Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Functional conservation of microbial communities determines composition predictability in anaerobic digestion

Abstract

A major challenge in managing and engineering microbial communities is determining whether and how microbial community responses to environmental alterations can be predicted and explained, especially in microorganism-driven systems. We addressed this challenge by monitoring microbial community responses to the periodic addition of the same feedstock throughout anaerobic digestion, a typical microorganism-driven system where microorganisms degrade and transform the feedstock. The immediate and delayed response consortia were assemblages of microorganisms whose abundances significantly increased on the first or third day after feedstock addition. The immediate response consortia were more predictable than the delayed response consortia and showed a reproducible and predictable order-level composition across multiple feedstock additions. These results stood in both present (16 S rRNA gene) and potentially active (16 S rRNA) microbial communities and in different feedstocks with different biodegradability and were validated by simulation modeling. Despite substantial species variability, the immediate response consortia aligned well with the reproducible CH4 production, which was attributed to the conservation of expressed functions by the response consortia throughout anaerobic digestion, based on metatranscriptomic data analyses. The high species variability might be attributed to intraspecific competition and contribute to biodiversity maintenance and functional redundancy. Our results demonstrate reproducible and predictable microbial community responses and their importance in stabilizing system functions.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Microbial community response and the response predictability.
Fig. 2: Experimental design.
Fig. 3: The characteristics of response consortia.
Fig. 4: The generic characteristics of immediate response consortia (the assemblages of immediate response microorganisms) at different taxonomic levels, based on the comparison between 100 simulated communities generalizing community features before feedstock addition, and 100 simulated communities generalizing community features on the first day after feedstock addition.
Fig. 5: Microbial transcriptional profiles.
Fig. 6: Variability and functional profiles of the response OTUs.

Data availability

The original amplicon sequencing data are deposited at the European Nucleotide Archive by accession number PRJEB59216. The original metatranscriptomic sequencing data are deposited in the project “mgp80358” at MG-RAST.

References

  1. Lin Q, De Vrieze J, Li C, Li J, Li J, Yao M, et al. Temperature regulates deterministic processes and the succession of microbial interactions in anaerobic digestion process. Water Res. 2017;123:134–43.

    CAS  PubMed  Google Scholar 

  2. Coban O, De Deyn GB, van der Ploeg M. Soil microbiota as game-changers in restoration of degraded lands. Science 2022;375:abe0725.

    PubMed  Google Scholar 

  3. Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: networks, competition, and stability. Science 2015;350:663–6.

    CAS  PubMed  Google Scholar 

  4. Goldford JE, Lu N, Bajić D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science 2018;361:469–74.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Hu J, Amor DR, Barbier M, Bunin G, Gore J. Emergent phases of ecological diversity and dynamics mapped in microcosms. Science. 2022;378:85–9.

    CAS  PubMed  Google Scholar 

  6. Pagaling E, Strathdee F, Spears BM, Cates ME, Allen RJ, Free A. Community history affects the predictability of microbial ecosystem development. ISME J. 2014;8:19–30.

    PubMed  Google Scholar 

  7. Lin Q, Dini-Andreote F, Meador TB, Angel R, Meszárošová L, Heděnec P, et al. Microbial phylogenetic relatedness links to distinct successional patterns of bacterial and fungal communities. Environ Microbiol. 2022;24:3985–4000.

    CAS  PubMed  Google Scholar 

  8. Martiny JB, Jones SE, Lennon JT, Martiny AC. Microbiomes in light of traits: A phylogenetic perspective. Science 2015;350:aac9323.

    PubMed  Google Scholar 

  9. Klang J, Szewzyk U, Bock D, Theuerl S. Effect of a profound feedstock change on the structure and performance of biogas microbiomes. Microorganisms. 2020;8:169.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Langer SG, Gabris C, Einfalt D, Wemheuer B, Kazda M, Bengelsdorf FR. Different response of bacteria, archaea and fungi to process parameters in nine full‐scale anaerobic digesters. Micro Biotechnol. 2019;12:1210–25.

    CAS  Google Scholar 

  11. Steinberg LM, Martino AJ, House CH. Convergent microbial community formation in replicate anaerobic reactors inoculated from different sources and treating Ersatz crew waste. Life. 2021;11:1374.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Long F, Wang L, Cai W, Lesnik K, Liu H. Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data. Water Res. 2021;199:117182.

    CAS  PubMed  Google Scholar 

  13. Lin Q, He G, Rui J, Fang X, Tao Y, Li J, et al. Microorganism-regulated mechanisms of temperature effects on the performance of anaerobic digestion. Micro Cell Fact. 2016;15:1–18.

    Google Scholar 

  14. Wang Z, Jiang Y, Wang S, Zhang Y, Hu Y, Hu Z-H, et al. Impact of total solids content on anaerobic co-digestion of pig manure and food waste: Insights into shifting of the methanogenic pathway. Waste Manag. 2020;114:96–106.

    CAS  PubMed  Google Scholar 

  15. Lin Q, De Vrieze J, Fang X, Li L, Li X. Labile carbon feedstocks trigger a priming effect in anaerobic digestion: An insight into microbial mechanisms. Bioresour Technol. 2022;344:126243.

    CAS  PubMed  Google Scholar 

  16. Cao Q, Zhang W, Lian T, Wang S, Yin F, Zhou T, et al. Revealing mechanism of micro-aeration for enhancing volatile fatty acids production from swine manure. Bioresour Technol. 2022;365:128140.

    CAS  PubMed  Google Scholar 

  17. Karki R, Chuenchart W, Surendra K, Shrestha S, Raskin L, Sung S, et al. Anaerobic co-digestion: Current status and perspectives. Bioresour Technol. 2021;330:125001.

    CAS  PubMed  Google Scholar 

  18. Lian T, Zhang W, Cao Q, Wang S, Yin F, Chen Y, et al. Optimization of lactate production from co-fermentation of swine manure with apple waste and dynamics of microbial communities. Bioresour Technol. 2021;336:125307.

    CAS  PubMed  Google Scholar 

  19. Li Y, Zhang R, Liu G, Chen C, He Y, Liu X. Comparison of methane production potential, biodegradability, and kinetics of different organic substrates. Bioresour Technol. 2013;149:565–9.

    CAS  PubMed  Google Scholar 

  20. Hart SC, Stark JM, Davidson EA, Firestone MK. Nitrogen Mineralization, Immobilization, and Nitrification. In: Weaver RW, Angle S, Bottomley P, Bezdicek D, Smith S, Tabatabai A, et al. editors. Methods of Soil Analysis. 1994. pp. 985–1018.

  21. Lin Q, De Vrieze J, Li J, Li X. Temperature affects microbial abundance, activity and interactions in anaerobic digestion. Bioresour Technol. 2016;209:228–36.

    CAS  PubMed  Google Scholar 

  22. Bludman SA, Vanriper KA. Equation of state of an ideal ferni gas. Astrophys J. 1977;212:859–72.

    CAS  Google Scholar 

  23. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–2200.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.

    CAS  PubMed  Google Scholar 

  27. Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–D642.

    CAS  PubMed  Google Scholar 

  28. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.

    CAS  PubMed  Google Scholar 

  29. Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, et al. The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinforma. 2008;9:386.

    CAS  Google Scholar 

  30. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.

    Google Scholar 

  31. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser. B 1995;57:289–300.

    Google Scholar 

  32. Ma S, Ren B, Mallick H, Moon YS, Schwager E, Maharjan S, et al. A statistical model for describing and simulating microbial community profiles. PLoS Comput Biol. 2021;17:e1008913.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.

    CAS  PubMed  Google Scholar 

  34. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’hara R, et al. Package ‘vegan’. Community Ecol package, version. 2013;2:1–295.

    Google Scholar 

  35. Wickham H. ggplot2. Wiley Interdiscip Rev Comput Stat. 2011;3:180–5.

    Google Scholar 

  36. Klappenbach JA, Dunbar JM, Schmidt TM. rRNA operon copy number reflects ecological strategies of bacteria. Appl Environ Micro. 2000;66:1328–33.

    CAS  Google Scholar 

  37. Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.

    PubMed  Google Scholar 

  38. De Vrieze J, Pinto AJ, Sloan WT, Ijaz UZ. The active microbial community more accurately reflects the anaerobic digestion process: 16S rRNA (gene) sequencing as a predictive tool. Microbiome 2018;6:63.

    PubMed  PubMed Central  Google Scholar 

  39. Kamke J, Taylor MW, Schmitt S. Activity profiles for marine sponge-associated bacteria obtained by 16S rRNA vs 16S rRNA gene comparisons. ISME J. 2010;4:498–508.

    CAS  PubMed  Google Scholar 

  40. Yang Y, Yang F, Huang W, Huang W, Li F, Lei Z, et al. Enhanced anaerobic digestion of ammonia-rich swine manure by zero-valent iron: With special focus on the enhancement effect on hydrogenotrophic methanogenesis activity. Bioresour Technol. 2018;270:172–9.

    CAS  PubMed  Google Scholar 

  41. Poirier S, Bize A, Bureau C, Bouchez T, Chapleur O. Community shifts within anaerobic digestion microbiota facing phenol inhibition: towards early warning microbial indicators? Water Res. 2016;100:296–305.

    CAS  PubMed  Google Scholar 

  42. Tsapekos P, Khoshnevisan B, Zhu X, Treu L, Alfaro N, Kougias PG, et al. Lab-and pilot-scale anaerobic digestion of municipal bio-waste and potential of digestate for biogas upgrading sustained by microbial analysis. Renew Energy. 2022;201:344–53.

    CAS  Google Scholar 

  43. Louca S, Jacques S, Pires AP, Leal JS, Srivastava DS, Parfrey LW, et al. High taxonomic variability despite stable functional structure across microbial communities. Nat Ecol Evol. 2016;1:1–12.

    Google Scholar 

  44. Lin Q, Li L, Adams JM, Heděnec P, Tu B, Li C, et al. Nutrient resource availability mediates niche differentiation and temporal co-occurrence of soil bacterial communities. App Soil Ecol. 2021;163:103965.

    Google Scholar 

  45. Chesson PL, Warner RR. Environmental variability promotes coexistence in lottery competitive systems. Am Nat. 1981;117:923–43.

    Google Scholar 

  46. LaManna JA, Walton ML, Turner BL, Myers JA. Negative density dependence is stronger in resource‐rich environments and diversifies communities when stronger for common but not rare species. Ecol Lett. 2016;19:657–67.

    PubMed  Google Scholar 

  47. Delgado-Baquerizo M, Maestre FT, Reich PB, Jeffries TC, Gaitan JJ, Encinar D, et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat Commun. 2016;7:10541.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Thank all people who work against COVID-19 pandemic and appreciate the support from our families for our works.

Funding

This study was supported by the Open Found of Key Laboratory of Environmental and Applied Microbiology CAS (11050004GB), and China Biodiversity Observation Networks (Sino BON).

Author information

Authors and Affiliations

Authors

Contributions

QL conceived this study, conducted the experiment, analyzed data, and wrote and revised the manuscript. LJL analyzed data and revised the manuscript. JDV, CNL, and XZL revised the manuscript. XYF conducted the experiment.

Corresponding author

Correspondence to Xiangzhen Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, Q., Li, L., De Vrieze, J. et al. Functional conservation of microbial communities determines composition predictability in anaerobic digestion. ISME J 17, 1920–1930 (2023). https://doi.org/10.1038/s41396-023-01505-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41396-023-01505-x

Search

Quick links