Large-scale chemical-genetics yields new M. tuberculosis inhibitor classes

Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery

The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs - a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

Fluorogenic Probes of the Mycobacterial Membrane as Reporters of Antibiotic Action

The genus Mycobacterium includes species such as Mycobacterium tuberculosis, which can cause deadly human diseases. These bacteria have a protective cell envelope that can be remodeled to facilitate their survival in challenging conditions. Understanding how such conditions affect membrane remodeling can facilitate antibiotic discovery and treatment. To this end, we describe an optimized fluorogenic probe, N-QTF, that reports on mycolyltransferase activity, which is vital for cell division and remodeling. N-QTF is a glycolipid probe that can reveal dynamic changes in the mycobacterial cell envelope in both fast- and slow-growing mycobacterial species. Using this probe to monitor the consequences of antibiotic treatment uncovered distinct cellular phenotypes. Even antibiotics that do not directly inhibit cell envelope biosynthesis cause conspicuous phenotypes. For instance, mycobacteria exposed to the RNA polymerase inhibitor rifampicin release fluorescent extracellular vesicles (EVs). While all mycobacteria release EVs, fluorescent EVs were detected only in the presence of RIF, indicating that exposure to the drug alters EV content. Macrophages exposed to the EVs derived from RIF-treated cells released lower levels of cytokines, suggesting the EVs moderate immune responses. These data suggest that antibiotics can alter EV content to impact immunity. Our ability to see such changes in EV constituents directly results from exploiting these chemical probes.

The antibiotic resistance crisis and the development of new antibiotics

The Global Burden of Disease report of 2019 estimated 14 million infection-related deaths, making it the second leading cause of death after ischaemic heart disease. Bacterial pathogens accounted for 7.7 million deaths and deaths attributable to bacterial antibiotic resistance amounted to 1.3 million, describing a clear demand for novel antibiotics. Antibiotic development had its golden age in 1930-1960. Following failures in the screening of chemical libraries for novel antibiotics at the beginning of this century, the high cost of launching new antibiotics (estimated at US$ 1.4 billion per registered drug) and difficulties in achieving a return of investment for novel antibiotics, pharmaceutical industry has mostly left the field. The current Lilliput review analyses the question whether scientific or economic hurdles prevented the registration of new antibiotics. Scientifically, substantial progress has been achieved over recent years to define the chemical properties needed to overcome the permeation barrier in Gram-negative pathogens; in extending the chemical space of antibiotic candidates by full modular synthesis of suitable molecules; by extending bioprospecting to previously 'unculturable' bacteria or unusual bacteria; by attacking bacterial targets on the outer bacterial membrane; and by looking for support from structural biology, genomics, molecular genetics, phylogenetic analyses and deep machine learning approaches. However, these research activities were mostly conducted by academic researchers and biotech companies with limited financial resources. It thus seems that the development of new antibiotics, frequently described as the drying of the pipeline, is less limited by lack of scientific insight than by lack of the mobilization of the monetary resources needed to bring these discoveries to the market despite recent financial push and pull efforts of the public sector.

CRISPRi functional genomics in bacteria and its application to medical and industrial research

SUMMARYFunctional genomics is the use of systematic gene perturbation approaches to determine the contributions of genes under conditions of interest. Although functional genomic strategies have been used in bacteria for decades, recent studies have taken advantage of CRISPR (clustered regularly interspaced short palindromic repeats) technologies, such as CRISPRi (CRISPR interference), that are capable of precisely modulating expression of all genes in the genome. Here, we discuss and review the use of CRISPRi and related technologies for bacterial functional genomics. We discuss the strengths and weaknesses of CRISPRi as well as design considerations for CRISPRi genetic screens. We also review examples of how CRISPRi screens have defined relevant genetic targets for medical and industrial applications. Finally, we outline a few of the many possible directions that could be pursued using CRISPR-based functional genomics in bacteria. Our view is that the most exciting screens and discoveries are yet to come.

Perturbation-Specific Transcriptional Mapping for unbiased target elucidation of antibiotics

The rising prevalence of antibiotic resistance threatens human health. While more sophisticated strategies for antibiotic discovery are being developed, target elucidation of new chemical entities remains challenging. In the post-genomic era, expression profiling can play an important role in mechanism-of-action (MOA) prediction by reporting on the cellular response to perturbation. However, the broad application of transcriptomics has yet to fulfill its promise of transforming target elucidation due to challenges in identifying the most relevant, direct responses to target inhibition. We developed an unbiased strategy for MOA prediction, called Perturbation-Specific Transcriptional Mapping (PerSpecTM), in which large-throughput expression profiling of wildtype or hypomorphic mutants, depleted for essential targets, enables a computational strategy to address this challenge. We applied PerSpecTM to perform reference-based MOA prediction based on the principle that similar perturbations, whether chemical or genetic, will elicit similar transcriptional responses. Using this approach, we elucidated the MOAs of three new molecules with activity against Pseudomonas aeruginosa by comparing their expression profiles to those of a reference set of antimicrobial compounds with known MOAs. We also show that transcriptional responses to small molecule inhibition resemble those resulting from genetic depletion of essential targets by CRISPRi by PerSpecTM, demonstrating proof-of-concept that correlations between expression profiles of small molecule and genetic perturbations can facilitate MOA prediction when no chemical entities exist to serve as a reference. Empowered by PerSpecTM, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts.

"Multiplexed screen identifies a Pseudomonas aeruginosa -specific small molecule targeting the outer membrane protein OprH and its interaction with LPS"

The surge of antimicrobial resistance threatens efficacy of current antibiotics, particularly against Pseudomonas aeruginosa , a highly resistant gram-negative pathogen. The asymmetric outer membrane (OM) of P. aeruginosa combined with its array of efflux pumps provide a barrier to xenobiotic accumulation, thus making antibiotic discovery challenging. We adapted PROSPECT 1 , a target-based, whole-cell screening strategy, to discover small molecule probes that kill P. aeruginosa mutants depleted for essential proteins localized at the OM. We identified BRD1401, a small molecule that has specific activity against a P. aeruginosa mutant depleted for the essential lipoprotein, OprL. Genetic and chemical biological studies identified that BRD1401 acts by targeting the OM β-barrel protein OprH to disrupt its interaction with LPS and increase membrane fluidity. Studies with BRD1401 also revealed an interaction between OprL and OprH, directly linking the OM with peptidoglycan. Thus, a whole-cell, multiplexed screen can identify species-specific chemical probes to reveal novel pathogen biology.

Transposon sequencing reveals metabolic pathways essential for Mycobacterium tuberculosis infection

New drugs are needed to shorten and simplify treatment of tuberculosis caused by Mycobacterium tuberculosis. Metabolic pathways that M. tuberculosis requires for growth or survival during infection represent potential targets for anti-tubercular drug development. Genes and metabolic pathways essential for M. tuberculosis growth in standard laboratory culture conditions have been defined by genome-wide genetic screens. However, whether M. tuberculosis requires these essential genes during infection has not been comprehensively explored because mutant strains cannot be generated using standard methods. Here we show that M. tuberculosis requires the phenylalanine (Phe) and de novo purine and thiamine biosynthetic pathways for mammalian infection. We used a defined collection of M. tuberculosis transposon (Tn) mutants in essential genes, which we generated using a custom nutrient-rich medium, and transposon sequencing (Tn-seq) to identify multiple central metabolic pathways required for fitness in a mouse infection model. We confirmed by individual retesting and complementation that mutations in pheA (Phe biosynthesis) or purF (purine and thiamine biosynthesis) cause death of M. tuberculosis in the absence of nutrient supplementation in vitro and strong attenuation in infected mice. Our findings show that Tn-seq with defined Tn mutant pools can be used to identify M. tuberculosis genes required during mouse lung infection. Our results also demonstrate that M. tuberculosis requires Phe and purine/thiamine biosynthesis for survival in the host, implicating these metabolic pathways as prime targets for the development of new antibiotics to combat tuberculosis.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

Revolutionizing Tuberculosis Treatment: Uncovering New Drugs and Breakthrough Inhibitors to Combat Drug-Resistant Mycobacterium tuberculosis

Tuberculosis (TB) is a global health threat that causes significant mortality. This review explores chemotherapeutics that target essential processes in Mycobacterium tuberculosis, such as DNA replication, protein synthesis, cell wall formation, energy metabolism, and proteolysis. We emphasize the need for new drugs to treat drug-resistant strains and shorten the treatment duration. Emerging targets and promising inhibitors were identified by examining the intricate biology of TB. This review provides an overview of recent developments in the search for anti-TB drugs with a focus on newly validated targets and inhibitors. We aimed to contribute to efforts to combat TB and improve therapeutic outcomes.

Chemical genetic approaches for the discovery of bacterial cell wall inhibitors

Antimicrobial resistance (AMR) in bacterial pathogens is a worldwide health issue. The innovation gap in discovering new antibiotics has remained a significant hurdle in combating the AMR problem. Currently, antibiotics target various vital components of the bacterial cell envelope, nucleic acid and protein biosynthesis machinery and metabolic pathways essential for bacterial survival. The critical role of the bacterial cell envelope in cell morphogenesis and integrity makes it an attractive drug target. While a significant number of in-clinic antibiotics target peptidoglycan biosynthesis, several components of the bacterial cell envelope have been overlooked. This review focuses on various antibacterial targets in the bacterial cell wall and the strategies employed to find their novel inhibitors. This review will further elaborate on combining forward and reverse chemical genetic approaches to discover antibacterials that target the bacterial cell envelope.

Mycobacterial RNase E cleaves with a distinct sequence preference and controls the degradation rates of most Mycolicibacterium smegmatis mRNAs

The mechanisms and regulation of RNA degradation in mycobacteria have been subject to increased interest following the identification of interplay between RNA metabolism and drug resistance. Mycobacteria encode multiple ribonucleases predicted to participate in mRNA degradation and/or processing of stable RNAs. RNase E is hypothesized to play a major role in mRNA degradation because of its essentiality in mycobacteria and its role in mRNA degradation in gram-negative bacteria. Here, we defined the impact of RNase E on mRNA degradation rates transcriptome-wide in the nonpathogenic model Mycolicibacterium smegmatis. RNase E played a rate-limiting role in degradation of the transcripts encoded by at least 89% of protein-coding genes, with leadered transcripts often being more affected by RNase E repression than leaderless transcripts. There was an apparent global slowing of transcription in response to knockdown of RNase E, suggesting that M. smegmatis regulates transcription in responses to changes in mRNA degradation. This compensation was incomplete, as the abundance of most transcripts increased upon RNase E knockdown. We assessed the sequence preferences for cleavage by RNase E transcriptome-wide in M. smegmatis and Mycobacterium tuberculosis and found a consistent bias for cleavage in C-rich regions. Purified RNase E had a clear preference for cleavage immediately upstream of cytidines, distinct from the sequence preferences of RNase E in gram-negative bacteria. We furthermore report a high-resolution map of mRNA cleavage sites in M. tuberculosis, which occur primarily within the RNase E-preferred sequence context, confirming that RNase E has a broad impact on the M. tuberculosis transcriptome.

KatG catalase deficiency confers bedaquiline hyper-susceptibility to isoniazid resistant Mycobacterium tuberculosis

Multidrug-resistant tuberculosis (MDR-TB) is a growing source of global mortality and threatens global control of tuberculosis (TB) disease. The diarylquinoline bedaquiline (BDQ) recently emerged as a highly efficacious drug against MDR-TB, defined as resistance to the first-line drugs isoniazid (INH) and rifampin. INH resistance is primarily caused by loss-of-function mutations in the catalase KatG, but mechanisms underlying BDQ's efficacy against MDR-TB remain unknown. Here we employ a systems biology approach to investigate BDQ hyper-susceptibility in INH-resistant Mycobacterium tuberculosis . We found hyper-susceptibility to BDQ in INH-resistant cells is due to several physiological changes induced by KatG deficiency, including increased susceptibility to reactive oxygen species and DNA damage, remodeling of transcriptional programs, and metabolic repression of folate biosynthesis. We demonstrate BDQ hyper-susceptibility is common in INH-resistant clinical isolates. Collectively, these results highlight how altered bacterial physiology can impact drug efficacy in drug-resistant bacteria.

Benzene Amide Ether Scaffold is Active against Non-replicating and Intracellular Mycobacterium tuberculosis

New drugs to treat tuberculosis which target intractable bacterial populations are required to develop shorter and more effective treatment regimens. The benzene amide ether scaffold has activity against intracellular Mycobacterium tuberculosis, but low activity against extracellular, actively replicating M. tuberculosis. We determined that these molecules have bactericidal activity against non-replicating M. tuberculosis but not actively replicating bacteria. Exposure to compounds depleted ATP levels in non-replicating bacteria and increased the oxygen consumption rate; a subset of molecules led to the accumulation of intrabacterial reactive oxygen species. A comprehensive screen of M. tuberculosis strains identified a number of under-expressing strains as more sensitive to compounds under replicating conditions including QcrA and QcrB hypomorphs. We determined the global gene expression profile after compound treatment for both replicating and nutrient-starved M. tuberculosis. We saw compound-dependent changes in the expression of genes involved in energy metabolism under both conditions. Taken together, our data suggest that the scaffold targets respiration in M. tuberculosis.

Epidemiological features and temporal trends of HIV-negative tuberculosis burden from 1990 to 2019: a retrospective analysis based on the Global Burden of Disease Study 2019

OBJECTIVE

This study aimed to analyse the burden and temporal trends of tuberculosis (TB) incidence and mortality globally, as well as the association between mortality-to-incidence ratio (MIR) and Socio-Demographic Index (SDI).

DESIGN

A retrospective analysis of TB data from 1990 to 2019 was conducted using the Global Burden of Disease Study database.

RESULTS

Between 1990 and 2019, there was a declining trend in the global incidence and mortality of TB. High SDI regions experienced a higher declining rate than in low SDI regions during the same period. Nearly half of the new patients occurred in South Asia. In addition, there is a sex-age imbalance in the overall burden of TB, with young males having higher incidence and mortality than females. In terms of the three subtypes of TB, drug-sensitive (DS)-TB accounted for more than 90% of the incidents and deaths and experienced a decline over the past 30 years. However, drug-resistant TB (multidrug-resistant (MDR)-TB and extensively drug-resistant (XDR)-TB) showed an overall increasing trend in age-standardised incidence rates and age-standardised mortality rates, with an inflection point after the year 2000. At the regional level, South Asia and Eastern Europe remained a high burden of drug-resistant TB incidence and mortality. Interestingly, a negative correlation was found between the MIR and SDI for TB, including DS-TB, MDR-TB and XDR-TB. Notably, central sub-Saharan Africa had the highest MIR, which indicated a higher-than-expected burden given its level of sociodemographic development.

CONCLUSION

This study provides comprehensive insights into the global burden and temporal trends of TB incidence and mortality, as well as the relationship between MIR and SDI. These findings contribute to our understanding of TB epidemiology and can inform public health strategies for prevention and management.

MtrA modulates Mycobacterium tuberculosis cell division in host microenvironments to mediate intrinsic resistance and drug tolerance

The success of Mycobacterium tuberculosis (Mtb) is largely attributed to its ability to physiologically adapt and withstand diverse localized stresses within host microenvironments. Here, we present a data-driven model (EGRIN 2.0) that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. Analysis of EGRIN 2.0 shows how modulation of the MtrAB two-component signaling system tunes Mtb growth in response to related host microenvironmental cues. Disruption of MtrAB by tunable CRISPR interference confirms that the signaling system regulates multiple peptidoglycan hydrolases, among other targets, that are important for cell division. Further, MtrA decreases the effectiveness of antibiotics by mechanisms of both intrinsic resistance and drug tolerance. Together, the model-enabled dissection of complex MtrA regulation highlights its importance as a drug target and illustrates how EGRIN 2.0 facilitates discovery and mechanistic characterization of Mtb adaptation to specific host microenvironments within the host.

Applications of machine learning in microbial natural product drug discovery

INTRODUCTION

Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased. To accelerate the microbial NP discovery process, machine learning (ML) is being applied to numerous areas of NP discovery and development.

AREAS COVERED

This review explores the utility of ML at various phases of the microbial NP drug discovery pipeline, discussing concrete examples throughout each major phase: genome mining, dereplication, and biological target prediction. Moreover, the authors discuss how ML approaches can be applied to semi-synthetic approaches to drug discovery.

EXPERT OPINION

Despite the important role that microbial NPs play in the development of novel drugs, their discovery has declined due to challenges associated with the conventional discovery process. ML is positioned to overcome these limitations given its ability to model complex datasets and generalize to novel chemical and sequence space. Unsurprisingly, ML comes with its own limitations that must be considered for its successful implementation. The authors stress the importance of continuing to build high quality and open access NP datasets to further increase the utility of ML in NP discovery.

Protein targets in Mycobacterium tuberculosis and their inhibitors for therapeutic implications: A narrative review

Advancement in the area of anti-tubercular drug development has been full-fledged, yet, a very less number of drug molecules have reached phase II clinical trials, and therefore "End-TB" is still a global challenge. Inhibitors to specific metabolic pathways of Mycobacterium tuberculosis (Mtb) gain importance in strategizing anti-tuberculosis drug discovery. The lead compounds that target DNA replication, protein synthesis, cell wall biosynthesis, bacterial virulence and energy metabolism are emerging as potential chemotherapeutic options against Mtb growth and survival within the host. In recent times, the in silico approaches have become most promising tools in the identification of suitable inhibitors for specific protein targets of Mtb. An update in the fundamental understanding of these inhibitors and the mechanism of interaction may bring hope to future perspectives in novel drug development and delivery approaches. This review provides a collective impression of the small molecules with potential antimycobacterial activities and their target pathways in Mtb such as cell wall biosynthesis, DNA replication, transcription and translation, efflux pumps, antivirulence pathways and general metabolism. The mechanism of interaction of specific inhibitor with their respective protein targets has been discussed. The comprehensive knowledge of such an impactful area of research would essentially reflect in the discovery of novel drug molecules and effective delivery approaches. This narrative review encompasses the knowledge of emerging targets and promising n that could potentially translate in to the anti-TB-drug discovery.

Modern Trends in Natural Antibiotic Discovery

Natural scaffolds remain an important basis for drug development. Therefore, approaches to natural bioactive compound discovery attract significant attention. In this account, we summarize modern and emerging trends in the screening and identification of natural antibiotics. The methods are divided into three large groups: approaches based on microbiology, chemistry, and molecular biology. The scientific potential of the methods is illustrated with the most prominent and recent results.

Personal Care Products as a Contributing Factor to Antimicrobial Resistance: Current State and Novel Approach to Investigation

Antimicrobial resistance (AMR) is one of the world's industrialized nations' biggest issues. It has a significant influence on the ecosystem and negatively affects human health. The overuse of antibiotics in the healthcare and agri-food industries has historically been defined as a leading factor, although the use of antimicrobial-containing personal care products plays a significant role in the spread of AMR. Lotions, creams, shampoos, soaps, shower gels, toothpaste, fragrances, and other items are used for everyday grooming and hygiene. However, in addition to the primary ingredients, additives are included to help preserve the product by lowering its microbial load and provide disinfection properties. These same substances are released into the environment, escaping traditional wastewater treatment methods and remaining in ecosystems where they contact microbial communities and promote the spread of resistance. The study of antimicrobial compounds, which are often solely researched from a toxicological point of view, must be resumed considering the recent discoveries, to highlight their contribution to AMR. Parabens, triclocarban, and triclosan are among the most worrying chemicals. To investigate this issue, more effective models must be chosen. Among them, zebrafish is a crucial study system because it allows for the assessment of both the risks associated with exposure to these substances as well as environmental monitoring. Furthermore, artificial intelligence-based computer systems are useful in simplifying the handling of antibiotic resistance data and speeding up drug discovery processes.

Targeting LPS biosynthesis and transport in gram-negative bacteria in the era of multi-drug resistance

Gram-negative bacteria pose a major threat to human health in an era fraught with multi-drug resistant bacterial infections. Despite extensive drug discovery campaigns over the past decades, no new antibiotic target class effective against gram-negative bacteria has become available to patients since the advent of the carbapenems in 1985. Antibiotic discovery efforts against gram-negative bacteria have been hampered by limited intracellular accumulation of xenobiotics, in large part due to the impermeable cell envelope comprising lipopolysaccharide (LPS) in the outer leaflet of the outer membrane, as well as a panoply of efflux pumps. The biosynthesis and transport of LPS are essential to the viability and virulence of most gram-negative bacteria. Thus, both LPS biosynthesis and transport are attractive pathways to target therapeutically. In this review, we summarize the LPS biosynthesis and transport pathways and discuss efforts to find small molecule inhibitors against targets within these pathways.

ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction

Graph convolutional neural networks (GCNs) have been repeatedly shown to have robust capacities for modeling graph data such as small molecules. Message-passing neural networks (MPNNs), a group of GCN variants that can learn and aggregate local information of molecules through iterative message-passing iterations, have exhibited advancements in molecular modeling and property prediction. Moreover, given the merits of Transformers in multiple artificial intelligence domains, it is desirable to combine the self-attention mechanism with MPNNs for better molecular representation. We propose an atom-bond transformer-based message-passing neural network (ABT-MPNN), to improve the molecular representation embedding process for molecular property predictions. By designing corresponding attention mechanisms in the message-passing and readout phases of the MPNN, our method provides a novel architecture that integrates molecular representations at the bond, atom and molecule levels in an end-to-end way. The experimental results across nine datasets show that the proposed ABT-MPNN outperforms or is comparable to the state-of-the-art baseline models in quantitative structure-property relationship tasks. We provide case examples of Mycobacterium tuberculosis growth inhibitors and demonstrate that our model's visualization modality of attention at the atomic level could be an insightful way to investigate molecular atoms or functional groups associated with desired biological properties. The new model provides an innovative way to investigate the effect of self-attention on chemical substructures and functional groups in molecular representation learning, which increases the interpretability of the traditional MPNN and can serve as a valuable way to investigate the mechanism of action of drugs.

Nutrient stress is a target for new antibiotics

Novel approaches are required to address the looming threat of pan-resistant Gram-negative pathogens and forestall the rise of untreatable infections. Unconventional targets that are uniquely important during infection and tractable to high-throughput drug discovery methods hold high potential for innovation in antibiotic discovery programs. In this context, inhibitors of bacterial nutrient stress are particularly exciting candidates for future antibiotic development. Amino acid, nucleotide, and vitamin biosynthesis pathways are critical for bacterial growth in nutrient-limiting conditions in the laboratory and the host. Although historically dismissed as dispensable for pathogens, a wealth of transposon mutagenesis and single-mutant studies have emerged which demonstrate that several such pathways are critical for infection. Indeed, high-throughput screens of diverse synthetic compounds and natural products have uncovered inhibitors of nutrient biosynthesis. Herein, we review bacterial nutrient biosynthesis and its role during host infection. Further, we explore screening platforms developed to search for inhibitors of these targets and highlight successes among these. Finally, we feature important and sometimes surprising connections between bacterial nutrient biosynthesis, antibiotic activity, and antibiotic resistance.

Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections

Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.

Multivariate chemogenomic screening prioritizes new macrofilaricidal leads

Development of direct acting macrofilaricides for the treatment of human filariases is hampered by limitations in screening throughput imposed by the parasite life cycle. In vitro adult screens typically assess single phenotypes without prior enrichment for chemicals with antifilarial potential. We developed a multivariate screen that identified dozens of compounds with submicromolar macrofilaricidal activity, achieving a hit rate of >50% by leveraging abundantly accessible microfilariae. Adult assays were multiplexed to thoroughly characterize compound activity across relevant parasite fitness traits, including neuromuscular control, fecundity, metabolism, and viability. Seventeen compounds from a diverse chemogenomic library elicited strong effects on at least one adult trait, with differential potency against microfilariae and adults. Our screen identified five compounds with high potency against adults but low potency or slow-acting microfilaricidal effects, at least one of which acts through a novel mechanism. We show that the use of microfilariae in a primary screen outperforms model nematode developmental assays and virtual screening of protein structures inferred with deep learning. These data provide new leads for drug development, and the high-content and multiplex assays set a new foundation for antifilarial discovery.

Advances in the design of combination therapies for the treatment of tuberculosis

INTRODUCTION

Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects.

AREAS COVERED

This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken.

EXPERT OPINION

We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.

An efficient CRISPR interference-based prediction method for synergistic/additive effects of novel combinations of anti-tuberculosis drugs

Tuberculosis (TB) is treated by chemotherapy with multiple anti-TB drugs for a long period, spanning 6 months even in a standard course. In perspective, to prevent the emergence of antimicrobial resistance, novel drugs that act synergistically or additively in combination with major anti-TB drugs and, if possible, shorten the duration of TB therapy are needed. However, their combinatorial effect cannot be predicted until the lead identification phase of the drug development. Clustered regularly interspaced short palindromic repeats interference (CRISPRi) is a powerful genetic tool that enables high-throughput screening of novel drug targets. The development of anti-TB drugs promises to be accelerated by CRISPRi. This study determined whether CRISPRi could be applicable for predictive screening of the combinatorial effect between major anti-TB drugs and an inhibitor of a novel target. In the checkerboard assay, isoniazid killed Mycobacterium smegmatis synergistically or additively in combinations with rifampicin or ethambutol, respectively. The susceptibility to rifampicin and ethambutol was increased by knockdown of inhA, which encodes a target molecule of isoniazid. Additionally, knockdown of rpoB, which encodes a target molecule of rifampicin, increased the susceptibility to isoniazid and ethambutol, which act synergistically with rifampicin in the checkerboard assay. Moreover, CRISPRi could successfully predict the synergistic action of cyclomarin A, a novel TB drug candidate, with isoniazid or rifampicin. These results demonstrate that CRISPRi is a useful tool not only for drug target exploration but also for screening the combinatorial effects of novel combinations of anti-TB drugs. This study provides a rationale for anti-TB drug development using CRISPRi.

Application of combined CRISPR screening for genetic and chemical-genetic interaction profiling in Mycobacterium tuberculosis

CRISPR screening, including CRISPR interference (CRISPRi) and CRISPR-knockout (CRISPR-KO) screening, has become a powerful technology in the genetic screening of eukaryotes. In contrast with eukaryotes, CRISPR-KO screening has not yet been applied to functional genomics studies in bacteria. Here, we constructed genome-scale CRISPR-KO and also CRISPRi libraries in Mycobacterium tuberculosis (Mtb). We first examined these libraries to identify genes essential for Mtb viability. Subsequent screening identified dozens of genes associated with resistance/susceptibility to the antitubercular drug bedaquiline (BDQ). Genetic and chemical validation of the screening results suggested that it provided a valuable resource to investigate mechanisms of action underlying the effects of BDQ and to identify chemical-genetic synergies that can be used to optimize tuberculosis therapy. In summary, our results demonstrate the potential for efficient genome-wide CRISPR-KO screening in bacteria and establish a combined CRISPR screening approach for high-throughput investigation of genetic and chemical-genetic interactions in Mtb.

Construction of a synthetic methodology-based library and its application in identifying a GIT/PIX protein-protein interaction inhibitor

In recent years, the flourishing of synthetic methodology studies has provided concise access to numerous molecules with new chemical space. These compounds form a large library with unique scaffolds, but their application in hit discovery is not systematically evaluated. In this work, we establish a synthetic methodology-based compound library (SMBL), integrated with compounds obtained from our synthetic researches, as well as their virtual derivatives in significantly larger scale. We screen the library and identify small-molecule inhibitors to interrupt the protein-protein interaction (PPI) of GIT1/β-Pix complex, an unrevealed target involved in gastric cancer metastasis. The inhibitor 14-5-18 with a spiro[bicyclo[2.2.1]heptane-2,3'-indolin]-2'-one scaffold, considerably retards gastric cancer metastasis in vitro and in vivo. Since the PPI targets are considered undruggable as they are hard to target, the successful application illustrates the structural specificity of SMBL, demonstrating its potential to be utilized as compound source for more challenging targets.

Unraveling the mechanisms of intrinsic drug resistance in Mycobacterium tuberculosis

Tuberculosis (TB) is among the most difficult infections to treat, requiring several months of multidrug therapy to produce a durable cure. The reasons necessitating long treatment times are complex and multifactorial. However, one major difficulty of treating TB is the resistance of the infecting bacterium, Mycobacterium tuberculosis (Mtb), to many distinct classes of antimicrobials. This review will focus on the major gaps in our understanding of intrinsic drug resistance in Mtb and how functional and chemical-genetics can help close those gaps. A better understanding of intrinsic drug resistance will help lay the foundation for strategies to disarm and circumvent these mechanisms to develop more potent antitubercular therapies.

“Upcycling” known molecules and targets for drug-resistant TB

Despite reinvigorated efforts in Tuberculosis (TB) drug discovery over the past 20 years, relatively few new drugs and candidates have emerged with clear utility against drug resistant TB. Over the same period, significant technological advances and learnings around target value have taken place. This has offered opportunities to re-assess the potential for optimization of previously discovered chemical matter against Mycobacterium tuberculosis (M.tb) and for reconsideration of clinically validated targets encumbered by drug resistance. A re-assessment of discarded compounds and programs from the “golden age of antibiotics” has yielded new scaffolds and targets against TB and uncovered classes, for example beta-lactams, with previously unappreciated utility for TB. Leveraging validated classes and targets has also met with success: booster technologies and efforts to thwart efflux have improved the potential of ethionamide and spectinomycin classes. Multiple programs to rescue high value targets while avoiding cross-resistance are making progress. These attempts to make the most of known classes, drugs and targets complement efforts to discover new chemical matter against novel targets, enhancing the chances of success of discovering effective novel regimens against drug-resistant TB.

Mutations in rv0678 Confer Low-Level Resistance to Benzothiazinone DprE1 Inhibitors in Mycobacterium tuberculosis

Tuberculosis (TB) is the leading cause of death from any bacterial infection, causing 1.5 million deaths worldwide each year. Due to the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) there have been significant efforts aimed at developing novel drugs to treat TB. One promising drug target in Mtb is the arabinogalactan biosynthetic enzyme DprE1, and there have been over a dozen unique chemical scaffolds identified which inhibit the activity of this protein. Among the most promising lead compounds are the benzothiazinones BTZ043 and PBTZ169, both of which are currently in or have completed phase IIa clinical trials. Due to the potential clinical utility of these drugs, we sought to identify potential synergistic interactions and new mechanisms of resistance using a genome-scale CRISPRi chemical-genetic screen with PBTZ169. We found that knockdown of rv0678, the negative regulator of the mmpS5/L5 drug efflux pump, confers resistance to PBTZ169. Mutations in rv0678 are the most common form of resistance to bedaquiline and there is already abundant evidence of these mutations emerging in bedaquiline-treated patients. We confirmed that rv0678 mutations from clinical isolates confer low level cross-resistance to BTZ043 and PBTZ169. While it is yet unclear whether rv0678 mutations would render benzothiazinones ineffective in treating TB, these results highlight the importance of monitoring for clinically prevalent rv0678 mutations during ongoing BTZ043 and PBTZ169 clinical trials.

How Mycobacterium tuberculosis drug resistance has shaped anti-tubercular drug discovery

Drug resistance is an increasing problem for the treatment of tuberculosis. The prevalence of clinical isolates with pre-existing resistance needs to be considered in any drug discovery program. Non-specific mechanisms of resistance such as increased efflux or decreased permeability need to be considered both in developing individual drug candidates and when designing novel regimens. We review a number of different approaches to develop new analogs and drug combinations or improve efficacy of existing drugs that may overcome or delay the appearance of clinical resistance. We also discuss the need to fully characterize mechanisms of resistance and cross- resistance to existing drugs to ensure that novel drugs will be clinically effective.

Myths and Misconceptions around Antibiotic Resistance: Time to Get Rid of Them

The antibiotic resistance arena is fraught with myths and misconceptions, leading to wrong strategies to combat it. It is crucial to identify them, discuss them in light of current evidence, and dispel those that are unequivocally wrong. This article proposes some concepts that may qualify as misconceptions around antibiotic resistance: the susceptible-resistant dichotomy; that incomplete antibiotic courses cause resistance; that resistance "emerges" in patients and hospitals; that antibiotics are mostly abused clinically; that resistance is higher in countries that use more antibiotics; that reducing antibiotic usage would reduce resistance; that financial incentives would "jumpstart" research and development of antibiotics; that generic and "original" antibiotics are the same; and that new anti-infective therapies are just around the corner. While some of these issues are still controversial, it is important to recognize their controversial status, instead of repeating them in specialized literature and lectures and, especially, in the planning of strategies to cope with resistance.

Bottlenecks and opportunities in antibiotic discovery against Mycobacterium tuberculosis

Tuberculosis (TB) persists as a major global health issue and a leading cause of death by a single infectious agent. The global burden of TB is further exacerbated by the continuing emergence and dissemination of strains of Mycobacterium tuberculosis resistant to multiple antibiotics. The need for novel drugs that can be used to shorten the course for current TB drug regimens as well as combat the persistent threat of antibiotic resistance has never been greater. There have been significant advances in the discovery of de novo TB treatments, with the first TB-specific drugs in 45 years approved for use. However, there are still issues that restrict the pipeline of new antitubercular chemotherapies. The rate of failure of TB drug candidates in clinical trials remains high, while the validation of new TB drug targets and subsequent identification of novel inhibitors remains modest.

A Target Engagement Assay to Assess Uptake, Potency, and Retention of Antibiotics in Living Bacteria

New antibiotics are urgently needed to counter the emergence of antimicrobial-resistant pathogenic bacteria. A major challenge in antibiotic drug discovery is to turn potent biochemical inhibitors of essential bacterial components into effective antimicrobials. This difficulty is underpinned by a lack of methods to investigate the physicochemical properties needed for candidate antibiotics to permeate the bacterial cell envelope and avoid clearance by the action of bacterial efflux pumps. To address these issues, here we used a target engagement assay to measure the equilibrium and kinetic binding parameters of antibiotics targeting dihydrofolate reductase (DHFR) in live bacteria. We also used this assay to identify novel DHFR ligands having antimicrobial activity. We validated this approach using the Gram-negative bacteria Escherichia coli and the emerging human pathogen Mycobacterium abscessus. We expect the use of target engagement assays in bacteria to expedite the discovery and progression of novel, cell-permeable antibiotics with on-target activity.

Low-cost anti-mycobacterial drug discovery using engineered E. coli

Whole-cell screening for Mycobacterium tuberculosis (Mtb) inhibitors is complicated by the pathogen's slow growth and biocontainment requirements. Here we present a synthetic biology framework for assaying Mtb drug targets in engineered E. coli. We construct Target Essential Surrogate E. coli (TESEC) in which an essential metabolic enzyme is deleted and replaced with an Mtb-derived functional analog, linking bacterial growth to the activity of the target enzyme. High throughput screening of a TESEC model for Mtb alanine racemase (Alr) revealed benazepril as a targeted inhibitor, a result validated in whole-cell Mtb. In vitro biochemical assays indicated a noncompetitive mechanism unlike that of clinical Alr inhibitors. We establish the scalability of TESEC for drug discovery by characterizing TESEC strains for four additional targets.

CRISPRi chemical genetics and comparative genomics identify genes mediating drug potency in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) infection is notoriously difficult to treat. Treatment efficacy is limited by Mtb's intrinsic drug resistance, as well as its ability to evolve acquired resistance to all antituberculars in clinical use. A deeper understanding of the bacterial pathways that influence drug efficacy could facilitate the development of more effective therapies, identify new mechanisms of acquired resistance, and reveal overlooked therapeutic opportunities. Here we developed a CRISPR interference chemical-genetics platform to titrate the expression of Mtb genes and quantify bacterial fitness in the presence of different drugs. We discovered diverse mechanisms of intrinsic drug resistance, unveiling hundreds of potential targets for synergistic drug combinations. Combining chemical genetics with comparative genomics of Mtb clinical isolates, we further identified several previously unknown mechanisms of acquired drug resistance, one of which is associated with a multidrug-resistant tuberculosis outbreak in South America. Lastly, we found that the intrinsic resistance factor whiB7 was inactivated in an entire Mtb sublineage endemic to Southeast Asia, presenting an opportunity to potentially repurpose the macrolide antibiotic clarithromycin to treat tuberculosis. This chemical-genetic map provides a rich resource to understand drug efficacy in Mtb and guide future tuberculosis drug development and treatment.

Tuberculosis Drug Discovery: Challenges and New Horizons

Over the past 2000 years, tuberculosis (TB) has claimed more lives than any other infectious disease. In 2020 alone, TB was responsible for 1.5 million deaths worldwide, comparable to the 1.8 million deaths caused by COVID-19. The World Health Organization has stated that new TB drugs must be developed to end this pandemic. After decades of neglect in this field, a renaissance era of TB drug discovery has arrived, in which many novel candidates have entered clinical trials. However, while hundreds of molecules are reported annually as promising anti-TB agents, very few successfully progress to clinical development. In this Perspective, we critically review those anti-TB compounds published in the last 6 years that demonstrate good in vivo efficacy against Mycobacterium tuberculosis. Additionally, we highlight the main challenges and strategies for developing new TB drugs and the current global pipeline of drug candidates in clinical studies to foment fresh research perspectives.

Expanding the search for small-molecule antibacterials by multidimensional profiling

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Integrated genomics and chemical biology herald an era of sophisticated antibacterial discovery, from defining essential genes to target elucidation

The golden age of antibiotic discovery in the 1940s-1960s saw the development and deployment of many different classes of antibiotics, revolutionizing the field of medicine. Since that time, our ability to discover antibiotics of novel structural classes or mechanisms has not kept pace with the ever-growing threat of antibiotic resistance. Recently, advances at the intersection of genomics and chemical biology have enabled efforts to better define the vulnerabilities of essential gene targets, to develop sophisticated whole-cell chemical screening methods that reveal target biology early, and to elucidate small molecule targets and modes of action more effectively. These new technologies have the potential to expand the chemical diversity of antibiotic candidates, as well as the breadth of targets. We illustrate how the latest tools of genomics and chemical biology are being integrated to better understand pathogen vulnerabilities and antibiotic mechanisms in order to inform a new era of antibiotic discovery.

Identification of cell wall synthesis inhibitors active against Mycobacterium tuberculosis by competitive activity-based protein profiling

The identification and validation of a small molecule's targets is a major bottleneck in the discovery process for tuberculosis antibiotics. Activity-based protein profiling (ABPP) is an efficient tool for determining a small molecule's targets within complex proteomes. However, how target inhibition relates to biological activity is often left unexplored. Here, we study the effects of 1,2,3-triazole ureas on Mycobacterium tuberculosis (Mtb). After screening ∼200 compounds, we focus on 4 compounds that form a structure-activity series. The compound with negligible activity reveals targets, the inhibition of which is functionally less relevant for Mtb growth and viability, an aspect not addressed in other ABPP studies. Biochemistry, computational docking, and morphological analysis confirms that active compounds preferentially inhibit serine hydrolases with cell wall and lipid metabolism functions and that disruption of the cell wall underlies biological activity. Our findings show that ABPP identifies the targets most likely relevant to a compound's antibacterial activity.

Gradients in gene essentiality reshape antibacterial research

Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.

Spiropyrimidinetrione DNA Gyrase Inhibitors with Potent and Selective Antituberculosis Activity

New antibiotics with either a novel mode of action or novel mode of inhibition are urgently needed to overcome the threat of drug-resistant tuberculosis (TB). The present study profiles new spiropyrimidinetriones (SPTs), DNA gyrase inhibitors having activity against drug-resistant Mycobacterium tuberculosis (Mtb), the causative agent of TB. While the clinical candidate zoliflodacin has progressed to phase 3 trials for the treatment of gonorrhea, compounds herein demonstrated higher inhibitory potency against Mtb DNA gyrase (e.g., compound 42 with IC₅₀ = 2.0) and lower Mtb minimum inhibitor concentrations (0.49 μM for 42). Notably, 42 and analogues showed selective Mtb activity relative to representative Gram-positive and Gram-negative bacteria. DNA gyrase inhibition was shown to involve stabilization of double-cleaved DNA, while on-target activity was supported by hypersensitivity against a gyrA hypomorph. Finally, a docking model for SPTs with Mtb DNA gyrase was developed, and a structural hypothesis was built for structure-activity relationship expansion.

Elucidating the Mechanisms of Action of Antimicrobial Agents

Despite the ever-growing antibiotic resistance crisis, the rate at which new antimicrobials are being discovered and approved for human use has rapidly declined over the past 75 years. A barrier for advancing newly identified antibiotics beyond discovery is elucidating their mechanism(s) of action.

Deep clustering of small molecules at large-scale via variational autoencoder embedding and K-means

Background

Converting molecules into computer-interpretable features with rich molecular information is a core problem of data-driven machine learning applications in chemical and drug-related tasks. Generally speaking, there are global and local features to represent a given molecule. As most algorithms have been developed based on one type of feature, a remaining bottleneck is to combine both feature sets for advanced molecule-based machine learning analysis. Here, we explored a novel analytical framework to make embeddings of the molecular features and apply them in the clustering of a large number of small molecules.

Results

In this novel framework, we first introduced a principal component analysis method encoding the molecule-specific atom and bond information. We then used a variational autoencoder (AE)-based method to make embeddings of the global chemical properties and the local atom and bond features. Next, using the embeddings from the encoded local and global features, we implemented and compared several unsupervised clustering algorithms to group the molecule-specific embeddings. The number of clusters was treated as a hyper-parameter and determined by the Silhouette method. Finally, we evaluated the corresponding results using three internal indices. Applying the analysis framework to a large chemical library of more than 47,000 molecules, we successfully identified 50 molecular clusters using the K-means method with 32 embeddings based on the AE method. We visualized the clustering result via t-SNE for the overall distribution of molecules and the similarity maps for the structural analysis of randomly selected cluster-specific molecules.

Conclusions

This study developed a novel analytical framework that comprises a feature engineering scheme for molecule-specific atomic and bonding features and a deep learning-based embedding strategy for different molecular features. By applying the identified embeddings, we show their usefulness for clustering a large molecule dataset. Our novel analytic algorithms can be applied to any virtual library of chemical compounds with diverse molecular structures. Hence, these tools have the potential of optimizing drug discovery, as they can decrease the number of compounds to be screened in any drug screening campaign.

Chemical-genetic interaction mapping links carbon metabolism and cell wall structure to tuberculosis drug efficacy

Efforts to improve tuberculosis therapy include optimizing multidrug regimens to take advantage of drug–drug synergies. However, the complex host environment has a profound effect on bacterial metabolic state and drug activity, making predictions of optimal drug combinations difficult. In this study, we leverage a newly developed library of conditional knockdown Mycobacterium tuberculosis mutants in which genetic depletion of essential genes mimics the effect of drug therapy. This tractable system allowed us to assess the effect of growth condition on predicted drug–drug interactions. We found that these interactions can be differentially sensitive to the metabolic state, and select in vitro–defined interactions can be leveraged to accelerate bacterial killing during infection. These findings suggest strategies for optimizing tuberculosis therapy.

Current chemotherapy against Mycobacterium tuberculosis (Mtb), an important human pathogen, requires a multidrug regimen lasting several months. While efforts have been made to optimize therapy by exploiting drug–drug synergies, testing new drug combinations in relevant host environments remains arduous. In particular, host environments profoundly affect the bacterial metabolic state and drug efficacy, limiting the accuracy of predictions based on in vitro assays alone. In this study, we utilized conditional Mtb knockdown mutants of essential genes as an experimentally tractable surrogate for drug treatment and probe the relationship between Mtb carbon metabolism and chemical–genetic interactions (CGIs). We examined the antitubercular drugs isoniazid, rifampicin, and moxifloxacin and found that CGIs are differentially responsive to the metabolic state, defining both environment-independent and -dependent interactions. Specifically, growth on the in vivo–relevant carbon source, cholesterol, reduced rifampicin efficacy by altering mycobacterial cell surface lipid composition. We report that a variety of perturbations in cell wall synthesis pathways restore rifampicin efficacy during growth on cholesterol, and that both environment-independent and cholesterol-dependent in vitro CGIs could be leveraged to enhance bacterial clearance in the mouse infection model. Our findings present an atlas of chemical–genetic–environmental interactions that can be used to optimize drug–drug interactions, as well as provide a framework for understanding in vitro correlates of in vivo efficacy.

Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles

Motivation

Chemical–genetic interaction profiling is a genetic approach that quantifies the susceptibility of a set of mutants depleted in specific gene product(s) to a set of chemical compounds. With the recent advances in artificial intelligence, chemical–genetic interaction profiles (CGIPs) can be leveraged to predict mechanism of action of compounds. This can be achieved by using machine learning, where the data from a CGIP is fed into the machine learning platform along with the chemical descriptors to develop a chemogenetically trained model. As small molecules can be considered non-structural data, graph convolutional neural networks, which can learn from the chemical structures directly, can be used to successfully predict molecular properties. Clustering analysis, on the other hand, is a critical approach to get insights into the underlying biological relationships between the gene products in the high-dimensional chemical-genetic data.

Methods and results

In this study, we proposed a comprehensive framework based on the large-scale chemical-genetics dataset built in Mycobacterium tuberculosis for predicting CGIPs using graph-based deep learning models. Our approach is structured into three parts. First, by matching M. tuberculosis genes with homologous genes in Escherichia coli (E. coli) according to their gene products, we grouped the genes into clusters with distinct biological functions. Second, we employed a directed message passing neural network to predict growth inhibition against M. tuberculosis gene clusters using a collection of 50,000 chemicals with the profile. We compared the performance of different baseline models and implemented multi-label tasks in binary classification frameworks. Lastly, we applied the trained model to an externally curated drug set that had experimental results against M. tuberculosis genes to examine the effectiveness of our method. Overall, we demonstrate that our approach effectively created M. tuberculosis gene clusters, and the trained classifier is able to predict activity against essential M. tuberculosis targets with high accuracy.

Conclusion

This work provides an analytical framework for modeling large-scale chemical-genetic datasets for predicting CGIPs and generating hypothesis about mechanism of action of novel drugs. In addition, this work highlights the importance of graph-based deep neural networks in drug discovery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13321-022-00596-6.

DNA-Dependent Binding of Nargenicin to DnaE1 Inhibits Replication in Mycobacterium tuberculosis

Natural products provide a rich source of potential antimicrobials for treating infectious diseases for which drug resistance has emerged. Foremost among these diseases is tuberculosis. Assessment of the antimycobacterial activity of nargenicin, a natural product that targets the replicative DNA polymerase of Staphylococcus aureus, revealed that it is a bactericidal genotoxin that induces a DNA damage response in Mycobacterium tuberculosis (Mtb) and inhibits growth by blocking the replicative DNA polymerase, DnaE1. Cryo-electron microscopy revealed that binding of nargenicin to Mtb DnaE1 requires the DNA substrate such that nargenicin is wedged between the terminal base pair and the polymerase and occupies the position of both the incoming nucleotide and templating base. Comparative analysis across three bacterial species suggests that the activity of nargenicin is partly attributable to the DNA binding affinity of the replicative polymerase. This work has laid the foundation for target-led drug discovery efforts focused on Mtb DnaE1.

Combining CRISPRi and metabolomics for functional annotation of compound libraries

Molecular profiling of small-molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small molecule direct targets and secondary effects. However, current profiling methods are either limited in the number of measurable parameters or throughput. Here, we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from (CRISPR) interference with 352 genes in all major essential biological processes. Next, based on the comparison of genetic with 1342 drug-induced metabolic changes we made de novo predictions of compound functionality and revealed antibacterials with unconventional Modes of Action. We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality, from bacteria to human cell lines.