Are essential genes really essential?

Genome engineering on size reduction and complexity simplification: A review

BACKGROUND

Genome simplification is an important topic in the field of life sciences that has attracted attention from its conception to the present day. It can help uncover the essential components of the genome and, in turn, shed light on the underlying operating principles of complex biological systems. This has made it a central focus of both basic and applied research in the life sciences. With the recent advancements in related technologies and our increasing knowledge of the genome, now is an opportune time to delve into this topic.

AIM OF REVIEW

Our review investigates the progress of genome simplification from two perspectives: genome size reduction and complexity simplification. In addition, we provide insights into the future development trends of genome simplification.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Reducing genome size requires eliminating non-essential elements as much as possible. This process has been facilitated by advances in genome manipulation and synthesis techniques. However, we still need a better and clearer understanding of living systems to reduce genome complexity. As there is a lack of quantitative and clearly defined standards for this task, we have opted to approach the topic from various perspectives and present our findings accordingly.

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.

A mobile CRISPRi collection enables genetic interaction studies for the essential genes of Escherichia coli

Advances in gene editing, in particular CRISPR interference (CRISPRi), have enabled depletion of essential cellular machinery to study the downstream effects on bacterial physiology. Here, we describe the construction of an ordered E. coli CRISPRi collection, designed to knock down the expression of 356 essential genes with the induction of a catalytically inactive Cas9, harbored on the conjugative plasmid pFD152. This mobile CRISPRi library can be conjugated into other ordered genetic libraries to assess combined effects of essential gene knockdowns with non-essential gene deletions. As proof of concept, we probed cell envelope synthesis with two complementary crosses: (1) an Lpp deletion into every CRISPRi knockdown strain and (2) the lolA knockdown plasmid into the Keio collection. These experiments revealed a number of notable genetic interactions for the essential phenotype probed and, in particular, showed suppressing interactions for the loci in question.

Comparative genomics hints at dispensability of multiple essential genes in two Escherichia coli L-form strains

Despite the critical role of bacterial cell walls in maintaining cell shapes, certain environmental stressors can induce the transition of many bacterial species into a wall-deficient state called L-form. Long-term induced Escherichia coli L-forms lose their rod shape and usually hold significant mutations that affect cell division and growth. Besides this, the genetic background of L-form bacteria is still poorly understood. In the present study, the genomes of two stable L-form strains of E. coli (NC-7 and LWF+) were sequenced and their gene mutation status was determined and compared with their parental strains. Comparative genomic analysis between two L-forms reveals both unique adaptions and common mutated genes, many of which belong to essential gene categories not involved in cell wall biosynthesis, indicating that L-form genetic adaptation impacts crucial metabolic pathways. Missense variants from L-forms and Lenski's long-term evolution experiment (LTEE) were analyzed in parallel using an optimized DeepSequence pipeline to investigate predicted mutation effects (α) on protein functions. We report that the two L-form strains analyzed display a frequency of 6-10% (0% for LTEE) in mutated essential genes where the missense variants have substantial impact on protein functions (α<0.5). This indicates the emergence of different survival strategies in L-forms through changes in essential genes during adaptions to cell wall deficiency. Collectively, our results shed light on the detailed genetic background of two E. coli L-forms and pave the way for further investigations of the gene functions in L-form bacterial models.

DXP Synthase Function in a Bacterial Metabolic Adaptation and Implications for Antibacterial Strategies

Pathogenic bacteria possess a remarkable ability to adapt to fluctuating host environments and cause infection. Disturbing bacterial central metabolism through inhibition of 1-deoxy-d-xylulose 5-phosphate synthase (DXPS) has the potential to hinder bacterial adaptation, representing a new antibacterial strategy. DXPS functions at a critical metabolic branchpoint to produce the metabolite DXP, a precursor to pyridoxal-5-phosphate (PLP), thiamin diphosphate (ThDP) and isoprenoids presumed essential for metabolic adaptation in nutrient-limited host environments. However, specific roles of DXPS in bacterial adaptations that rely on vitamins or isoprenoids have not been studied. Here we investigate DXPS function in an adaptation of uropathogenic E. coli (UPEC) to d-serine (d-Ser), a bacteriostatic host metabolite that is present at high concentrations in the urinary tract. UPEC adapt to d-Ser by producing a PLP-dependent deaminase, DsdA, that converts d-Ser to pyruvate, pointing to a role for DXPS-dependent PLP synthesis in this adaptation. Using a DXPS-selective probe, butyl acetylphosphonate (BAP), and leveraging the toxic effects of d-Ser, we reveal a link between DXPS activity and d-Ser catabolism. We find that UPEC are sensitized to d-Ser and produce sustained higher levels of DsdA to catabolize d-Ser in the presence of BAP. In addition, BAP activity in the presence of d-Ser is suppressed by β-alanine, the product of aspartate decarboxylase PanD targeted by d-Ser. This BAP-dependent sensitivity to d-Ser marks a metabolic vulnerability that can be exploited to design combination therapies. As a starting point, we show that combining inhibitors of DXPS and CoA biosynthesis displays synergy against UPEC grown in urine where there is increased dependence on the TCA cycle and gluconeogenesis from amino acids. Thus, this study provides the first evidence for a DXPS-dependent metabolic adaptation in a bacterial pathogen and demonstrates how this might be leveraged for development of antibacterial strategies against clinically relevant pathogens.

CRISPRi-Mediated Gene Suppression Reveals Putative Reverse Transcriptase Gene PA0715 to Be a Global Regulator of Pseudomonas aeruginosa

Purpose

Pseudomonas aeruginosa is a common pathogen of infection in burn and trauma patients, and multi-drug resistant P. aeruginosa has become an increasingly important pathogen. Essential genes are key to the development of novel antibiotics. The PA0715 gene is a novel unidentified essential gene that has attracted our interest as a potential antibiotic target. Our study aims to determine the exact role of PA0715 in cell physiology and bacterial pathogenicity, providing important clues for antibiotic development.

Patients and Methods

The shuttle vector pHERD20T containing an arabinose inducible promoter was used to construct the CRISPRi system. Alterations in cellular physiology and bacterial pathogenicity of P. aeruginosa PAO1 after PA0715 inhibition were characterized. High-throughput RNA-seq was performed to gain more insight into the mechanisms by which PA0715 regulates the vital activity of P. aeruginosa.

Results

We found that down-regulation of PA0715 significantly reduced PAO1 growth rate, motility and chemotaxis, antibiotic resistance, pyocyanin and biofilm production. In addition, PA0715 inhibition reduced the pathogenicity of PAO1 to the greater galleria mellonella larvae. Transcriptional profiling identified 1757 genes including those related to amino acid, carbohydrate, ketone body and organic salt metabolism, whose expression was directly or indirectly controlled by PA0715. Unexpectedly, genes involved in oxidative phosphorylation also varied with PA0715 levels, and these findings support a hitherto unrecognized critical role for PA0715 in oxidative respiration in P. aeruginosa.

Conclusion

We identified PA0715 as a global regulator of the metabolic network that is indispensable for the survival and reproduction of P. aeruginosa. Our results provide a basis for future studies of potential antibiotic targets for P. aeruginosa and offer new ideas for P. aeruginosa infection control.

Synthetic Genetic Interactions Reveal a Dense and Cryptic Regulatory Network of Small Noncoding RNAs in Escherichia coli

Over the past 20 years, we have learned that bacterial small noncoding RNAs (sRNAs) can rapidly effect changes in gene expression in response to stress. However, the broader role and impact of sRNA-mediated regulation in promoting bacterial survival has remained elusive. Indeed, there are few examples where disruption of sRNA-mediated gene regulation results in a discernible change in bacterial growth or survival. The lack of phenotypes attributable to loss of sRNA function suggests that either sRNAs are wholly dispensable or functional redundancies mask the impact of deleting a single sRNA. We investigated synthetic genetic interactions among sRNA genes in Escherichia coli by constructing pairwise deletions in 54 genes, including 52 sRNAs. Some 1,373 double deletion strains were studied for growth defects under 32 different nutrient stress conditions and revealed 1,131 genetic interactions. In one example, we identified a profound synthetic lethal interaction between ArcZ and CsrC when E. coli was grown on pyruvate, lactate, oxaloacetate, or d-/l-alanine, and we provide evidence that the expression of ppsA is dysregulated in the double deletion background, causing the conditionally lethal phenotype. This work employs a unique platform for studying sRNA-mediated gene regulation and sheds new light on the genetic network of sRNAs that underpins bacterial growth.

High-Throughput Screen for Inhibitors of Klebsiella pneumoniae Virulence Using a Tetrahymena pyriformis Co-Culture Surrogate Host Model

The continuing emergence of antibacterial resistance reduces the effectiveness of antibiotics and drives an ongoing search for effective replacements. Screening compound libraries for antibacterial activity in standard growth media has been extensively explored and may be showing diminishing returns. Inhibition of bacterial targets that are selectively important under in vivo (infection) conditions and, therefore, would be missed by conventional in vitro screens might be an alternative. Surrogate host models of infection, however, are often not suitable for high-throughput screens. Here, we adapted a medium-throughput Tetrahymena pyriformis surrogate host model that was successfully used to identify inhibitors of a hyperviscous Klebsiella pneumoniae strain to a high-throughput format and screened circa 1.2 million compounds. The screen was robust and identified confirmed hits from different chemical classes with potent inhibition of K. pneumoniae growth in the presence of T. pyriformis that lacked any appreciable direct antibacterial activity. Several of these appeared to inhibit capsule/mucoidy, which are key virulence factors in hypervirulent K. pneumoniae. A weakly antibacterial inhibitor of LpxC (essential for the synthesis of the lipid A moiety of lipopolysaccharides) also appeared to be more active in the presence of T. pyriformis, which is consistent with the role of LPS in virulence as well as viability in K. pneumoniae.

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.

Machine learning approach to gene essentiality prediction: a review

 

Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions.

SHORT ABSTRACT

Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

How to kill Pseudomonas-emerging therapies for a challenging pathogen

As the number of effective antibiotics dwindled, antibiotic resistance (AR) became a pressing concern. Some Pseudomonas aeruginosa isolates are resistant to all available antibiotics. In this review, we identify the mechanisms that P. aeruginosa uses to evade antibiotics, including intrinsic, acquired, and adaptive resistance. Our review summarizes many different approaches to overcome resistance. Antimicrobial peptides have potential as therapeutics with low levels of resistance evolution. Rationally designed bacteriophage therapy can circumvent and direct evolution of AR and virulence. Vaccines and monoclonal antibodies are highlighted as immune-based treatments targeting specific P. aeruginosa antigens. This review also identifies promising drug combinations, antivirulence therapies, and considerations for new antipseudomonal discovery. Finally, we provide an update on the clinical pipeline for antipseudomonal therapies and recommend future avenues for research.

OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines

OGEE is an Online GEne Essentiality database. Gene essentiality is not a static and binary property, rather a context-dependent and evolvable property in all forms of life. In OGEE we collect not only experimentally tested essential and non-essential genes, but also associated gene properties that contributes to gene essentiality. We tagged conditionally essential genes that show variable essentiality statuses across datasets to highlight complex interplays between gene functions and environmental/experimental perturbations. OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581. These human cancer cell lines were tested with high-throughput experiments such as CRISPR-Cas9 and RNAi; in total, 150 of which were tested by both techniques. We also included factors known to contribute to gene essentiality for these cell lines, such as genomic mutation, methylation and gene expression, along with extensive graphical visualizations for ease of understanding of these factors. OGEE v3 can be accessible freely at https://v3.ogee.info.

Insights on recent approaches in drug discovery strategies and untapped drug targets against drug resistance

BACKGROUND

Despite the various strategies undertaken in the clinical practice, the mortality rate due to antibiotic-resistant microbes has been markedly increasing worldwide. In addition to multidrug-resistant (MDR) microbes, the "ESKAPE" bacteria are also emerging. Of course, the infection caused by ESKAPE cannot be treated even with lethal doses of antibiotics. Now, the drug resistance is also more prevalent in antiviral, anticancer, antimalarial and antifungal chemotherapies.

MAIN BODY

To date, in the literature, the quantum of research reported on the discovery strategies for new antibiotics is remarkable but the milestone is still far away. Considering the need of the updated strategies and drug discovery approaches in the area of drug resistance among researchers, in this communication, we consolidated the insights pertaining to new drug development against drug-resistant microbes. It includes drug discovery void, gene paradox, transposon mutagenesis, vitamin biosynthesis inhibition, use of non-conventional media, host model, target through quorum sensing, genomic-chemical network, synthetic viability to targets, chemical versus biological space, combinational approach, photosensitization, antimicrobial peptides and transcriptome profiling. Furthermore, we optimally briefed about antievolution drugs, nanotheranostics and antimicrobial adjuvants and then followed by twelve selected new feasible drug targets for new drug design against drug resistance. Finally, we have also tabulated the chemical structures of potent molecules against antimicrobial resistance.

CONCLUSION

It is highly recommended to execute the anti-drug resistance research as integrated approach where both molecular and genetic research needs to be as integrative objective of drug discovery. This is time to accelerate new drug discovery research with advanced genetic approaches instead of conventional blind screening.

Gene Dispensability in Escherichia coli Grown in Thirty Different Carbon Environments

While there has been much study of bacterial gene dispensability, there is a lack of comprehensive genome-scale examinations of the impact of gene deletion on growth in different carbon sources. In this context, a lot can be learned from such experiments in the model microbe Escherichia coli where much is already understood and there are existing tools for the investigation of carbon metabolism and physiology (1). Gene deletion studies have practical potential in the field of antibiotic drug discovery where there is emerging interest in bacterial central metabolism as a target for new antibiotics (2). Furthermore, some carbon utilization pathways have been shown to be critical for initiating and maintaining infection for certain pathogens and sites of infection (3–5). Here, with the use of high-throughput solid medium phenotyping methods, we have generated kinetic growth measurements for 3,796 genes under 30 different carbon source conditions. This data set provides a foundation for research that will improve our understanding of genes with unknown function, aid in predicting potential antibiotic targets, validate and advance metabolic models, and help to develop our understanding of E. coli metabolism.

Central metabolism is a topic that has been studied for decades, and yet, this process is still not fully understood in Escherichia coli, perhaps the most amenable and well-studied model organism in biology. To further our understanding, we used a high-throughput method to measure the growth kinetics of each of 3,796 E. coli single-gene deletion mutants in 30 different carbon sources. In total, there were 342 genes (9.01%) encompassing a breadth of biological functions that showed a growth phenotype on at least 1 carbon source, demonstrating that carbon metabolism is closely linked to a large number of processes in the cell. We identified 74 genes that showed low growth in 90% of conditions, defining a set of genes which are essential in nutrient-limited media, regardless of the carbon source. The data are compiled into a Web application, Carbon Phenotype Explorer (CarPE), to facilitate easy visualization of growth curves for each mutant strain in each carbon source. Our experimental data matched closely with the predictions from the EcoCyc metabolic model which uses flux balance analysis to predict growth phenotypes. From our comparisons to the model, we found that, unexpectedly, phosphoenolpyruvate carboxylase (ppc) was required for robust growth in most carbon sources other than most trichloroacetic acid (TCA) cycle intermediates. We also identified 51 poorly annotated genes that showed a low growth phenotype in at least 1 carbon source, which allowed us to form hypotheses about the functions of these genes. From this list, we further characterized the ydhC gene and demonstrated its role in adenosine efflux.

In vitro exploration of the Xanthomonas hortorum pv. vitians genome using transposon insertion sequencing and comparative genomics to discriminate between core and contextual essential genes

The essential genome of a bacterium encompasses core genes associated with basic cellular processes and conditionally essential genes dependent upon environmental conditions or the genetic context. Comprehensive knowledge of those gene sets allows for a better understanding of fundamental bacterial biology and offers new perspectives for antimicrobial drug research against detrimental bacteria such as pathogens. We investigated the essential genome of Xanthomonas hortorum pv. vitians, a gammaproteobacterial plant pathogen of lettuce (Lactuca sativa L.) which belongs to the plant-pathogen reservoir genus Xanthomonas and is affiliated to the family Xanthomonadaceae. No practical means of disease control or prevention against this pathogen is currently available, and its molecular biology is virtually unknown. To reach a comprehensive overview of the essential genome of X. hortorum pv. vitians LM16734, we developed a mixed approach combining high-quality full genome sequencing, saturated transposon insertion sequencing (Tn-Seq) in optimal growth conditions, and coupled computational analyses such as comparative genomics, synteny assessment and phylogenomics. Among the 370 essential loci identified by Tn-Seq, a majority was bound to critical cell processes conserved across bacteria. The remaining genes were either related to specific ecological features of Xanthomonas or Xanthomonadaceae species, or acquired through horizontal gene transfer of mobile genetic elements and associated with ancestral parasitic gene behaviour and bacterial defence systems. Our study sheds new light on our usual concepts about gene essentiality and is pioneering in the molecular and genomic study of X. hortorum pv. vitians.

Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering

Producing 'designer cells' with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering.

Genomewide Assessment of Mycobacterium tuberculosis Conditionally Essential Metabolic Pathways

A better understanding of essential cellular functions in pathogenic bacteria is important for the development of more effective antimicrobial agents. We performed a comprehensive identification of essential genes in Mycobacterium tuberculosis, the major causative agent of tuberculosis, using a combination of transposon insertion sequencing (Tn-seq) and comparative genomic analysis. To identify conditionally essential genes by Tn-seq, we used media with different nutrient compositions. Although many conditional gene essentialities were affected by the presence of relevant nutrient sources, we also found that the essentiality of genes in a subset of metabolic pathways was unaffected by metabolite availability. Comparative genomic analysis revealed that not all essential genes identified by Tn-seq were fully conserved within the M. tuberculosis complex, including some existing antitubercular drug target genes. In addition, we utilized an available M. tuberculosis genome-scale metabolic model, iSM810, to predict M. tuberculosis gene essentiality in silico Comparing the sets of essential genes experimentally identified by Tn-seq to those predicted in silico reveals the capabilities and limitations of gene essentiality predictions, highlighting the complexity of M. tuberculosis essential metabolic functions. This study provides a promising platform to study essential cellular functions in M. tuberculosis IMPORTANCE Mycobacterium tuberculosis causes 10 million cases of tuberculosis (TB), resulting in over 1 million deaths each year. TB therapy is challenging because it requires a minimum of 6 months of treatment with multiple drugs. Protracted treatment times and the emergent spread of drug-resistant M. tuberculosis necessitate the identification of novel targets for drug discovery to curb this global health threat. Essential functions, defined as those indispensable for growth and/or survival, are potential targets for new antimicrobial drugs. In this study, we aimed to define gene essentialities of M. tuberculosis on a genomewide scale to comprehensively identify potential targets for drug discovery. We utilized a combination of experimental (functional genomics) and in silico approaches (comparative genomics and flux balance analysis). Our functional genomics approach identified sets of genes whose essentiality was affected by nutrient availability. Comparative genomics revealed that not all essential genes were fully conserved within the M. tuberculosis complex. Comparing sets of essential genes identified by functional genomics to those predicted by flux balance analysis highlighted gaps in current knowledge regarding M. tuberculosis metabolic capabilities. Thus, our study identifies numerous potential antitubercular drug targets and provides a comprehensive picture of the complexity of M. tuberculosis essential cellular functions.

A Survey on Computational Methods for Essential Proteins and Genes Prediction

Background:

Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research.

Objective:

The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

Reframing gene essentiality in terms of adaptive flexibility

BACKGROUND

Essentiality assays are important tools commonly utilized for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are also often utilized to test the predictive power of genome-scale models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. One explanation for this inconsistency is that the model contains the wrong information, possibly an incorrectly annotated alternative pathway or isozyme reaction. Inconsistencies could also be attributed to experimental limitations, such as growth tests with arbitrary time cut-offs. The focus of this study was to resolve such inconsistencies to better understand isozyme activities and gene essentiality.

RESULTS

In this study, we explored the definition of conditional essentiality from a phenotypic and genomic perspective. Gene-deletion strains associated with false positive predictions of gene essentiality on defined minimal medium for Escherichia coli were targeted for extended growth tests followed by population sequencing and transcriptome analysis. Of the twenty false positive strains available and confirmed from the Keio single gene knock-out collection, 11 strains were shown to grow with longer incubation periods making these actual true positives. These strains grew reproducibly with a diverse range of growth phenotypes. The lag phase observed for these strains ranged from less than one day to more than 7 days. It was found that 9 out of 11 of the false positive strains that grew acquired mutations in at least one replicate experiment and the types of mutations ranged from SNPs and small indels associated with regulatory or metabolic elements to large regions of genome duplication. Comparison of the detected adaptive mutations, modeling predictions of alternate pathways and isozymes, and transcriptome analysis of KO strains suggested agreement for the observed growth phenotype for 6 out of the 9 cases where mutations were observed.

CONCLUSIONS

Longer-term growth experiments followed by whole genome sequencing and transcriptome analysis can provide a better understanding of conditional gene essentiality and mechanisms of adaptation to such perturbations. Compensatory mutations are largely reproducible mechanisms and are in agreement with genome-scale modeling predictions to loss of function gene deletion events.

Use of genetic and chemical synthetic lethality as probes of complexity in bacterial cell systems

Different conditions and genomic contexts are known to have an impact on gene essentiality and interactions. Synthetic lethal interactions occur when a combination of perturbations, either genetic or chemical, result in a more profound fitness defect than expected based on the effect of each perturbation alone. Synthetic lethality in bacterial systems has long been studied; however, during the past decade, the emerging fields of genomics and chemical genomics have led to an increase in the scale and throughput of these studies. Here, we review the concepts of genomics and chemical genomics in the context of synthetic lethality and their revolutionary roles in uncovering novel biology such as the characterization of genes of unknown function and in antibacterial drug discovery. We provide an overview of the methodologies, examples and challenges of both genetic and chemical synthetic lethal screening platforms. Finally, we discuss how to apply genetic and chemical synthetic lethal approaches to rationalize the synergies of drugs, screen for new and improved antibacterial therapies and predict drug mechanism of action.

Advances and perspectives in computational prediction of microbial gene essentiality

The minimal subset of genes required for cellular growth, survival and viability of an organism are classified as essential genes. Knowledge of essential genes gives insight into the core structure and functioning of a cell. This might lead to more efficient antimicrobial drug discovery, to elucidation of the correlations between genotype and phenotype, and a better understanding of the minimal requirements for a (synthetic) cell. Traditionally, constructing a catalog of essential genes for a given microbe involved costly and time-consuming laboratory experiments. While experimental methods have produced abundant gene essentiality data for model organisms like Escherichia coli and Bacillus subtilis, the knowledge generated cannot automatically be extrapolated to predict essential genes in all bacteria. In addition, essential genes identified in the laboratory are by definition 'conditionally essential', as they are essential under the specified experimental conditions: these might not resemble conditions in the microorganisms' natural habitat(s). Also, large-scale experimental assaying for essential genes is not always feasible because of the time investment required to setup these assays. The ability to rapidly and precisely identify essential genes in silico is therefore important and has great potential for applications in medicine, biotechnology and basic biological research. Here, we review the advances made in the use of computational methods to predict microbial gene essentiality, perspectives for the future of these techniques and the possible practical applications of essential genes.

Sequence-based information-theoretic features for gene essentiality prediction

Background

Identification of essential genes is not only useful for our understanding of the minimal gene set required for cellular life but also aids the identification of novel drug targets in pathogens. In this work, we present a simple and effective gene essentiality prediction method using information-theoretic features that are derived exclusively from the gene sequences.

Results

We developed a Random Forest classifier and performed an extensive model performance evaluation among and within 15 selected bacteria. In intra-organism predictions, where training and testing sets are taken from the same organism, AUC (Area Under the Curve) scores ranging from 0.73 to 0.90, 0.84 on average, were obtained. Cross-organism predictions using 5-fold cross-validation, pairwise, leave-one-species-out, leave-one-taxon-out, and cross-taxon yielded average AUC scores of 0.88, 0.75, 0.80, 0.82, and 0.78, respectively. To further show the applicability of our method in other domains of life, we predicted the essential genes of the yeast Schizosaccharomyces pombe and obtained a similar accuracy (AUC 0.84).

Conclusions

The proposed method enables a simple and reliable identification of essential genes without searching in databases for orthologs and demanding further experimental data such as network topology and gene-expression.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1884-5) contains supplementary material, which is available to authorized users.

Emerging and evolving concepts in gene essentiality

Gene essentiality is a founding concept of genetics with important implications in both fundamental and applied research. Multiple screens have been performed over the years in bacteria, yeasts, animals and more recently in human cells to identify essential genes. A mounting body of evidence suggests that gene essentiality, rather than being a static and binary property, is both context dependent and evolvable in all kingdoms of life. This concept of a non-absolute nature of gene essentiality changes our fundamental understanding of essential biological processes and could directly affect future treatment strategies for cancer and infectious diseases.

A small-molecule allosteric inhibitor of Mycobacterium tuberculosis tryptophan synthase

New antibiotics with novel targets are greatly needed. Bacteria have numerous essential functions, but only a small fraction of such processes-primarily those involved in macromolecular synthesis-are inhibited by current drugs. Targeting metabolic enzymes has been the focus of recent interest, but effective inhibitors have been difficult to identify. We describe a synthetic azetidine derivative, BRD4592, that kills Mycobacterium tuberculosis (Mtb) through allosteric inhibition of tryptophan synthase (TrpAB), a previously untargeted, highly allosterically regulated enzyme. BRD4592 binds at the TrpAB α-β-subunit interface and affects multiple steps in the enzyme's overall reaction, resulting in inhibition not easily overcome by changes in metabolic environment. We show that TrpAB is required for the survival of Mtb and Mycobacterium marinum in vivo and that this requirement may be independent of an adaptive immune response. This work highlights the effectiveness of allosteric inhibition for targeting proteins that are naturally highly dynamic and that are essential in vivo, despite their apparent dispensability under in vitro conditions, and suggests a framework for the discovery of a next generation of allosteric inhibitors.

An Essential Regulatory System Originating from Polygenic Transcriptional Rewiring of PhoP-PhoQ of Xanthomonas campestris

How essential, regulatory genes originate and evolve is intriguing because mutations of these genes not only lead to lethality in organisms, but also have pleiotropic effects since they control the expression of multiple downstream genes. Therefore, the evolution of essential, regulatory genes is not only determined by genetic variations of their own sequences, but also by the biological function of downstream genes and molecular mechanisms of regulation. To understand the origin of essential, regulatory genes, experimental dissection of the complete regulatory cascade is needed. Here, we provide genetic evidences to reveal that PhoP-PhoQ is an essential two-component signal transduction system in the gram-negative bacterium Xanthomonas campestris, but that its orthologs in other bacteria belonging to Proteobacteria are nonessential. Mutational, biochemical, and chromatin immunoprecipitation together with high-throughput sequencing analyses revealed that phoP and phoQ of X. campestris and its close relative Pseudomonas aeruginosa are replaceable, and that the consensus binding motifs of the transcription factor PhoP are also highly conserved. PhoP _Xcc in X. campestris regulates the transcription of a number of essential, structural genes by directly binding to cis-regulatory elements (CREs); however, these CREs are lacking in the orthologous essential, structural genes in P. aeruginosa, and thus the regulatory relationships between PhoP _Pae and these downstream essential genes are disassociated. Our findings suggested that the recruitment of regulatory proteins by critical structural genes via transcription factor-CRE rewiring is a driving force in the origin and functional divergence of essential, regulatory genes.

OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines

OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info.

TarO-specific inhibitors of wall teichoic acid biosynthesis restore β-lactam efficacy against methicillin-resistant staphylococci

The widespread emergence of methicillin-resistant Staphylococcus aureus (MRSA) has dramatically eroded the efficacy of current β-lactam antibiotics and created an urgent need for new treatment options. We report an S. aureus phenotypic screening strategy involving chemical suppression of the growth inhibitory consequences of depleting late-stage wall teichoic acid biosynthesis. This enabled us to identify early-stage pathway-specific inhibitors of wall teichoic acid biosynthesis predicted to be chemically synergistic with β-lactams. We demonstrated by genetic and biochemical means that each of the new chemical series discovered, herein named tarocin A and tarocin B, inhibited the first step in wall teichoic acid biosynthesis (TarO). Tarocins do not have intrinsic bioactivity but rather demonstrated potent bactericidal synergy in combination with broad-spectrum β-lactam antibiotics against diverse clinical isolates of methicillin-resistant staphylococci as well as robust efficacy in a murine infection model of MRSA. Tarocins and other inhibitors of wall teichoic acid biosynthesis may provide a rational strategy to develop Gram-positive bactericidal β-lactam combination agents active against methicillin-resistant staphylococci.

The Genome-Wide Interaction Network of Nutrient Stress Genes in Escherichia coli

Conventional efforts to describe essential genes in bacteria have typically emphasized nutrient-rich growth conditions. Of note, however, are the set of genes that become essential when bacteria are grown under nutrient stress. For example, more than 100 genes become indispensable when the model bacterium Escherichia coli is grown on nutrient-limited media, and many of these nutrient stress genes have also been shown to be important for the growth of various bacterial pathogens in vivo To better understand the genetic network that underpins nutrient stress in E. coli, we performed a genome-scale cross of strains harboring deletions in some 82 nutrient stress genes with the entire E. coli gene deletion collection (Keio) to create 315,400 double deletion mutants. An analysis of the growth of the resulting strains on rich microbiological media revealed an average of 23 synthetic sick or lethal genetic interactions for each nutrient stress gene, suggesting that the network defining nutrient stress is surprisingly complex. A vast majority of these interactions involved genes of unknown function or genes of unrelated pathways. The most profound synthetic lethal interactions were between nutrient acquisition and biosynthesis. Further, the interaction map reveals remarkable metabolic robustness in E. coli through pathway redundancies. In all, the genetic interaction network provides a powerful tool to mine and identify missing links in nutrient synthesis and to further characterize genes of unknown function in E. coli Moreover, understanding of bacterial growth under nutrient stress could aid in the development of novel antibiotic discovery platforms.

IMPORTANCE

With the rise of antibiotic drug resistance, there is an urgent need for new antibacterial drugs. Here, we studied a group of genes that are essential for the growth of Escherichia coli under nutrient limitation, culture conditions that arguably better represent nutrient availability during an infection than rich microbiological media. Indeed, many such nutrient stress genes are essential for infection in a variety of pathogens. Thus, the respective proteins represent a pool of potential new targets for antibacterial drugs that have been largely unexplored. We have created all possible double deletion mutants through a genetic cross of nutrient stress genes and the E. coli deletion collection. An analysis of the growth of the resulting clones on rich media revealed a robust, dense, and complex network for nutrient acquisition and biosynthesis. Importantly, our data reveal new genetic connections to guide innovative approaches for the development of new antibacterial compounds targeting bacteria under nutrient stress.

The nonessentiality of essential genes in yeast provides therapeutic insights into a human disease

Essential genes refer to those whose null mutation leads to lethality or sterility. Theoretical reasoning and empirical data both suggest that the fatal effect of inactivating an essential gene can be attributed to either the loss of indispensable core cellular function (Type I), or the gain of fatal side effects after losing dispensable periphery function (Type II). In principle, inactivation of Type I essential genes can be rescued only by re-gain of the core functions, whereas inactivation of Type II essential genes could be rescued by a further loss of function of another gene to eliminate the otherwise fatal side effects. Because such loss-of-function rescuing mutations may occur spontaneously, Type II essential genes may become nonessential in a few individuals of a large population. Motivated by this reasoning, we here carried out a systematic screening for Type II essentiality in the yeast Saccharomyces cerevisiae Large-scale whole-genome sequencing of essentiality-reversing mutants reveals 14 cases whereby the inactivation of an essential gene is rescued by loss-of-function mutations on another gene. In particular, the essential gene encoding the enzyme adenylosuccinate lyase (ADSL) is shown to be Type II, suggesting a loss-of-function therapeutic strategy for the human disorder ADSL deficiency. A proof-of-principle test of this strategy in the nematode Caenorhabditis elegans shows promising results.

Complementation Plasmids, Inducible Gene-Expression Systems, and Reporters for Staphylococci

A cornucopia of methods and molecular tools is available for genetic modification of staphylococci, as shown for at least ten different species to date (Prax et al. Microbiology 159:421-435, 2013). This chapter reviews a number of frequently used vectors for complementation purposes that usually replicate in E. coli and staphylococci and differ in parameters including copy number, mode of replication, and sequence length. Systems for the artificial control of gene expression are described that are modulated by low-molecular-weight effectors such as metal cations, carbohydrates, and antibiotics. Finally, the usefulness of reporter proteins that exhibit enzymatic or autofluorescent characteristics in staphylococci is highlighted.

Chemical Genetic Analysis and Functional Characterization of Staphylococcal Wall Teichoic Acid 2-Epimerases Reveals Unconventional Antibiotic Drug Targets

Here we describe a chemical biology strategy performed in Staphylococcus aureus and Staphylococcus epidermidis to identify MnaA, a 2-epimerase that we demonstrate interconverts UDP-GlcNAc and UDP-ManNAc to modulate substrate levels of TarO and TarA wall teichoic acid (WTA) biosynthesis enzymes. Genetic inactivation of mnaA results in complete loss of WTA and dramatic in vitro β-lactam hypersensitivity in methicillin-resistant S. aureus (MRSA) and S. epidermidis (MRSE). Likewise, the β-lactam antibiotic imipenem exhibits restored bactericidal activity against mnaA mutants in vitro and concomitant efficacy against 2-epimerase defective strains in a mouse thigh model of MRSA and MRSE infection. Interestingly, whereas MnaA serves as the sole 2-epimerase required for WTA biosynthesis in S. epidermidis, MnaA and Cap5P provide compensatory WTA functional roles in S. aureus. We also demonstrate that MnaA and other enzymes of WTA biosynthesis are required for biofilm formation in MRSA and MRSE. We further determine the 1.9Å crystal structure of S. aureus MnaA and identify critical residues for enzymatic dimerization, stability, and substrate binding. Finally, the natural product antibiotic tunicamycin is shown to physically bind MnaA and Cap5P and inhibit 2-epimerase activity, demonstrating that it inhibits a previously unanticipated step in WTA biosynthesis. In summary, MnaA serves as a new Staphylococcal antibiotic target with cognate inhibitors predicted to possess dual therapeutic benefit: as combination agents to restore β-lactam efficacy against MRSA and MRSE and as non-bioactive prophylactic agents to prevent Staphylococcal biofilm formation.

Staphylococcus aureus and Staphylococcus epidermidis cause life-threatening infections that are commonly acquired in hospitals as well as the community and remain difficult to treat with current antibiotics. In part, this is due to the emergence of methicillin-resistant S. aureus and S. epidermidis (MRSA and MRSE), which exhibit broad resistance to β-lactams such as penicillin and other members of this important founding class of antibiotics. Compounding this problem, Staphylococci commonly colonize the surface of catheters and other medical devices, forming bacterial communities that are intrinsically resistant to antibiotics. Here we functionally characterize a family of 2-epimerases, named MnaA and Cap5P, that we demonstrate by genetic, biochemical, and X-ray crystallography means are essential for wall teichoic acid biosynthesis and that upon their genetic inactivation render methicillin-resistant Staphylococci unable to form biofilms as well as broadly hypersusceptible to β-lactam antibiotics both in vitro and in a host infection setting. WTA 2-epimerases therefore constitute a novel class of methicillin-resistant Staphylococcal drug targets.

Antibacterial drug discovery in the resistance era

The looming antibiotic-resistance crisis has penetrated the consciousness of clinicians, researchers, policymakers, politicians and the public at large. The evolution and widespread distribution of antibiotic-resistance elements in bacterial pathogens has made diseases that were once easily treatable deadly again. Unfortunately, accompanying the rise in global resistance is a failure in antibacterial drug discovery. Lessons from the history of antibiotic discovery and fresh understanding of antibiotic action and the cell biology of microorganisms have the potential to deliver twenty-first century medicines that are able to control infection in the resistance era.

New Structural Templates for Clinically Validated and Novel Targets in Antimicrobial Drug Research and Development

The development of bacterial resistance against current antibiotic drugs necessitates a continuous renewal of the arsenal of efficacious drugs. This imperative has not been met by the output of antibiotic research and development of the past decades for various reasons, including the declining efforts of large pharma companies in this area. Moreover, the majority of novel antibiotics are chemical derivatives of existing structures that represent mostly step innovations, implying that the available chemical space may be exhausted. This review negates this impression by showcasing recent achievements in lead finding and optimization of antibiotics that have novel or unexplored chemical structures. Not surprisingly, many of the novel structural templates like teixobactins, lysocin, griselimycin, or the albicidin/cystobactamid pair were discovered from natural sources. Additional compounds were obtained from the screening of synthetic libraries and chemical synthesis, including the gyrase-inhibiting NTBI's and spiropyrimidinetrione, the tarocin and targocil inhibitors of wall teichoic acid synthesis, or the boronates and diazabicyclo[3.2.1]octane as novel β-lactamase inhibitors. A motif that is common to most clinically validated antibiotics is that they address hotspots in complex biosynthetic machineries, whose functioning is essential for the bacterial cell. Therefore, an introduction to the biological targets-cell wall synthesis, topoisomerases, the DNA sliding clamp, and membrane-bound electron transport-is given for each of the leads presented here.

Why are essential genes essential? - The essentiality of Saccharomyces genes

Essential genes are defined as required for the survival of an organism or a cell. They are of particular interests, not only for their essential biological functions, but also in practical applications, such as identifying effective drug targets to pathogenic bacteria and fungi. The budding yeast Saccharomyces cerevisiae has approximately 6,000 open reading frames, 15 to 20% of which are deemed as essential. Some of the essential genes, however, appear to perform non-essential functions, such as aging and cell death, while many of the non-essential genes play critical roles in cell survival. In this paper, we reviewed and analyzed the levels of essentiality of the Saccharomyces cerevisiae genes and have grouped the genes into four categories: (1) Conditional essential: essential only under certain circumstances or growth conditions; (2) Essential: required for survival under optimal growth conditions; (3) Redundant essential: synthetic lethal due to redundant pathways or gene duplication; and (4) Absolute essential: the minimal genes required for maintaining a cellular life under a stress-free environment. The essential and non-essential functions of the essential genes were further analyzed.

Unconventional screening approaches for antibiotic discovery

The dramatic rise in microbial drug resistance in recent years has led to ongoing searches for novel drugs to add to the armory against infectious disease. Nevertheless, a paucity of new antibacterial drugs in discovery and development pipelines using traditional approaches has prompted a variety of unconventional and disruptive strategies for antibacterial drug discovery. Herein, we review recent nontraditional approaches that have been piloted for early drug discovery efforts. These unique methodologies open new avenues for finding the next generation of antimicrobials.

Essential Genes in the Core Genome of the Human Pathogen Streptococcus pyogenes

Streptococcus pyogenes (Group A Streptococcus, GAS) remains a major public health burden worldwide, infecting over 750 million people leading to over 500,000 deaths annually. GAS pathogenesis is complex, involving genetically distinct GAS strains and multiple infection sites. To overcome fastidious genetic manipulations and accelerate pathogenesis investigations in GAS, we developed a mariner-based system (Krmit) for en masse monitoring of complex mutant pools by transposon sequencing (Tn-seq). Highly saturated transposant libraries (Krmit insertions in ca. every 25 nucleotides) were generated in two distinct GAS clinical isolates, a serotype M1T1 invasive strain 5448 and a nephritogenic serotype M49 strain NZ131, and analyzed using a Bayesian statistical model to predict GAS essential genes, identifying sets of 227 and 241 of those genes in 5448 and NZ131, respectively. A large proportion of GAS essential genes corresponded to key cellular processes and metabolic pathways, and 177 were found conserved within the GAS core genome established from 20 available GAS genomes. Selected essential genes were validated using conditional-expression mutants. Finally, comparison to previous essentiality analyses in S. sanguinis and S. pneumoniae revealed significant overlaps, providing valuable insights for the development of new antimicrobials to treat infections by GAS and other pathogenic streptococci.

Genomic tools to profile antibiotic mode of action

The increasing emergence of antimicrobial multiresistant bacteria is of great concern to public health. While these bacteria are becoming an ever more prominent cause of nosocomial and community-acquired infections worldwide, the antibiotic discovery pipeline has been stalled in the last few years with very few efforts in the research and development of novel antibacterial therapies. Some of the root causes that have hampered current antibiotic drug development are the lack of understanding of the mode of action (MOA) of novel antibiotic molecules and the poor characterization of the bacterial physiological response to antibiotics that ultimately causes resistance. Here, we review how bacterial genetic tools can be applied at the genomic level with the goal of profiling resistance to antibiotics and elucidating antibiotic MOAs. Specifically, we highlight how chemical genomic detection of the MOA of novel antibiotic molecules and antibiotic profiling by next-generation sequencing are leveraging basic antibiotic research to unprecedented levels with great opportunities for knowledge translation.

Training set selection for the prediction of essential genes

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.

A new computational strategy for predicting essential genes

Background

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.

Results

We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naïve Bayes model (FWM), which is based on Naïve Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naïve Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction.

Conclusions

FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.

Discovery of wall teichoic acid inhibitors as potential anti-MRSA β-lactam combination agents

Innovative strategies are needed to combat drug resistance associated with methicillin-resistant Staphylococcus aureus (MRSA). Here, we investigate the potential of wall teichoic acid (WTA) biosynthesis inhibitors as combination agents to restore β-lactam efficacy against MRSA. Performing a whole-cell pathway-based screen, we identified a series of WTA inhibitors (WTAIs) targeting the WTA transporter protein, TarG. Whole-genome sequencing of WTAI-resistant isolates across two methicillin-resistant Staphylococci spp. revealed TarG as their common target, as well as a broad assortment of drug-resistant bypass mutants mapping to earlier steps of WTA biosynthesis. Extensive in vitro microbiological analysis and animal infection studies provide strong genetic and pharmacological evidence of the potential effectiveness of WTAIs as anti-MRSA β-lactam combination agents. This work also highlights the emerging role of whole-genome sequencing in antibiotic mode-of-action and resistance studies.

Auxiliary factors: a chink in the armor of MRSA resistance to β-lactam antibiotics

Combination agents provide an important orthogonal approach to treat infectious diseases, particularly those caused by drug resistant pathogens. Indeed, applying a biologically 'rational' and systems-level paradigm to discover potent, selective, and synergistic agents would augment current (and arguably overly relied upon) empirical and serendipitous approaches to such discovery efforts. Here, we review the cellular mechanisms of β-lactam drug resistance and tolerance achieved amongst methicillin-resistant Staphylococcus aureus (MRSA) as well as their molecular targets and strategies to identify cognate inhibitors as potential combination agents to restore β-lactam efficacy against MRSA.

The role of protein interactions in mediating essentiality and synthetic lethality

Genes are characterized as essential if their knockout is associated with a lethal phenotype, and these "essential genes" play a central role in biological function. In addition, some genes are only essential when deleted in pairs, a phenomenon known as synthetic lethality. Here we consider genes displaying synthetic lethality as "essential pairs" of genes, and analyze the properties of yeast essential genes and synthetic lethal pairs together. As gene duplication initially produces an identical pair or sets of genes, it is often invoked as an explanation for synthetic lethality. However, we find that duplication explains only a minority of cases of synthetic lethality. Similarly, disruption of metabolic pathways leads to relatively few examples of synthetic lethality. By contrast, the vast majority of synthetic lethal gene pairs code for proteins with related functions that share interaction partners. We also find that essential genes and synthetic lethal pairs cluster in the protein-protein interaction network. These results suggest that synthetic lethality is strongly dependent on the formation of protein-protein interactions. Compensation by duplicates does not usually occur mainly because the genes involved are recent duplicates, but is more commonly due to functional similarity that permits preservation of essential protein complexes. This unified view, combining genes that are individually essential with those that form essential pairs, suggests that essentiality is a feature of physical interactions between proteins protein-protein interactions, rather than being inherent in gene and protein products themselves.

Is the GAIN Act a turning point in new antibiotic discovery?

The United States GAIN (Generating Antibiotic Incentives Now) Act is a call to action for new antibiotic discovery and development that arises from a ground swell of concern over declining activity in this therapeutic area in the pharmaceutical sector. The GAIN Act aims to provide economic incentives for antibiotic drug discovery in the form of market exclusivity and accelerated drug approval processes. The legislation comes on the heels of nearly two decades of failure using the tools of modern drug discovery to find new antibiotic drugs. The lessons of failure are examined herein as are the prospects for a renewed effort in antibiotic drug discovery and development stimulated by new investments in both the public and private sector.

Extrachromosomal, extraordinary and essential--the plasmids of the Roseobacter clade

The alphaproteobacterial Roseobacter clade (Rhodobacterales) is one of the most important global players in carbon and sulfur cycles of marine ecosystems. The remarkable metabolic versatility of this bacterial lineage provides access to diverse habitats and correlates with a multitude of extrachromosomal elements. Four non-homologous replication systems and additional subsets of individual compatibility groups ensure the stable maintenance of up to a dozen replicons representing up to one third of the bacterial genome. This complexity presents the challenge of successful partitioning of all low copy number replicons. Based on the phenomenon of plasmid incompatibility, we developed molecular tools for target-oriented plasmid curing and could generate customized mutants lacking hundreds of genes. This approach allows one to analyze the relevance of specific replicons including so-called chromids that are known as lifestyle determinants of bacteria. Chromids are extrachromosomal elements with a chromosome-like genetic imprint (codon usage, GC content) that are essential for competitive survival in the natural habitat, whereas classical dispensable plasmids exhibit a deviating codon usage and typically contain type IV secretion systems for conjugation. The impact of horizontal plasmid transfer is exemplified by the scattered occurrence of the characteristic aerobic anoxygenic photosynthesis among the Roseobacter clade and the recently reported transfer of the 45-kb photosynthesis gene cluster to extrachromosomal elements. Conjugative transmission may be the crucial driving force for rapid adaptations and hence the ecological prosperousness of this lineage of pink bacteria.