Microbial Bioactives
Microbial Bioactives | Online ISSN 2209-2161
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Volume 8 Number 1 2025
REVIEWS (Open Access)
Deep Mining the Microbial Biosphere: Genomics-Driven Discovery of Natural Products in the Era of Antimicrobial Resistance
Dhiraj Kumar Chaudhary 1*, Ram Hari Dahal 2, Ramesh Prasad Pandey 3
Microbial Bioactives 8 (1) 1-8 https://doi.org/10.25163/microbbioacts.8110649
Submitted: 20 November 2024 Revised: 13 January 2025 Accepted: 22 January 2025 Published: 24 January 2025
Abstract
The accelerating crisis of antimicrobial resistance (AMR) has exposed the limitations of conventional natural product discovery pipelines and renewed interest in microbial secondary metabolites as a foundation for future therapeutics. Historically, bioactivity-guided screening of cultivable microorganisms yielded many of today’s frontline antibiotics; however, this approach now suffers from diminishing returns due to frequent rediscovery of known compounds and a narrow exploration of microbial diversity. Advances in genome sequencing and systems-level analytics have fundamentally reshaped this landscape. This systematic review and meta-analytical synthesis examines how genomics-driven “deep mining” of the microbial biosphere has transformed natural product discovery, shifting the field from serendipity toward predictive, data-informed strategies. Evidence across diverse studies demonstrates that microbial genomes harbor a vast reservoir of cryptic biosynthetic gene clusters (BGCs), most of which remain silent under standard laboratory conditions. Genome mining, resistance-guided prioritization, metagenomics, and integrative metabolomics have collectively expanded access to this hidden biosynthetic potential. The review further highlights how marine and rare biosphere microbes contribute disproportionately to chemical novelty, reinforcing the value of underexplored environments. Recent incorporation of artificial intelligence and machine learning has improved BGC detection, dereplication, and gene–metabolite linking, increasing discovery efficiency and reducing redundancy. Meta-analytical trends indicate that integrative, genomics-centered workflows consistently outperform traditional screening in novelty yield and mechanistic insight. Despite remaining challenges in pathway activation, compound expression, and scalable production, deep mining strategies represent a robust and necessary response to the AMR crisis. Harnessing microbial genomic diversity is therefore not only a scientific opportunity but a strategic imperative for sustaining the antibiotic pipeline.
Keywords: Antimicrobial resistance; microbial natural products; genome mining; biosynthetic gene clusters; metagenomics; deep mining; artificial intelligence
References
Alcock, B. P., Huynh, W., Chalil, R., Smith, K. W., Raphenya, A. R., Wlodarski, M. A., Edalatmand, A., Petkau, A., Syed, S. A., Tsang, K. K., Baker, S. J. C., Dave, M., Nguyen, T., Jaramillo, M. F., Pon, A., Prasad, S., Zaidi, F., Yao, R., Jin, L., … McArthur, A. G. (2023). CARD 2023: Expanded curation and resistome prediction. Nucleic Acids Research, 51(D1), D690–D699. https://doi.org/10.1093/nar/gkac920
Albarano, L., Esposito, R., Ruocco, N., & Costantini, M. (2020). Genome mining as new challenge in natural products discovery. Marine Drugs, 18(4), 199. https://doi.org/10.3390/md18040199
Arnison, P. G., Bibb, M. J., Bierbaum, G., Bowers, A. A., Bugni, T. S., Bulaj, G., Camarero, J. A., Campopiano, D. J., Challis, G. L., Clardy, J., Cotter, P. D., Craik, D. J., Dawson, M., Dittmann, E., Donadio, S., Dorrestein, P. C., Entian, K. D., Fischbach, M. A., Garavelli, J. S., … van der Donk, W. A. (2013). Ribosomally synthesized and post-translationally modified peptide natural products. Natural Product Reports, 30(1), 108–160. https://doi.org/10.1039/C2NP20085F
Baltz, R. H. (2017). Gifted microbes for genome mining and natural product discovery. Journal of Industrial Microbiology & Biotechnology, 44(4–5), 573–588. https://doi.org/10.1007/s10295-016-1815-x
Berdy, J. (2012). Thoughts and facts about antibiotics: Where we are now and where we are heading. The Journal of Antibiotics, 65(8), 385–395. https://doi.org/10.1038/ja.2012.38
Brown, E. D., & Wright, G. D. (2016). Antibacterial drug discovery in the resistance era. Nature, 529(7586), 336–343. https://doi.org/10.1038/nature17048
Dong, H., & Ming, D. (2023). A comprehensive self-resistance gene database for natural-product discovery. International Journal of Molecular Sciences, 24(15), 12446. https://doi.org/10.3390/ijms241512446
El-Sayed, A. S. A., Shindia, A. A., AbouZid, S. F., El-Sayed, M. M., & Ali, G. S. (2020). Exploiting the biosynthetic potency of Taxol from fungal endophytes. Molecules, 25(13), 3000. https://doi.org/10.3390/molecules25133000
Fischbach, M. A., & Walsh, C. T. (2009). Antibiotics for emerging pathogens. Science, 325(5944), 1089–1093. https://doi.org/10.1126/science.1176667
Gaudêncio, S. P., Pereira, F., & Pinto, F. (2023). Advanced methods for natural products discovery. Marine Drugs, 21(5), 308. https://doi.org/10.3390/md21050308
Handelsman, J. (2004). Metagenomics: Application of genomics to uncultured microorganisms. Microbiology and Molecular Biology Reviews, 68(4), 669–685. https://doi.org/10.1128/MMBR.68.4.669-685.2004
Knerr, P. J., & van der Donk, W. A. (2012). Discovery, biosynthesis, and engineering of lantipeptides. Annual Review of Biochemistry, 81, 479–505. https://doi.org/10.1146/annurev-biochem-060110-113521
Kunyavskaya, O., Khmelinskaia, A., Gurevich, A., & Pevzner, P. A. (2021). Nerpa: A tool for discovering biosynthetic gene clusters. Metabolites, 11(10), 693. https://doi.org/10.3390/metabo11100693
Ling, L. L., Schneider, T., Peoples, A. J., Spoering, A. L., Engels, I., Conlon, B. P., Mueller, A., Schäberle, T. F., Hughes, D. E., Epstein, S., Jones, M., Lazarides, L., Steadman, V. A., Cohen, D. R., Felix, C. R., Fetterman, K. A., Millett, W. P., Nitti, A. G., Zullo, A. M., … Lewis, K. (2015). A new antibiotic kills pathogens without detectable resistance. Nature, 517(7535), 455–459. https://doi.org/10.1038/nature14098
Lopatkin, A. J., Yang, J. H., Bening, S. C., Manson, A. L., Stokes, J. M., Kohanski, M. A., Badran, A. H., Ford, C. B., & Collins, J. J. (2021). Clinically relevant mutations in core metabolic genes confer antibiotic resistance. Science, 371(6531), eaba0862. https://doi.org/10.1126/science.aba0862
Landwehr, W., Wolf, C., & Wink, J. (2016). Actinobacteria and myxobacteria—Two of the most important bacterial resources for novel antibiotics. Current Topics in Microbiology and Immunology, 398, 273–302. https://doi.org/10.1007/82_2016_499
Malit, J. J. L., Li, Z., Kasanah, N., & Lin, Z. (2022). Targeted large-scale genome mining and candidate prioritization. Marine Drugs, 20(6), 398. https://doi.org/10.3390/md20060398
Meena, S. N., Sharma, R., Gupta, P., & Kaur, G. (2024). High-throughput mining of novel compounds from known microbes. Molecules, 29(13), 3237. https://doi.org/10.3390/molecules29133237
Mungan, M. D., Alanjary, M., Blin, K., Weber, T., Medema, M. H., & Ziemert, N. (2022). ARTS-DB: A database for antibiotic resistant targets. Nucleic Acids Research, 50(D1), D736–D740. https://doi.org/10.1093/nar/gkab940
Murray, C. J. L., Ikuta, K. S., Sharara, F., Swetschinski, L., Robles Aguilar, G., Gray, A., Han, C., Bisignano, C., Rao, P., Wool, E., Johnson, S. C., Browne, A. J., Chipeta, M. G., Fell, F., Hackett, S., Haines-Woodhouse, G., Kashef Hamadani, B. H., Kumaran, E. A. P., McManigal, B., … Naghavi, M. (2022). Global burden of bacterial antimicrobial resistance in 2019. The Lancet, 399(10325), 629–655. https://doi.org/10.1016/S0140-6736(21)02724-0
O’Neill, E. C., Schorn, M., Larson, C. B., & Tietze, A. (2019). Targeted antibiotic discovery through resistance determinants. Critical Reviews in Microbiology, 45(3), 255–277. https://doi.org/10.1080/1040841X.2019.1577234
Rosic, N. (2022). Genome mining as an alternative way for screening marine organisms. Marine Drugs, 20(8), 478. https://doi.org/10.3390/md20080478
Rutledge, P. J., & Challis, G. L. (2015). Discovery of microbial natural products by activation of silent biosynthetic gene clusters. Nature Reviews Microbiology, 13(8), 509–523. https://doi.org/10.1038/nrmicro3496
Silver, L. L. (2008). Are natural products still the best source for antibacterial discovery? Expert Opinion on Drug Discovery, 3(5), 487–500. https://doi.org/10.1517/17460441.3.5.487
Silver, L. L. (2011). Challenges of antibacterial discovery. Clinical Microbiology Reviews, 24(1), 71–109. https://doi.org/10.1128/CMR.00030-10
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J. (2019). A deep learning approach to antibiotic discovery. Cell Metabolism, 30(2), 251–259. https://doi.org/10.1016/j.cmet.2019.06.009
Sogin, M. L., Morrison, H. G., Huber, J. A., Mark Welch, D., Huse, S. M., Neal, P. R., Arrieta, J. M., & Herndl, G. J. (2006). Microbial diversity in the deep sea and the rare biosphere. Proceedings of the National Academy of Sciences of the United States of America, 103(32), 12115–12120. https://doi.org/10.1073/pnas.0605127103
Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D., Eisen, J. A., Wu, D., Paulsen, I., Nelson, K. E., Nelson, W., Fouts, D. E., Levy, S., Knap, A. H., Lomas, M. W., Nealson, K., White, O., Peterson, J., Hoffman, J., Parsons, R., … Smith, H. O. (2004). Environmental genome shotgun sequencing of the Sargasso Sea. Science, 304(5667), 66–74. https://doi.org/10.1126/science.1093857
Wang, M., Carver, J. J., Phelan, V. V., Sanchez, L. M., Garg, N., Peng, Y., Nguyen, D. D., Watrous, J., Kapono, C. A., Luzzatto-Knaan, T., Porto, C., Bouslimani, A., Melnik, A. V., Meehan, M. J., Liu, W. T., Crüsemann, M., Boudreau, P. D., Esquenazi, E., Sandoval-Calderón, M., … Dorrestein, P. C. (2016). Sharing and community curation of mass spectrometry data with GNPS. Nature Biotechnology, 34(8), 828–837. https://doi.org/10.1038/nbt.3597
Wang, Z., Li, J., Zhang, Y., & Liu, H. (2025). The deep mining era: Genomic, metabolomic, and integrative approaches. Marine Drugs, 23(7), 261. https://doi.org/10.3390/md23070261
Walsh, C. T., & Wencewicz, T. A. (2014). Prospects for new antibiotics. The Journal of Antibiotics, 67(1), 7–22. https://doi.org/10.1038/ja.2013.49
Wietz, M., Mansson, M., Gotfredsen, C. H., Larsen, T. O., & Gram, L. (2010). Antibacterial compounds from marine Vibrionaceae. Marine Drugs, 8(12), 2946–2960. https://doi.org/10.3390/md8122946
Wright, G. D. (2014). Something old, something new: Revisiting natural products in antibiotic drug discovery. Canadian Journal of Microbiology, 60(3), 147–154. https://doi.org/10.1139/cjm-2014-0063
Ziemert, N., Alanjary, M., & Weber, T. (2016). The evolution of genome mining in microbes. Natural Product Reports, 33(8), 988–1005. https://doi.org/10.1039/C6NP00025H
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