Modest functional diversity decline and pronounced composition shifts of microbial communities in a mixed waste-contaminated aquifer

Yupeng Fan^#^{1

2}, Dongyu Wang^#², Joy X Yang¹, Daliang Ning^{1

2}, Zhili He^{1

3}, Ping Zhang^{1

4}, Andrea M Rocha⁵, Naijia Xiao^{1

2}, Jonathan P Michael^{1

2}, Katie F Walker^{5

6}, Dominique C Joyner^{5

6}, Chongle Pan^{2

7}, Michael W W Adams⁸, Matthew W Fields⁹, Eric J Alm¹⁰, David A Stahl¹¹, Terry C Hazen^{5

6}, Paul D Adams^{12

13}, Adam P Arkin^{13

14}, Jizhong Zhou^{15

16

17

18

19}

Affiliations

¹ Institute for Environmental Genomics, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA.
² School of Biological Sciences, University of Oklahoma, Norman, OK, 73019, USA.
³ Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, Guangdong, 519080, China.
⁴ Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
⁵ Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
⁶ Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, 37996, USA.
⁷ School of Computer Science, University of Oklahoma, Norman, OK, 73019, USA.
⁸ Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, 30602, USA.
⁹ Center for Biofilm Engineering and Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, 59717, USA.
¹⁰ Department of Biological Engineering, Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
¹¹ Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, 98195, USA.
¹² Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
¹³ Department of Bioengineering, University of California, Berkeley, CA, 94720, USA.
¹⁴ Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
¹⁵ Institute for Environmental Genomics, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA. jzhou@ou.edu.
¹⁶ School of Biological Sciences, University of Oklahoma, Norman, OK, 73019, USA. jzhou@ou.edu.
¹⁷ School of Computer Science, University of Oklahoma, Norman, OK, 73019, USA. jzhou@ou.edu.
¹⁸ Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. jzhou@ou.edu.
¹⁹ School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, 73019, USA. jzhou@ou.edu.

^# Contributed equally.

PMID: 40296156
PMCID: PMC12036129
DOI: 10.1186/s40168-025-02105-x

Modest functional diversity decline and pronounced composition shifts of microbial communities in a mixed waste-contaminated aquifer

Yupeng Fan et al. Microbiome. 2025.

. 2025 Apr 28;13(1):106.

doi: 10.1186/s40168-025-02105-x.

Authors

Affiliations

¹ Institute for Environmental Genomics, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA.
² School of Biological Sciences, University of Oklahoma, Norman, OK, 73019, USA.
³ Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, Guangdong, 519080, China.
⁴ Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
⁵ Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
⁶ Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, 37996, USA.
⁷ School of Computer Science, University of Oklahoma, Norman, OK, 73019, USA.
⁸ Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, 30602, USA.
⁹ Center for Biofilm Engineering and Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, 59717, USA.
¹⁰ Department of Biological Engineering, Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
¹¹ Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, 98195, USA.
¹² Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
¹³ Department of Bioengineering, University of California, Berkeley, CA, 94720, USA.
¹⁴ Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
¹⁵ Institute for Environmental Genomics, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA. jzhou@ou.edu.
¹⁶ School of Biological Sciences, University of Oklahoma, Norman, OK, 73019, USA. jzhou@ou.edu.
¹⁷ School of Computer Science, University of Oklahoma, Norman, OK, 73019, USA. jzhou@ou.edu.
¹⁸ Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. jzhou@ou.edu.
¹⁹ School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, 73019, USA. jzhou@ou.edu.

^# Contributed equally.

PMID: 40296156
PMCID: PMC12036129
DOI: 10.1186/s40168-025-02105-x

Abstract

Background: Microbial taxonomic diversity declines with increased environmental stress. Yet, few studies have explored whether phylogenetic and functional diversities track taxonomic diversity along the stress gradient. Here, we investigated microbial communities within an aquifer in Oak Ridge, Tennessee, USA, which is characterized by a broad spectrum of stressors, including extremely high levels of nitrate, heavy metals like cadmium and chromium, radionuclides such as uranium, and extremely low pH (< 3).

Results: Both taxonomic and phylogenetic α-diversities were reduced in the most impacted wells, while the decline in functional α-diversity was modest and statistically insignificant, indicating a more robust buffering capacity to environmental stress. Differences in functional gene composition (i.e., functional β-diversity) were pronounced in highly contaminated wells, while convergent functional gene composition was observed in uncontaminated wells. The relative abundances of most carbon degradation genes were decreased in contaminated wells, but genes associated with denitrification, adenylylsulfate reduction, and sulfite reduction were increased. Compared to taxonomic and phylogenetic compositions, environmental variables played a more significant role in shaping functional gene composition, suggesting that niche selection could be more closely related to microbial functionality than taxonomy.

Conclusions: Overall, we demonstrated that despite a reduced taxonomic α-diversity, microbial communities under stress maintained functionality underpinned by environmental selection. Video Abstract.

Keywords: Contaminated aquifer; Functional diversity; Phylogenetic diversity; Taxonomic diversity.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

**Fig. 1**
Geographical location of the study sites. Aquifer samples consist of uncontaminated wells (UC) FW300, FW301, and FW303; low-contaminated wells (LC) GW199, GW715, and GW928; mid-contaminated wells (MC) FW215, FW602, and DP16D; high-contaminated wells (HC) FW104, FW106, and FW021

**Fig. 2**
Microbial α-diversity of aquifer samples from uncontaminated wells (UC), low-contaminated wells (LC), mid-contaminated wells (MC), and high-contaminated wells (HC). Diversity metrics are represented as follows: A taxonomic diversity, B phylogenetic diversity, and C functional diversity, calculated by richness; and D taxonomic diversity, E phylogenetic diversity, and F functional diversity, calculated by the Shannon index. Statistical differences between groups were assessed using ANOVA followed by a post hoc test, with a significance threshold set at p < 0.05. Letters indicate significant differences between groups

**Fig. 3**
Microbial β-diversity of aquifer microbial communities for different diversity indices in uncontaminated wells (UC), low-contaminated wells (LC), mid-contaminated wells (MC), and high-contaminated wells (HC). Non-metric multidimensional scaling (NMDS) plots based on weighted Bray-Curties index for A taxonomic and C functional diversities, normalized weighted Unifrac (phylogenetic Bray-Curties) for phylogenetic diversity (B). Dispersion test (D) based on weighted Bray-Curties index for taxonomic and functional diversities, normalized weighted Unifrac (phylogenetic Bray-Curties) for phylogenetic diversity. Statistical differences between groups were assessed using ANOVA followed by a post hoc test, with a significance threshold set at p < 0.05. Letters indicate significant differences between groups

**Fig. 4**
Differences in relative gene abundance of selected genes for 12 aquifer samples functional community based on response ratio. A Carbon degradation genes, B nitrogen cycling genes, C sulfur cycling genes, D metal homeostatic genes, E stress response genes, F organic pollutant degradation genes, and G electron transfer genes. All genes presented here are significantly different from those in unpolluted wells, judged by a 95% confidence interval

**Fig. 5**
The linkage between aquifer microbial communities and environmental factors. A Variance partition analysis (VPA) showing relative contributions of geographical distance (Geo.) and environmental variables (Env.) to the different diversity indexes based on the PLS method. B Variable influence on projection (VIP) values based on the PLS model for different diversity indexes, where VIP value larger than 1 is filled, VIP value smaller than 1 is blank

See this image and copyright information in PMC

References

1. Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, Thomas BC, Singh A, Wilkins MJ, Karaoz U, Brodie EL, Williams KH, Hubbard SS, Banfield JF. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:13219. - DOI - PMC - PubMed
1. Anderson MJ. Distance-based tests for homogeneity of multivariate dispersions. Biometrics. 2006;62(1):245–53. - DOI - PubMed
1. Anderson MJ. Permutational Multivariate Analysis of Variance (PERMANOVA). In: Balakrishnan N, Colton T, Everitt B, Piegorsch W, Ruggeri F, Teugels JL, editors. ‘Wiley StatsRef: Statistics Reference Online’. Chichester: Wiley; 2017. pp. 1–15
1. Arnault G, Mony C, Vandenkoornhuyse P. Plant microbiota dysbiosis and the Anna Karenina Principle. Trends Plant Sci. 2023;28(1):18–30. - DOI - PubMed
1. Blanco-Miguez A, Beghini F, Cumbo F, McIver LJ, Thompson KN, Zolfo M, Manghi P, Dubois L, Huang KD, Thomas AM, Nickols WA, Piccinno G, Piperni E, Puncochar M, Valles-Colomer M, Tett A, Giordano F, Davies R, Wolf J, Berry SE, Spector TD, Franzosa EA, Pasolli E, Asnicar F, Huttenhower C, Segata N. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol. 2023;41(11):1633–44. - DOI - PMC - PubMed

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Modest functional diversity decline and pronounced composition shifts of microbial communities in a mixed waste-contaminated aquifer

Affiliations

Modest functional diversity decline and pronounced composition shifts of microbial communities in a mixed waste-contaminated aquifer

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources