IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-52532-x.html
   My bibliography  Save this article

Metabolic interdependencies in thermophilic communities are revealed using co-occurrence and complementarity networks

Author

Listed:
  • Xi Peng

    (Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (CAS)
    University of Chinese Academy of Sciences)

  • Shang Wang

    (Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (CAS))

  • Miaoxiao Wang

    (ETH Zürich
    Eawag)

  • Kai Feng

    (Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (CAS))

  • Qing He

    (Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (CAS))

  • Xingsheng Yang

    (Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (CAS)
    University of Chinese Academy of Sciences)

  • Weiguo Hou

    (China University of Geosciences)

  • Fangru Li

    (China University of Geosciences)

  • Yuxiang Zhao

    (Zhejiang University)

  • Baolan Hu

    (Zhejiang University
    Zhejiang Province Key Laboratory for Water Pollution Control and Environmental Safety
    College of Environmental Resource Sciences, Zhejiang University)

  • Xiao Zou

    (College of Life Sciences, Guizhou University)

  • Ye Deng

    (Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (CAS)
    University of Chinese Academy of Sciences)

Abstract

Microbial communities exhibit intricate interactions underpinned by metabolic dependencies. To elucidate these dependencies, we present a workflow utilizing random matrix theory on metagenome-assembled genomes to construct co-occurrence and metabolic complementarity networks. We apply this approach to a temperature gradient hot spring, unraveling the interplay between thermal stress and metabolic cooperation. Our analysis reveals an increase in the frequency of metabolic interactions with rising temperatures. Amino acids, coenzyme A derivatives, and carbohydrates emerge as key exchange metabolites, forming the foundation for syntrophic dependencies, in which commensalistic interactions take a greater proportion than mutualistic ones. These metabolic exchanges are most prevalent between phylogenetically distant species, especially archaea-bacteria collaborations, as a crucial adaptation to harsh environments. Furthermore, we identify a significant positive correlation between basal metabolite exchange and genome size disparity, potentially signifying a means for streamlined genomes to leverage cooperation with metabolically richer partners. This phenomenon is also confirmed by another composting system which has a similar wide range of temperature fluctuations. Our workflow provides a feasible way to decipher the metabolic complementarity mechanisms underlying microbial interactions, and our findings suggested environmental stress regulates the cooperative strategies of thermophiles, while these dependencies have been potentially hardwired into their genomes during co-evolutions.

Suggested Citation

  • Xi Peng & Shang Wang & Miaoxiao Wang & Kai Feng & Qing He & Xingsheng Yang & Weiguo Hou & Fangru Li & Yuxiang Zhao & Baolan Hu & Xiao Zou & Ye Deng, 2024. "Metabolic interdependencies in thermophilic communities are revealed using co-occurrence and complementarity networks," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52532-x
    DOI: 10.1038/s41467-024-52532-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-52532-x
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-52532-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Emiley A. Eloe-Fadrosh & David Paez-Espino & Jessica Jarett & Peter F. Dunfield & Brian P. Hedlund & Anne E. Dekas & Stephen E. Grasby & Allyson L. Brady & Hailiang Dong & Brandon R. Briggs & Wen-Jun , 2016. "Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs," Nature Communications, Nature, vol. 7(1), pages 1-10, April.
    2. Zachary D Kurtz & Christian L Müller & Emily R Miraldi & Dan R Littman & Martin J Blaser & Richard A Bonneau, 2015. "Sparse and Compositionally Robust Inference of Microbial Ecological Networks," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-25, May.
    3. Yuxiang Zhao & Zishu Liu & Baofeng Zhang & Jingjie Cai & Xiangwu Yao & Meng Zhang & Ye Deng & Baolan Hu, 2023. "Inter-bacterial mutualism promoted by public goods in a system characterized by deterministic temperature variation," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Duo Jiang & Thomas Sharpton & Yuan Jiang, 2021. "Microbial Interaction Network Estimation via Bias-Corrected Graphical Lasso," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 329-350, July.
    2. Jiarui Lu & Pixu Shi & Hongzhe Li, 2019. "Generalized linear models with linear constraints for microbiome compositional data," Biometrics, The International Biometric Society, vol. 75(1), pages 235-244, March.
    3. Dina in ‘t Zandt & Zuzana Kolaříková & Tomáš Cajthaml & Zuzana Münzbergová, 2023. "Plant community stability is associated with a decoupling of prokaryote and fungal soil networks," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Li, Lianwei & Li, Wendy & Zou, Quan & Ma, Zhanshan (Sam), 2020. "Network analysis of the hot spring microbiome sketches out possible niche differentiations among ecological guilds," Ecological Modelling, Elsevier, vol. 431(C).
    5. Qin Liu & Qi Su & Fen Zhang & Hein M. Tun & Joyce Wing Yan Mak & Grace Chung-Yan Lui & Susanna So Shan Ng & Jessica Y. L. Ching & Amy Li & Wenqi Lu & Chenyu Liu & Chun Pan Cheung & David S. C. Hui & P, 2022. "Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Pratheepa Jeganathan & Susan P. Holmes, 2021. "A Statistical Perspective on the Challenges in Molecular Microbial Biology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 131-160, June.
    7. Ana Popovic & Celine Bourdon & Pauline W. Wang & David S. Guttman & Sajid Soofi & Zulfiqar A. Bhutta & Robert H. J. Bandsma & John Parkinson & Lisa G. Pell, 2021. "Micronutrient supplements can promote disruptive protozoan and fungal communities in the developing infant gut," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    8. Rieser, Christopher & Filzmoser, Peter, 2023. "Extending compositional data analysis from a graph signal processing perspective," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    9. Runtan Cheng & Lu Wang & Shenglong Le & Yifan Yang & Can Zhao & Xiangqi Zhang & Xin Yang & Ting Xu & Leiting Xu & Petri Wiklund & Jun Ge & Dajiang Lu & Chenhong Zhang & Luonan Chen & Sulin Cheng, 2022. "A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    10. Yu Shang & Johannes Sikorski & Michael Bonkowski & Anna-Maria Fiore-Donno & Ellen Kandeler & Sven Marhan & Runa S Boeddinghaus & Emily F Solly & Marion Schrumpf & Ingo Schöning & Tesfaye Wubet & Franc, 2017. "Inferring interactions in complex microbial communities from nucleotide sequence data and environmental parameters," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-24, March.
    11. Oliver Aasmets & Kertu Liis Krigul & Kreete Lüll & Andres Metspalu & Elin Org, 2022. "Gut metagenome associations with extensive digital health data in a volunteer-based Estonian microbiome cohort," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    12. Emma Schwager & Himel Mallick & Steffen Ventz & Curtis Huttenhower, 2017. "A Bayesian method for detecting pairwise associations in compositional data," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-21, November.
    13. Koplin, Eric & Forzani, Liliana & Tomassi, Diego & Pfeiffer, Ruth M., 2024. "Sufficient dimension reduction for a novel class of zero-inflated graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
    14. Huang Lin & Merete Eggesbø & Shyamal Das Peddada, 2022. "Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    15. Lingjing Jiang & Niina Haiminen & Anna‐Paola Carrieri & Shi Huang & Yoshiki Vázquez‐Baeza & Laxmi Parida & Ho‐Cheol Kim & Austin D. Swafford & Rob Knight & Loki Natarajan, 2022. "Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data," Biometrics, The International Biometric Society, vol. 78(3), pages 1155-1167, September.
    16. Ines Wilms & Jacob Bien, 2021. "Tree-based Node Aggregation in Sparse Graphical Models," Papers 2101.12503, arXiv.org.
    17. Sara Del Duca & Stefano Mocali & Francesco Vitali & Arturo Fabiani & Maria Alexandra Cucu & Giuseppe Valboa & Giada d’Errico & Francesco Binazzi & Paolo Storchi & Rita Perria & Silvia Landi, 2024. "Impacts of Soil Management and Sustainable Plant Protection Strategies on Soil Biodiversity in a Sangiovese Vineyard," Land, MDPI, vol. 13(5), pages 1-20, April.
    18. Mendler, Isabella-Hilda & Drossel, Barbara & Hütt, Marc-Thorsten, 2024. "Microbiome abundance patterns as attractors and the implications for the inference of microbial interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    19. Zhepu Ruan & Kai Chen & Weimiao Cao & Lei Meng & Bingang Yang & Mengjun Xu & Youwen Xing & Pengfa Li & Shiri Freilich & Chen Chen & Yanzheng Gao & Jiandong Jiang & Xihui Xu, 2024. "Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    20. Juan José Egozcue & Vera Pawlowsky-Glahn, 2019. "Compositional data: the sample space and its structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 599-638, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52532-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.