Skip to content
1887

Microbial Genomics logo Microbial Genomics

Volume 10, Issue 5

bioprospecting from ore-forming environments Open Access

Abstract

Natural products from have served as inspiration for many clinically relevant therapeutics. Despite early triumphs in natural product discovery, the rate of unearthing new compounds has decreased, necessitating inventive approaches. One promising strategy is to explore environments where survival is challenging. These harsh environments are hypothesized to lead to bacteria developing chemical adaptations (e.g. natural products) to enable their survival. This investigation focuses on ore-forming environments, particularly fluoride mines, which typically have extreme pH, salinity and nutrient scarcity. Herein, we have utilized metagenomics, metabolomics and evolutionary genome mining to dissect the biodiversity and metabolism in these harsh environments. This work has unveiled the promising biosynthetic potential of these bacteria and has demonstrated their ability to produce bioactive secondary metabolites. This research constitutes a pioneering endeavour in bioprospection within fluoride mining regions, providing insights into uncharted microbial ecosystems and their previously unexplored natural products.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): Actinomycetota , fluoride mines , genome mining , natural products and ore-forming environments
Funding
This study was supported by the:
  • Instituto de Ecología, Universidad Nacional Autónoma de México (Award DGAPA 2023)
    • Principle Award Recipient: HaydeéContreras-Peruyero
  • Foundation for the National Institutes of Health (Award R35GM138002)
    • Principle Award Recipient: ElizabethIvy Parkinson
© 2024 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.001253
2024-05-14
2025-06-19
Loading full text...

Full text loading...

/deliver/fulltext/mgen/10/5/mgen001253.html?itemId=/content/journal/mgen/10.1099/mgen.0.001253&mimeType=html&fmt=ahah

References

  1. Hutchings MI, Truman AW, Wilkinson B. Antibiotics: past, present and future. Curr Opin Microbiol 2019; 51:72–80 [View Article] [PubMed]
     [Google Scholar]
  2. Demain AL. Importance of microbial natural products and the need to revitalize their discovery. J Ind Microbiol Biotechnol 2014; 41:185–201 [View Article] [PubMed]
     [Google Scholar]
  3. Demain AL, Sanchez S. Microbial drug discovery: 80 years of progress. J Antibiot (Tokyo) 2009; 62:5–16 [View Article] [PubMed]
     [Google Scholar]
  4. Chevrette MG, Gutiérrez-García K, Selem-Mojica N, Aguilar-Martínez C, Yañez-Olvera A et al. Evolutionary dynamics of natural product biosynthesis in bacteria. Nat Prod Rep 2020; 37:566–599 [View Article] [PubMed]
     [Google Scholar]
  5. Pye CR, Bertin MJ, Lokey RS, Gerwick WH, Linington RG. Retrospective analysis of natural products provides insights for future discovery trends. Proc Natl Acad Sci USA 2017; 114:5601–5606 [View Article]
     [Google Scholar]
  6. Gavriilidou A, Kautsar SA, Zaburannyi N, Krug D, Müller R et al. Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes. Nat Microbiol 2022; 7:726–735 [View Article] [PubMed]
     [Google Scholar]
  7. Covington BC, Spraggins JM, Ynigez-Gutierrez AE, Hylton ZB, Bachmann BO. Response of secondary metabolism of hypogean actinobacterial genera to chemical and biological stimuli. Appl Environ Microbiol 2018; 84:e01125-18 [View Article] [PubMed]
     [Google Scholar]
  8. Giordano D. Bioactive molecules from extreme environments. Mar Drugs 2020; 18:640 [View Article] [PubMed]
     [Google Scholar]
  9. Elfeki M, Mantri S, Clark CM, Green SJ, Ziemert N et al. Evaluating the distribution of bacterial natural product biosynthetic Genes across lake huron sediment. ACS Chem Biol 2021; 16:2623–2631 [View Article]
     [Google Scholar]
  10. Mohammadipanah F, Wink J. Actinobacteria from arid and desert habitats: diversity and biological activity. Front Microbiol 2015; 6:1541 [View Article] [PubMed]
     [Google Scholar]
  11. Stach EM, Bull AT. Estimating and comparing the diversity of marine actinobacteria. Antonie van Leeuwenhoek 2005; 87:3–9 [View Article]
     [Google Scholar]
  12. Golinska P, Wypij M, Agarkar G, Rathod D, Dahm H et al. Endophytic actinobacteria of medicinal plants: diversity and bioactivity. Antonie van Leeuwenhoek 2015; 108:267–289 [View Article] [PubMed]
     [Google Scholar]
  13. Chevrette MG, Carlson CM, Ortega HE, Thomas C, Ananiev GE et al. The antimicrobial potential of Streptomyces from insect microbiomes. Nat Commun 2019; 10:516 [View Article] [PubMed]
     [Google Scholar]
  14. Flórez LV, Biedermann PHW, Engl T, Kaltenpoth M. Defensive symbioses of animals with prokaryotic and eukaryotic microorganisms. Nat Prod Rep 2015; 32:904–936 [View Article] [PubMed]
     [Google Scholar]
  15. Sayed AM, Hassan MHA, Alhadrami HA, Hassan HM, Goodfellow M et al. Extreme environments: microbiology leading to specialized metabolites. J Appl Microbiol 2020; 128:630–657 [View Article] [PubMed]
     [Google Scholar]
  16. van der Meij A, Worsley SF, Hutchings MI, van Wezel GP. Chemical ecology of antibiotic production by actinomycetes. FEMS Microbiol Rev 2017; 41:392–416 [View Article] [PubMed]
     [Google Scholar]
  17. Núñez-Pons L, Shilling A, Verde C, Baker BJ, Giordano D. Marine terpenoids from polar latitudes and their potential applications in biotechnology. Mar Drugs 2020; 18:401 [View Article] [PubMed]
     [Google Scholar]
  18. Shang Z, Winter JM, Kauffman CA, Yang I, Fenical W. Salinipeptins: integrated genomic and chemical approaches reveal unusual d -amino acid-containing ribosomally synthesized and post-translationally modified peptides (RiPPs) from a great salt lake Streptomyces sp. ACS Chem Biol 2019; 14:415–425 [View Article]
     [Google Scholar]
  19. Bull AT, Goodfellow M. Dark, rare and inspirational microbial matter in the extremobiosphere: 16 000 m of bioprospecting campaigns. Microbiology 2019; 165:1252–1264 [View Article]
     [Google Scholar]
  20. Trujillo ME, Riesco R, Benito P, Carro L. Endophytic actinobacteria and the interaction of micromonospora and nitrogen fixing plants. Front Microbiol 2015; 6:1341 [View Article] [PubMed]
     [Google Scholar]
  21. Kaltenpoth M. Actinobacteria as mutualists: general healthcare for insects?. Trends Microbiol 2009; 17:529–535 [View Article] [PubMed]
     [Google Scholar]
  22. Jose PA, Sundari SI, Sivakala KK, Jebakumar SRD. n.d. Molecular Phylogeny and plant growth promoting traits of Endophytic bacteria isolated from roots of Seagrass Cymodocea Serrulata. Indian Journal of Geo-Marine Sciences 43:
     [Google Scholar]
  23. Rey T, Dumas B. Plenty is no plague: streptomyces symbiosis with crops. Trends Plant Sci 2017; 22:30–37 [View Article] [PubMed]
     [Google Scholar]
  24. Marmion M, Macori G, Ferone M, Whyte P, Scannell AGM. Survive and thrive: control mechanisms that facilitate bacterial adaptation to survive manufacturing-related stress. Int J Food Microbiol 2022; 368:109612 [View Article] [PubMed]
     [Google Scholar]
  25. Kenderes SM, Appold MS. Fluorine concentrations of ore fluids in the Illinois-Kentucky district: evidence from SEM-EDS analysis of fluid inclusion decrepitates. Geochim Cosmochim Acta 2017; 210:132–151 [View Article]
     [Google Scholar]
  26. Kyzer JL, Martens M. Metabolism and toxicity of fluorine compounds. Chem Res Toxicol 2021; 34:678–680 [View Article] [PubMed]
     [Google Scholar]
  27. Inoue M, Sumii Y, Shibata N. Contribution of organofluorine compounds to pharmaceuticals. ACS Omega 2020; 5:10633–10640 [View Article] [PubMed]
     [Google Scholar]
  28. SW-846 Test Method 9056A: Determination of Inorganic Anions by Ion Chromatography. https://www.epa.gov/hw-sw846/sw-846-test-method-9056a-determination-inorganic-anions-ion-chromatography
  29. Frankenberger WT, Tabatabai MA, Adriano DC, Doner HE. Bromine, chlorine, & fluorine. In Methods of Soil Analysis: Part 3 Chemical Methods vol 5 pp 833–867 [View Article]
     [Google Scholar]
  30. Ausubel FM, Brent R, Kingston RE, Moore DD, Seidman JG et al. Current Protocols in Molecular Biology Brooklyn, New York: John Wiley & Sons. Inc; 2003 p 1
     [Google Scholar]
  31. Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. n.d https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ accessed 30 March 2024
  32. Wattam AR, Davis JJ, Assaf R, Boisvert S, Brettin T et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 2017; 45:D535–D542 [View Article] [PubMed]
     [Google Scholar]
  33. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 2017; 13:e1005595 [View Article]
     [Google Scholar]
  34. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
     [Google Scholar]
  35. Lu J, Rincon N, Wood DE, Breitwieser FP, Pockrandt C et al. Metagenome analysis using the Kraken software suite. Nat Protoc 2022; 17:2815–2839 [View Article] [PubMed]
     [Google Scholar]
  36. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018; 34:i884–i890 [View Article] [PubMed]
     [Google Scholar]
  37. Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res 2010; 38:e191 [View Article] [PubMed]
     [Google Scholar]
  38. Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 2019; 47:D309–D314 [View Article] [PubMed]
     [Google Scholar]
  39. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 2016; 44:D457–62 [View Article] [PubMed]
     [Google Scholar]
  40. Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci 2017; 3:e104 [View Article]
     [Google Scholar]
  41. Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol 1991; 173:697–703 [View Article] [PubMed]
     [Google Scholar]
  42. Bown L, Hirota R, Goettge MN, Cui J, Krist DT et al. A novel pathway for biosynthesis of the herbicidal phosphonate natural product phosphonothrixin is widespread in actinobacteria. J Bacteriol 2023; 205:e0048522 [View Article] [PubMed]
     [Google Scholar]
  43. Tietz JI, Schwalen CJ, Patel PS, Maxson T, Blair PM et al. A new genome-mining tool redefines the lasso peptide biosynthetic landscape. Nat Chem Biol 2017; 13:470–478 [View Article] [PubMed]
     [Google Scholar]
  44. Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 2015; 12:523–526 [View Article] [PubMed]
     [Google Scholar]
  45. Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 2012; 30:918–920 [View Article] [PubMed]
     [Google Scholar]
  46. GNPS - The Future of Natural Products Research and Mass Spectrometry. n.d http://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp accessed 18 November 2015
  47. Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 2016; 34:828–837 [View Article]
     [Google Scholar]
  48. Ernst M, Kang KB, Caraballo-Rodríguez AM, Nothias L-F, Wandy J et al. MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools. Metabolites 2019; 9:144 [View Article] [PubMed]
     [Google Scholar]
  49. Sumner LW, Amberg A, Barrett D, Beale MH, Beger R et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 2007; 3:211–221 [View Article]
     [Google Scholar]
  50. Freeman TC, Horsewell S, Patir A, Harling-Lee J, Regan T et al. Graphia: a platform for the graph-based visualisation and analysis of high dimensional data. PLoS Comput Biol 2022; 18:e1010310 [View Article] [PubMed]
     [Google Scholar]
  51. Morehouse NJ, Clark TN, McMann EJ, van Santen JA, Haeckl FPJ et al. Annotation of natural product compound families using molecular networking topology and structural similarity fingerprinting. Nat Commun 2023; 14:308 [View Article] [PubMed]
     [Google Scholar]
  52. Chevrette MG, Selem-Mojica N, Aguilar C, Labby K, Bustos-Diaz ED et al. Evolutionary genome mining for the discovery and engineering of natural product biosynthesis. In Methods in Molecular Biology pp 129–155
     [Google Scholar]
  53. Blin K, Shaw S, Augustijn HE, Reitz ZL, Biermann F et al. antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Res 2023; 51:W46–W50 [View Article]
     [Google Scholar]
  54. Navarro-Muñoz JC, Selem-Mojica N, Mullowney MW, Kautsar SA, Tryon JH et al. A computational framework to explore large-scale biosynthetic diversity. Nat Chem Biol 2020; 16:60–68 [View Article] [PubMed]
     [Google Scholar]
  55. Terlouw BR, Blin K, Navarro-Muñoz JC, Avalon NE, Chevrette MG et al. MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters. Nucleic Acids Res 2023; 51:D603–D610 [View Article]
     [Google Scholar]
  56. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13:2498–2504 [View Article]
     [Google Scholar]
  57. González-Salazar LA, Quezada M, Rodríguez-Orduña L, Ramos-Aboites H, Capon RJ et al. Biosynthetic novelty index reveals the metabolic potential of rare actinobacteria isolated from highly oligotrophic sediments. Microb Genom 2023; 9:mgen000921 [View Article] [PubMed]
     [Google Scholar]
  58. Meier-Kolthoff JP, Göker M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat Commun 2019; 10:2182 [View Article] [PubMed]
     [Google Scholar]
  59. Meier-Kolthoff JP, Carbasse JS, Peinado-Olarte RL, Göker M. TYGS and LPSN: a database tandem for fast and reliable genome-based classification and nomenclature of prokaryotes. Nucleic Acids Res 2022; 50:D801–D807 [View Article] [PubMed]
     [Google Scholar]
  60. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 2016; 17:132 [View Article] [PubMed]
     [Google Scholar]
  61. Lagesen K, Hallin P, Rødland EA, Staerfeldt H-H, Rognes T et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 2007; 35:3100–3108 [View Article] [PubMed]
     [Google Scholar]
  62. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J et al. BLAST+: architecture and applications. BMC Bioinformatics 2009; 10:421 [View Article] [PubMed]
     [Google Scholar]
  63. Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics 2013; 14:60 [View Article] [PubMed]
     [Google Scholar]
  64. Lefort V, Desper R, Gascuel O. FastME 2.0: a comprehensive, accurate, and fast distance-based phylogeny inference program: Table 1. Mol Biol Evol 2015; 32:2798–2800 [View Article]
     [Google Scholar]
  65. Kreft L, Botzki A, Coppens F, Vandepoele K, Van Bel M. PhyD3: a phylogenetic tree viewer with extended phyloXML support for functional genomics data visualization. Bioinformatics 2017; 33:2946–2947 [View Article] [PubMed]
     [Google Scholar]
  66. Vichai V, Kirtikara K. Sulforhodamine B colorimetric assay for cytotoxicity screening. Nat Protoc 2006; 1:1112–1116 [View Article] [PubMed]
     [Google Scholar]
  67. Vásquez-Dean J, Maza F, Morel I, Pulgar R, González M. Microbial communities from arid environments on a global scale. A systematic review. Biol Res 2020; 53:29 [View Article] [PubMed]
     [Google Scholar]
  68. Rumble J. n.d. CRC handbook of chemistry and physics. J Mol Struct 268:
     [Google Scholar]
  69. Fluorspar PGM. Uranium, and beryllium deposits at Spor mountain and historical overview of the discovery and geology of the topaz mountains, Utah. In Mining Districts of Utah, Utah Geological Association vol 32 2006 pp 565–593
     [Google Scholar]
  70. Denny FB, Nelson WJ, Breeden JR, Lillie RC. Mines in the Illinois portion of the Illinois-Kentucky Fluorspar District. In Illinois State Geological Survey, Prairie Research Institute 2020
     [Google Scholar]
  71. Feng G, Xie T, Wang X, Bai J, Tang L et al. Metagenomic analysis of microbial community and function involved in cd-contaminated soil. BMC Microbiol 2018; 18:11 [View Article] [PubMed]
     [Google Scholar]
  72. Hong C, Si Y, Xing Y, Li Y. Illumina MiSeq sequencing investigation on the contrasting soil bacterial community structures in different iron mining areas. Environ Sci Pollut Res Int 2015; 22:10788–10799 [View Article] [PubMed]
     [Google Scholar]
  73. Mendez MO, Neilson JW, Maier RM. Characterization of a bacterial community in an abandoned semiarid lead-zinc mine tailing site. Appl Environ Microbiol 2008; 74:3899–3907 [View Article] [PubMed]
     [Google Scholar]
  74. Fernandes CC, Kishi LT, Lopes EM, Omori WP, Souza JAM de et al. Bacterial communities in mining soils and surrounding areas under regeneration process in a former ore mine. Braz J Microbiol 2018; 49:489–502 [View Article] [PubMed]
     [Google Scholar]
  75. Edwards J, Johnson C, Santos-Medellín C, Lurie E, Podishetty NK et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci U S A 2015; 112:E911–20 [View Article] [PubMed]
     [Google Scholar]
  76. An D-S, Siddiqi MZ, Kim K-H, Yu H-S, Im W-T. Baekduia soli gen. nov., sp. nov., a novel bacterium isolated from the soil of Baekdu Mountain and proposal of a novel family name, Baekduiaceae fam. nov. J Microbiol 2018; 56:24–29 [View Article] [PubMed]
     [Google Scholar]
  77. Wang Y, Cheng X, Wang H, Zhou J, Liu X et al. The characterization of microbiome and interactions on weathered rocks in a subsurface Karst Cave, Central China. Front Microbiol 2022; 13:909494 [View Article] [PubMed]
     [Google Scholar]
  78. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
     [Google Scholar]
  79. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28:27–30 [View Article] [PubMed]
     [Google Scholar]
  80. Kanehisa M, Sato Y, Kawashima M. KEGG mapping tools for uncovering hidden features in biological data. Protein Sci 2022; 31:47–53 [View Article] [PubMed]
     [Google Scholar]
  81. Mirete S, Mora-Ruiz MR, Lamprecht-Grandío M, de Figueras CG, Rosselló-Móra R et al. Salt resistance genes revealed by functional metagenomics from brines and moderate-salinity rhizosphere within a hypersaline environment. Front Microbiol 2015; 6:1121 [View Article] [PubMed]
     [Google Scholar]
  82. Ma B, France M, Ravel J. Meta-pangenome: at the crossroad of pangenomics and metagenomics. In The Pangenome Cham: Springer International Publishing; 2020 pp 205–218 [View Article]
     [Google Scholar]
  83. Imchen M, Vennapu RK, Kumavath R. Pan-metagenomics: an overview of the human microbiome. In Pan-Genomics: Applications, Challenges, and Future Prospects Elsevier; 2020 pp 335–342
     [Google Scholar]
  84. Wang Z, Peng D, Fu C, Luo X, Guo S et al. Pan-metagenome reveals the abiotic stress resistome of cigar tobacco phyllosphere microbiome. Front Plant Sci 2023; 14:1248476 [View Article] [PubMed]
     [Google Scholar]
  85. Hertweck C. The biosynthetic logic of polyketide diversity. Angew Chem Int Ed Engl 2009; 48:4688–4716 [View Article] [PubMed]
     [Google Scholar]
  86. West A-KR, Bailey CB. Crosstalk between primary and secondary metabolism: interconnected fatty acid and polyketide biosynthesis in prokaryotes. Bioorg Med Chem Lett 2023; 91:129377 [View Article]
     [Google Scholar]
  87. Jenke-Kodama H, Sandmann A, Müller R, Dittmann E. Evolutionary implications of bacterial polyketide synthases. Mol Biol Evol 2005; 22:2027–2039 [View Article] [PubMed]
     [Google Scholar]
  88. Xie S, Zhang L. Type II polyketide synthases: a bioinformatics-driven approach. Chembiochem 2023; 24:e202200775 [View Article] [PubMed]
     [Google Scholar]
  89. Wang B, Guo F, Huang C, Zhao H. Unraveling the iterative type I polyketide synthases hidden in Streptomyces. Proc Natl Acad Sci USA 2020; 117:8449–8454 [View Article]
     [Google Scholar]
  90. Stincone A, Prigione A, Cramer T, Wamelink MMC, Campbell K et al. The return of metabolism: biochemistry and physiology of the pentose phosphate pathway. Biol Rev Camb Philos Soc 2015; 90:927–963 [View Article] [PubMed]
     [Google Scholar]
  91. Feehily C, Karatzas KAG. Role of glutamate metabolism in bacterial responses towards acid and other stresses. J Appl Microbiol 2013; 114:11–24 [View Article] [PubMed]
     [Google Scholar]
  92. Shirling EB, Gottlieb D. Cooperative description of type strains of Streptomyces: V. additional descriptions. Int J Syst Bacteriol 1972; 22:265–394 [View Article]
     [Google Scholar]
  93. Zhao G-Z, Li J, Qin S, Zhang Y-Q, Zhu W-Y et al. Micrococcus yunnanensis sp. nov., a novel actinobacterium isolated from surface-sterilized Polyspora axillaris roots. Int J Syst Evol Microbiol 2009; 59:2383–2387 [View Article] [PubMed]
     [Google Scholar]
  94. Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 2016; 34:828–837 [View Article] [PubMed]
     [Google Scholar]
  95. Du Y, Wang Y, Huang T, Tao M, Deng Z et al. Identification and characterization of the biosynthetic gene cluster of polyoxypeptin A, A potent apoptosis inducer. BMC Microbiol 2014; 14:30 [View Article] [PubMed]
     [Google Scholar]
  96. Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ et al. A global atlas of the dominant bacteria found in soil. Science 2018; 359:320–325 [View Article]
     [Google Scholar]
  97. Chandy S, Honu YAK, Gibson DJ. Temporal changes in species composition in permanent plots across the Shawnee National Forest, Illinois, USA 1. J Torrey Bot Soc 2009; 136:487–499 [View Article]
     [Google Scholar]
  98. Bernardet J-F, Nakagawa Y. An introduction to the family Flavobacteriaceae. In The Prokaryotes New York, NY: Springer New York; pp 455–480
     [Google Scholar]
  99. Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol 2009; 75:5111–5120 [View Article] [PubMed]
     [Google Scholar]
  100. Thomas F, Hehemann J-H, Rebuffet E, Czjzek M, Michel G. Environmental and gut bacteroidetes: the food connection. Front Microbiol 2011; 2:93 [View Article] [PubMed]
     [Google Scholar]
  101. Pontefract A, Zhu TF, Walker VK, Hepburn H, Lui C et al. Microbial diversity in a hypersaline sulfate lake: a terrestrial analog of ancient mars. Front Microbiol 2017; 8:1819 [View Article] [PubMed]
     [Google Scholar]
  102. Brinkmann S, Spohn MS, Schäberle TF. Bioactive natural products from bacteroidetes. Nat Prod Rep 2022; 39:1045–1065 [View Article] [PubMed]
     [Google Scholar]
  103. Garritano AN, Song W, Thomas T. Carbon fixation pathways across the bacterial and archaeal tree of life. PNAS Nexus 2022; 1:pgac226 [View Article] [PubMed]
     [Google Scholar]
  104. Vavourakis CD, Andrei A-S, Mehrshad M, Ghai R, Sorokin DY et al. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome 2018; 6:168 [View Article] [PubMed]
     [Google Scholar]
  105. Jiao J-Y, Fu L, Hua Z-S, Liu L, Salam N et al. Insight into the function and evolution of the Wood-Ljungdahl pathway in Actinobacteria. ISME J 2021; 15:3005–3018 [View Article] [PubMed]
     [Google Scholar]
  106. Park CJ, Smith JT, Andam CP. Horizontal gene transfer and genome evolution in the phylum actinobacteria. In Horizontal Gene Transfer Cham: Springer International Publishing; 2019 pp 155–174 [View Article]
     [Google Scholar]
  107. Egan S, Wiener P, Kallifidas D, Wellington EMH. Phylogeny of Streptomyces species and evidence for horizontal transfer of entire and partial antibiotic gene clusters. Antonie van Leeuwenhoek 2001; 79:127–133 [View Article] [PubMed]
     [Google Scholar]
  108. Schorn MA, Verhoeven S, Ridder L, Huber F, Acharya DD et al. A community resource for paired genomic and metabolomic data mining. Nat Chem Biol 2021; 17:363–368 [View Article] [PubMed]
     [Google Scholar]
  109. Mallick H, Franzosa EA, Mclver LJ, Banerjee S, Sirota-Madi A et al. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat Commun 2019; 10:3136 [View Article] [PubMed]
     [Google Scholar]
  110. Morton JT, Aksenov AA, Nothias LF, Foulds JR, Quinn RA et al. Learning representations of microbe–metabolite interactions. Nat Methods 2019; 16:1306–1314 [View Article]
     [Google Scholar]
/content/journal/mgen/10.1099/mgen.0.001253
Loading
Actinomycetota bioprospecting from ore-forming environments
M Gen 10, 001253 (2024); https://doi.org/10.1099/mgen.0.001253
/content/journal/mgen/10.1099/mgen.0.001253
/content/journal/mgen/10.1099/mgen.0.001253
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Supplementary material 2

EXCEL

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error