and pluripotency, herein defined as cell reproduction, by genome-scale loss-of-function CRISPR screens in an inducible S. pyogenes Cas9 H1 hPSC line. To address questions of context-dependent gene essentiality, we include protocols for screening hPSCs cultured on feeder cells and laminin, two commonly used growth substrates. This method establishes parameters for genome-wide screens in hPSCs, making human stem cells amenable for functional genomics approaches to facilitate investigation of hPSC biology.

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

Transcriptome Analysis in Yeast Reveals the Externality of Position Effects

The activity of a gene newly integrated into a chromosome depends on the genomic context of the integration site. This “position effect” has been widely reported, although the other side of the coin, that is, how integration affects the local chromosomal environment, has remained largely unexplored, as have the mechanism and phenotypic consequences of this “externality” of the position effect. Here, we examined the transcriptome profiles of approximately 250 Saccharomyces cerevisiae strains, each with GFP integrated into a different locus of the wild-type strain. We found that in genomic regions enriched in essential genes, GFP expression tended to be lower, and the genes near the integration site tended to show greater expression reduction. Further joint analysis with public genome-wide histone modification profiles indicated that this effect was associated with H3K4me2. More importantly, we found that changes in the expression of neighboring genes, but not GFP expression, significantly altered the cellular growth rate. As a result, genomic loci that showed high GFP expression immediately after integration were associated with growth disadvantages caused by elevated expression of neighboring genes, ultimately leading to a low total yield of GFP in the long run. Our results were consistent with competition for transcriptional resources among neighboring genes and revealed a previously unappreciated facet of position effects. This study highlights the impact of position effects on the fate of exogenous gene integration and has significant implications for biological engineering and the pathology of viral integration into the host genome.

Improving target assessment in biomedical research: the GOT-IT recommendations

Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.

Cyclic di-AMP Signaling in Bacteria

The second messenger molecule cyclic di-AMP (c-di-AMP) is formed by many bacteria and archaea. In many species that produce c-di-AMP, this second messenger is essential for viability on rich medium. Recent research has demonstrated that c-di-AMP binds to a large number of proteins and riboswitches, which are often involved in potassium and osmotic homeostasis. c-di-AMP becomes dispensable if the bacteria are cultivated on minimal media with low concentrations of osmotically active compounds. Thus, the essentiality of c-di-AMP does not result from an interaction with a single essential target but rather from the multilevel control of complex homeostatic processes. This review summarizes current knowledge on the homeostasis of c-di-AMP and its function(s) in the control of cellular processes.