Use of genomics to select antibacterial targets

Molecular engineering of antimicrobial peptides: microbial targets, peptide motifs and translation opportunities

The global public health threat of antimicrobial resistance has led the scientific community to highly engage into research on alternative strategies to the traditional small molecule therapeutics. Here, we review one of the most popular alternatives amongst basic and applied research scientists, synthetic antimicrobial peptides. The ease of peptide chemical synthesis combined with emerging engineering principles and potent broad-spectrum activity, including against multidrug-resistant strains, has motivated intense scientific focus on these compounds for the past decade. This global effort has resulted in significant advances in our understanding of peptide antimicrobial activity at the molecular scale. Recent evidence of molecular targets other than the microbial lipid membrane, and efforts towards consensus antimicrobial peptide motifs, have supported the rise of molecular engineering approaches and design tools, including machine learning. Beyond molecular concepts, supramolecular chemistry has been lately added to the debate; and helped unravel the impact of peptide self-assembly on activity, including on biofilms and secondary targets, while providing new directions in pharmaceutical formulation through taking advantage of peptide self-assembled nanostructures. We argue that these basic research advances constitute a solid basis for promising industry translation of rationally designed synthetic peptide antimicrobials, not only as novel drugs against multidrug-resistant strains but also as components of emerging antimicrobial biomaterials. This perspective is supported by recent developments of innovative peptide-based and peptide-carrier nanobiomaterials that we also review.

Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery

Of the more than 190 distinct species of Mycobacterium genus, many are economically and clinically important pathogens of humans or animals. Among those mycobacteria that infect humans, three species namely Mycobacterium tuberculosis (causative agent of tuberculosis), Mycobacterium leprae (causative agent of leprosy) and Mycobacterium abscessus (causative agent of chronic pulmonary infections) pose concern to global public health. Although antibiotics have been successfully developed to combat each of these, the emergence of drug-resistant strains is an increasing challenge for treatment and drug discovery. Here we describe the impact of the rapid expansion of genome sequencing and genome/pathway annotations that have greatly improved the progress of structure-guided drug discovery. We focus on the applications of comparative genomics, metabolomics, evolutionary bioinformatics and structural proteomics to identify potential drug targets. The opportunities and challenges for the design of drugs for M. tuberculosis, M. leprae and M. abscessus to combat resistance are discussed.

Transcriptomics as a tool to discover new antibacterial targets

The emergence of antibiotic-resistant pathogens, multiple drug-resistance, and extremely drug-resistant strains demonstrates the need for improved strategies to discover new drug-based compounds. The development of transcriptomics, proteomics, and metabolomics has provided new tools for global studies of living organisms. However, the compendium of expression profiles produced by these methods has introduced new scientific challenges into antimicrobial research. In this review, we discuss the practical value of transcriptomic techniques as well as their difficulties and pitfalls. We advocate the construction of new databases of transcriptomic data, using standardized formats in addition to standardized models of bacterial and yeast similar to those used in systems biology. The inclusion of proteomic and metabolomic data is also essential, as the resulting networks can provide a landscape to rationally predict and exploit new drug targets and to understand drug synergies.

An integrated machine-learning model to predict prokaryotic essential genes

Essential genes are indispensable for the target organism's survival. Large-scale identification and characterization of essential genes has shown to be beneficial in both fundamental biology and medicine fields. Current existing genome-scale experimental screenings of essential genes are time consuming and costly, also sometimes confer erroneous essential gene annotations. To circumvent these difficulties, many research groups turn to computational approaches as the alternative to identify essential genes. Here, we developed an integrative machine-learning based statistical framework to accurately predict essential genes in microorganisms. First we extracted a variety of relevant features derived from different aspects of an organism's genomic sequences. Then we selected a subset of features have high predictive power of gene essentiality through a carefully designed feature selection system. Using the selected features as input, we constructed an ensemble classifier and trained the model on a well-studied microorganism. After fine-tuning the model parameters in cross-validation, we tested the model on the other microorganism. We found that the tenfold cross-validation results within the same organism achieves a high predictive accuracy (AUC ~0.9), and cross-organism predictions between distant related organisms yield the AUC scores from 0.69 to 0.89, which significantly outperformed homology mapping.

Value of a newly sequenced bacterial genome

Next-generation sequencing (NGS) technologies have made high-throughput sequencing available to medium- and small-size laboratories, culminating in a tidal wave of genomic information. The quantity of sequenced bacterial genomes has not only brought excitement to the field of genomics but also heightened expectations that NGS would boost antibacterial discovery and vaccine development. Although many possible drug and vaccine targets have been discovered, the success rate of genome-based analysis has remained below expectations. Furthermore, NGS has had consequences for genome quality, resulting in an exponential increase in draft (partial data) genome deposits in public databases. If no further interests are expressed for a particular bacterial genome, it is more likely that the sequencing of its genome will be limited to a draft stage, and the painstaking tasks of completing the sequencing of its genome and annotation will not be undertaken. It is important to know what is lost when we settle for a draft genome and to determine the “scientific value” of a newly sequenced genome. This review addresses the expected impact of newly sequenced genomes on antibacterial discovery and vaccinology. Also, it discusses the factors that could be leading to the increase in the number of draft deposits and the consequent loss of relevant biological information.

Prioritizing drug targets in Clostridium botulinum with a computational systems biology approach

A computational and in silico system level framework was developed to identify and prioritize the antibacterial drug targets in Clostridium botulinum (Clb), the causative agent of flaccid paralysis in humans that can be fatal in 5 to 10% of cases. This disease is difficult to control due to the emergence of drug-resistant pathogenic strains and the only available treatment antitoxin which can target the neurotoxin at the extracellular level and cannot reverse the paralysis. This study framework is based on comprehensive systems-scale analysis of genomic sequence homology and phylogenetic relationships among Clostridium, other infectious bacteria, host and human gut flora. First, the entire 2628-annotated genes of this bacterial genome were categorized into essential, non-essential and virulence genes. The results obtained showed that 39% of essential proteins that functionally interact with virulence proteins were identified, which could be a key to new interventions that may kill the bacteria and minimize the host damage caused by the virulence factors. Second, a comprehensive comparative COGs and blast sequence analysis of these proteins and host proteins to minimize the risks of side effects was carried out. This revealed that 47% of a set of C. botulinum proteins were evolutionary related with Homo sapiens proteins to sort out the non-human homologs. Third, orthology analysis with other infectious bacteria to assess broad-spectrum effects was executed and COGs were mostly found in Clostridia, Bacilli (Firmicutes), and in alpha and beta Proteobacteria. Fourth, a comparative phylogenetic analysis was performed with human microbiota to filter out drug targets that may also affect human gut flora. This reduced the list of candidate proteins down to 131. Finally, the role of these putative drug targets in clostridial biological pathways was studied while subcellular localization of these candidate proteins in bacterial cellular system exhibited that 68% of the proteins were located in the cytoplasm, out of which 6% was virulent. Finally, this framework may serve as a general computational strategy for future drug target identification in infectious diseases.

Photodynamic therapy based on 5-aminolevulinic acid and its use as an antimicrobial agent

Exogenous 5-aminolevulinic acid (ALA) is taken up directly by bacteria, yeasts, fungi, and some parasites, which then induces the accumulation of protoporphyrin IX (PPIX). Subsequent light irradiation of PPIX leads to the inactivation of these organisms via photodamage to their cellular structures. ALA uptake and light irradiation of PPIX produced by host cells leads to the inactivation of other parasites, along with some viruses, via the induction of an immune response. ALA-mediated PPIX production by host cells and light irradiation result in the inactivation of other viruses via either the induction of a host cell response or direct photodynamic attack on viral particles. This ALA-mediated production of light-activated PPIX has been extensively used as a form of photodynamic therapy (PDT) and has shown varying levels of efficacy in treating conditions that are associated with microbial infection, ranging from acne and verrucae to leishmaniasis and onychomycosis. However, for the treatment of some of these conditions by ALA-based PDT, the role of an antimicrobial effect has been disputed and in general, the mechanisms by which the technique inactivates microbes are not well understood. In this study, we review current understanding of the antimicrobial mechanisms used by ALA-based PDT and its role in the treatment of microbial infections along with its potential medical and nonmedical applications.

Using genomics to develop novel antibacterial therapeutics

As the genomics era matures, the availability of complete microbial genome sequences is facilitating computational approaches to understand bacterial genomes and DNA structure/function relationships. From the genome of pathogens, we can derive invaluable information on potential targets for new antimicrobial agents. Advancements in high-throughput 'omics' technologies and the availability of multiple isolates of the same species have significantly changed the time frame and scope for identifying novel therapeutic targets. This article aims to discuss selected aspects of the bacterial genome, and advocates 'omics'-based techniques to advance the discovery of new therapeutic targets against extracellular bacterial pathogens.

Investigating the predictability of essential genes across distantly related organisms using an integrative approach

Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs.

The antigenome: from protein subunit vaccines to antibody treatments of bacterial infections?

New strategies are needed to master infectious diseases. The so-called "passive vaccination", i.e., prevention and treatment with specific antibodies, has a proven record and potential in the management of infections and entered the medical arena more than 100 years ago. Progress in the identification of specific antigens has become the hallmark in the development of novel subunit vaccines that often contain only a single immunogen, frequently proteins, derived from the microbe in order to induce protective immunity. On the other hand, the monoclonal antibody technology has enabled biotechnology to produce antibody species in unlimited quantities and at reasonable costs that are more or less identical to their human counterparts and bind with high affinity to only one specific site of a given antigen. Although, this technology has provided a robust platform for launching novel and successful treatments against a variety of devastating diseases, it is up till now only exceptionally employed in therapy of infectious diseases. Monoclonal antibodies engaged in the treatment of specific cancers seem to work by a dual mode; they mark the cancerous cells for decontamination by the immune system, but also block a function that intervenes with cell growth. The availability of the entire genome sequence of pathogens has strongly facilitated the identification of highly specific protein antigens that are suitable targets for neutralizing antibodies, but also often seem to play an important role in the microbe's life cycle. Thus, the growing repertoire of well-characterized protein antigens will open the perspective to develop monoclonal antibodies against bacterial infections, at least as last resort treatment, when vaccination and antibiotics are no options for prevention or therapy. In the following chapter we describe and compare various technologies regarding the identification of suitable target antigens and the foundation of cognate monoclonal antibodies and discuss their possible applications in the treatment of bacterial infections together with an overview of current efforts.

Theoretical methods for identifying important functional genes in bacterial genomes

Some functional genes, such as essential genes, highly expressed genes and horizontally transferred genes, play important roles in the survival and pathogenicity of bacteria. This review attempts to summarize current computational methods in identifying the above functional genes from bacterial genomes, which is of significant importance in exploring the bacterial genomes.

Natural products in drug discovery: present status and perspectives

Natural products and their derivatives have been and continue to be rich sources for drug discovery. However, natural products are not drugs. They are produce in nature and through biological assays they are identified as leads, which become candidates for drug development. More than 60% of the drugs that are in the market derive from natural sources. During the last two decades, research aimed at exploiting natural products as a resource has seriously declined. This is in part due to the development of new technologies such as combinatorial chemistry, metagenomics and high-throughput screening. However, the new drug discovery approaches did not fulfilled the initial expectations. This has lead to a renewed interest in natural products, determined by the urgent need for new drugs, in particular to fight against infections caused by multi-resistant pathogens.

Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

Target-based antimicrobial drug discovery

The continued increase in antibiotic resistance among bacterial pathogens, coupled with a decrease in infectious disease research among pharmaceutical companies, has escalated the need for novel and effective antibacterial chemotherapies. While current agents have emerged almost exclusively from whole-cell screening of natural products and small molecules that cause microbial death, recent advances in target identification and assay development have resulted in a flood of target-driven drug discovery methods. Whether genome-based methodologies will yield new classes of agents that conventional methods have been unable to is yet to be seen. At the end of the day, perhaps a synergy between old and new approaches will harvest the next generation of antibacterial treatments.

Novel approaches to developing new antibiotics for bacterial infections

Antibiotics are an essential part of modern medicine. The emergence of antibiotic-resistant mutants among bacteria is seemingly inevitable, and results, within a few decades, in decreased efficacy and withdrawal of the antibiotic from widespread usage. The traditional answer to this problem has been to introduce new antibiotics that kill the resistant mutants. Unfortunately, after more than 50 years of success, the pharmaceutical industry is now producing too few antibiotics, particularly against Gram-negative organisms, to replace antibiotics that are no longer effective for many types of infection. This paper reviews possible new ways to discover novel antibiotics. The genomics route has proven to be target rich, but has not led to the introduction of a marketed antibiotic as yet. Non-culturable bacteria may be an alternative source of new antibiotics. Bacteriophages have been shown to be antibacterial in animals, and may find use in specific infectious diseases. Developing new antibiotics that target non-multiplying bacteria is another approach that may lead to drugs that reduce the emergence of antibiotic resistance and increase patient compliance by shortening the duration of antibiotic therapy. These new discovery routes have given rise to compounds that are in preclinical development, but, with one exception, have not yet entered clinical trials. For the time being, the majority of new antibiotics that reach the marketplace are likely to be structural analogues of existing families of antibiotics or new compounds, both natural and non-natural which are screened in a conventional way against live multiplying bacteria.

NAD kinases use substrate-assisted catalysis for specific recognition of NAD

Here we describe the crystal structures of the NAD kinase (LmNADK1) from Listeria monocytogenes in complex with its substrate NAD, its product NADP, or two synthesized NAD mimics. We identified one of the NAD mimics, di-adenosine diphosphate, as a new substrate for LmNADK1, whereas we showed that the closely related compound di-5'-thioadenosine is a novel non-natural inhibitor for this enzyme. These structures suggest a mechanism involving substrate-assisted catalysis. Indeed, sequence/structure comparison and directed mutagenesis have previously shown that NAD kinases (NADKs) and the distantly related 6-phosphofructokinases share the same catalytically important GGDGT motif. However, in this study we have shown that these enzymes use the central aspartate of this motif differently. Although this acidic residue chelates the catalytic Mg(2+) ion in 6-phosphofructokinases, it activates the phospho-acceptor (NAD) in NADKs. Sequence/structure comparisons suggest that the role of this aspartate would be conserved in NADKs and the related sphingosine and diacylglycerol kinases.

Inhibition of lysine biosynthesis: an evolving antibiotic strategy

Bacterial biosynthesis of lysine has come under increased scrutiny as a target for novel antibacterial agents as it provides lysine for protein synthesis and both lysine and meso-diaminopimelate for construction of the bacterial peptidoglycan cell wall. In this Highlight article we review recent advances in the validation of antibiotic targets, studies of the enzymes of the lysine biosynthetic pathway and development of inhibitors of these enzymes.

Antibacterial discovery and development--the failure of success?

A combination of approaches and compounds-many of which failed to yield immediate results in the past-will ultimately prove invaluable to the drug industry in the ongoing battle against infectious disease.

Multi-targeting by monotherapeutic antibacterials

Antibacterial discovery research has been driven, medically, commercially and intellectually, by the need for new therapeutics that are not subject to the resistance mechanisms that have evolved to combat previous generations of antibacterial agents. This need has often been equated with the identification and exploitation of novel targets. But efforts towards discovery and development of inhibitors of novel targets have proved frustrating. It might be that the 'good old targets' are qualitatively different from the crop of all possible novel targets. What has been learned from existing targets that can be applied to the quest for new antibacterials?

Novel Genetic Techniques and Approaches in the Microbial Genomics Era

The availability of microbial genome sequences has ushered in the genomics era and has led to numerous technical advancements over the past decade. These advances have been both in the bioinformatics field that has integrated computer-based approaches with biology and in research methods in the laboratory. The advances have assisted scientists in their study of bacterial gene complements and the roles of their gene products in the bacterial life cycle. Assignment of genes as essential to the bacterial cell nominated them as potential targets for antibacterial drugs and spurred attempts to exploit this information and convert it into drugs. At present, these efforts have met with minimal success. There are several possible reasons for these disappointing results including choice of targets and screen designs, compound libraries chosen for screens, and decreased commitment to antibacterial drug discovery by many large pharmaceutical companies. Structure-based approaches could become very effective in the future as methodologies continue to improve.