IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v399y2019icp1-12.html
   My bibliography  Save this article

Metagenome level metabolic network reconstruction analysis reveals the microbiome in the Bogotá River is functionally close to the microbiome in produced water

Author

Listed:
  • Ruiz-Moreno, Héctor Alejandro
  • López-Tamayo, Ana María
  • Caro-Quintero, Alejandro
  • Husserl, Johana
  • González Barrios, Andrés Fernando

Abstract

The microbiomes of contaminated water bodies have altered microbial communities and functional capacity. Metagenomic data provides information on the microbiome at taxonomic and functional level. However, metagenomes from different environmental samples have shown that differences in taxonomy don’t always reflect changes in metabolic functions. Here, curated metagenome level network reconstructions for the highly contaminated Bogotá River and five additional samples were constructed and compared through topological analysis and flux balance analysis, using a constraint-based optimization method. Active metabolic fluxes change depending on simulated conditions evidencing a distance between potential and active functions. Higher microbial diversity was found to have little effect on potential functions but a positive effect on metabolic network robustness. A high degree of contamination in the Bogotá River has altered the microbiome’s metabolic functions making the system distant from a natural state. The microbiome of the Bogotá River was found to be functionally similar to the microbiome of Produced Water. Metabolic network reconstructions increase the information obtainable from metagenomic data and allow modelling of complex ecosystems.

Suggested Citation

  • Ruiz-Moreno, Héctor Alejandro & López-Tamayo, Ana María & Caro-Quintero, Alejandro & Husserl, Johana & González Barrios, Andrés Fernando, 2019. "Metagenome level metabolic network reconstruction analysis reveals the microbiome in the Bogotá River is functionally close to the microbiome in produced water," Ecological Modelling, Elsevier, vol. 399(C), pages 1-12.
    • Handle: RePEc:eee:ecomod:v:399:y:2019:i:c:p:1-12
    DOI: 10.1016/j.ecolmodel.2019.02.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380019300560
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2019.02.001?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alvarez-Yela, Astrid Catalina & Alvarez-Silva, María Camila & Restrepo, Silvia & Husserl, Johana & Zambrano, María Mercedes & Danies, Giovanna & Gómez, Jorge M. & González Barrios, Andrés Fernando, 2017. "Influence of agricultural activities in the structure and metabolic functionality of paramo soil samples in Colombia studied using a metagenomics analysis in dynamic state," Ecological Modelling, Elsevier, vol. 351(C), pages 63-76.
    2. Peter J. Turnbaugh & Ruth E. Ley & Micah Hamady & Claire M. Fraser-Liggett & Rob Knight & Jeffrey I. Gordon, 2007. "The Human Microbiome Project," Nature, Nature, vol. 449(7164), pages 804-810, October.
    3. H. Jeong & B. Tombor & R. Albert & Z. N. Oltvai & A.-L. Barabási, 2000. "The large-scale organization of metabolic networks," Nature, Nature, vol. 407(6804), pages 651-654, October.
    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. Jin Wang & Bo Huang & Xuefeng Xia & Zhirong Sun, 2006. "Funneled Landscape Leads to Robustness of Cell Networks: Yeast Cell Cycle," PLOS Computational Biology, Public Library of Science, vol. 2(11), pages 1-10, November.
    2. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    3. Shilan Li & Jianxin Shi & Paul Albert & Hong-Bin Fang, 2022. "Dependence Structure Analysis and Its Application in Human Microbiome," Mathematics, MDPI, vol. 11(1), pages 1-14, December.
    4. Jae-Chang Cho, 2021. "Human microbiome privacy risks associated with summary statistics," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-11, April.
    5. Tamás Nepusz & Tamás Vicsek, 2013. "Hierarchical Self-Organization of Non-Cooperating Individuals," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    6. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    7. Pirjo Wacklin & Harri Mäkivuokko & Noora Alakulppi & Janne Nikkilä & Heli Tenkanen & Jarkko Räbinä & Jukka Partanen & Kari Aranko & Jaana Mättö, 2011. "Secretor Genotype (FUT2 gene) Is Strongly Associated with the Composition of Bifidobacteria in the Human Intestine," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-10, May.
    8. Jiang, Jingchi & Zheng, Jichuan & Zhao, Chao & Su, Jia & Guan, Yi & Yu, Qiubin, 2016. "Clinical-decision support based on medical literature: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 42-54.
    9. Disha Tandon & Mohammed Monzoorul Haque & Sharmila S Mande, 2016. "Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-16, April.
    10. Gerhardt, Günther J.L. & Lemke, Ney & Corso, Gilberto, 2006. "Network clustering coefficient approach to DNA sequence analysis," Chaos, Solitons & Fractals, Elsevier, vol. 28(4), pages 1037-1045.
    11. Laurienti, Paul J. & Joyce, Karen E. & Telesford, Qawi K. & Burdette, Jonathan H. & Hayasaka, Satoru, 2011. "Universal fractal scaling of self-organized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3608-3613.
    12. Chen, Qinghua & Shi, Dinghua, 2004. "The modeling of scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 240-248.
    13. Liu, X. & Murata, T., 2010. "Advanced modularity-specialized label propagation algorithm for detecting communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1493-1500.
    14. Wen, Xiangxi & Tu, Congliang & Wu, Minggong, 2018. "Node importance evaluation in aviation network based on “No Return” node deletion method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 546-559.
    15. Lawford, Steve & Mehmeti, Yll, 2020. "Cliques and a new measure of clustering: With application to U.S. domestic airlines," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    16. J. J. Esquivel-Gómez & J. G. Barajas-Ramírez, 2024. "Rapid disease spread on dense networks with power-law topology," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(5), pages 1-10, May.
    17. Wang, Huan & Xu, Chuan-Yun & Hu, Jing-Bo & Cao, Ke-Fei, 2014. "A complex network analysis of hypertension-related genes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 166-176.
    18. Selen Onel & Abe Zeid & Sagar Kamarthi, 2011. "The structure and analysis of nanotechnology co-author and citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 119-138, October.
    19. Chung-Yen Yu & Yung-Ting Chuang & Hsi-Peng Kuan, 2017. "Understanding Faculty Collaboration and Productivity: A Case Study," Asian Social Science, Canadian Center of Science and Education, vol. 13(3), pages 1-1, March.
    20. Ashty S. Karim & Dylan M. Brown & Chloé M. Archuleta & Sharisse Grannan & Ludmilla Aristilde & Yogesh Goyal & Josh N. Leonard & Niall M. Mangan & Arthur Prindle & Gabriel J. Rocklin & Keith J. Tyo & L, 2024. "Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

    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:eee:ecomod:v:399:y:2019:i:c:p:1-12. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.