Abstract
Association networks are widely applied for the prediction of bacterial interactions in studies of human gut microbiomes. However, the experimental validation of the predicted interactions is challenging due to the complexity of gut microbiomes and the limited number of cultivated bacteria. In this study, we addressed this challenge by integrating in vitro time series network (TSN) associations and co-cultivation of TSN taxon pairs. Fecal samples were collected and used for cultivation and enrichment of gut microbiome on YCFA agar plates for 13 days. Enriched cells were harvested for DNA extraction and metagenomic sequencing. A total of 198 metagenome-assembled genomes (MAGs) were recovered. Temporal dynamics of bacteria growing on the YCFA agar were used to infer microbial association networks. To experimentally validate the interactions of taxon pairs in networks, we selected 24 and 19 bacterial strains from this study and from the previously established human gut microbial biobank, respectively, for pairwise co-cultures. The co-culture experiments revealed that most of the interactions between taxa in networks were identified as neutralism (51.67%), followed by commensalism (21.67%), amensalism (18.33%), competition (5%) and exploitation (3.33%). Genome-centric analysis further revealed that the commensal gut bacteria (helpers and beneficiaries) might interact with each other via the exchanges of amino acids with high biosynthetic costs, short-chain fatty acids, and/or vitamins. We also validated 12 beneficiaries by adding 16 additives into the basic YCFA medium and found that the growth of 66.7% of these strains was significantly promoted. This approach provides new insights into the gut microbiome complexity and microbial interactions in association networks. Our work highlights that the positive relationships in gut microbial communities tend to be overestimated, and that amino acids, short-chain fatty acids, and vitamins are contributed to the positive relationships.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and materials
The reconstructed metagenome-assembled genomes in the present study have been deposited in China National Microbiology Data Center (NMDC) with accession numbers NMDC10018525.
References
Bäckhed, F., Roswall, J., Peng, Y., Feng, Q., Jia, H., Kovatcheva-Datchary, P., Li, Y., Xia, Y., Xie, H., Zhong, H., et al. (2015). Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 852.
Bankevich, A., Nurk, S., Antipov, D., Gurevich, A.A., Dvorkin, M., Kulikov, A.S., Lesin, V.M., Nikolenko, S.I., Pham, S., Prjibelski, A.D., et al. (2012). SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19, 455–477.
Baxter, N.T., Schmidt, A.W., Venkataraman, A., Kim, K.S., Waldron, C., and Schmidt, T.M. (2019). Dynamics of human gut microbiota and short-chain fatty acids in response to dietary interventions with three fermentable fibers. mBio 10, e02566–02518.
Browne, H.P., Forster, S.C., Anonye, B.O., Kumar, N., Neville, B.A., Stares, M.D., Goulding, D., and Lawley, T.D. (2016). Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 533, 543–546.
Chaumeil, P.A., Mussig, A.J., Hugenholtz, P., and Parks, D.H. (2020). GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927.
Chen, Y., Chen, Y., Shi, C., Huang, Z., Zhang, Y., Li, S., Li, Y., Ye, J., Yu, C., Li, Z., et al. (2018). SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience 7, 1–6.
Clavel, T., Henderson, G., Engst, W., Doré, J., and Blaut, M. (2006). Phylogeny of human intestinal bacteria that activate the dietary lignan secoisolariciresinol diglucoside. FEMS Microbiol Ecol 55, 471–478.
Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., Landys, N., Workman, C., Christmas, R., Avila-Campilo, I., Creech, M., Gross, B., et al. (2007). Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2, 2366–2382.
Coker, O.O., Dai, Z., Nie, Y., Zhao, G., Cao, L., Nakatsu, G., Wu, W.K., Wong, S.H., Chen, Z., Sung, J.J.Y., et al. (2018). Mucosal microbiome dysbiosis in gastric carcinogenesis. Gut 67, 1024–1032.
Duncan, S.H., Hold, G.L., Harmsen, H.J.M., Stewart, C.S., and Flint, H.J. (2002). Growth requirements and fermentation products of Fusobacterium prausnitzii, and a proposal to reclassify it as Faecalibacterium prausnitzii gen. nov., comb. nov. Int J Syst Evol Microbiol 52, 2141–2146.
Fan, Y., and Pedersen, O. (2021). Gut microbiota in human metabolic health and disease. Nat Rev Microbiol 19, 55–71.
Faust, K., and Raes, J. (2012). Microbial interactions: from networks to models. Nat Rev Microbiol 10, 538–550.
Fisher, C.K., and Mehta, P. (2014). Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451arXiv: 1402.0511.
Forster, S.C., Kumar, N., Anonye, B.O., Almeida, A., Viciani, E., Stares, M.D., Dunn, M., Mkandawire, T.T., Zhu, A., Shao, Y., et al. (2019). A human gut bacterial genome and culture collection for improved metagenomic analyses. Nat Biotechnol 37, 186–192.
Ghoul, M., and Mitri, S. (2016). The ecology and evolution of microbial competition. Trends Microbiol 24, 833–845.
Halfvarson, J., Brislawn, C.J., Lamendella, R., Vázquez-Baeza, Y., Walters, W.A., Bramer, L.M., D’Amato, M., Bonfiglio, F., McDonald, D., Gonzalez, A., et al. (2017). Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol 2, 17004.
Harcombe, W. (2010). Novel cooperation experimentally evolved between species. Evolution 64, 2166–2172.
Hijová, E., Bertková, I., and Stofilová, J. (2019). Dietary fibre as prebiotics in nutrition. Cent Eur J Public Health 27, 251–255.
Hsieh, C.H., Glaser, S.M., Lucas, A.J., and Sugihara, G. (2005). Distinguishing random environmental fluctuations from ecological catastrophes for the North Pacific Ocean. Nature 435, 336–340.
Jiang, M.Z., Zhu, H.Z., Zhou, N., Liu, C., Jiang, C.Y., Wang, Y., and Liu, S.J. (2022). Droplet microfluidics-based high-throughput bacterial cultivation for validation of taxon pairs in microbial co-occurrence networks. Sci Rep 12, 18145.
Johnson, M.D., Scott, J.J., Leray, M., Lucey, N., Bravo, L.M.R., Wied, W.L., and Altieri, A.H. (2021). Rapid ecosystem-scale consequences of acute deoxygenation on a Caribbean coral reef. Nat Commun 12, 4522.
Kanazawa, A., Aida, M., Yoshida, Y., Kaga, H., Katahira, T., Suzuki, L., Tamaki, S., Sato, J., Goto, H., Azuma, K., et al. (2021). Effects of synbiotic supplementation on chronic inflammation and the gut microbiota in obese patients with type 2 diabetes mellitus: a randomized controlled study. Nutrients 13, 558.
La Fata, G., Weber, P., and Mohajeri, M.H. (2018). Probiotics and the gut immune system: indirect regulation. Probiotics Antimicro Prot 10, 11–21.
Lagkouvardos, I., Overmann, J., and Clavel, T. (2017). Cultured microbes represent a substantial fraction ofthe human and mouse gut microbiota. Gut Microbes 8, 493–503.
Langmead, B., and Salzberg, S.L. (2012). Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357–359.
Liu, C., Du, M.X., Abuduaini, R., Yu, H.Y., Li, D.H., Wang, Y.J., Zhou, N., Jiang, M.Z., Niu, P.X., Han, S.S., et al. (2021a). Enlightening the taxonomy darkness of human gut microbiomes with a cultured biobank. Microbiome 9, 119.
Liu, C., Zhou, N., Du, M.X., Sun, Y.T., Wang, K., Wang, Y.J., Li, D.H., Yu, H.Y., Song, Y., Bai, B.B., et al. (2020). The Mouse Gut Microbial Biobank expands the coverage of cultured bacteria. Nat Commun 11, 79.
Liu, P., Zhang, T., Zheng, Y., Li, Q., Su, T., and Qi, Q. (2021b). Potential one-step strategy for PET degradation and PHB biosynthesis through co-cultivation of two engineered microorganisms. Eng Microbiol 1, 100003.
Liu, W., Fang, X., Zhou, Y., Dou, L., and Dou, T. (2022). Machine learning-based investigation of the relationship between gut microbiome and obesity status. Microbes Infect 24, 104892.
Lohia, S., Vlahou, A., and Zoidakis, J. (2022). Microbiome in chronic kidney disease (CKD): an omics perspective. Toxins 14, 176.
Macmicking, J.D. (2017). Bacteria disarm host-defence proteins. Nature 551, 303–304.
Mars, R.A.T., Yang, Y., Ward, T., Houtti, M., Priya, S., Lekatz, H.R., Tang, X., Sun, Z., Kalari, K.R., Korem, T., et al. (2020). Longitudinal multi-omics reveals subset-specific mechanisms underlying irritable bowel syndrome. Cell 183, 1137–1140.
Matchado, M.S., Lauber, M., Reitmeier, S., Kacprowski, T., Baumbach, J., Haller, D., and List, M. (2021). Network analysis methods for studying microbial communities: A mini review. Comput Struct Biotechnol J 19, 2687–2698.
Mitri, S., and Foster, K.R. (2013). The genotypic view of social interactions in microbial communities. Annu Rev Genet 47, 247–273.
Nash, A.K., Auchtung, T.A., Wong, M.C., Smith, D.P., Gesell, J.R., Ross, M.C., Stewart, C.J., Metcalf, G.A., Muzny, D.M., Gibbs, R.A., et al. (2017). The gut mycobiome of the Human Microbiome Project healthy cohort. Microbiome 5, 153.
Odamaki, T., Kato, K., Sugahara, H., Hashikura, N., Takahashi, S., Xiao, J., Abe, F., and Osawa, R. (2016). Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol 16, 90.
Olm, M.R., Brown, C.T., Brooks, B., and Banfield, J.F. (2017). dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J 11, 2864–2868.
Palmer, J.D., and Foster, K.R. (2022). Bacterial species rarely work together. Science 376, 581–582.
Pan, Z., Hu, Y., Huang, Z., Han, N., Li, Y., Zhuang, X., Yin, J., Peng, H., Gao, Q., Zhang, W., et al. (2022). Alterations in gut microbiota and metabolites associated with altitude-induced cardiac hypertrophy in rats during hypobaric hypoxia challenge. Sci China Life Sci 65, 2093–2113
Parks, D.H., Chuvochina, M., Waite, D.W., Rinke, C., Skarshewski, A., Chaumeil, P.A., and Hugenholtz, P. (2018). A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol 36, 996–1004.
Peng, W., Yi, P., Yang, J., Xu, P., Wang, Y., Zhang, Z., Huang, S., Wang, Z., and Zhang, C. (2018). Association of gut microbiota composition and function with a senescence-accelerated mouse model of Alzheimer’s Disease using 16S rRNA gene and metagenomic sequencing analysis. Aging 10, 4054–4065.
Petrov, V.A., Saltykova, I.V., Zhukova, I.A., Alifirova, V.M., Zhukova, N.G., Dorofeeva, Y.B., Tyakht, A.V., Kovarsky, B.A., Alekseev, D.G., Kostryukova, E.S., et al. (2017). Analysis of gut microbiota in patients with Parkinson’s disease. Bull Exp Biol Med 162, 734–737.
Poyet, M., Groussin, M., Gibbons, S.M., Avila-Pacheco, J., Jiang, X., Kearney, S.M., Perrotta, A.R., Berdy, B., Zhao, S., Lieberman, T.D., et al. (2019). A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research. Nat Med 25, 1442–1452.
Qian, Y., Yang, X., Xu, S., Huang, P., Li, B., Du, J., He, Y., Su, B., Xu, L.M., Wang, L., et al. (2020). Gut metagenomics-derived genes as potential biomarkers of Parkinson’s disease. Brain 143, 2474–2489.
Ruaud, A., Esquivel-Elizondo, S., de la Cuesta-Zuluaga, J., Waters, J.L., Angenent, L.T., Youngblut, N.D., and Ley, R.E. (2020). Syntrophy via interspecies H2 transfer between Christensenella and Methanobrevibacter underlies their global cooccurrence in the human gut. mBio 11, e03235.
Seemann, T. (2014). Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069.
Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., and Munch, S. (2012). Detecting causality in complex ecosystems. Science 338, 496–500.
Uritskiy, G.V., DiRuggiero, J., and Taylor, J. (2018). MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158.
Venturelli, O.S., Carr, A.V., Fisher, G., Hsu, R.H., Lau, R., Bowen, B.P., Hromada, S., Northen, T., and Arkin, A.P. (2018). Deciphering microbial interactions in synthetic human gut microbiome communities. Mol Syst Biol 14, e8157.
Weiss, A.S., Burrichter, A.G., Durai Raj, A.C., von Strempel, A., Meng, C., Kleigrewe, K., Münch, P.C., Rössler, L., Huber, C., Eisenreich, W., et al. (2022). In vitro interaction network of a synthetic gut bacterial community. ISME J 16, 1095–1109.
Xia, L.C., Steele, J.A., Cram, J.A., Cardon, Z.G., Simmons, S.L., Vallino, J.J., Fuhrman, J.A., and Sun, F. (2011). Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates. BMC Syst Biol 5, S15.
Yuan, J., Wen, T., Yang, S., Zhang, C., Zhao, M., Niu, G., Xie, P., Liu, X., Zhao, X., Shen, Q., et al. (2023). Growth substrates alter aboveground plant microbial and metabolic properties thereby influencing insect herbivore performance. Sci China Life Sci 66, 1728–1741.
Zimmermann, J., Kaleta, C., and Waschina, S. (2021). gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol 22, 81.
Acknowledgement
This work was supported by the National Key Research and Development Program of China (2021YFA0717002) and Taishan Young Scholars (tsqn202306029).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The author(s) declare that they have no conflict of interest. This study was approved by the Research Ethics Committee of the Institute of Microbiology, Chinese Academy of Science. All subjects provided informed consent to be included in the study.
Rights and permissions
About this article
Cite this article
Jiang, MZ., Liu, C., Xu, C. et al. Gut microbial interactions based on network construction and bacterial pairwise cultivation. Sci. China Life Sci. (2024). https://doi.org/10.1007/s11427-023-2537-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11427-023-2537-0