The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Bacterial community structures are unique and resilient in full-scale bioenergy systems
Open accessAbstract
Anaerobic digestion is the most successful bioenergy technology worldwide with, at its core, undefined microbial communities that have poorly understood dynamics. Here, we investigated the relationships of bacterial community structure (>400,000 16S rRNA gene sequences for 112 samples) with function (i.e., bioreactor performance) and environment (i.e., operating conditions) in a yearlong monthly time series of nine full-scale bioreactor facilities treating brewery wastewater (>20,000 measurements). Each of the nine facilities had a unique community structure with an unprecedented level of stability. Using machine learning, we identified a small subset of operational taxonomic units (OTUs; 145 out of 4,962), which predicted the location of the facility of origin for almost every sample (96.4% accuracy). Of these 145 OTUs, syntrophic bacteria were systematically overrepresented, demonstrating that syntrophs rebounded following disturbances. This indicates that resilience, rather than dynamic competition, played an important role in maintaining the necessary syntrophic populations. In addition, we explained the observed phylogenetic differences between all samples on the basis of a subset of environmental gradients (using constrained ordination) and found stronger relationships between community structure and its function rather than its environment. These relationships were strongest for two performance variables—methanogenic activity and substrate removal efficiency—both of which were also affected by microbial ecology because these variables were correlated with community evenness (at any given time) and variability in phylogenetic structure (over time), respectively. Thus, we quantified relationships between community structure and function, which opens the door to engineer communities with superior functions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
Self-citationNaïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.00062-07 · Cited by 19,885
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 19,549
FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments
Price, M. N., Dehal, P. S., & Arkin, A. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009490 · Cited by 14,894
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P., & Andersen, G. L. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.03006-05 · Cited by 10,891
UniFrac: a New Phylogenetic Method for Comparing Microbial Communities
Lozupone, C., & Knight, R. (2005) · Applied and Environmental Microbiology · doi:10.1128/aem.71.12.8228-8235.2005 · Cited by 8,227
Self-citationPyNAST: a flexible tool for aligning sequences to a template alignment
Caporaso, J. G., Bittinger, K., Bushman, F. D., DeSantis, T. Z., Andersen, G. L., & Knight, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp636 · Cited by 3,396
Self-citationResistance, resilience, and redundancy in microbial communities
Allison, S. D., & Martiny, J. B. H. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801925105 · Cited by 3,151
Diagnosis of multiple cancer types by shrunken centroids of gene expression
Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082099299 · Cited by 2,930
Legendre, P., & Anderson, M. J. (1999) · Ecological Monographs · doi:10.1890/0012-9615(1999)069[0001:dbratm]2.0.co;2 · Cited by 2,695
Cited by
Promoting direct interspecies electron transfer with activated carbon
Liu, F., Rotaru, A.-E., Shrestha, P. M., Malvankar, N. S., Nevin, K. P., & Lovley, D. R. (2012) · Energy & Environmental Science · doi:10.1039/c2ee22459c · Cited by 879
Chain elongation with reactor microbiomes: open-culture biotechnology to produce biochemicals
Angenent, L. T., Richter, H., Buckel, W., Spirito, C. M., Steinbusch, K. J. J., Plugge, C. M., Strik, D. P. B. T. B., Grootscholten, T. I. M., Buisman, C. J. N., & Hamelers, H. V. M. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b04847 · Cited by 689
Self-citationImpact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys
Werner, J. J., Koren, O., Hugenholtz, P., DeSantis, T. Z., Walters, W. A., Caporaso, J. G., Angenent, L. T., Knight, R., & Ley, R. E. (2012) · The ISME Journal · doi:10.1038/ismej.2011.82 · Cited by 607
Self-citationChain elongation in anaerobic reactor microbiomes to recover resources from waste
Spirito, C. M., Richter, H., Rabaey, K., Stams, A. J., & Angenent, L. T. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.01.003 · Cited by 479
Self-citationAnukam, A., Mohammadi, A., Naqvi, M., & Granström, K. (2019) · Processes · doi:10.3390/pr7080504 · Cited by 452
Metadata
- Authors
- Werner, J. J., Knights, D., Garcia, M. L., Scalfone, N. B., Smith, S., Yarasheski, K., Cummings, T. A., Beers, A. R., Knight, R., & Angenent, L. T.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2011-02-22
- Volume
- 108
- Issue
- 10
- Pages
- 4158-4163
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2127862315
- Cited by
- 469
- References
- 29
- FWCI
- 70.62
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Bacterial community structures are unique and resilient in full-scale bioenergy systems. https://app.cochat.ai/papers/10.1073/pnas.1015676108
- The paper
- Werner, J. J., Knights, D., Garcia, M. L., Scalfone, N. B., Smith, S., Yarasheski, K., Cummings, T. A., Beers, A. R., Knight, R., & Angenent, L. T. (2011). Bacterial community structures are unique and resilient in full-scale bioenergy systems. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1015676108
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
