Rational identification of new antibacterial drug targets that are essential for viability using a genomics-based approach

Differentially used codons among essential genes in bacteria identified by machine learning-based analysis

Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CUB is known to be influenced by gene expression and accordingly, CUB differs between the high-expression and low-expression genes in several bacteria. In this article, we have extended codon usage study considering gene essentiality as a feature. Using machine learning (ML) based approaches, we have analysed Relative Synonymous Codon Usage (RSCU) values between essential and non-essential genes in Escherichia coli and thirty-four other bacterial genomes whose gene essentiality features were available in public databases. We observed significant differences in codon usage patterns between essential and non-essential genes for majority of the bacterial genomes and accordingly, ML based classifiers achieved high area under curve (AUC) scores, with a minimum score of 70.0 across twenty-eight organisms. Further, importance of the codons towards classifying genes found to differ among the codons in each genome. Arg codon CGT and Gly codon GGT were observed to be the most preferred codons among essential genes in Escherichia coli. Interestingly, some of the codons like CGT, ATA, GGT and GGG observed to be contributing consistently towards classifying essential genes across thirty-five bacteria genomes studied. In other hand, codons TGY and CAY encoding amino acids Cys and His respectively were among the least contributing codons towards classification among all these bacteria. This study demonstrates the gene essentiality based differences in synonymous codon usage in bacteria genomes and presents a common codon usage pattern across bacteria.

Development of a telomere vector-based approach to overcome limitations caused by lethal phenotypes in the study of essential genes in Magnaporthe oryzae

Reverse genetic approaches are common tools in genomics for elucidating gene functions, involving techniques such as gene deletion followed by screening for aberrant phenotypes. If the generation of gene deletion mutants fails, the question arises whether the failure stems from technical issues or because the gene of interest (GOI) is essential, meaning that the deletion causes lethality. In this report, we introduce a novel method for assessing gene essentiality using the phytopathogenic ascomycete Magnaporthe oryzae. The method is based on the observation that telomere vectors are lost in transformants during cultivation without selection pressure. We tested the hypothesis that essential genes can be identified in deletion mutants co-transformed with a telomere vector. The M. oryzae gene MoPKC, described in literature as essential, was chosen as GOI. Using CRISPR/Cas9 technology transformants with deleted GOI were generated and backed up by a telomere vector carrying a copy of the GOI and conferring fenhexamid resistance. Transformants in which the GOI deletion in the genome was not successful lost the telomere vector on media without fenhexamid. In contrast, transformants with confirmed GOI deletion retained the telomere vector even in absence of fenhexamid selection. In the latter case, the maintenance of the telomere indicates that the GOI is essential for the surveillance of the fungi, as it would have been lost otherwise. The method presented here allows to test for essentiality of genes when no mutants can be obtained from gene deletion approaches, thereby expanding the toolbox for studying gene function in ascomycetes.

Homology modeling, virtual screening, molecular docking, and dynamics studies for discovering Staphylococcus epidermidis FtsZ inhibitors

Staphylococcus epidermidis is the most common cause of medical device-associated infections and is an opportunistic biofilm former. Among hospitalized patients, S. epidermidis infections are the most prevalent, and resistant to most antibiotics. In order to overcome this resistance, it is imperative to treat the infection at a cellular level. The present study aims to identify inhibitors of the prokaryotic cell division protein FtsZ a widely conserved component of bacterial cytokinesis. Two substrate binding sites are present on the FtsZ protein; the nucleotide-binding domain and the inter-domain binding sites. Molecular modeling was used to identify potential inhibitors against the binding sites of the FtsZ protein. One hundred thirty-eight chemical entities were virtually screened for the binding sites and revealed ten molecules, each with good binding affinities (docking score range -9.549 to -4.290 kcal/mol) compared to the reference control drug, i.e., Dacomitinib (-4.450 kcal/mol) and PC190723 (-4.694 kcal/mol) at nucleotide and inter-domain binding sites respectively. These top 10 hits were further analyzed for their ADMET properties and molecular dynamics simulations. The Chloro-derivative of GTP, naphthalene-1,3-diyl bis(3,4,5-trihydroxybenzoate), Guanosine triphosphate (GTP), morpholine and methylpiperazine derivative of GTP were identified as the lead molecules for nucleotide binding site whereas for inter-domain binding site, 1-(((amino(iminio)methyl)amino)methyl)-3-(3-(tert-butyl)phenyl)-6,7-dimethoxyisoquinolin-2-ium, and Chlorogenic acidwere identified as lead molecules. Molecular dynamics simulation and post MM/GBSA analysis of the complexes revealed good protein-ligand stability predicting them as potential inhibitors of FtsZ (Figure 1). Thus, identified FtsZ inhibitors are a promising lead compounds for S. epidermidis related infections.

Acridinium-conjugated aromatic heterocycles as highly potent FtsZ inhibitors: Design, synthesis, and biological evaluation

The epidemic of multidrug resistance (MDR) is a serious threat to public health, and new classes of antibiotics with novel mechanisms of action are in critical need. We rationally designed and efficiently synthesized three series of new chemical entities with potential antibacterial activity targeting filamenting temperature-sensitive mutant Z (FtsZ). Evaluation of these compounds against a panel of Gram-positive bacteria including MDR and vancomycin-resistant Enterococcus strains indicated that most compounds showed enhanced antibacterial efficacy, comparable or even superior to the reference drugs. The newly synthesized compounds proved to be substrates of the Escherichia coli efflux pump AcrB, thus affecting the activity. Their structure-activity relationships were summarized in detail. The most potent compound 10f quickly eliminated bacteria in a bactericidal mode, with low susceptibility to induce bacterial resistance. Further mechanistic studies with the BsFtsZ protein revealed that 10f functioned as an effective FtsZ inhibitor through altering the dynamics of FtsZ self-polymerization via a stimulatory mechanism, which leads to inhibition of cell division and cell death. Besides, 10f not only displayed no obvious cytotoxicity to mammalian cells but also had a high efficacy in a murine model of bacteremia in vivo. Regarded as a whole, our findings highlight 10f as a promising new FtsZ-targeting bactericidal agent.

A review on the application of bioinformatics tools in food microbiome studies

There is currently a transformed interest toward understanding the impact of fermentation on functional food development due to growing consumer interest on modified health benefits of sustainable foods. In this review, we attempt to summarize recent findings regarding the impact of Next-generation sequencing and other bioinformatics methods in the food microbiome and use prediction software to understand the critical role of microbes in producing fermented foods. Traditionally, fermentation methods and starter culture development were considered conventional methods needing optimization to eliminate errors in technique and were influenced by technical knowledge of fermentation. Recent advances in high-output omics innovations permit the implementation of additional logical tactics for developing fermentation methods. Further, the review describes the multiple functions of the predictions based on docking studies and the correlation of genomic and metabolomic analysis to develop trends to understand the potential food microbiome interactions and associated products to become a part of a healthy diet.

Predicting essential genes of 37 prokaryotes by combining information-theoretic features

Essential genes are required for the reproduction and survival of an organism. Rapid identification of essential genes has practical application value in biomedicine. Information theory is a discipline that studies information transmission. Based on the similarity between heredity and information transmission, measures derived from information theory can be applied to genetic sequence analysis on different scales. In this study, we employed 114 features extracted by information theory methods to construct an essential gene prediction model. We applied a backpropagation neural network to construct a classifier and employed it to predict essential genes of 37 prokaryotes. The performance of the classifier was evaluated by applying intra-organism prediction and leave-one-species-out prediction. Among 37 prokaryotes, intra-organism prediction and leave-one-species-out prediction yielded average AUC scores of 0.791 and 0.717, respectively. Considering the potential redundancy in the feature set, we performed feature selection and constructed a key feature subset. In the above two prediction methods, the average AUC scores of 37 organisms obtained by using key features were 0.786 and 0.714, respectively. The results show the potential and universality of information-theoretic features in the study of prokaryotic essential gene prediction.

FtsZ inhibitors as a new genera of antibacterial agents

The continuous emergence and rapid spread of a multidrug-resistant strain of bacterial pathogens have demanded the discovery and development of new antibacterial agents. A highly conserved prokaryotic cell division protein FtsZ is considered as a promising target by inhibiting bacterial cytokinesis. Inhibition of FtsZ assembly restrains the cell-division complex known as divisome, which results in filamentation, leading to lysis of the cell. This review focuses on details relating to the structure, function, and influence of FtsZ in bacterial cytokinesis. It also summarizes on the recent perspective of the known natural and synthetic inhibitors directly acting on FtsZ protein, with prominent antibacterial activities. A series of benzamides, trisubstituted benzimidazoles, isoquinolene, guanine nucleotides, zantrins, carbonylpyridine, 4 and 5-Substituted 1-phenyl naphthalenes, sulindac, vanillin analogues were studied here and recognized as FtsZ inhibitors that act either by disturbing FtsZ polymerization and/or GTPase activity. Doxorubicin, from a U.S. FDA, approved drug library displayed strong interaction with FtsZ. Several of the molecules discussed, include the prodrugs of benzamide based compound PC190723 (TXA-709 and TXA707). These molecules have exhibited the most prominent antibacterial activity against several strains of Staphylococcus aureus with minimal toxicity and good pharmacokinetics properties. The evidence of research reports and patent documentations on FtsZ protein has disclosed distinct support in the field of antibacterial drug discovery. The pressing need and interest shall facilitate the discovery of novel clinical molecules targeting FtsZ in the upcoming days.

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.

Bacterial histidine kinases as novel antibacterial drug targets

Bacterial histidine kinases (HKs) are promising targets for novel antibacterials. Bacterial HKs are part of bacterial two-component systems (TCSs), the main signal transduction pathways in bacteria, regulating various processes including virulence, secretion systems and antibiotic resistance. In this review, we discuss the biological importance of TCSs and bacterial HKs for the discovery of novel antibacterials, as well as published TCS and HK inhibitors that can be used as a starting point for structure-based approaches to develop novel antibacterials.

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus

BACKGROUND

Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, "the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs". Expanding the target space by rapidly identifying new essential genes has thus been described as "the most important task of genomics-based target validation".

RESULTS

In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus Neurospora crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach.

CONCLUSIONS

The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to predict an inventory of essential genes in Eukaryotic fungal species, amongst which a preferred subset of suitable drug targets may be selected. By selecting the best new targets, we believe that resultant drugs would exhibit an unparalleled clinical impact against a naive pathogen population. Additional benefits that a compendium of essential genes can provide are important information on cell function and evolutionary biology. Furthermore, mapping essential genes to pathways may also reveal critical check points in the pathogen's metabolism. Finally, this approach is highly reproducible and portable, and can be easily applied to predict essential genes in many more pathogenic microbes, especially those unculturable.

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.

The evolutionary rate of antibacterial drug targets

Background

One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis.

Results

In this study we show that pN/pS ratio of genes coding for known drug targets is significantly lower than the genome average and also lower than that for essential genes identified by experimental methods. Similar results are observed in the case of dN/dS analysis. Both analyzes suggest that drug targets tend to evolve slowly and that the rate of evolution is a better predictor of drugability than essentiality.

Conclusions

Evolutionary rate can be used to score and find potential drug targets. The results presented here may become a useful addition to a repertoire of drug target prediction methods. As a proof of concept, we analyzed GO enrichment among the slowest evolving genes. These may become the starting point in the search for antibiotics with a novel mechanism.

Comparative study of RNA-seq- and microarray-derived coexpression networks in Arabidopsis thaliana

MOTIVATION

Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays; however, the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset.

RESULTS

We collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. We show how Variance-Stabilizing Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein-protein interaction network and edge ontological agreement. Different coexpression network centralities are investigated; in particular, we show how betweenness centrality is generally a positive marker for essential genes in A.thaliana, regardless of the platform originating the data. In the end, we focus on a specific gene network case, showing that although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome.

Antibiotics and the post-genome revolution

The emergence of pathogenic bacteria resistant to multiple antimicrobial agents is turning into a major crisis in human and veterinary medicine. This necessitates a serious re-evaluation of our approaches toward antibacterial drug discovery and use. Concurrent advances in genomics including whole-genome sequencing, genotyping, and gene expression profiling have the potential to transform our basic understanding of antimicrobial pathways and lead to the discovery of novel targets and therapeutics.

Chemical genomics in Escherichia coli identifies an inhibitor of bacterial lipoprotein targeting

One of the most significant hurdles to developing new chemical probes of biological systems and new drugs to treat disease is that of understanding the mechanism of action of small molecules discovered with cell-based small-molecule screening. Here we have assembled an ordered, high-expression clone set of all of the essential genes from Escherichia coli and used it to systematically screen for suppressors of growth inhibitory compounds. Using this chemical genomic approach, we demonstrate that the targets of well-known antibiotics can be identified as high copy suppressors of chemical lethality. This approach led to the discovery of MAC13243, a molecule that belongs to a new chemical class and that has a unique mechanism and promising activity against multidrug-resistant Pseudomonas aeruginosa. We show that MAC13243 inhibits the function of the LolA protein and represents a new chemical probe of lipoprotein targeting in bacteria with promise as an antibacterial lead with Gram-negative selectivity.

Relationship between insertion/deletion (indel) frequency of proteins and essentiality

BACKGROUND

In a previous study, we demonstrated that some essential proteins from pathogenic organisms contained sizable insertions/deletions (indels) when aligned to human proteins of high sequence similarity. Such indels may provide sufficient spatial differences between the pathogenic protein and human proteins to allow for selective targeting. In one example, an indel difference was targeted via large scale in-silico screening. This resulted in selective antibodies and small compounds which were capable of binding to the deletion-bearing essential pathogen protein without any cross-reactivity to the highly similar human protein. The objective of the current study was to investigate whether indels were found more frequently in essential than non-essential proteins.

RESULTS

We have investigated three species, Bacillus subtilis, Escherichia coli, and Saccharomyces cerevisiae, for which high-quality protein essentiality data is available. Using these data, we demonstrated with t-test calculations that the mean indel frequencies in essential proteins were greater than that of non-essential proteins in the three proteomes. The abundance of indels in both types of proteins was also shown to be accurately modeled by the Weibull distribution. However, Receiver Operator Characteristic (ROC) curves showed that indel frequencies alone could not be used as a marker to accurately discriminate between essential and non-essential proteins in the three proteomes. Finally, we analyzed the protein interaction data available for S. cerevisiae and observed that indel-bearing proteins were involved in more interactions and had greater betweenness values within Protein Interaction Networks (PINs).

CONCLUSION

Overall, our findings demonstrated that indels were not randomly distributed across the studied proteomes and were likely to occur more often in essential proteins and those that were highly connected, indicating a possible role of sequence insertions and deletions in the regulation and modification of protein-protein interactions. Such observations will provide new insights into indel-based drug design using bioinformatics and cheminformatics tools.

Antibacterial natural products in medicinal chemistry--exodus or revival?

To create a drug, nature's blueprints often have to be improved through semisynthesis or total synthesis (chemical postevolution). Selected contributions from industrial and academic groups highlight the arduous but rewarding path from natural products to drugs. Principle modification types for natural products are discussed herein, such as decoration, substitution, and degradation. The biological, chemical, and socioeconomic environments of antibacterial research are dealt with in context. Natural products, many from soil organisms, have provided the majority of lead structures for marketed anti-infectives. Surprisingly, numerous "old" classes of antibacterial natural products have never been intensively explored by medicinal chemists. Nevertheless, research on antibacterial natural products is flagging. Apparently, the "old fashioned" natural products no longer fit into modern drug discovery. The handling of natural products is cumbersome, requiring nonstandardized workflows and extended timelines. Revisiting natural products with modern chemistry and target-finding tools from biology (reversed genomics) is one option for their revival.

From Bacterial Genomes to Novel Antibacterial Agents: Discovery, Characterization, and Antibacterial Activity of Compounds that Bind to HI0065 (YjeE) from Haemophilus influenzae

As part of a fully integrated and comprehensive strategy to discover novel antibacterial agents, NMR- and mass spectrometry-based affinity selection screens were performed to identify compounds that bind to protein targets uniquely found in bacteria and encoded by genes essential for microbial viability. A biphenyl acid lead series emerged from an NMR-based screen with the Haemophilus influenzae protein HI0065, a member of a family of probable ATP-binding proteins found exclusively in eubacteria. The structure-activity relationships developed around the NMR-derived biphenyl acid lead were consistent with on-target antibacterial activity as the Staphylococcus aureus antibacterial activity of the series correlated extremely well with binding affinity to HI0065, while the correlation of binding affinity with B-cell cytotoxicity was relatively poor. Although further studies are needed to conclusively establish the mode of action of the biphenyl series, these compounds represent novel leads that can serve as the basis for the development of novel antibacterial agents that appear to work via an unprecedented mechanism of action. Overall, these results support the genomics-driven hypothesis that targeting bacterial essential gene products that are not present in eukaryotic cells can identify novel antibacterial agents.

Molecular recognition by Escherichia coli UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase is modulated by bound metal ions

The metal-dependent enzyme UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC) catalyzes the conversion of UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine to UDP-3-O-(R-3-hydroxymyristoyl)glucosamine and acetate. This is the committed step in the biosynthesis of lipid A, and for this reason, LpxC is a target for the development of antibiotics in the treatment of Gram-negative bacterial infections. Here we examine the importance of bound metal ion(s) and fatty acids for molecular recognition of ligands by LpxC. The KDproduct value increases >1000-fold with the loss of the hydroxymyristoyl moiety, indicating that the enhanced catalytic efficiency of substrates containing this acyl group is mainly due to increased binding affinity. New fluorescent binding assays for measuring the affinity of LpxC for fatty acids indicate that myristate binds to LpxC 10-fold less tightly than palmitate and that fatty acid affinity is only modestly dependent on pH. Furthermore, LpxC homologues from different species have similar affinities for fatty acids despite alterations in protein sequence. In contrast, the affinity of LpxC for both product and fatty acids is significantly influenced (< or =40-fold) by changes in the number and identity of metal ions bound to the LpxC active site. Therefore, interactions with these metal ions are critical for molecular recognition of ligands by LpxC and may mimic similar contacts with active site inhibitors. These data indicate that the potency of LpxC inhibitors in vitro can be altered by assay conditions used in screening and/or development of LpxC inhibitors and that the metal ion status of LpxC in vivo will likely influence the effectiveness of LpxC inhibitors as antibiotics.

A comprehensive transposon mutant library of Francisella novicida, a bioweapon surrogate

Francisella tularensis, the causative agent of tularemia, is one of the most infectious bacterial pathogens known and is a category A select agent. We created a sequence-defined, near-saturation transposon mutant library of F. tularensis novicida, a subspecies that causes a tularemia-like disease in rodents. The library consists of 16,508 unique insertions, an average of >9 insertions per gene, which is a coverage nearly twice that of the greatest previously achieved for any bacterial species. Insertions were recovered in 84% (1,490) of the predicted genes. To achieve high coverage, it was necessary to construct transposons carrying an endogenous Francisella promoter to drive expression of antibiotic resistance. An analysis of genes lacking (or with few) insertions identified nearly 400 candidate essential genes, most of which are likely to be required for growth on rich medium and which represent potential therapeutic targets. To facilitate genome-scale screening using the mutant collection, we assembled a sublibrary made up of two purified mutants per gene. The library provides a resource for virtually complete identification of genes involved in virulence and other nonessential processes.

Predicting essential genes in fungal genomes

Essential genes are required for an organism's viability, and the ability to identify these genes in pathogens is crucial to directed drug development. Predicting essential genes through computational methods is appealing because it circumvents expensive and difficult experimental screens. Most such prediction is based on homology mapping to experimentally verified essential genes in model organisms. We present here a different approach, one that relies exclusively on sequence features of a gene to estimate essentiality and offers a promising way to identify essential genes in unstudied or uncultured organisms. We identified 14 characteristic sequence features potentially associated with essentiality, such as localization signals, codon adaptation, GC content, and overall hydrophobicity. Using the well-characterized baker's yeast Saccharomyces cerevisiae, we employed a simple Bayesian framework to measure the correlation of each of these features with essentiality. We then employed the 14 features to learn the parameters of a machine learning classifier capable of predicting essential genes. We trained our classifier on known essential genes in S. cerevisiae and applied it to the closely related and relatively unstudied yeast Saccharomyces mikatae. We assessed predictive success in two ways: First, we compared all of our predictions with those generated by homology mapping between these two species. Second, we verified a subset of our predictions with eight in vivo knockouts in S. mikatae, and we present here the first experimentally confirmed essential genes in this species.

Bacterial resistance: a sensitive issue complexity of the challenge and containment strategy in Europe

The development of antimicrobial agents has been a key achievement of modern medicine. However, their overuse has led to an increasing incidence of infections due to antibiotic-resistant microorganisms. Quantitative figures on the current economic and health impact of antimicrobial resistance are scant, but it is clearly a growing challenge that requires timely action. That action should be at the educational, ethical, economic and political level. An important first step would be to increase public awareness and willingness to take the necessary measures to curb resistance. Hence, studies are needed that would provide solid, quantitative data on the societal impact of antibiotic resistance. This review discusses the complexity of resistance, identifies its main drivers and proposes measures to contain it on a European scale.

Rapid test for distinguishing membrane-active antibacterial agents

In the search for antibacterial agents with a novel mode-of-action (MOA) many targeted cellular and cell-free assays are developed and used to screen chemical and natural product libraries. Frequently, hits identified by the primary screens include compounds with nonspecific activities that can affect the integrity and function of bacterial membrane. For a rapid dereplication of membrane-active compounds, a simple method was established using a commercially available Live/Dead(R) Bacterial Viability Kit. This method utilized two fluorescent nucleic acid stains, SYTO9 (stains all cells green) and propidium iodide (stains cells with damaged membrane red) for the drug-treated bacterial cells. The cells were then either examined visually by fluorescence microscopy or their fluorescence emissions were recorded using a multi-label plate reader set to measure emissions at two different wavelengths. The ratio of green versus red was compared to a standard curve indicating the percentage of live versus dead bacteria. Nine known antibiotics and 14 lead compounds from various antibacterial screens were tested with results consistent with their MOA.

Use of genomics to select antibacterial targets

The problem of antibiotic resistance has eroded the usefulness of our arsenal of effective antibiotics. There is a need for new strategies to discover and develop new, effective drugs. The advent of the microbial genomics era has provided a wealth of information on a variety of microorganisms. This has allowed the identification and/or validation of a number of gene products that could serve as targets for the discovery of novel antibacterial agents. New genetic techniques and approaches have arisen in an attempt to exploit this newly available genomic data. Both random and targeted gene disruption efforts have proven effective in this process. Many of these methods would have been difficult to accomplish without DNA sequence and bioinformatics analyses. Several targets have been selected to further characterize and screen for inhibitors and one has yielded two clinical candidates.

How essential are nonessential genes?

Gene essentiality in bacteria has been identified in silico, focusing on gene persistence, or experimentally, focusing on the growth of knockouts in rich media. Comparing 55 genomes of Firmicutes and Gamma-proteobacteria to identify the genes which, while persistent among genomes, do not lead to a lethal phenotype when inactivated, we show that the characteristics of persistence, conservation, expression, and location are shared between persistent nonessential (PNE) genes and experimentally essential genes. PNE genes show an overrepresentation of genes related to maintenance and stress response. This outlines the limits of current experimental techniques to define gene essentiality and highlights the essential role of genes implicated in maintenance which, although dispensable for growth, are not dispensable from an evolutionary point of view. Firmicutes and Gamma-proteobacteria are mostly differing in the construction of the cell envelope, DNA replication and proofreading, and RNA degradation. In addition to suggesting functions for persistent genes that had until now resisted identification, we show that these genes have many characters in common with experimentally identified essential genes. They should then be regarded as truly essential genes.

The magic bullets and tuberculosis drug targets

Modern chemotherapy has played a major role in our control of tuberculosis. Yet tuberculosis still remains a leading infectious disease worldwide, largely owing to persistence of tubercle bacillus and inadequacy of the current chemotherapy. The increasing emergence of drug-resistant tuberculosis along with the HIV pandemic threatens disease control and highlights both the need to understand how our current drugs work and the need to develop new and more effective drugs. This review provides a brief historical account of tuberculosis drugs, examines the problem of current chemotherapy, discusses the targets of current tuberculosis drugs, focuses on some promising new drug candidates, and proposes a range of novel drug targets for intervention. Finally, this review addresses the problem of conventional drug screens based on inhibition of replicating bacilli and the challenge to develop drugs that target nonreplicating persistent bacilli. A new generation of drugs that target persistent bacilli is needed for more effective treatment of tuberculosis.

Molecular cloning and characterization of a new peptide deformylase from human pathogenic bacterium Helicobacter pylori

Helicobacter pylori is a gram-negative pathogenic bacterium, which is associated with peptic ulcer disease and gastric cancer. It is urgent to discover novel drug targets for appropriate antimicrobial agents against this human pathogen. In bacteria, peptide deformylase (PDF) catalyzes the removal of a formyl group from the N-termini of nascent polypeptides. Due to its essentiality and absence in mammalian cells, PDF has been considered as an attractive target for the discovery of novel antibiotics. In this work, a new PDF gene (def) from H. pylori strain SS1 was cloned, expressed, and purified in Escherichia coli system. Sequence alignment shows that H. pylori PDF (HpPDF) shares about 40% identity to E. coli PDF (EcPDF). The enzymatic properties of HpPDF demonstrate its relatively high activity toward formyl-Met-Ala-Ser, with K(cat) of 3.4s(-1), K(m) of 1.7 mM, and K(cat) / K(m) of 2000M(-1)s(-1). HpPDF enzyme appears to be fully active at pH between 8.0 and 9.0, and temperature 50 degrees C. The enzyme activity of Co(2+)-containing HpPDF is apparently higher than that of Zn(2+)-containing HpPDF. This present work thereby supplies a potential platform that facilitates the discovery of novel HpPDF inhibitors and further of possible antimicrobial agents against H. pylori.

What is the relevance of bioinformatics to pharmacology?

Although bioinformatics achieved prominence because of its central role in genome data storage, management and analysis, its focus has shifted as the life sciences exploit these data. In pharmacology, genomic, transcriptomic and proteomic data are being used in the quest for drugs that fulfill unmet medical needs, are disease modifying or curative and are more effective and safer than current drugs. Bioinformatics is used in drug target identification and validation and in the development of biomarkers and toxicogenomic and pharmacogenomic tools to maximize the therapeutic benefit of drugs. Now that the 'parts list' of cellular signalling pathways is available, integrated computational and experimental programmes are being developed, with the goal of enabling in silico pharmacology by linking the genome, transcriptome and proteome to cellular pathophysiology.

Exploiting genomics, genetics and chemistry to combat antibiotic resistance

To address the worsening problem of antibiotic-resistant bacteria there is an urgent need to develop new antibiotics. Comparative genomics and molecular genetics are being applied to produce lists of essential new targets for compound screening programmes. Combinatorial chemistry and structural biology are being applied to rapidly explore and optimize the interactions between lead compounds and their biological targets. Several compounds that have been identified from target-based screens are now in development, but technical and economic constraints might result in a trickle, rather than a flood, of new antibiotics onto the market in the near future.

Genetic strategies for antibacterial drug discovery

The availability of genome sequences is revolutionizing the field of microbiology. Genetic methods are being modified to facilitate rapid analysis at a genome-wide level and are blossoming for human pathogens that were previously considered intractable. This revolution coincided with a growing concern about the emergence of microbial drug resistance, compelling the pharmaceutical industry to search for new antimicrobial agents. The availability of the new technologies, combined with many genetic strategies, has changed the way that researchers approach antibacterial drug discovery.