MS network-based screening for new antibiotics discovery

Mavintramycin A is a promising antibiotic for treating Mycobacterium avium complex infectious disease

Mycobacterium avium complex (MAC) is a serious disease that is mainly caused by infection with the non-tuberculous mycobacteria (NTM), Mycobacterium avium and Mycobacterium intracellulare. Seven new compounds, designated mavintramycins A-G (1-7), were isolated along with structurally related compounds, including amicetin (9) and plicacetin (10), from the culture broth of Streptomyces sp. OPMA40551 as anti-MAC compounds that were active against M. avium and M. intracellulare. Among them, mavintramycin A showed the most potent and selective inhibition of M. avium and M. intracellulare. Furthermore, mavintramycin A was active against more than 40 clinically isolated M. avium, including multidrug-resistant strains, and inhibited the growth of M. avium in a persistent infection cell model using THP-1 macrophages. Mavintramycin A also exhibited in vivo efficacy in silkworm and mouse infection assays with NTM. An experiment to elucidate its mechanism of action revealed that mavintramycin A inhibits protein synthesis by binding to 23S ribosomal RNA in NTM. Mavintramycin A, with a different chemical structure from those of clinically used agents, is a promising drug candidate for the treatment of MAC infectious disease.

Kimidinomycin, a new antibiotic against Mycobacterium avium complex, produced by Streptomyces sp. KKTA-0263

During our screening for antibiotics against Mycobacterium avium complex (MAC) with a mass spectrometry network-based indexing approach, a new compound named kimidinomycin was isolated from the culture broth of Streptomyces sp. KKTA-0263 by solvent extraction, HP20 column chromatography, and preparative HPLC. From the structural elucidation, the compound possesses a 38-membered macrolide structure with an N-methylguanidyl group at the terminal side chain. The compound exhibited antimycobacterial activity against M. avium, M. intracellulare, M. smegmatis, and M. bovis BCG with respective MIC values of 12.5, 0.78, 12.5, and 25.0 µg ml-1.

Integrated Omics Strategy Reveals Cyclic Lipopeptides Empedopeptins from Massilia sp. YMA4 and Their Biosynthetic Pathway

Empedopeptins—eight amino acid cyclic lipopeptides—are calcium-dependent antibiotics that act against Gram-positive bacteria such as Staphylococcus aureus by inhibiting cell wall biosynthesis. However, to date, the biosynthetic mechanism of the empedopeptins has not been well identified. Through comparative genomics and metabolomics analysis, we identified empedopeptin and its new analogs from a marine bacterium, Massilia sp. YMA4. We then unveiled the empedopeptin biosynthetic gene cluster. The core nonribosomal peptide gene null-mutant strains (ΔempC, ΔempD, and ΔempE) could not produce empedopeptin, while dioxygenase gene null-mutant strains (ΔempA and ΔempB) produced several unique empedopeptin analogs. However, the antibiotic activity of ΔempA and ΔempB was significantly reduced compared with the wild-type, demonstrating that the hydroxylated amino acid residues of empedopeptin and its analogs are important to their antibiotic activity. Furthermore, we found seven bacterial strains that could produce empedopeptin-like cyclic lipopeptides using a genome mining approach. In summary, this study demonstrated that an integrated omics strategy can facilitate the discovery of potential bioactive metabolites from microbial sources without further isolation and purification.

Penicillic Acid Congener, a New Inhibitor of BMP-Induced Alkaline Phosphatase Activity in Myoblasts, Produced by the Fungus Penicillium sp. BF-0343

During our screening for microbial regulators of bone metabolism, a new compound, 6-ethoxy-5,6-dihydropenillic acid (1), was isolated together with a known and structurally related penicillic acid (2) from the culture broth of the soil-derived fungus Penicillium sp. BF-0343. The structure of 1 was elucidated by various spectroscopic data including nuclear magnetic resonance experiments. Compounds 1 and 2 dose-dependently inhibited bone morphogenetic protein–induced alkaline phosphatase activity in myoblasts with half-maximal inhibitory concentration values of 19.8 and 2.1 μM, respectively.

Genomic and Metabolomic Analysis of Antarctic Bacteria Revealed Culture and Elicitation Conditions for the Production of Antimicrobial Compounds

Concern about finding new antibiotics against drug-resistant pathogens is increasing every year. Antarctic bacteria have been proposed as an unexplored source of bioactive metabolites; however, most biosynthetic gene clusters (BGCs) producing secondary metabolites remain silent under common culture conditions. Our work aimed to characterize elicitation conditions for the production of antibacterial secondary metabolites from 34 Antarctic bacterial strains based on MS/MS metabolomics and genome mining approaches. Bacterial strains were cultivated under different nutrient and elicitation conditions, including the addition of lipopolysaccharide (LPS), sodium nitroprusside (SNP), and coculture. Metabolomes were obtained by HPLC-QTOF-MS/MS and analyzed through molecular networking. Antibacterial activity was determined, and seven strains were selected for genome sequencing and analysis. Biosynthesis pathways were activated by all the elicitation treatments, which varies among strains and dependents of culture media. Increased antibacterial activity was observed for a few strains and addition of LPS was related with inhibition of Gram-negative pathogens. Antibiotic BGCs were found for all selected strains and the expressions of putative actinomycin, carotenoids, and bacillibactin were characterized by comparison of genomic and metabolomic data. This work established the use of promising new elicitors for bioprospection of Antarctic bacteria and highlights the importance of new "-omics" comparative approaches for drug discovery.

An overview of tools, software, and methods for natural product fragment and mass spectral analysis

One major challenge in natural product (NP) discovery is the determination of the chemical structure of unknown metabolites using automated software tools from either GC–mass spectrometry (MS) or liquid chromatography–MS/MS data only. This chapter reviews the existing spectral libraries and predictive computational tools used in MS-based untargeted metabolomics, which is currently a hot topic in NP structure elucidation. We begin by focusing on spectral databases and the general workflow of MS annotation. We then describe software and tools used in MS, particularly those used to predict fragmentation patterns, mass spectral classifiers, and tools for fragmentation trees analysis. We then round up the chapter by looking at more advanced approaches implemented in tools for competitive fragmentation modeling and quantum chemical approaches.