Metabolomics Tools Assisting Classic Screening Methods in Discovering New Antibiotics from Mangrove Actinomycetia in Leizhou Peninsula

Mangrove actinomycetia are considered one of the promising sources for discovering novel biologically active compounds. Traditional bioactivity- and/or taxonomy-based methods are inefficient and usually result in the re-discovery of known metabolites. Thus, improving selection efficiency among strain candidates is of interest especially in the early stage of the antibiotic discovery program. In this study, an integrated strategy of combining phylogenetic data and bioactivity tests with a metabolomics-based dereplication approach was applied to fast track the selection process. A total of 521 actinomycetial strains affiliated to 40 genera in 23 families were isolated from 13 different mangrove soil samples by the culture-dependent method. A total of 179 strains affiliated to 40 different genera with a unique colony morphology were selected to evaluate antibacterial activity against 12 indicator bacteria. Of the 179 tested isolates, 47 showed activities against at least one of the tested pathogens. Analysis of 23 out of 47 active isolates using UPLC-HRMS-PCA revealed six outliers. Further analysis using the OPLS-DA model identified five compounds from two outliers contributing to the bioactivity against drug-sensitive A. baumannii. Molecular networking was used to determine the relationship of significant metabolites in six outliers and to find their potentially new congeners. Finally, two Streptomyces strains (M22, H37) producing potentially new compounds were rapidly prioritized on the basis of their distinct chemistry profiles, dereplication results, and antibacterial activities, as well as taxonomical information. Two new trioxacarcins with keto-reduced trioxacarcinose B, gutingimycin B (16) and trioxacarcin G (20), together with known gutingimycin (12), were isolated from the scale-up fermentation broth of Streptomyces sp. M22. Our study demonstrated that metabolomics tools could greatly assist classic antibiotic discovery methods in strain prioritization to improve efficiency in discovering novel antibiotics from those highly productive and rich diversity ecosystems.

Artificial Intelligence and Antibiotic Discovery

Over recent decades, a new antibiotic crisis has been unfolding due to a decreased research in this domain, a low return of investment for the companies that developed the drug, a lengthy and difficult research process, a low success rate for candidate molecules, an increased use of antibiotics in farms and an overall inappropriate use of antibiotics. This has led to a series of pathogens developing antibiotic resistance, which poses severe threats to public health systems while also driving up the costs of hospitalization and treatment. Moreover, without proper action and collaboration between academic and health institutions, a catastrophic trend might develop, with the possibility of returning to a pre-antibiotic era. Nevertheless, new emerging AI-based technologies have started to enter the field of antibiotic and drug development, offering a new perspective to an ever-growing problem. Cheaper and faster research can be achieved through algorithms that identify hit compounds, thereby further accelerating the development of new antibiotics, which represents a vital step in solving the current antibiotic crisis. The aim of this review is to provide an extended overview of the current artificial intelligence-based technologies that are used for antibiotic discovery, together with their technological and economic impact on the industrial sector.

In Silico/In Vitro Strategies Leading to the Discovery of New Nonribosomal Peptide and Polyketide Antibiotics Active against Human Pathogens

Antibiotics are majorly important molecules for human health. Following the golden age of antibiotic discovery, a period of decline ensued, characterised by the rediscovery of the same molecules. At the same time, new culture techniques and high-throughput sequencing enabled the discovery of new microorganisms that represent a potential source of interesting new antimicrobial substances to explore. The aim of this review is to present recently discovered nonribosomal peptide (NRP) and polyketide (PK) molecules with antimicrobial activity against human pathogens. We highlight the different in silico/in vitro strategies and approaches that led to their discovery. As a result of technological progress and a better understanding of the NRP and PK synthesis mechanisms, these new antibiotic compounds provide an additional option in human medical treatment and a potential way out of the impasse of antibiotic resistance.

Looking Back to Amycolatopsis: History of the Antibiotic Discovery and Future Prospects

The emergence of antibiotic-resistant pathogenic bacteria in recent decades leads us to an urgent need for the development of new antibacterial agents. The species of the genus Amycolatopsis are known as producers of secondary metabolites that are used in medicine and agriculture. The complete genome sequences of the Amycolatopsis demonstrate a wide variety of biosynthetic gene clusters, which highlights the potential ability of actinomycetes of this genus to produce new antibiotics. In this review, we summarize information about antibiotics produced by Amycolatopsis species. This knowledge demonstrates the prospects for further study of this genus as an enormous source of antibiotics.

New from old: discovery of the novel antibiotic actinomycin L in Streptomyces sp. MBT27

ABSTRACTStreptomycetes are major producers of bioactive natural products, including the majority of the antibiotics. While much if the low-hanging fruit has been discovered, it is predited that less than 5% of the chemical space has been mined. Here, we describe the novel actinomycins L1 and L2, which are produced by Streptomyces sp. MBT27. The molecules were discovered via metabolic analysis combined with molecular networking of cultures grown with different combinations of carbon sources. Actinomycins L1 and L2 are diastereoisomers, and the structure of actinomycin L2 was resolved using NMR and single crystal X-ray crystallography. Actinomycin L is formed via a unique spirolinkage of anthranilamide to the 4-oxoproline moiety of actinomycin X2, prior to the condensation of the actinomycin halves. Feeding anthranilamide to cultures of Streptomyces antibioticus, which has the same biosynthetic gene cluster as Streptomyces sp. MBT27 but only produces actinomycin X2, resulted in the production of actinomycin L. This shows that actinomycin L results from joining two distinct metabolic pathways, namely those for actinomycin X2 and for anthranilamide. Actinomycins L1 and L2 showed significant antimicrobial activity against Gram- positive bacteria. Our work shows how new molecules can still be identified even in the oldest of natural product families.IMPORTANCEActinomycin was the first antibiotic discovered in an actinobacterium by Selman Waksman and colleagues, as early as 1940. This period essentially marks the start of the ‘golden era’ of antibiotic discovery. Over time, emerging antimicrobial resistance (AMR) and the declining success rate of antibiotic discovery resulted in the current antibiotic crisis. We surprisingly discovered that under some growth conditions, Streptomyces sp. MBT27 can produce actinomycins that are significantly different from those that have been published so far. The impact of this work is not only that we have discovered a novel molecule with very interesting chemical modifications in one of the oldest antibiotics ever described, but also that this requires the combined action of primary and secondary metabolic pathways, namely the biosynthesis of anthranilamide and of actinomycin X2, respectively. The implication of the discovery is that even the most well-studied families of natural products may still have surprises in store for us.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Membrane Sensor Histidine Kinases: Insights from Structural, Ligand and Inhibitor Studies of Full-Length Proteins and Signalling Domains for Antibiotic Discovery

There is an urgent need to find new antibacterial agents to combat bacterial infections, including agents that inhibit novel, hitherto unexploited targets in bacterial cells. Amongst novel targets are two-component signal transduction systems (TCSs) which are the main mechanism by which bacteria sense and respond to environmental changes. TCSs typically comprise a membrane-embedded sensory protein (the sensor histidine kinase, SHK) and a partner response regulator protein. Amongst promising targets within SHKs are those involved in environmental signal detection (useful for targeting specific SHKs) and the common themes of signal transmission across the membrane and propagation to catalytic domains (for targeting multiple SHKs). However, the nature of environmental signals for the vast majority of SHKs is still lacking, and there is a paucity of structural information based on full-length membrane-bound SHKs with and without ligand. Reasons for this lack of knowledge lie in the technical challenges associated with investigations of these relatively hydrophobic membrane proteins and the inherent flexibility of these multidomain proteins that reduces the chances of successful crystallisation for structural determination by X-ray crystallography. However, in recent years there has been an explosion of information published on (a) methodology for producing active forms of full-length detergent-, liposome- and nanodisc-solubilised membrane SHKs and their use in structural studies and identification of signalling ligands and inhibitors; and (b) mechanisms of signal sensing and transduction across the membrane obtained using sensory and transmembrane domains in isolation, which reveal some commonalities as well as unique features. Here we review the most recent advances in these areas and highlight those of potential use in future strategies for antibiotic discovery. This Review is part of a Special Issue entitled “Interactions of Bacterial Molecules with Their Ligands and Other Chemical Agents” edited by Mary K. Phillips-Jones.

Review on the Antibacterial Mechanism of Plant-Derived Compounds against Multidrug-Resistant Bacteria (MDR)

Microbial resistance has progressed rapidly and is becoming the leading cause of death globally. The spread of antibiotic-resistant microorganisms has been a significant threat to the successful therapy against microbial infections. Scientists have become more concerned about the possibility of a return to the pre-antibiotic era. Thus, searching for alternatives to fight microorganisms has become a necessity. Some bacteria are naturally resistant to antibiotics, while others acquire resistance mainly by the misuse of antibiotics and the emergence of new resistant variants through mutation. Since ancient times, plants represent the leading source of drugs and alternative medicine for fighting against diseases. Plants are rich sources of valuable secondary metabolites, such as alkaloids, quinones, tannins, terpenoids, flavonoids, and polyphenols. Many studies focus on plant secondary metabolites as a potential source for antibiotic discovery. They have the required structural properties and can act by different mechanisms. This review analyses the antibiotic resistance strategies produced by multidrug-resistant bacteria and explores the phytochemicals from different classes with documented antimicrobial action against resistant bacteria, either alone or in combination with traditional antibiotics.