Deciphering Nature's Secret: Genome Mining in Natural Product Drug Discovery.

Abstract

Background: Antimicrobial resistance (AMR) stands as a threat against global health, rendering once-effective antibiotics impotent against bacterial infections. Various factors contribute to the emergence of AMR, including overuse and misuse of antibiotics in human healthcare and agriculture sectors, among others. Moreover, the challenge is further intensified by the antimicrobial discovery void that has emerged subsequent to the golden era of antimicrobials. During this period, the development of novel antimicrobials stagnated, creating a scarcity of effective therapeutic agents. In this context, natural products have regained prominence as valuable reservoirs for new antimicrobial agents. Microorganisms, led by evolutionary pressures to outcompete each other, are producers of secondary metabolites (SM) with antimicrobial properties. Focusing on the microbial frontlines, this thesis proposes exploring the untapped potential of soil microorganisms through genome mining, utilizing both metagenomics and isolate genomics approaches. While traditional culture-based methods have limitations in capturing the full range of microbial diversity present in complex environments, Metagenomics offers a powerful tool to unlock biosynthetic potential within the genomes of uncultured bacteria. Simultaneously, isolate genomics provides a targeted approach to study individual microorganisms, enhancing our understanding of their genomic makeup and biosynthetic capabilities. This integrated approach holds the key to discovering novel antimicrobial compounds that may evade traditional cultivation methods. Through a combination of innovative techniques and a focus on nature's own defenses, this study seeks to contribute to the revitalization of antimicrobial drug discovery in the face of an evolving global health crisis.

Materials and Methods: Soil samples from various locations in Lebanon undergo DNA extraction, followed by sequencing using short-read and long-read platforms. Bioinformatics analysis, including assembly, binning, taxonomic classification, and gene annotation, is conducted using tools like antiSMASH for biosynthetic gene cluster (BGC) identification. Genetic Networking and Molecular networking via a high-resolution liquid chromatography mass spectrometer (HR-LC-MS) and metabolomics aid in compound identification and biosynthetic pathway elucidation.

Results: We observed striking differences in BGC abundance across microbial taxa, with Streptomyces species exhibiting a significantly higher number of BGCs compared to other groups. Additionally, the limited number of BGCs in certain microorganisms may facilitate their validation and downstream application, simplifying the process of identifying and characterizing bioactive compounds. Furthermore, our investigation into relatedness revealed that BGCs were only related across the same Streptomyces genera, emphasizing the need for comprehensive exploration and characterization of BGCs across microbial taxa. Comparing these results to the molecular network, we observed that many BGCs producing known compounds were not represented, indicating the presence of unexpressed BGCs, and underscoring the importance of genome mining in identifying cryptic biosynthetic potential.