Approximately two thirds of all knockouts of individual mouse genes give rise to viable fertile mice. These genes have thus been termed 'non-essential' in contrast to 'essential' genes, the knockouts of which result in death or infertility. Although non-essential genes are likely to be under selection that favours sequence conservation [1], it is predicted that they are less subject to such stabilising selection than essential genes, and hence evolve faster [2]. We have addressed this issue by analysing the molecular evolution of 108 non-essential and 67 essential genes that have been sequenced in both mouse and rat. On preliminary analysis, the non-essential genes appeared to be faster evolving than the essential ones. We found, however, that the non-essential class contains a disproportionate number of immune-system genes that may be under directional selection (that is, selection favouring change) because of host-parasite coevolution. After correction for this bias, we found that the rate at which genes evolve does not correlate with the severity of the knockout phenotype. This was corroborated by the finding that, whereas neuron-specific genes have significantly lower rates of change than other genes, essential and non-essential neuronal genes have comparable rates of evolution. Our findings most probably reflect strong selection acting against even very subtle deleterious phenotypes, and indicate that the putative involvement of directional selection in host-parasite coevolution and gene expression within the nervous system explains much more of the variance in rates of gene evolution than does the knockout phenotype.

Essential genes are those that are critical for life. In the specific case of the mouse, they are the set of genes whose deletion means that a mouse is unable to survive after birth. As such, they are the key minimal set of genes needed for all the steps of development to produce an organism capable of life ex utero. We explored a wide range of sequence and functional features to characterise essential (lethal) and non-essential (viable) genes in mice. Experimental data curated manually identified 1301 essential genes and 3451 viable genes. Very many sequence features show highly significant differences between essential and viable mouse genes. Essential genes generally encode complex proteins, with multiple domains and many introns. These genes tend to be: long, highly expressed, old and evolutionarily conserved. These genes tend to encode ligases, transferases, phosphorylated proteins, intracellular proteins, nuclear proteins, and hubs in protein-protein interaction networks. They are involved with regulating protein-protein interactions, gene expression and metabolic processes, cell morphogenesis, cell division, cell proliferation, DNA replication, cell differentiation, DNA repair and transcription, cell differentiation and embryonic development. Viable genes tend to encode: membrane proteins or secreted proteins, and are associated with functions such as cellular communication, apoptosis, behaviour and immune response, as well as housekeeping and tissue specific functions. Viable genes are linked to transport, ion channels, signal transduction, calcium binding and lipid binding, consistent with their location in membranes and involvement with cell-cell communication. From the analysis of the composite features of essential and viable genes, we conclude that essential genes tend to be required for intracellular functions, and viable genes tend to be involved with extracellular functions and cell-cell communication. Knowledge of the features that are over-represented in essential genes allows for a deeper understanding of the functions and processes implemented during mammalian development.

Understanding the basis for cellular growth, proliferation, and function requires determining the roles of essential genes in diverse cellular processes, including visualizing their contributions to cellular organization and morphology. Here, we combined pooled CRISPR-Cas9-based functional screening of 5,072 fitness-conferring genes in human HeLa cells with microscopy-based imaging of DNA, the DNA damage response, actin, and microtubules. Analysis of >31 million individual cells identified measurable phenotypes for >90% of gene knockouts, implicating gene targets in specific cellular processes. Clustering of phenotypic similarities based on hundreds of quantitative parameters further revealed co-functional genes across diverse cellular activities, providing predictions for gene functions and associations. By conducting pooled live-cell screening of ∼450,000 cell division events for 239 genes, we additionally identified diverse genes with functional contributions to chromosome segregation. Our work establishes a resource detailing the consequences of disrupting core cellular processes that represents the functional landscape of essential human genes.

Essential genes, those indispensable for the survival of an organism, play a key role in the emerging field, synthetic biology. Characterization of functions encoded by essential genes not only has important practical implications, such as in identifying antibiotic drug targets, but can also enhance our understanding of basic biology, such as functions needed to support cellular life. Enzymes are critical for almost all cellular activities. However, essential genes have not been systematically examined from the aspect of enzymes and the chemical reactions that they catalyze. Here, by comprehensively analyzing essential genes in 14 bacterial genomes in which large-scale gene essentiality screens have been performed, we found that enzymes are enriched in essential genes. Essential enzymes have overrepresented ligases (especially those forming carbon-oxygen bonds and carbon-nitrogen bonds), nucleotidyltransferases and phosphotransferases, while have underrepresented oxidoreductases. Furthermore, essential enzymes tend to associate with more gene ontology domains. These results, from the aspect of chemical reactions, provide further insights into the understanding of functions needed to support natural cellular life, as well as synthetic cells, and provide additional parameters that can be integrated into gene essentiality prediction algorithms.

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.

The genes that are required for organismal survival are annotated as 'essential genes'. Identifying all the essential genes of an animal species can reveal critical functions that are needed during the development of the organism. To inform studies on mouse development, we developed a supervised machine learning classifier based on phenotype data from mouse knockout experiments. We used this classifier to predict the essentiality of mouse genes lacking experimental data. Validation of our predictions against a blind test set of recent mouse knockout experimental data indicated a high level of accuracy (>80%). We also validated our predictions for other mouse mutagenesis methodologies, demonstrating that the predictions are accurate for lethal phenotypes isolated in random chemical mutagenesis screens and embryonic stem cell screens. The biological functions that are enriched in essential and non-essential genes have been identified, showing that essential genes tend to encode intracellular proteins that interact with nucleic acids. The genome distribution of predicted essential and non-essential genes was analysed, demonstrating that the density of essential genes varies throughout the genome. A comparison with human essential and non-essential genes was performed, revealing conservation between human and mouse gene essentiality status. Our genome-wide predictions of mouse essential genes will be of value for the planning of mouse knockout experiments and phenotyping assays, for understanding the functional processes required during mouse development, and for the prioritisation of disease candidate genes identified in human genome and exome sequence datasets.

Essential genes, those critical for the survival of an organism under certain conditions, play a significant role in pharmaceutics and synthetic biology. Knowledge of protein localization is invaluable for understanding their function as well as the interaction of different proteins. However, systematical examination of essential genes from the aspect of the localizations of proteins they encode has not been explored before. Here, a comprehensive protein localization analysis of essential genes in 27 prokaryotes including 24 bacteria, 2 mycoplasmas and 1 archaeon has been performed. Both statistical analysis of localization information in these genomes and GO (Gene Ontology) terms enriched in the essential genes show that proteins encoded by essential genes are enriched in internal location sites, while exist in cell envelope with a lower proportion compared with non-essential ones. Meanwhile, there are few essential proteins in the external subcellular location sites such as flagellum and fimbrium, and proteins encoded by non-essential genes tend to have diverse localizations. These results would provide further insights into the understanding of fundamental functions needed to support a cellular life and improve gene essentiality prediction by taking the protein localization and enriched GO terms into consideration.

The WHO has recently classified Neisseria gonorrhoeae as a super-bacterium due to the rapid spread of antibiotic resistant derivatives and an overall dramatic increase in infection incidences. Genome sequencing has identified potential genes, however, little is known about the transcriptional organization and the presence of non-coding RNAs in gonococci. We performed RNA sequencing to define the transcriptome and the transcriptional start sites of all gonococcal genes and operons. Numerous new transcripts including 253 potentially non-coding RNAs transcribed from intergenic regions or antisense to coding genes were identified. Strikingly, strong antisense transcription was detected for the phase-variable opa genes coding for a family of adhesins and invasins in pathogenic Neisseria, that may have regulatory functions. Based on the defined transcriptional start sites, promoter motifs were identified. We further generated and sequenced a high density Tn5 transposon library to predict a core of 827 gonococcal essential genes, 133 of which have no known function. Our combined RNA-Seq and Tn-Seq approach establishes a detailed map of gonococcal genes and defines the first core set of essential gonococcal genes.

The overproduction and secretion of inositol (i.e., Opi(-)) phenotype is associated with defects in regulation of phospholipid biosynthesis in yeast. Here we report a screen of the essential yeast gene set using a conditional-expression library. This screen identified novel functions previously unknown to affect phospholipid synthesis.

Deciphering the fundamental mechanisms controlling cardiac specification is critical for our understanding of how heart formation is initiated during embryonic development and for applying stem cell biology to regenerative medicine and disease modeling. Using systematic and unbiased functional screening approaches, we discovered that the Id family of helix-loop-helix proteins is both necessary and sufficient to direct cardiac mesoderm formation in frog embryos and human embryonic stem cells. Mechanistically, Id proteins specify cardiac cell fate by repressing two inhibitors of cardiogenic mesoderm formation-Tcf3 and Foxa2-and activating inducers Evx1, Grrp1, and Mesp1. Most importantly, CRISPR/Cas9-mediated ablation of the entire Id (Id1-4) family in mouse embryos leads to failure of anterior cardiac progenitor specification and the development of heartless embryos. Thus, Id proteins play a central and evolutionarily conserved role during heart formation and provide a novel means to efficiently produce cardiovascular progenitors for regenerative medicine and drug discovery applications.

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.

Essential genes tend to be highly conserved across eukaryotes, but, in some cases, their critical roles can be bypassed through genetic rewiring. From a systematic analysis of 728 different essential yeast genes, we discovered that 124 (17%) were dispensable essential genes. Through whole-genome sequencing and detailed genetic analysis, we investigated the genetic interactions and genome alterations underlying bypass suppression. Dispensable essential genes often had paralogs, were enriched for genes encoding membrane-associated proteins, and were depleted for members of protein complexes. Functionally related genes frequently drove the bypass suppression interactions. These gene properties were predictive of essential gene dispensability and of specific suppressors among hundreds of genes on aneuploid chromosomes. Our findings identify yeast's core essential gene set and reveal that the properties of dispensable essential genes are conserved from yeast to human cells, correlating with human genes that display cell line-specific essentiality in the Cancer Dependency Map (DepMap) project.

Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms.

When a duplicate gene has no apparent loss-of-function phenotype, it is commonly considered that the phenotype has been masked as a result of functional redundancy with the remaining paralog. This is supported by indirect evidence showing that multi-copy genes show loss-of-function phenotypes less often than single-copy genes and by direct tests of phenotype masking using select gene sets. Here we take a systematic genome-wide RNA interference approach to assess phenotype masking in paralog pairs in the Caenorhabditis elegans genome. Remarkably, in contrast to expectations, we find that phenotype masking makes only a minor contribution to the low knockdown phenotype rate for duplicate genes. Instead, we find that non-essential genes are highly over-represented among duplicates, leading to a low observed loss-of-function phenotype rate. We further find that duplicate pairs derived from essential and non-essential genes have contrasting evolutionary dynamics: whereas non-essential genes are both more often successfully duplicated (fixed) and lost, essential genes are less often duplicated but upon successful duplication are maintained over longer periods. We expect the fundamental evolutionary duplication dynamics presented here to be broadly applicable.

Essential genes are absolutely required for the survival of an organism. The identification of essential genes, besides being one of the most fundamental questions in biology, is also of interest for the emerging science of synthetic biology and for the development of novel antimicrobials. New antimicrobial therapies are desperately needed to treat multidrug-resistant pathogens, such as members of the Burkholderia cepacia complex.

Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.

Evolutionarily conserved mechanisms that control aging are predicted to have prereproductive functions in order to be subject to natural selection. Genes that are essential for growth and development are highly conserved in evolution, but their role in longevity has not previously been assessed. We screened 2,700 genes essential for Caenorhabditis elegans development and identified 64 genes that extend lifespan when inactivated postdevelopmentally. These candidate lifespan regulators are highly conserved from yeast to humans. Classification of the candidate lifespan regulators into functional groups identified the expected insulin and metabolic pathways but also revealed enrichment for translation, RNA, and chromatin factors. Many of these essential gene inactivations extend lifespan as much as the strongest known regulators of aging. Early gene inactivations of these essential genes caused growth arrest at larval stages, and some of these arrested animals live much longer than wild-type adults. daf-16 is required for the enhanced survival of arrested larvae, suggesting that the increased longevity is a physiological response to the essential gene inactivation. These results suggest that insulin-signaling pathways play a role in regulation of aging at any stage in life.

Essential genes are commonly assumed to function in basic cellular processes and to change slowly. However, it remains unclear whether all essential genes are similarly conserved or if their evolutionary rates can be accelerated by specific factors. To address these questions, we replaced 86 essential genes of Saccharomyces cerevisiae with orthologues from four other species that diverged from S. cerevisiae about 50, 100, 270 and 420 Myr ago. We identify a group of fast-evolving genes that often encode subunits of large protein complexes, including anaphase-promoting complex/cyclosome (APC/C). Incompatibility of fast-evolving genes is rescued by simultaneously replacing interacting components, suggesting it is caused by protein co-evolution. Detailed investigation of APC/C further revealed that co-evolution involves not only primary interacting proteins but also secondary ones, suggesting the evolutionary impact of epistasis. Multiple intermolecular interactions in protein complexes may provide a microenvironment facilitating rapid evolution of their subunits.

Essential genes are thought to encode proteins that carry out the basic functions to sustain a cellular life, and genomic islands (GIs) usually contain clusters of horizontally transferred genes. It has been assumed that essential genes are not likely to be located in GIs, but systematical analysis of essential genes in GIs has not been explored before. Here, we have analyzed the essential genes in 28 prokaryotes by statistical method and reached a conclusion that essential genes in GIs are significantly fewer than those outside GIs. The function of 362 essential genes found in GIs has been explored further by BLAST against the Virulence Factor Database (VFDB) and the phage/prophage sequence database of PHAge Search Tool (PHAST). Consequently, 64 and 60 eligible essential genes are found to share the sequence similarity with the virulence factors and phage/prophages-related genes, respectively. Meanwhile, we find several toxin-related proteins and repressors encoded by these essential genes in GIs. The comparative analysis of essential genes in genomic islands will not only shed new light on the development of the prediction algorithm of essential genes, but also give a clue to detect the functionality of essential genes in genomic islands.

A greater understanding of the causes of human disease can come from identifying characteristics that are specific to disease genes. However, a full understanding of the contribution of essential genes to human disease is lacking, due to the premise that these genes tend to cause developmental abnormalities rather than adult disease. We tested the hypothesis that human orthologs of mouse essential genes are associated with a variety of human diseases, rather than only those related to miscarriage and birth defects. We segregated human disease genes according to whether the knockout phenotype of their mouse ortholog was lethal or viable, defining those with orthologs producing lethal knockouts as essential disease genes. We show that the human orthologs of mouse essential genes are associated with a wide spectrum of diseases affecting diverse physiological systems. Notably, human disease genes with essential mouse orthologs are over-represented among disease genes associated with cancer, suggesting links between adult cellular abnormalities and developmental functions. The proteins encoded by essential genes are highly connected in protein-protein interaction networks, which we find correlates with an over-representation of nuclear proteins amongst essential disease genes. Disease genes associated with essential orthologs also are more likely than those with non-essential orthologs to contribute to disease through an autosomal dominant inheritance pattern, suggesting that these diseases may actually result from semi-dominant mutant alleles. Overall, we have described attributes found in disease genes according to the essentiality status of their mouse orthologs. These findings demonstrate that disease genes do occupy highly connected positions in protein-protein interaction networks, and that due to the complexity of disease-associated alleles, essential genes cannot be ignored as candidates for causing diverse human diseases.