Chemical genomics reveals mechanistic hypotheses for uncharacterized bioactive molecules in bacteria

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

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

Quantitative approaches to study phenotypic effects of large-scale genetic perturbations

How microbes interact with their environment and how the complex interplay of their genes enables them to survive and thrive under stress is a fundamental question in microbial system biology, which is also important from a public health perspective. Large-scale studies of gene-gene, gene-drug, and drug-drug interactions have proven to be powerful tools for elucidating gene function and functional modules in the cell. Approaches that systematically quantify phenotypes in libraries of microbial strains with genome-wide genetic perturbations are crucial for progress in this area. Here, we review recent advances in this field, and point out applications to the study of gene-drug interactions. We highlight newly developed techniques for the rapid generation of genome-wide mutant libraries and the high-throughput measurement of more complex phenotypes and other observables, such as cell morphology or thermal stability of the proteome.

A Staphylococcus aureus clpX Mutant Used as a Unique Screening Tool to Identify Cell Wall Synthesis Inhibitors that Reverse β-Lactam Resistance in MRSA

Staphylococcus aureus is a leading cause of bacterial infections world-wide. Staphylococcal infections are preferentially treated with β-lactam antibiotics, however, methicillin-resistant S. aureus (MRSA) strains have acquired resistance to this superior class of antibiotics. We have developed a growth-based, high-throughput screening approach that directly identifies cell wall synthesis inhibitors capable of reversing β-lactam resistance in MRSA. The screen is based on the finding that S. aureus mutants lacking the ClpX chaperone grow very poorly at 30°C unless specific steps in teichoic acid synthesis or penicillin binding protein (PBP) activity are inhibited. This property allowed us to exploit the S. aureus clpX mutant as a unique screening tool to rapidly identify biologically active compounds that target cell wall synthesis. We tested a library of ∼50,000 small chemical compounds and searched for compounds that inhibited growth of the wild type while stimulating growth of the clpX mutant. Fifty-eight compounds met these screening criteria, and preliminary tests of 10 compounds identified seven compounds that reverse β-lactam resistance of MRSA as expected for inhibitors of teichoic acid synthesis. The hit compounds are therefore promising candidates for further development as novel combination agents to restore β-lactam efficacy against MRSA.

Technologies for High-Throughput Identification of Antibiotic Mechanism of Action

There are two main strategies for antibiotic discovery: target-based and phenotypic screening. The latter has been much more successful in delivering first-in-class antibiotics, despite the major bottleneck of delayed Mechanism-of-Action (MOA) identification. Although finding new antimicrobial compounds is a very challenging task, identifying their MOA has proven equally challenging. MOA identification is important because it is a great facilitator of lead optimization and improves the chances of commercialization. Moreover, the ability to rapidly detect MOA could enable a shift from an activity-based discovery paradigm towards a mechanism-based approach. This would allow to probe the grey chemical matter, an underexplored source of structural novelty. In this study we review techniques with throughput suitable to screen large libraries and sufficient sensitivity to distinguish MOA. In particular, the techniques used in chemical genetics (e.g., based on overexpression and knockout/knockdown collections), promoter-reporter libraries, transcriptomics (e.g., using microarrays and RNA sequencing), proteomics (e.g., either gel-based or gel-free techniques), metabolomics (e.g., resourcing to nuclear magnetic resonance or mass spectrometry techniques), bacterial cytological profiling, and vibrational spectroscopy (e.g., Fourier-transform infrared or Raman scattering spectroscopy) were discussed. Ultimately, new and reinvigorated phenotypic assays bring renewed hope in the discovery of a new generation of antibiotics.

Simultaneous elucidation of antibiotic mechanism of action and potency with high-throughput Fourier-transform infrared (FTIR) spectroscopy and machine learning

The low rate of discovery and rapid spread of resistant pathogens have made antibiotic discovery a worldwide priority. In cell-based screening, the mechanism of action (MOA) is identified after antimicrobial activity. This increases rediscovery, impairs low potency candidate detection, and does not guide lead optimization. In this study, high-throughput Fourier-transform infrared (FTIR) spectroscopy was used to discriminate the MOA of 14 antibiotics at pathway, class, and individual antibiotic level. For that, the optimal combinations and parametrizations of spectral preprocessing were selected with cross-validated partial least squares discriminant analysis, to which various machine learning algorithms were applied. This coherently resulted in very good accuracies, independently of the algorithms, and at all levels of MOA. Particularly, an ensemble of subspace discriminants predicted the known pathway (98.6%), antibiotic classes (100%), and individual antibiotics (97.8%) with exceptional accuracy, and similar results were obtained for simulated novel MOA. Even at very low concentrations (1 μg/mL) and growth inhibition (15%), over 70% pathway and class accuracy was achieved, suggesting FTIR spectroscopy can probe the grey chemical matter. Prediction of inhibitory effect was also examined, for which a squared exponential Gaussian process regression yielded a root mean square error of 0.33 and a R² of 0.92, indicating that metabolic alterations leading to growth inhibition are intrinsically reflected on FTIR spectra beyond cell density. KEY POINTS: • Antibiotic MOA and potency estimated with high-throughput FTIR spectroscopy • Sub-inhibitory MOA identification suggests ability to explore grey chemical matter • Data analysis optimization improved MOA identification at antibiotic level by 38.

Potentiation of Antibiotics against Gram-Negative Bacteria by Polymyxin B Analogue SPR741 from Unique Perturbation of the Outer Membrane

Therapeutics targeting Gram-negative bacteria have the challenge of overcoming a formidable outer membrane (OM) barrier. Here, we characterize the action of SPR741, a novel polymyxin B (PMB) analogue shown to potentiate several large-scaffold antibiotics in Gram-negative pathogens. Probing the surface topology of Escherichia coli using atomic force microscopy revealed substantial OM disorder at concentrations of SPR741 that lead to antibiotic potentiation. Conversely, very little cytoplasmic membrane depolarization was observed at these same concentrations, indicating that SPR741 acts predominately on the OM. Truncating the lipopolysaccharide (LPS) core with genetic perturbations uniquely sensitized E. coli to SPR741, suggesting that LPS core residues keep SPR741 at the OM, where it can potentiate a codrug, rather than permit its entry to the cytoplasmic membrane. Further, a promoter activity assay revealed that SPR741 challenge induced the expression of RcsAB, a stress sensor for OM perturbation. Together, these results indicate that SPR741 interacts predominately with the OM, in contrast to the dual action of PMB and colistin at both the outer and cytoplasmic membranes.

Metabolic Fingerprinting with Fourier-Transform Infrared (FTIR) Spectroscopy: Towards a High-Throughput Screening Assay for Antibiotic Discovery and Mechanism-of-Action Elucidation

The discovery of antibiotics has been slowing to a halt. Phenotypic screening is once again at the forefront of antibiotic discovery, yet Mechanism-Of-Action (MOA) identification is still a major bottleneck. As such, methods capable of MOA elucidation coupled with the high-throughput screening of whole cells are required now more than ever, for which Fourier-Transform Infrared (FTIR) spectroscopy is a promising metabolic fingerprinting technique. A high-throughput whole-cell FTIR spectroscopy-based bioassay was developed to reveal the metabolic fingerprint induced by 15 antibiotics on the Escherichia coli metabolism. Cells were briefly exposed to four times the minimum inhibitory concentration and spectra were quickly acquired in the high-throughput mode. After preprocessing optimization, a partial least squares discriminant analysis and principal component analysis were conducted. The metabolic fingerprints obtained with FTIR spectroscopy were sufficiently specific to allow a clear distinction between different antibiotics, across three independent cultures, with either analysis algorithm. These fingerprints were coherent with the known MOA of all the antibiotics tested, which include examples that target the protein, DNA, RNA, and cell wall biosynthesis. Because FTIR spectroscopy acquires a holistic fingerprint of the effect of antibiotics on the cellular metabolism, it holds great potential to be used for high-throughput screening in antibiotic discovery and possibly towards a better understanding of the MOA of current antibiotics.

Impact of ∼omics in the detection and validation of potential anti-infective drugs

New anti-infective drugs are an unmet necessity of modern medicine. The use of ∼omics technologies has exponentially increased the knowledge on active anti-infective structures, where to search for them and their mechanisms of action. Research involving extreme and unique environments (such as endophytes) revealed their potential for many yet unknown active molecules. This work intends to review a recent research involving discovery of secondary metabolites with an established anti-infective action which was mediated by one of the ∼omics sciences: genomics, proteomics, transcriptomics, metabolomics, glycomics or their combinations, as well as the software at the base of these discoveries.

Rapid Inhibition Profiling Identifies a Keystone Target in the Nucleotide Biosynthesis Pathway

Understanding the mechanism of action (MOA) of new antimicrobial agents is a critical step in drug discovery but is notoriously difficult for compounds that appear to inhibit multiple cellular pathways. We recently described image-based approaches [bacterial cytological profiling and rapid inducible profiling (RIP)] for identifying the cellular pathways targeted by antibiotics. Here we have applied these methods to examine the effects of proteolytically degrading enzymes involved in pyrimidine nucleotide biosynthesis, a pathway that produces intermediates for transcription, DNA replication, and cell envelope synthesis. We show that rapid removal of enzymes directly involved in deoxyribonucleotide synthesis blocks DNA replication. However, degradation of cytidylate kinase (CMK), which catalyzes reactions involved in the synthesis of both ribonucleotides and deoxyribonucleotides, blocks both DNA replication and wall teichoic acid biosynthesis, producing cytological effects identical to those created by simultaneously inhibiting both processes with the antibiotics ciprofloxacin and tunicamycin. Our results suggest that RIP can be used to identify and characterize potential keystone enzymes like CMK whose inhibition dramatically affects multiple pathways, thereby revealing important metabolic connections. Identifying and understanding the role of keystone targets might also help to determine the MOAs of drugs that appear to inhibit multiple targets.