Journals / Black Sea Journal of Agriculture / 2026 / Cilt: 9 Sayı: 2

Biosynthetic Potential and Antibiotic Resistance in Environmental Streptomyces across Agricultural and Natural Ecosystems

Elif Coşkun Dağgeçen(Kahramanmaraş Sütçü İmam Üniversitesi)0000-0003-4688-5855
Yaşar Alptekin(KAHRAMANMARAS SUTCU IMAM UNIVERSITY)0000-0002-4321-2834
Pages
262–270
Publication Date
2026-03-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall, our findings show that biosynthetic capacity and clinically defined antibiotic resistance are separate genomic traits in environmental Streptomyces.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Streptomyces species play key roles in producing natural products and are important in soil and agricultural environments.
📊
Key Findings & Data:In this study, we compared the genomes of 90 environmental Streptomyces strains from diverse agricultural and natural sites worldwide to examine how their biosynthetic capabilities and antibiotic resistance relate to their evolutionary history and environments.
💡
Conclusion & Impact:Overall, our findings show that biosynthetic capacity and clinically defined antibiotic resistance are separate genomic traits in environmental Streptomyces.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Streptomyces · Biosynthetic gene clusters · Antibiotic resistance genes

Abstract

Streptomyces species play key roles in producing natural products and are important in soil and agricultural environments. The link between their biosynthetic gene clusters (BGCs) and clinically known antibiotic resistance genes (ARGs) in the environment remains poorly understood. In this study, we compared the genomes of 90 environmental Streptomyces strains from diverse agricultural and natural sites worldwide to examine how their biosynthetic capabilities and antibiotic resistance relate to their evolutionary history and environments. Phylogenomic analyses based on average nucleotide identity (ANI) revealed substantial genetic diversity with limited clustering by geographic origin or isolation source. Genome-wide prediction of biosynthetic gene clusters using GECCO revealed extensive, highly variable biosynthetic potential across genomes, with a large proportion of BGCs remaining uncharacterized. In contrast, clinically referenced ARGs identified using AMRFinderPlus and CARD were unevenly distributed and often absent, indicating limited overlap between environmental resistance mechanisms and clinical resistance determinants. Our quantitative analysis found a significant negative correlation between the total number of BGCs and the number of clinically identified ARGs (Spearman’s ρ=−0.277, P=0.0081). This result challenges the common belief that antibiotic production and clinical resistance are closely connected. When we measured antibiotic-specific biosynthetic potential using the Antibiotic Biosynthetic Potential Index (ABPI), we also found no association with ARG content, further supporting the idea that biosynthesis and clinical resistance are separate. We also observed substantial variation in both BGC and ARG content, even among closely related genomes, indicating that Streptomyces genomes are highly flexible. Overall, our findings show that biosynthetic capacity and clinically defined antibiotic resistance are separate genomic traits in environmental Streptomyces. This highlights the need for genome-based, context-aware methods to assess environmental resistance risks. It also shows that Streptomyces are a valuable source of new biosynthetic diversity for discovering natural products, improving agricultural biotechnology, and supporting One Health research.

Özet

Streptomyces species play key roles in producing natural products and are important in soil and agricultural environments. The link between their biosynthetic gene clusters (BGCs) and clinically known antibiotic resistance genes (ARGs) in the environment remains poorly understood. In this study, we compared the genomes of 90 environmental Streptomyces strains from diverse agricultural and natural sites worldwide to examine how their biosynthetic capabilities and antibiotic resistance relate to their evolutionary history and environments. Phylogenomic analyses based on average nucleotide identity (ANI) revealed substantial genetic diversity with limited clustering by geographic origin or isolation source. Genome-wide prediction of biosynthetic gene clusters using GECCO revealed extensive, highly variable biosynthetic potential across genomes, with a large proportion of BGCs remaining uncharacterized. In contrast, clinically referenced ARGs identified using AMRFinderPlus and CARD were unevenly distributed and often absent, indicating limited overlap between environmental resistance mechanisms and clinical resistance determinants. Our quantitative analysis found a significant negative correlation between the total number of BGCs and the number of clinically identified ARGs (Spearman’s ρ=−0.277, P=0.0081). This result challenges the common belief that antibiotic production and clinical resistance are closely connected. When we measured antibiotic-specific biosynthetic potential using the Antibiotic Biosynthetic Potential Index (ABPI), we also found no association with ARG content, further supporting the idea that biosynthesis and clinical resistance are separate. We also observed substantial variation in both BGC and ARG content, even among closely related genomes, indicating that Streptomyces genomes are highly flexible. Overall, our findings show that biosynthetic capacity and clinically defined antibiotic resistance are separate genomic traits in environmental Streptomyces. This highlights the need for genome-based, context-aware methods to assess environmental resistance risks. It also shows that Streptomyces are a valuable source of new biosynthetic diversity for discovering natural products, improving agricultural biotechnology, and supporting One Health research.

🔖

Cite This Article

Dağgeçen, E. C., Alptekin, Y. (2026). Biosynthetic Potential and Antibiotic Resistance in Environmental Streptomyces across Agricultural and Natural Ecosystems. Black Sea Journal of Agriculture, Cilt: 9 Sayı: 2, 262–270. https://doi.org/10.47115/bsagriculture.1850732

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (31)

  1. Alcock, B. P., Huynh, W., Chalil, R., Smith, K. W., Raphenya, A. R., Wlodarski, M. A., Edalatmand, A., Petkau, A., Syed, S. A., …, & McArthur, A. G. (2023). CARD 2023: Expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research, 51(D1), D690-D699. https://doi.org/10.1093/nar/gkac920
  2. Alkorta, I., & Garbisu, C. (2025). Expanding the focus of the One Health concept: Links between the Earth-system processes of the planetary boundaries framework and antibiotic resistance. Reviews on Environmental Health, 40(1), 159-173. https://doi.org/10.1515/reveh-2024-0013
  3. Baltz, R. H. (2019). Natural product drug discovery in the genomic era: realities, conjectures, misconceptions, and opportunities. Journal of Industrial Microbiology and Biotechnology, 46(3-4), 281-299. https://doi.org/10.1007/s10295-018-2115-4
  4. Berendonk, T. U., Manaia, C. M., Merlin, C., Fatta-Kassinos, D., Cytryn, E., Walsh, F., Bürgmann, H., Sørum, H., Norström, M., Pons, M. N., Kreuzinger, N., Huovinen, P., Stefani, S., Schwartz, T., Kisand, V., Baquero, F., & Martinez, J. L. (2015). Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13(5), 310-317. https://doi.org/10.1038/nrmicro3439
  5. Carroll, L. M., Larralde, M., Fleck, J. S., Ponnudurai, R., Milanese, A., Cappio, E., & Zeller, G. (2021). Accurate de novo identification of biosynthetic gene clusters with GECCO. BioRxiv, 2021-05. https://doi.org/10.1101/2021.05.03.442509
  6. Chater, K. F. (2016). Recent advances in understanding Streptomyces. F1000Research, 5, 2795. https://doi.org/10.12688/f1000research.9534.1
  7. Cimermancic, P., Medema, M. H., Claesen, J., Kurita, K., Brown, L. C. W., Mavrommatis, K., Pati, A., Godfrey, P. A., Koehrsen, M., Clardy, J., Birren, B. W., Takano, E., Sali, A., Linington, P. G., & Fischbach, M. A. (2014). Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters. Cell, 158(2), 412-421.
  8. Doroghazi, J. R., Albright, J. C., Goering, A. W., Ju, K. S., Haines, R. R., Tchalukov, K. A., Labeda, D. P., Kelleher, N. L., & Metcalf, W. W. (2014). A roadmap for natural product discovery based on large-scale genomics and metabolomics. Nature Chemical Biology, 10(11), 963-968. https://doi.org/10.1038/nchembio.1659
  9. Feldgarden, M., Brover, V., Gonzalez-Escalona, N., Frye, J. G., Haendiges, J., Haft, D. H., Hoffmann, M., Pettengill, J. B., Prasad, A. B., Tillman, G. E., Tyson, G. H., & Klimke, W. (2021). AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Scientific Reports, 11(1), 12728. https://doi.org/10.1038/s41598-021-91456-0
  10. Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T., & Aluru, S. (2018). High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nature Communications, 9(1), 5114. https://doi.org/10.1038/s41467-018-07641-9
  11. Kautsar, S. A., Blin, K., Shaw, S., Navarro-Muñoz, J. C., Terlouw, B. R., Van Der Hooft, J. J.,. & Medema, M. H. (2020). MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Research, 48(D1), D454-D458. https://doi.org/10.1093/nar/gkz882
  12. Kolde, R., & Kolde, M. R. (2015). Package ‘pheatmap’. R package, 1(7), 790.
  13. Larsson, D. J., & Flach, C. F. (2022). Antibiotic resistance in the environment. Nature Reviews Microbiology, 20(5), 257-269. https://doi.org/10.1038/s41579-021-00649-x
  14. Martínez, J. L. (2008). Antibiotics and antibiotic resistance genes in natural environments. Science, 321(5887), 365-367. https://doi.org/10.1126/science.1159483
  15. Martínez, J. L., Coque, T. M., & Baquero, F. (2015). What is a resistance gene? Ranking risk in resistomes. Nature Reviews Microbiology, 13(2), 116-123. https://doi.org/10.1038/nrmicro3399
  16. Medema, M. H., & Fischbach, M. A. (2015). Computational approaches to natural product discovery. Nature Chemical Biology, 11(9), 639-648. https://doi.org/10.1038/nchembio.1884
  17. Nesme, J., & Simonet, P. (2015). The soil resistome: a critical review on antibiotic resistance origins, ecology and dissemination potential in telluric bacteria. Environmental Microbiology, 17(4), 913-930. https://doi.org/10.1111/1462-2920.12631
  18. Nett, M., Ikeda, H., & Moore, B. S. (2009). Genomic basis for natural product biosynthetic diversity in the actinomycetes. Natural Product Reports, 26(11), 1362-1384. https://doi.org/10.1039/B817069J
  19. Ogawara, H. (2016). Self-resistance in Streptomyces, with special reference to β-lactam antibiotics. Molecules, 21(5), 605. https://doi.org/10.3390/molecules21050605
  20. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., & Tyson, G. W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research, 25(7), 1043-1055. https://doi.org/10.1101/gr.186072.114
  21. Perry, J. A., Westman, E. L., & Wright, G. D. (2014). The antibiotic resistome: what's new?. Current Opinion in Microbiology, 21, 45-50. https://doi.org/10.1016/j.mib.2014.09.002
  22. R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www. R-project. org/.
  23. Scherlach, K., & Hertweck, C. (2020). Chemical mediators at the bacterial-fungal interface. Annual Review of Microbiology, 74(1), 267-290. https://doi.org/10.1146/annurev-micro-012420-081224
  24. Schlatter, D., Kinkel, L., Thomashow, L., Weller, D., & Paulitz, T. (2017). Disease suppressive soils: new insights from the soil microbiome. Phytopathology, 107(11), 1284-1297. https://doi.org/10.1094/PHYTO-03-17-0111-RVW
  25. Spearman, C. (1961). The Proof and Measurement of Association Between Two Things. In J. J. Jenkins & D. G. Paterson (Eds.), Studies in individual differences: The search for intelligence (pp. 45–58). Appleton-Century-Crofts. https://doi.org/10.1037/11491-005
  26. Walsh, C. T., & Fischbach, M. A. (2010). Natural products version 2.0: connecting genes to molecules. Journal of the American Chemical Society, 132(8), 2469-2493. https://doi.org/10.1021/ja909118a
  27. Watve, M. G., Tickoo, R., Jog, M. M., & Bhole, B. D. (2001). How many antibiotics are produced by the genus Streptomyces?. Archives of Microbiology, 176(5), 386-390. https://doi.org/10.1007/s002030100345
  28. WHO. (2022). World Health Organization, UNEP United Nations Environment Programme, & World Organisation for Animal Health. One health joint plan of action (2022‒2026): working together for the health of humans, animals, plants and the environment.
  29. Wickham, H. (2016). Data analysis. In ggplot2: elegant graphics for data analysis. Springer international publishing.
  30. Wright, G. D. (2007). The antibiotic resistome: the nexus of chemical and genetic diversity. Nature Reviews Microbiology, 5(3), 175-186. https://doi.org/10.1038/nrmicro1614
  31. Kautsar, S. A., Blin, K., Shaw, S., Navarro-Muñoz, J. C., Terlouw, B. R., Van Der Hooft, J. J.,. & Medema, M. H. (2020). MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Research, 48(D1), D454-D458. https://doi.org/10.1093/nar/gkz882