Extreme trophic tales: deciphering bacterial diversity and potential functions in oligotrophic and hypereutrophic lakes.

Guijuan Xie, Yuqing Zhang, Yi Gong, Wenlei Luo, Xiangming Tang
Author Information
  1. Guijuan Xie: College of Biology and Pharmaceutical Engineering, West Anhui University, Lu'an, 237012, China.
  2. Yuqing Zhang: Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
  3. Yi Gong: Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
  4. Wenlei Luo: Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
  5. Xiangming Tang: Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China. xmtang@niglas.ac.cn.

Abstract

BACKGROUND: Oligotrophy and hypereutrophy represent the two extremes of lake trophic states, and understanding the distribution of bacterial communities across these contrasting conditions is crucial for advancing aquatic microbial research. Despite the significance of these extreme trophic states, bacterial community characteristics and co-occurrence patterns in such environments have been scarcely interpreted. To bridge this knowledge gap, we collected 60 water samples from Lake Fuxian (oligotrophic) and Lake Xingyun (hypereutrophic) during different hydrological periods.
RESULTS: Employing 16S rRNA gene sequencing, our findings revealed distinct community structures and metabolic potentials in bacterial communities of hypereutrophic and oligotrophic lake ecosystems. The hypereutrophic ecosystem exhibited higher bacterial α- and β-diversity compared to the oligotrophic ecosystem. Actinobacteria dominated the oligotrophic Lake Fuxian, while Cyanobacteria, Proteobacteria, and Bacteroidetes were more prevalent in the hypereutrophic Lake Xingyun. Functions associated with methanol oxidation, methylotrophy, fermentation, aromatic compound degradation, nitrogen/nitrate respiration, and nitrogen/nitrate denitrification were enriched in the oligotrophic lake, underscoring the vital role of bacteria in carbon and nitrogen cycling. In contrast, functions related to ureolysis, human pathogens, animal parasites or symbionts, and phototrophy were enriched in the hypereutrophic lake, highlighting human activity-related disturbances and potential pathogenic risks. Co-occurrence network analysis unveiled a more complex and stable bacterial network in the hypereutrophic lake compared to the oligotrophic lake.
CONCLUSION: Our study provides insights into the intricate relationships between trophic states and bacterial community structure, emphasizing significant differences in diversity, community composition, and network characteristics between extreme states of oligotrophy and hypereutrophy. Additionally, it explores the nuanced responses of bacterial communities to environmental conditions in these two contrasting trophic states.

Keywords

References

  1. Front Microbiol. 2017 Dec 04;8:2387 [PMID: 29255452]
  2. Sci Total Environ. 2022 Jan 20;805:150294 [PMID: 34536882]
  3. Environ Sci Technol. 2017 Jun 6;51(11):6018-6026 [PMID: 28466638]
  4. Microbiologyopen. 2017 Oct;6(5): [PMID: 28872219]
  5. Appl Environ Microbiol. 2015 Oct;81(20):7114-24 [PMID: 26231655]
  6. Imeta. 2022 Mar 16;1(2):e13 [PMID: 38868563]
  7. FEMS Microbiol Ecol. 2015 Oct;91(10): [PMID: 26324853]
  8. PLoS One. 2011;6(7):e21884 [PMID: 21779347]
  9. Ambio. 2015 Jun;44 Suppl 3:402-12 [PMID: 26022323]
  10. J Environ Sci (China). 2015 Apr 1;30:140-7 [PMID: 25872720]
  11. Ecotoxicol Environ Saf. 2018 Jul 30;156:366-374 [PMID: 29574319]
  12. Nat Rev Microbiol. 2012 Jul 16;10(8):538-50 [PMID: 22796884]
  13. PeerJ. 2022 Sep 16;10:e13999 [PMID: 36132223]
  14. Ecotoxicol Environ Saf. 2018 Aug 15;157:388-394 [PMID: 29649784]
  15. Bioinformatics. 2010 Sep 15;26(18):2347-8 [PMID: 20656902]
  16. Sci Rep. 2019 Jul 31;9(1):11144 [PMID: 31366993]
  17. Sci Total Environ. 2021 Jan 10;751:141618 [PMID: 33167190]
  18. J Environ Manage. 2024 Feb 14;352:120119 [PMID: 38244411]
  19. Microorganisms. 2021 Jan 20;9(2): [PMID: 33498349]
  20. Proc Natl Acad Sci U S A. 2011 Apr 5;108(14):5638-42 [PMID: 21415368]
  21. Am Nat. 2012 Aug;180(2):186-99 [PMID: 22766930]
  22. PeerJ. 2019 Jul 12;7:e7318 [PMID: 31338262]
  23. Chemosphere. 2017 Jun;177:317-325 [PMID: 28319885]
  24. Front Microbiol. 2023 Feb 14;14:1091818 [PMID: 36865780]
  25. Environ Pollut. 2024 Feb 1;342:123058 [PMID: 38042466]
  26. Front Microbiol. 2023 Jan 19;13:1056147 [PMID: 36741896]
  27. ISME J. 2014 Apr;8(4):816-29 [PMID: 24196323]
  28. Microbiome. 2014 Feb 24;2(1):6 [PMID: 24558975]
  29. J Environ Manage. 2020 Jul 15;266:110590 [PMID: 32310123]
  30. Front Microbiol. 2020 Dec 04;11:593589 [PMID: 33343534]
  31. ISME J. 2013 Mar;7(3):680-4 [PMID: 23051691]
  32. Glob Chang Biol. 2022 Jan;28(1):140-153 [PMID: 34610173]
  33. Microbes Environ. 2018 Jul 4;33(2):120-126 [PMID: 29681561]
  34. Environ Microbiol. 2013 Sep;15(9):2489-504 [PMID: 23663352]
  35. ISME J. 2014 Dec;8(12):2503-16 [PMID: 25093637]
  36. Environ Pollut. 2019 Sep;252(Pt A):682-688 [PMID: 31185357]
  37. Science. 2012 Jul 20;337(6092):349-51 [PMID: 22822151]
  38. Water Res. 2019 Mar 15;151:500-514 [PMID: 30641465]
  39. Limnol Oceanogr. 2009 Nov;54(6):2283-2297 [PMID: 20396409]
  40. Can J Microbiol. 2023 Jun 1;69(6):228-239 [PMID: 36753712]
  41. Microb Ecol. 2006 Feb;51(2):137-46 [PMID: 16435168]
  42. BMC Bioinformatics. 2012 May 30;13:113 [PMID: 22646978]
  43. Science. 2016 Sep 16;353(6305):1272-7 [PMID: 27634532]
  44. Environ Microbiol Rep. 2009 Oct;1(5):385-92 [PMID: 23765891]
  45. Microbiol Mol Biol Rev. 2011 Mar;75(1):14-49 [PMID: 21372319]
  46. Environ Pollut. 2022 Dec 1;314:120305 [PMID: 36181942]
  47. Genome Biol. 2011 Jun 24;12(6):R60 [PMID: 21702898]
  48. Science. 2022 Oct 7;378(6615):29-30 [PMID: 36201571]
  49. Appl Environ Microbiol. 2007 Jun;73(11):3511-8 [PMID: 17435002]
  50. Sci Total Environ. 2022 Jan 10;803:150049 [PMID: 34500271]
  51. Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Oct;76(4 Pt 2):045103 [PMID: 17995048]
  52. Microbiome. 2023 Jun 26;11(1):142 [PMID: 37365664]
  53. Front Microbiol. 2015 Oct 28;6:1168 [PMID: 26579082]
  54. Microbiome. 2018 May 17;6(1):90 [PMID: 29773078]
  55. Water Res. 2021 Nov 1;206:117724 [PMID: 34637974]
  56. Microorganisms. 2019 Nov 27;7(12): [PMID: 31783682]
  57. Environ Microbiol. 2018 Mar;20(3):1120-1133 [PMID: 29377517]
  58. Sci Total Environ. 2022 Dec 1;850:158011 [PMID: 35970466]
  59. Sci Total Environ. 2022 Mar 25;814:152804 [PMID: 34982987]
  60. Microorganisms. 2020 Jun 11;8(6): [PMID: 32545218]
  61. Front Microbiol. 2019 Nov 07;10:2560 [PMID: 31787952]
  62. Water Res. 2011 Feb;45(5):1973-83 [PMID: 20934736]
  63. FEMS Microbiol Ecol. 2005 Mar 1;52(1):115-28 [PMID: 16329898]
  64. Nature. 2006 Jul 20;442(7100):259-64 [PMID: 16855581]
  65. Nat Microbiol. 2017 May 30;2:17065 [PMID: 28555622]
  66. Nat Biotechnol. 2019 Aug;37(8):852-857 [PMID: 31341288]
  67. Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Jun;69(6 Pt 2):066133 [PMID: 15244693]

Grants

  1. WGKQ2022032/West Anhui University
  2. 41971062/National Natural Science Foundation of China

MeSH Term

Lakes
Bacteria
RNA, Ribosomal, 16S
Phylogeny
Biodiversity
DNA, Bacterial
Microbiota
Ecosystem
Water Microbiology
China
Nitrogen
Sequence Analysis, DNA

Chemicals

RNA, Ribosomal, 16S
DNA, Bacterial
Nitrogen

Word Cloud

Created with Highcharts 10.0.0bacterialoligotrophichypereutrophiclakeLaketrophicstatescommunitycommunitiesFuxianXingyunnetworkdiversityhypereutrophytwocontrastingconditionsextremecharacteristicsecosystemcomparednitrogen/nitrateenrichedfunctionshumanpotentialBACKGROUND:OligotrophyrepresentextremesunderstandingdistributionacrosscrucialadvancingaquaticmicrobialresearchDespitesignificanceco-occurrencepatternsenvironmentsscarcelyinterpretedbridgeknowledgegapcollected60watersamplesdifferenthydrologicalperiodsRESULTS:Employing16SrRNAgenesequencingfindingsrevealeddistinctstructuresmetabolicpotentialsecosystemsexhibitedhigherα-β-diversityActinobacteriadominatedCyanobacteriaProteobacteriaBacteroidetesprevalentFunctionsassociatedmethanoloxidationmethylotrophyfermentationaromaticcompounddegradationrespirationdenitrificationunderscoringvitalrolebacteriacarbonnitrogencyclingcontrastrelatedureolysispathogensanimalparasitessymbiontsphototrophyhighlightingactivity-relateddisturbancespathogenicrisksCo-occurrenceanalysisunveiledcomplexstableCONCLUSION:studyprovidesinsightsintricaterelationshipsstructureemphasizingsignificantdifferencescompositionoligotrophyAdditionallyexploresnuancedresponsesenvironmentalExtremetales:decipheringlakesBacterialHypereutrophicNetworkcomplexitystabilityOligotrophic

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