Exploring antibiotic resistance genes, mobile gene elements, and virulence gene factors in an urban freshwater samples using metagenomic analysis.
Antibiotic resistance genes
Bacterial diversity
Environmental infections
Freshwater
Metagenomic
Mobile genetic elements
Virulence gene factors
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Jan 2023
Jan 2023
Historique:
received:
08
02
2022
accepted:
20
07
2022
pubmed:
9
8
2022
medline:
7
2
2023
entrez:
8
8
2022
Statut:
ppublish
Résumé
Antibiotic resistance genes (ARGs) and antimicrobial resistance elements (AMR) are novel environmental contaminants that pose a significant risk to human health globally. Freshwater contains a variety of microorganisms that might affect human health; its quality must be assessed before use. However, the dynamics of mobile genetic elements (MGEs) and ARG propagation in freshwater have rarely been studied in Singapore. Therefore, this study used metagenomics to compare diversity, virulence factor composition, and ARG and MGE co-occurrence with bacterial communities in paired (n = 8) environmental freshwater samples. KneadData, FMAP, and Kraken2 were used for bioinformatics analysis and R (v4.1.1) for statistical analysis. Sequence reads with a total of 9043 species were taxonomically classified into 66 phyla, 130 classes, 261 orders, 584 families, and 2477 genera. Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes were found the Phyla in all samples. Analysis of QIIME output by PICRUSt and ß-diversity showed unique clusters and functional microbial community structures. A total of 2961 ARGs were found that conferred resistance to multidrug, aminoglycosides, tetracyclines, elfamycins, and more. The classified ARG mechanism revealed significant distribution of virulence factors in bacterial cells. Transposes and transposon were highly correlated to ARG gene transfer. Co-occurrence network analysis showed several MGEs appear to use the same ARGs (intI and rho) and were dominant in all samples. Furthermore, ARGs are also highly correlated with bacteria like Campylobacter and Escherichia. This study enhances the understanding of antibiotic risk assessment and provides a new perspective on bacterial assembly contamination and the functional prevalence of ARGs and MGEs with antibiotic resistance bacteria. Moreover, it raises public awareness because these contaminants put people's lives at risk of acquiring bacterial infections. In addition, it can also help propose hybrid water treatment approaches.
Identifiants
pubmed: 35939194
doi: 10.1007/s11356-022-22197-4
pii: 10.1007/s11356-022-22197-4
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2977-2990Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Ahmad T, Gupta G, Sharma A, Kaur B, El-Sheikh MA, Alyemeni MN (2021) Metagenomic analysis exploring taxonomic and functional diversity of bacterial communities of a Himalayan urban fresh water lake. PLoS ONE 16(3 March), 1–19. https://doi.org/10.1371/journal.pone.0248116
Akhil D, Lakshmi D, Senthil Kumar P, Vo DVN, Kartik A (2021) Occurrence and removal of antibiotics from industrial wastewater. Environ Chem Lett 19(2):1477–1507. https://doi.org/10.1007/S10311-020-01152-0
doi: 10.1007/S10311-020-01152-0
Alexander J, Hembach N, Schwartz T (2020) Evaluation of antibiotic resistance dissemination by wastewater treatment plant effluents with different catchment areas in Germany. Scientific Reports 2020 10:1, 10(1), 1–9. https://doi.org/10.1038/s41598-020-65635-4
Anh HQ, Le TPQ, Da Le N, Lu XX, Duong TT, Garnier J, Rochelle-Newall E, Zhang S, Oh NH, Oeurng C, Ekkawatpanit C, Nguyen TD, Nguyen QT, Nguyen TD, Nguyen TN, Tran TL, Kunisue T, Tanoue R, Takahashi S, … Nguyen TAH (2021) Antibiotics in surface water of East and Southeast Asian countries: a focused review on contamination status, pollution sources, potential risks, and future perspectives Sci Total Environ 764(1):142873. https://doi.org/10.1016/J.SCITOTENV.2020.142865
Antipov D, Raiko M, Lapidus A, Pevzner PA (2020) MetaviralSPAdes: assembly of viruses from metagenomic data. Bioinformatics 36(14):4126–4129. https://doi.org/10.1093/BIOINFORMATICS/BTAA490
doi: 10.1093/BIOINFORMATICS/BTAA490
Arenas F, Navarro-Ródenas A, Marqués-Gálvez JE, Ghignone S, Mello A, Morte A (2021) Different patterns in root and soil fungal diversity drive plant productivity of the desert truffle Terfezia claveryi in plantation. Environ Microbiol 23(10):5917–5933. https://doi.org/10.1111/1462-2920.15688
doi: 10.1111/1462-2920.15688
Baniya A, Digennaro P (2021) Genome announcement of Steinernema khuongi and its associated symbiont from Florida. G3 Genes|Genomes|Genetics, 11(4), 1–16. https://doi.org/10.1093/G3JOURNAL/JKAB053
Barancheshme F, Munir M (2018) Strategies to combat antibiotic resistance in the wastewater treatment plants. Front Microbiol 8(JAN):2615. https://doi.org/10.3389/FMICB.2017.02603/BIBTEX
Bibi S, Hasan MM, Wang Y-B, Papadakos SP, Yu H (2021) Cordycepin as a promising inhibitor of SARS-CoV-2 RNA dependent RNA polymerase (RdRp). Curr Med Chem 28(1):33–41. https://doi.org/10.2174/0929867328666210820114025
doi: 10.2174/0929867328666210820114025
Bibi S, Wang Y-B, Tang D-X, Kamal MA, Yu H (2019) Prospects for discovering the secondary metabolites of Cordyceps sensu lato by the integrated strategy. Med Chem 17(2):97–120. https://doi.org/10.2174/1573406416666191227120425
doi: 10.2174/1573406416666191227120425
Biswas P, Hasan MM, Dey D, dos Santos Costa AC, Polash SA, Bibi S, Ferdous N, Kaium MA, Rahman MH, Jeet FK, Papadakos S, Islam K, Uddin MS (2021) Candidate antiviral drugs for COVID-19 and their environmental implications: a comprehensive analysis. Environ Sci Pollut Res 2021 28:42, 28(42), 59570–59593. https://doi.org/10.1007/S11356-021-16096-3
Brown J, Pirrung M, Mccue LA (2017) FQC Dashboard: integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool. Bioinformatics 33(19):3137–3139. https://doi.org/10.1093/BIOINFORMATICS/BTX373
doi: 10.1093/BIOINFORMATICS/BTX373
Che Y, Xia Y, Liu L, Li AD, Yang Y, Zhang T (2019) Mobile antibiotic resistome in wastewater treatment plants revealed by Nanopore metagenomic sequencing. Microbiome 7(1):1–13. https://doi.org/10.1186/S40168-019-0663-0/FIGURES/5
doi: 10.1186/S40168-019-0663-0/FIGURES/5
Chopyk J, Nasko DJ, Allard S, Callahan MT, Bui A, Ferelli AMC, Chattopadhyay S, Mongodin EF, Pop M, Micallef SA, Sapkota AR (2020) Metagenomic analysis of bacterial and viral assemblages from a freshwater creek and irrigated field reveals temporal and spatial dynamics. Sci Total Environ 706(1):135409. https://doi.org/10.1016/J.SCITOTENV.2019.135395
doi: 10.1016/J.SCITOTENV.2019.135395
Danko D, Bezdan D, Afshin EE, Ahsanuddin S, Bhattacharya C, Butler DJ, Chng KR, Donnellan D, Hecht J, Jackson K, Kuchin K, Karasikov M, Lyons A, Mak L, Meleshko D, Mustafa H, Mutai B, Neches RY, Ng A, … Zubenko S (2021) A global metagenomic map of urban microbiomes and antimicrobial resistance. Cell 184(13):3376-3393.e17 https://doi.org/10.1016/J.CELL.2021.05.002
Dávalos AF, Garcia PK, Montoya-Pachongo C, Rengifo A, Guerrero D, Díaz-Ordoñez L, Díaz G, Ferro BE (2021) Identification of nontuberculous mycobacteria in drinking water in Cali, Colombia. Int J Environ Res Public Health 2021, Vol. 18, Page 8451, 18(16), 8462. https://doi.org/10.3390/IJERPH18168451
Davis JJ, Boisvert S, Brettin T, Kenyon RW, Mao C, Olson R, Overbeek R, Santerre J, Shukla M, Wattam AR, Will R, Xia F, Stevens R (2016) Antimicrobial resistance prediction in PATRIC and RAST. Sci Rep 6(1):1–12. https://doi.org/10.1038/srep27930
doi: 10.1038/srep27930
Davis JJ, Wattam AR, Aziz RK, Brettin T, Butler R, Butler RM, Chlenski P, Conrad N, Dickerman A, Dietrich EM, Gabbard JL, Gerdes S, Guard A, Kenyon RW, MacHi D, Mao C, Murphy-Olson D, Nguyen M, Nordberg EK, … Stevens R (2020) The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities. Nucleic Acids Res 48(D1):D606-D612 https://doi.org/10.1093/NAR/GKZ943
Fang H, Huang K, Yu J, Ding C, Wang Z, Zhao C, Yuan H, Wang Z, Wang S, Hu J, Cui Y (2019) Metagenomic analysis of bacterial communities and antibiotic resistance genes in the Eriocheir sinensis freshwater aquaculture environment. Chemosphere 224(1):202–211. https://doi.org/10.1016/J.CHEMOSPHERE.2019.02.068
doi: 10.1016/J.CHEMOSPHERE.2019.02.068
Fang H, Cai L, Yang Y, Ju F, Li X, Yu Y, Zhang T (2014) Metagenomic analysis reveals potential biodegradation pathways of persistent pesticides in freshwater and marine sediments. Sci Total Environ 470–471:983–992. https://doi.org/10.1016/J.SCITOTENV.2013.10.076
doi: 10.1016/J.SCITOTENV.2013.10.076
Farman M, Yasir M, Al-Hindi RR, Farraj SA, Jiman-Fatani AA, Alawi M, Azhar EI (2019) Genomic analysis of multidrug-resistant clinical Enterococcus faecalis isolates for antimicrobial resistance genes and virulence factors from the western region of Saudi Arabia. Antimicrob Resist Infect Control 8(1):1–11. https://doi.org/10.1186/S13756-019-0508-4/FIGURES/5
doi: 10.1186/S13756-019-0508-4/FIGURES/5
Frank H (2021) Harrell Miscellaneous Package ‘Hmisc.’ https://hbiostat.org/R/Hmisc/
Gabashvili E, Kobakhidze S, Chkhikvishvili T, Tabatadze L, Tsiklauri R, Dadiani K, Koulouris S, Kotetishvili M (2022) Metagenomic and recombination analyses of antimicrobial resistance genes from recreational waters of Black Sea coastal areas and other marine environments unveil extensive evidence for their both intrageneric and intergeneric transmission across Genetical. Mar Genomics 61(1):100931. https://doi.org/10.1016/J.MARGEN.2021.100916
doi: 10.1016/J.MARGEN.2021.100916
Goh JL, Jaffar JLY, Xu H, Lum VWM (2020) A retrospective study on end-of-life care in the emergency department of a tertiary hospital in Singapore. Proceedings of Singapore Healthcare 30(2):138–144. https://doi.org/10.1177/2010105820960175
doi: 10.1177/2010105820960175
Grant KR (2022) Next-Generation Amplicon Sequencing: A cost-effective method for exploring microbial biodiversity. In Molecular genetics and genomics tools in biodiversity conservation (1st ed., Vol. 1, pp. 203–236). Springer, Singapore. https://doi.org/10.1007/978-981-16-6005-4_10
Kori JA, Mahar RB, Vistro MR, Tariq H, Khan IA, Goel R (2019) Metagenomic analysis of drinking water samples collected from treatment plants of Hyderabad City and Mehran University Employees Cooperative Housing Society. Environ Sci Pollut Res 26(28):29052–29064. https://doi.org/10.1007/s11356-019-05859-8
doi: 10.1007/s11356-019-05859-8
Kumar Awasthi, M., Ravindran, B., Sarsaiya, S., Chen, H., Wainaina, S., Singh, E., Liu, T., Kumar, S., Pandey, A., Singh, L., & Zhang, Z. (2020). Metagenomics for taxonomy profiling: tools and approaches. 11(1):356–374. https://doi.org/10.1080/21655979.2020.1736238
Liu C, Cui Y, Li X, Yao M (2021) microeco: an R package for data mining in microbial community ecology. FEMS Microbiol Ecol 97(2):1–10. https://doi.org/10.1093/FEMSEC/FIAA255
doi: 10.1093/FEMSEC/FIAA255
Mammola S, Carmona CP, Guillerme T, Cardoso P (2021) Concepts and applications in functional diversity. Funct Ecol 35(9):1869–1885. https://doi.org/10.1111/1365-2435.13882/SUPPINFO
doi: 10.1111/1365-2435.13882/SUPPINFO
Meneghine AK, Nielsen S, Varani AM, Thomas T, Alves LMC (2017) Metagenomic analysis of soil and freshwater from zoo agricultural area with organic fertilization. PLoS ONE 12(12):e0190189. https://doi.org/10.1371/JOURNAL.PONE.0190178
doi: 10.1371/JOURNAL.PONE.0190178
Mitchell AL, Scheremetjew M, Denise H, Potter S, Tarkowska A, Qureshi M, Salazar GA, Pesseat S, Boland MA, Hunter FMI, Ten Hoopen P, Alako B, Amid C, Wilkinson DJ, Curtis TP, Cochrane G, Finn RD (2018) EBI Metagenomics in 2017: enriching the analysis of microbial communities, from sequence reads to assemblies. Nucleic Acids Res 46(1):D726–D735. https://doi.org/10.1093/nar/gkx967
doi: 10.1093/nar/gkx967
Mohiuddin M, Schellhorn HE (2015) Spatial and temporal dynamics of virus occurrence in two freshwater lakes captured through metagenomic analysis. Front Microbiol 6(SEP):971. https://doi.org/10.3389/FMICB.2015.00960/BIBTEX
Mreyoud Y, Song M, Lim J, Ahn TH (2022) MegaD: deep learning for rapid and accurate disease status prediction of metagenomic samples. Life 2022, Vol. 12, Page 669, 12(5):680. https://doi.org/10.3390/LIFE12050669
Mukherjee M, Laird E, Gentry TJ, Brooks JP, Karthikeyan R (2021) Increased antimicrobial and multidrug resistance downstream of wastewater treatment plants in an urban watershed. Front Microbiol 12(1):1278. https://doi.org/10.3389/FMICB.2021.657353/BIBTEX
doi: 10.3389/FMICB.2021.657353/BIBTEX
Palermo CN, Fulthorpe RR, Saati R, Short SM (2019) Metagenomic analysis of virus diversity and relative abundance in a eutrophic freshwater harbour. Viruses 2019, Vol. 11, Page 792, 11(9):802. https://doi.org/10.3390/V11090792
Parrello B, Butler R, Chlenski P, Pusch GD, Overbeek R (2021) Supervised extraction of near-complete genomes from metagenomic samples: A new service in PATRIC. PLoS ONE 16(4):250092–250102. https://doi.org/10.1371/JOURNAL.PONE.0250092
doi: 10.1371/JOURNAL.PONE.0250092
Rasche H, Hiltemann S (2020) Galactic Circos: user-friendly Circos plots within the Galaxy platform. GigaScience 9(6):1–6. https://doi.org/10.1093/GIGASCIENCE/GIAA065
doi: 10.1093/GIGASCIENCE/GIAA065
Revelle, W. (2021, September 22). PSYCH: Procedures for Psychological, Psychometric, and Personality Research. Cran R Projects; Comprehensive R Archive Network (CRAN). https://cran.r-project.org/package=psych
Sahani S, Hansa, Sharma YC, Kim TY (2022) Emerging contaminants in wastewater and surface water. New Trends in Emerging Environment Contaminants 9–30 https://doi.org/10.1007/978-981-16-8367-1_2/COVER/
SaparbaevnaAlexyuk M, SabirzhanovnaTurmagambetova A, GennadievichAlexyuk P, PavlinovichBogoyavlenskiy A, EleazarovichBerezin V (2017) Comparative study of viromes from freshwater samples of the Ile-Balkhash region of Kazakhstan captured through metagenomic analysis. VirusDisease 28(1):18–25. https://doi.org/10.1007/s13337-016-0353-5
doi: 10.1007/s13337-016-0353-5
Shilpa B, N., & Meena, S. S. (2022) Exploring the plastic degrading ability of microbial communities through metagenomic approach. Materials Today: Proceedings 57:1924–1932. https://doi.org/10.1016/J.MATPR.2022.02.308
doi: 10.1016/J.MATPR.2022.02.308
Sohail MN, Jiadong R, Uba MM, Irshad M, Iqbal W, Arshad J, John AV (2019a) A hybrid Forecast Cost Benefit Classification of diabetes mellitus prevalence based on epidemiological study on Real-life patient’s data. Sci Rep 9(1):1–10. https://doi.org/10.1038/s41598-019-46631-9
doi: 10.1038/s41598-019-46631-9
Sohail MN, Ren J, Muhammad MU (2019b) A euclidean group assessment on semi-supervised clustering for healthcare clinical implications based on real-life data. Int J Environ Res Public Health 16(9):1581–1593. https://doi.org/10.3390/ijerph16091581
doi: 10.3390/ijerph16091581
Sohail N, Jiadong R, Musa Uba M, Irshad M, Khan A (2018) Classification and cost benefit analysis of diabetes mellitus dominance. Int J Comput Sci Netw Secur 18(10):29–35. http://paper.ijcsns.org/07_book/201810/20181006.pdf
Szekeres E, Chiriac CM, Baricz A, Szőke-Nagy T, Lung I, Soran ML, Rudi K, Dragos N, Coman C (2018) Investigating antibiotics, antibiotic resistance genes, and microbial contaminants in groundwater in relation to the proximity of urban areas. Environ Pollut 236(1):734–744. https://doi.org/10.1016/J.ENVPOL.2018.01.107
doi: 10.1016/J.ENVPOL.2018.01.107
Taş N, de Jong AE, Li Y, Trubl G, Xue Y, Dove NC (2021) Metagenomic tools in microbial ecology research. Curr Opin Biotechnol 67(1):184–191. https://doi.org/10.1016/J.COPBIO.2021.01.019
doi: 10.1016/J.COPBIO.2021.01.019
Uba Muhammad M, Jiadong R, Sohail MN, Irshad M, Bilal M, Osi AA (2018) A logistic regression modeling on the prevalence of diabetes mellitus in the North Western Part of Nigeria. Benin J St 1(1):1–10. https://srg-uniben.org/pdf/1_bjs.pdf
UNEP (2021) Progress on SDG 6 Indicators | UNEP - UN Environment Programme. UN Environment Programme. https://www.unep.org/resources/report/progress-sdg-6-indicators
Vieira FR, Pecchia JA (2021) Bacterial community patterns in the Agaricus bisporus cultivation system, from compost raw materials to mushroom caps. Microb Ecol 1(1):1–13. https://doi.org/10.1007/S00248-021-01833-5/FIGURES/5
doi: 10.1007/S00248-021-01833-5/FIGURES/5
White RA, Chan AM, Gavelis GS, Leander BS, Brady AL, Slater GF, Lim DSS, Suttle CA (2016) Metagenomic analysis suggests modern freshwater microbialites harbor a distinct core microbial community. Front Microbiol 6(JAN):1543. https://doi.org/10.3389/FMICB.2015.01531/BIBTEX
Wu F, Speth DR, Philosof A, Crémière A, Narayanan A, Barco RA, Connon SA, Amend JP, Antoshechkin IA, Orphan VJ (2022) Unique mobile elements and scalable gene flow at the prokaryote–eukaryote boundary revealed by circularized Asgard archaea genomes. Nat Microbiol 2022:1–13. https://doi.org/10.1038/s41564-021-01039-y
doi: 10.1038/s41564-021-01039-y
Yadav S, Kapley A (2021) Antibiotic resistance: global health crisis and metagenomics. Biotechnology Reports 29(1):604–612. https://doi.org/10.1016/J.BTRE.2021.E00604
doi: 10.1016/J.BTRE.2021.E00604
Yang C, Chowdhury D, Zhang Z, Cheung WK, Lu A, Bian ZX, Zhang L (2021) A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data. Genomics 1(1):1–8. https://arxiv.org/abs/2109.00809v1
Yoo K, Yoo H, Lee J, Choi EJ, Park J (2019) Exploring the antibiotic resistome in activated sludge and anaerobic digestion sludge in an urban wastewater treatment plant via metagenomic analysis. J Microbiol 2020 58(2):123–130. https://doi.org/10.1007/S12275-020-9309-Y
Yoo K, Yoo H, Lee J, Choi EJ, Park J (2020) Exploring the antibiotic resistome in activated sludge and anaerobic digestion sludge in an urban wastewater treatment plant via metagenomic analysis. J Microbiol 58(2):123–130. https://doi.org/10.1007/s12275-020-9309-y
doi: 10.1007/s12275-020-9309-y
Yousafi Q, Amin H, Bibi S, Rafi R, Khan MS, Ali H, Masroor A (2021) Subtractive proteomics and immuno-informatics approaches for multi-peptide vaccine prediction against Klebsiella oxytoca and validation through in silico expression. Int J Pept Res Ther 27(4):2685–2701. https://doi.org/10.1007/S10989-021-10283-Z/FIGURES/8
doi: 10.1007/S10989-021-10283-Z/FIGURES/8
Yuan Y, Chen Y, Yao F, Zeng M, Xie Q, Shafiq M, Noman SM, Jiao X (2021) Microbiomes and resistomes in biopsy tissue and intestinal lavage fluid of colorectal cancer. Frontiers in Cell and Developmental Biology 9(1):737008. https://doi.org/10.3389/FCELL.2021.736994
doi: 10.3389/FCELL.2021.736994
Zhang L, Zhong M, Li X, Lu W, Li J (2020) River bacterial community structure and co-occurrence patterns under the influence of different domestic sewage types. J Environ Manage 266(February):110590. https://doi.org/10.1016/j.jenvman.2020.110590
doi: 10.1016/j.jenvman.2020.110590
Zhao R, Yu K, Zhang J, Zhang G, Huang J, Ma L, Deng C, Li X, Li B (2020) Deciphering the mobility and bacterial hosts of antibiotic resistance genes under antibiotic selection pressure by metagenomic assembly and binning approaches. Water Res 186(1):33–41. https://doi.org/10.1016/j.watres.2020.116318
doi: 10.1016/j.watres.2020.116318
Zhao Y, Zhang XX, Zhao Z, Duan C, Chen H, Wang M, Ren H, Yin Y, Ye L (2018) Metagenomic analysis revealed the prevalence of antibiotic resistance genes in the gut and living environment of freshwater shrimp. J Hazard Mater 350:10-18. https://doi.org/10.1016/J.JHAZMAT.2018.02.004