Abstract
Microorganisms in extreme environments represent a promising source of novel metabolites, yet their global diversity and biosynthetic potential remain underexplored. Here, we reconstruct 78,213 bacterial and archaeal genomes from 2293 publicly available metagenomes and 3214 microbial isolates to establish a unified database, the Extreme Environment Microbiome Catalog (EEMC). The EEMC expands known global phylogenetic diversity, encompassing 32,715 representative species and nearly 4 billion non-redundant genes, 63.00% and 19.21% of which are previously unannotated, respectively. It also comprises 163,693 biosynthetic gene clusters, grouped into 64,733 gene cluster families, 58.68% of which are classified as novel, underscoring the functional diversity of microbial communities across various extreme habitats. We further develop protein large language models to predict genome-encoded candidate antimicrobial peptides (cAMPs) from the EEMC, identifying 3032 non-toxic candidates. Of 100 synthesized peptides, 84% demonstrate antibacterial activity, and all 50 tested cAMPs exhibit low cytotoxicity. Notably, six of the most potent cAMPs show significant efficacy against multidrug-resistant, Gram-negative pathogens in vitro, indicating their biomedical potential. Together, our study establishes the EEMC as a foundational resource for uncovering novel microbial lineages and biosynthetic capabilities, highlighting its substantial potential for drug discovery and laying the foundation for future advances in biotechnology and biomedicine.
Data availability
All 74,999 MAGs generated in this study, together with 83 in-house isolate genomes from deep-sea, the non-redundant gene sets from assembled contigs and genomes, and 163,693 BGCs, have been deposited in the China National GeneBank DataBase (CNGBdb) with accession number CNP0007106. The accession IDs of publicly available bacterial and archaeal reference genomes from NCBI genome database are provided in Supplementary Data 2. The referenced representative genomes used in this study, including 113,104 from GTDB release R220, 22,732 from GEM47, 24,195 from GOMC19, and 957 from Tara Ocean46, are available at https://gtdb.ecogenomic.org/, https://portal.nersc.gov/GEM/genomes/, https://db.cngb.org/maya/datasets/MDB0000002, and https://merenlab.org/data/tara-oceans-mags/, respectively. The 4472 representative genomes from UHGG v2.0108 used in this study are available at https://www.ebi.ac.uk/metagenomics/genome-catalogues/human-gut-v2-0-2. All additional data supporting the findings of this study are provided within the main text, Supplementary Information files, or via the provided repositories. Source data are provided with this paper.
Code availability
The trained model weights and corresponding datasets are now publicly available at Zenodo (https://zenodo.org/records/17613552). The inference scripts enabling reproduction of the model results are publicly available on our GitHub repository (https://github.com/BGI-METAI/Metagenome-AI), together with Python Jupyter notebooks for creating the figures and tables with model results from this manuscript.
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Acknowledgements
This study was supported by the Key Research and Development Program of Hainan Province (grant No. ZDYF2024SHFZ046 to Haixin Chen), the Project of Sanya Yazhou Bay Science and Technology City (grant No. SKJC-2024-01-002 to X.F., SKJC-2024-01-001 to Haixin Chen, and SCKJ-JYRC-2023-41 to P.J.), Hainan Provincial Natural Science Foundation of China (grant No. 826QN0909 to P.J.), and Hainan Yazhou Bay Seed Lab (grant No. JBGS B23YQ2003 to Z.Y.). Computations in this study were supported by the High-performance Computing Platform of YaZhou Bay Science and Technology City Advanced Computing Center. The authors thank Kui Zhu (China Agricultural University) for kindly donating the strains S. aureus ATCC 29213, E. faecalis ATCC 29212, E. faecalis VRE10, and E. coli ATCC 25922 for AMP antibacterial tests; Cong Shen (Guangdong Provincial Hospital of Chinese Medicine) for providing bacterial strains E. faecium BM4105, P. aeruginosa ATCC 27853, and P. aeruginosa PAO1 for AMP antibacterial tests; and our colleague Jun Wang for his help with the bioinformatic analysis. The authors thank the China National GeneBank, BGI Research, Shenzhen 518120, China, for their support. The authors thank ChatGPT-4o mini (OpenAI) for assistance with language editing and phrasing. Supplementary Fig. 1 was created with Biorender.com.
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H.C. (Haixin Chen), P.Y., C.X., Z.Y., and P.J. conceived and supervised the study. P.J., Z.L., F.W., and L.L. collected the data and contributed to formal analyses. P.J. and Z.L. conducted bioinformatic analyses and data visualization. P.Y. and V.K. conceived the development of the MAI framework. V.K. and N.M. developed the MAI algorithm. C.P. and N.S. evaluated the MAI framework and published AMP models. M.H., Y.Z., Y.X.L., and J.H.L. provided computational resources and bioinformatic analysis support. C.X., J.S., and P.J. designed the experimental validation. C.X., X.F., and R.C. provided experimental platforms and resources. Y.L. isolated and sequenced microbial strains from cold seeps. J.S., X.L., L.W., S.W., H.C. (Haixian Cheng), J.N.L., and Y.J. performed all in vitro experiments. J.S. and P.J. analyzed and interpreted the experimental results. P.J., V.K., and Z.L. drafted the manuscript. All authors reviewed and approved the final version of the manuscript.
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Jiang, P., Liang, Z., Kovacevic, V. et al. The Extreme Environment Microbiome Catalog (EEMC): a global resource for microbial diversity and antimicrobial discovery. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71145-0
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DOI: https://doi.org/10.1038/s41467-026-71145-0
