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Bombaywala S, Bajaj A, Dafale NA. Oxygen mediated mobilization and co-occurrence of antibiotic resistance in lab-scale bioreactor using metagenomic binning. World J Microbiol Biotechnol 2024; 40:142. [PMID: 38519761 DOI: 10.1007/s11274-024-03952-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/11/2024] [Indexed: 03/25/2024]
Abstract
Sub-lethal levels of antibiotic stimulate bacteria to generate reactive oxygen species (ROS) that promotes emergence and spread of antibiotic resistance mediated by mobile genetic elements (MGEs). Nevertheless, the influence of dissolved oxygen (DO) levels on mobility of antibiotic resistance genes (ARGs) in response to ROS-induced stress remains elusive. Thus, the study employs metagenomic assembly and binning approaches to decipher mobility potential and co-occurrence frequency of ARGs and MGEs under hyperoxic (5.5-7 mgL- 1), normoxic (2.5-4 mgL- 1), and hypoxic (0.5-1 mgL- 1) conditions in lab-scale bioreactor for 6 months. Among 163 high-quality metagenome-assembled genomes (MAGs) recovered from 13 metagenomes, 42 MAGs harboured multiple ARGs and were assigned to priority pathogen group. Total ARG count increased by 4.3 and 2.5% in hyperoxic and normoxic, but decreased by 0.53% in hypoxic conditions after 150 days. On contrary, MGE count increased by 7.3-1.3% in all the DO levels, with only two ARGs showed positive correlation with MGEs in hypoxic compared to 20 ARGs under hyperoxic conditions. Opportunistic pathogens (Escherichia, Klebsiella, Clostridium, and Proteus) were detected as potential hosts of ARGs wherein co-localisation of critical ARG gene cassette (sul1, dfr1,adeF, and qacC) were identified in class 1 integron/Tn1 family transposons. Thus, enhanced co-occurrence frequency of ARGs with MGEs in pathogens suggested promotion of ARGs mobility under oxidative stress. The study offers valuable insights into ARG dissemination and hosts dynamics that is essential for controlling oxygen-related stress for mitigating MGEs and ARGs in the environment.
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Affiliation(s)
- Sakina Bombaywala
- Environmental Biotechnology & Genomics Division, CSIR-National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur, 440020, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Abhay Bajaj
- Environmental Biotechnology & Genomics Division, CSIR-National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur, 440020, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- Environmental Toxicology Group, FEST Division, CSIR-Indian Institute of Toxicology Research, 31 Mahatma Gandhi Marg, Lucknow, 226001, India
| | - Nishant A Dafale
- Environmental Biotechnology & Genomics Division, CSIR-National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur, 440020, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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Bombaywala S, Dafale NA. Mapping the spread and mobility of antibiotic resistance in wastewater due to COVID-19 surge. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:121734-121747. [PMID: 37955733 DOI: 10.1007/s11356-023-30932-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023]
Abstract
Large amounts of antibiotics have been discharged into wastewater during the COVID-19 pandemic due to overuse and misuse of antibiotics to treat patients. Wastewater-based surveillance can be used as an early warning for antibiotic resistance (AR) emergence. The present study analyzed municipal wastewater corresponding to the major pandemic waves (WW1, WW2, and WW3) in India along with hospital wastewater (Ho) taken as a benchmark for AR. Commonly prescribed antibiotics during a pandemic, azithromycin and cefixime residues, were found in the range of 2.1-2.6 μg/L in Ho and WW2. Total residual antibiotic concentration was less in WW2; however, the total antibiotic resistance gene (ARG) count was 1065.6 ppm compared to 85.2 ppm in Ho. Metagenome and RT-qPCR analysis indicated a positive correlation between antibiotics and non-corresponding ARGs (blaOXA, aadA, cat, aph3, and ere), where 7.2-7.5% was carried by plasmid in the bacterial community of WW1 and WW2. Moreover, as the abundance of the dfrA and int1 genes varied most among municipal wastewater, they can be suggested as AR markers for the pandemic. The common pathogens Streptococcus, Escherichia, Shigella, and Aeromonas were putative ARG hosts in metagenome-assembled genomes. The ARG profile and antibiotic levels varied between municipal wastewaters but were fairly similar for WW2 and Ho, suggesting the impact of the pandemic in shaping the resistome pattern. The study provides insights into the resistome dynamic, AR markers, and host-ARG association in wastewater during the COVID-19 surge. Continued surveillance and identification of intervention points for AR beyond the pandemic are essential to curbing the environmental spread of ARGs in the near future.
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Affiliation(s)
- Sakina Bombaywala
- Environmental Biotechnology & Genomics Division, CSIR-National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur, 4400 20, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Nishant A Dafale
- Environmental Biotechnology & Genomics Division, CSIR-National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur, 4400 20, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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Li M, Yao J, Sunahara G, Duran R, Liu B, Cao Y, Li H, Pang W, Liu H, Jiang S, Zhu J, Zhang Q. Assembly processes of bacterial and fungal communities in metal(loid)s smelter soil. JOURNAL OF HAZARDOUS MATERIALS 2023; 451:131153. [PMID: 36893604 DOI: 10.1016/j.jhazmat.2023.131153] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/20/2023] [Accepted: 03/03/2023] [Indexed: 06/18/2023]
Abstract
There are few studies on concurrent bacterial and fungal community assembly processes that govern the metal(loid)s biogeochemical cycles at smelters. Here, a systematic investigation combined geochemical characterization, co-occurrence patterns, and assembly mechanisms of bacterial and fungal communities inhabiting soils around an abandoned arsenic smelter. Acidobacteriota, Actinobacteriota, Chloroflexi, and Pseudomonadota were dominant in bacterial communities, whereas Ascomycota and Basidiomycota dominated fungal communities. The random forest model indicated the bioavailable fractions of Fe (9.58%) were the main positive factor driving the beta diversity of bacterial communities, and the total N (8.09%) was the main negative factor for fungal communities. Microbe-contaminant interactions demonstrate the positive impact of the bioavailable fractions of certain metal(loid)s on bacteria (Comamonadaceae and Rhodocyclaceae) and fungi (Meruliaceae and Pleosporaceae). The fungal co-occurrence networks exhibited more connectivity and complexity than the bacterial networks. The keystone taxa were identified in bacterial (including Diplorickettsiaceae, norank_o_Candidatus_Woesebacteria, norank_o_norank_c_AT-s3-28, norank_o_norank_c_bacteriap25, and Phycisphaeraceae) and fungal (including Biatriosporaceae, Ganodermataceae, Peniophoraceae, Phaeosphaeriaceae, Polyporaceae, Teichosporaceae, Trichomeriaceae, Wrightoporiaceae, and Xylariaceae) communities. Meanwhile, community assembly analysis revealed that deterministic processes dominated the microbial community assemblies, which were highly impacted by pH, total N, and total and bioavailable metal(loid) content. This study provides helpful information to develop bioremediation strategies for the mitigation of metal(loid)s-polluted soils.
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Affiliation(s)
- Miaomiao Li
- Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Jun Yao
- Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China.
| | - Geoffrey Sunahara
- Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China; Department of Natural Resource Sciences, McGill University, 21111 Lakeshore Drive, Ste-Anne-de-Bellevue, Quebec H9X 3V9, Canada
| | - Robert Duran
- Universite de Pau et des Pays de l'Adour, UPPA/E2S, IPREM CNRS 5254, Pau, France
| | - Bang Liu
- Universite de Pau et des Pays de l'Adour, UPPA/E2S, IPREM CNRS 5254, Pau, France
| | - Ying Cao
- Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Hao Li
- Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Wancheng Pang
- Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Houquan Liu
- Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Shun Jiang
- Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Junjie Zhu
- Science and Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing 100083, China
| | - Qinghua Zhang
- College of Bioscience and Engineering, Jiangxi Agricultural University, Nanchang 330045, China
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Liu HQ, Zhao ZL, Li HJ, Yu SJ, Cong L, Ding LL, Ran C, Wang XF. Accurate prediction of huanglongbing occurrence in citrus plants by machine learning-based analysis of symbiotic bacteria. FRONTIERS IN PLANT SCIENCE 2023; 14:1129508. [PMID: 37313258 PMCID: PMC10258322 DOI: 10.3389/fpls.2023.1129508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/02/2023] [Indexed: 06/15/2023]
Abstract
Huanglongbing (HLB), the most prevalent citrus disease worldwide, is responsible for substantial yield and economic losses. Phytobiomes, which have critical effects on plant health, are associated with HLB outcomes. The development of a refined model for predicting HLB outbreaks based on phytobiome markers may facilitate early disease detection, thus enabling growers to minimize damages. Although some investigations have focused on differences in the phytobiomes of HLB-infected citrus plants and healthy ones, individual studies are inappropriate for generating common biomarkers useful for detecting HLB on a global scale. In this study, we therefore obtained bacterial information from several independent datasets representing hundreds of citrus samples from six continents and used these data to construct HLB prediction models based on 10 machine learning algorithms. We detected clear differences in the phyllosphere and rhizosphere microbiomes of HLB-infected and healthy citrus samples. Moreover, phytobiome alpha diversity indices were consistently higher for healthy samples. Furthermore, the contribution of stochastic processes to citrus rhizosphere and phyllosphere microbiome assemblies decreased in response to HLB. Comparison of all constructed models indicated that a random forest model based on 28 bacterial genera in the rhizosphere and a bagging model based on 17 bacterial species in the phyllosphere predicted the health status of citrus plants with almost 100% accuracy. Our results thus demonstrate that machine learning models and phytobiome biomarkers may be applied to evaluate the health status of citrus plants.
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Affiliation(s)
- Hao-Qiang Liu
- Citrus Research Institute, Southwest University/Chinese Academy of Agricultural Sciences, National Engineering Research Center for Citrus, Chongqing, China
| | - Ze-long Zhao
- Shanghai BIOZERON Biotechnology Co., Ltd., Shanghai, China
| | - Hong-Jun Li
- Citrus Research Institute, Southwest University/Chinese Academy of Agricultural Sciences, National Engineering Research Center for Citrus, Chongqing, China
| | - Shi-Jiang Yu
- Citrus Research Institute, Southwest University/Chinese Academy of Agricultural Sciences, National Engineering Research Center for Citrus, Chongqing, China
| | - Lin Cong
- Citrus Research Institute, Southwest University/Chinese Academy of Agricultural Sciences, National Engineering Research Center for Citrus, Chongqing, China
| | - Li-Li Ding
- Citrus Research Institute, Southwest University/Chinese Academy of Agricultural Sciences, National Engineering Research Center for Citrus, Chongqing, China
| | - Chun Ran
- Citrus Research Institute, Southwest University/Chinese Academy of Agricultural Sciences, National Engineering Research Center for Citrus, Chongqing, China
| | - Xue-Feng Wang
- Citrus Research Institute, Southwest University/Chinese Academy of Agricultural Sciences, National Engineering Research Center for Citrus, Chongqing, China
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Ekundayo TC, Ijabadeniyi OA, Igbinosa EO, Okoh AI. Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 317:120734. [PMID: 36455774 DOI: 10.1016/j.envpol.2022.120734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R2 =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.
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Affiliation(s)
- Temitope C Ekundayo
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa; Department of Biotechnology and Food Science, Durban University of Technology, Steve Biko Campus, Steve Biko Rd, Musgrave, Berea, 4001, Durban, South Africa; Department of Microbiology, University of Medical Sciences, Ondo City, Ondo State, Nigeria.
| | - Oluwatosin A Ijabadeniyi
- Department of Biotechnology and Food Science, Durban University of Technology, Steve Biko Campus, Steve Biko Rd, Musgrave, Berea, 4001, Durban, South Africa
| | - Etinosa O Igbinosa
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa; Department of Microbiology, Faculty of Life Sciences University of Benin, Private Mail Bag 1154, Benin City, 300283, Nigeria
| | - Anthony I Okoh
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa; Department of Environmental Health Sciences, College of Health Sciences, University of Sharjah, Sharjah, P.O. Box 27272, United Arab Emirates
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Lan X, Lin W, Ning Z, Su X, Chen Y, Jia Y, Xiao E. Arsenic shapes the microbial community structures in tungsten mine waste rocks. ENVIRONMENTAL RESEARCH 2023; 216:114573. [PMID: 36243050 DOI: 10.1016/j.envres.2022.114573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/29/2022] [Accepted: 10/08/2022] [Indexed: 06/16/2023]
Abstract
Tungsten (W) is a critical material that is widely used in military applications, electronics, lighting technology, power engineering and the automotive and aerospace industries. In recent decades, overexploitation of W has generated large amounts of mine waste rocks, which generate elevated content of toxic elements and cause serious adverse effects on ecosystems and public health. Microorganisms are considered important players in toxic element migrations from waste rocks. However, the understanding of how the microbial community structure varies in W mine waste rocks and its key driving factors is still unknown. In this study, high-throughput sequencing methods were used to determine the microbial community profiles along a W content gradient in W mine waste rocks. We found that the microbial community structures showed clear differences across the different W levels in waste rocks. Notably, arsenic (As), instead of W and nutrients, was identified as the most important predictor influencing microbial diversity. Furthermore, our results also showed that As is the most important environmental factor that regulates the distribution patterns of ecological clusters and keystone ASVs. Importantly, we found that the dominant genera have been regulated by As and were widely involved in As biogeochemical cycling in waste rocks. Taken together, our results have provided useful information about the response of microbial communities to W mine waste rocks.
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Affiliation(s)
- Xiaolong Lan
- School of Chemistry and Environmental Engineering, Hanshan Normal University, Chaozhou, 521041, China
| | - Wenjie Lin
- School of Chemistry and Environmental Engineering, Hanshan Normal University, Chaozhou, 521041, China.
| | - Zengping Ning
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, 550081, China
| | - Xinyu Su
- School of Chemistry and Environmental Engineering, Hanshan Normal University, Chaozhou, 521041, China
| | - Yushuang Chen
- School of Chemistry and Environmental Engineering, Hanshan Normal University, Chaozhou, 521041, China
| | - Yanlong Jia
- School of Chemistry and Environmental Engineering, Hanshan Normal University, Chaozhou, 521041, China
| | - Enzong Xiao
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, China.
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