Disruption and recovery of river planktonic community during and after the COVID-19 outbreak in Wuhan, China

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Abstract

During the COVID-19 outbreak in Wuhan, large amounts of anti-coronavirus chemicals, such as antiviral drugs and disinfectants were discharged into the surrounding aquatic ecosystem, causing potential ecological damage. Here, we investigated plankton in the Wuhan reaches of the Yangtze River, before, during, and after COVID-19, with the river reaches of three adjacent cities sampled for comparison. During the COVID-19, planktonic microbial density declined significantly. Correspondingly, the eukaryotic and prokaryotic community compositions and functions shifted markedly, with increasing abundance of chlorine-resistant organisms. Abundance of antibiotic resistance genes, virulence factor genes, and bacteria containing both genes increased by 2.3-, 2.7-, and 7.9-fold, respectively, compared to other periods. After COVID-19, all measured plankton community compositional and functional traits recovered in the Yangtze River.

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Most cited references 29

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Basic local alignment search tool.

Stephen F Altschul, Warren Gish, Webb Miller … (1990)

A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.

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The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

Christian Quast, Elmar Pruesse, Pelin Yilmaz … (2012)

SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

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Prodigal: prokaryotic gene recognition and translation initiation site identification

Doug Hyatt, Gwo-Liang Chen, Philip LoCascio … (2010)

Background The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals. Results With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives. Conclusion We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.

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Author and article information

Contributors

Yaohui Bai:

ORCID: http://orcid.org/0000-0002-2086-4477

yhbai@rcees.ac.cn

Jiuhui Qu: jhqu@tsinghua.edu.cn

Journal

Journal ID (iso-abbrev): ISME COMMUN.

Title: ISME Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2730-6151

Publication date (Electronic): 19 September 2022

Publication date PMC-release: 19 September 2022

Publication date (Print): 2022

Volume: 2

Issue: 1

Electronic Location Identifier: 84

Affiliations

[1 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Center for Water and Ecology, , Tsinghua University, ; Beijing, 100084 China

[2 ]GRID grid.9227.e, ISNI 0000000119573309, Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, , Chinese Academy of Sciences, ; Beijing, 100085 China

[3 ]GRID grid.19373.3f, ISNI 0000 0001 0193 3564, School of Civil and Environmental Engineering, , Harbin Institute of Technology, ; Shenzhen, 518055 China

[4 ]GRID grid.7872.a, ISNI 0000000123318773, MaREI Centre, Environmental Research Institute, School of Engineering, , University College Cork, ; Cork, T23XE10 Ireland

Author information

Yaohui Bai http://orcid.org/0000-0002-2086-4477

Article

Publisher ID: 168

DOI: 10.1038/s43705-022-00168-7

PMC ID: 9483884

SO-VID: 8ce0ae61-0887-4512-9b29-1769bc949927

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 13 March 2022

Date revision received : 24 August 2022

Date accepted : 6 September 2022

Funding

Funded by: FundRef https://doi.org/10.13039/501100011282, State Key Joint Laboratory of Environmental Simulation and Pollution Control (State Key Laboratory of Environmental Simulation and Pollution Control);

Award ID: No. 21Z03ESPCR

Award ID: No.21Z03ESPCR

Award Recipient : Donglin Wang Qiaojuan Wang Hui Lin Guannan Mao Yaohui Bai

Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);

Award ID: No.52091542

Award ID: No.52170156

Award ID: No.52091542

Award Recipient : Junwen Chen Weixiao Qi Jinsong Liang Huijuan Liu Jiuhui Qu

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Keywords: environmental sciences,water microbiology

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Keywords: environmental sciences, water microbiology

Disruption and recovery of river planktonic community during and after the COVID-19 outbreak in Wuhan, China

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Novel Coronavirus Disease COVID-19

Most cited references 29

Basic local alignment search tool.

The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

Prodigal: prokaryotic gene recognition and translation initiation site identification

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