Impact of industrial wastewater on the microbiome and resistome of waterbodies: a systematic review

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Magehembe" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Industrial wastewater significantly affects the microbiome and resistome of aquatic ecosystems, posing environmental and public health risks through the dissemination of antibiotic resistance genes (ARGs). Methods This study systematically reviewed the influence of industrial effluents on microbial community structure and ARG prevalence in receiving water bodies. Results Our findings revealed that Proteobacteria and Bacteroidetes dominate polluted environments, with beta-lactamases, macrolides, and sulfonamide resistance genes that are frequently detected. Mobile genetic elements (MGEs), including integrons and plasmids, are critical drivers of ARG horizontal gene transfer. The limited efficiency of wastewater treatment plants (WWTPs) in removing ARGs highlights the urgent need for advanced treatment technologies. Comparisons with the existing literature reinforce the role of industrial activities in exacerbating ARG risks and disrupting microbial diversity. Mitigation strategies including stringent regulatory frameworks, advanced filtration methods, and a one-health approach are recommended to address these challenges. Conclusion This study emphasizes the urgent need for interdisciplinary collaboration and targeted interventions to safeguard environmental and public health from the impacts of industrial wastewater pollution. 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F1000Research 2025, 14 :603 ( https://doi.org/10.12688/f1000research.164918.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Systematic Review Impact of industrial wastewater on the microbiome and resistome of waterbodies: a systematic review [version 1; peer review: awaiting peer review] Olukayode Adebola Ibitoye https://orcid.org/0000-0002-9688-8045 1 , Abdulganiy Babatunde Agbaje 1 , Chinyere Anyanwu 1 , [...] Ilemobayo Victor Fasogbon 2 , Reuben Samson Dangana https://orcid.org/0000-0002-1077-3782 3 , Saheed Adekunle Akinola 4 , Reuben S. Magehembe 5 Olukayode Adebola Ibitoye https://orcid.org/0000-0002-9688-8045 1 , Abdulganiy Babatunde Agbaje 1 , [...] Chinyere Anyanwu 1 , Ilemobayo Victor Fasogbon 2 , Reuben Samson Dangana https://orcid.org/0000-0002-1077-3782 3 , Saheed Adekunle Akinola 4 , Reuben S. Magehembe 5 PUBLISHED 19 Jun 2025 Author details Author details 1 Microbiology and Immunology, Kampala International University - Western Campus, Bushenyi, Western Region, Uganda 2 Biochemistry, Kampala International University - Western Campus, Bushenyi, Western Region, Uganda 3 Discipline of Life Science, University of KwaZulu-Natal - Westville Campus, Durban, KwaZulu-Natal, South Africa 4 Microbiology and Parasitology, University of Rwanda School of Medicine and Pharmacy, Kigali, Kigali City, Rwanda 5 Department of Microbiology & Parasitology, Faculty of Medicine, St. Francis University College of Health and Allied Science, Ifakara, Tanzania Olukayode Adebola Ibitoye Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Abdulganiy Babatunde Agbaje Roles: Supervision, Writing – Review & Editing Chinyere Anyanwu Roles: Supervision, Writing – Review & Editing Ilemobayo Victor Fasogbon Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Reuben Samson Dangana Roles: Data Curation, Formal Analysis, Investigation Saheed Adekunle Akinola Roles: Supervision, Writing – Review & Editing Reuben S. Magehembe Roles: Conceptualization, Supervision OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW Abstract Background Industrial wastewater significantly affects the microbiome and resistome of aquatic ecosystems, posing environmental and public health risks through the dissemination of antibiotic resistance genes (ARGs). Methods This study systematically reviewed the influence of industrial effluents on microbial community structure and ARG prevalence in receiving water bodies. Results Our findings revealed that Proteobacteria and Bacteroidetes dominate polluted environments, with beta-lactamases, macrolides, and sulfonamide resistance genes that are frequently detected. Mobile genetic elements (MGEs), including integrons and plasmids, are critical drivers of ARG horizontal gene transfer. The limited efficiency of wastewater treatment plants (WWTPs) in removing ARGs highlights the urgent need for advanced treatment technologies. Comparisons with the existing literature reinforce the role of industrial activities in exacerbating ARG risks and disrupting microbial diversity. Mitigation strategies including stringent regulatory frameworks, advanced filtration methods, and a one-health approach are recommended to address these challenges. Conclusion This study emphasizes the urgent need for interdisciplinary collaboration and targeted interventions to safeguard environmental and public health from the impacts of industrial wastewater pollution. READ ALL READ LESS Keywords Microbiome, Resistome, Industrial Effluents, Pollution, Antibiotic Resistance, Environmental Microbiology, Anthropogenic activities, Wastewater treatment plant Corresponding Author(s) Olukayode Adebola Ibitoye ( [email protected] ) Close Corresponding author: Olukayode Adebola Ibitoye Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Ibitoye OA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Ibitoye OA, Agbaje AB, Anyanwu C et al. Impact of industrial wastewater on the microbiome and resistome of waterbodies: a systematic review [version 1; peer review: awaiting peer review] . F1000Research 2025, 14 :603 ( https://doi.org/10.12688/f1000research.164918.1 ) First published: 19 Jun 2025, 14 :603 ( https://doi.org/10.12688/f1000research.164918.1 ) Latest published: 19 Jun 2025, 14 :603 ( https://doi.org/10.12688/f1000research.164918.1 ) 1. Introduction Globally, urban rivers are progressively being subjected to significant pressures arising from anthropogenic activities, prominently including industrial wastewater effluents ( Akhtar et al., 2021 ). Some examples of these contaminants are heavy metals, biocides, and dyes are capable of disrupting the aquatic ecosystems, changing the microbiome structure, and creating a suitable environment for the proliferation of antibiotic-resistant bacteria and their respective resistomes, such as beta-lactamase resistance genes (blaOXA, blaCTX); tetracycline resistance genes (tetA, tetM); sulfonamide resistance (sul1, sul2); tetracyclines resistance (tetC, tet39); macrolide resistance (mphG, msrE); Mobile Genetic Elements: IncP-1 plasmids, class 1 integrons ( Lu et al., 2022 ). The rising global public health concern associated with antimicrobial resistance (AMR) has spotlighted urban water systems as reservoirs through which Antibiotic-Resistant Genes are disseminated ( Ogunlaja et al., 2022 ). Industrial effluents, also known as industrial wastes such as chemical wastewater, textile industrial waste (dyes, pigments, synthetic fibers), pharmaceutical wastewater containing residues of different antibiotics, organic solvents, heated water from thermal industries, and many more are unwanted liquid wastes released from industrial processes, which immensely contribute to the environment as pollutants. These pollutants or effluents have diverse origins, including chemical, agricultural, pharmaceutical, and textile to mention a few ( Kishor et al., 2021 ). Chemical and pharmaceutical wastes released from effluents from these industries are often heavily laden with high concentrations of heavy metals, xenobiotics, and organic matter ( Gupta et al., 2022 ). Agricultural or agro-allied industries contribute fertilizers, pesticides, and nutrients, such as phosphorous, nitrogen, potassium, and many more, as constituents of these environmental pollutants ( Irewale et al., 2024 ). Many developing and underdeveloped regions of the world do not adequately ensure proper treatment of effluents due to insufficient infrastructure, thereby exacerbating the condition of aquatic ecosystems into which the effluents are finally channeled, resulting in grave consequences for water quality and biodiversity ( Singh et al., 2023 ). Industrial wastewater contains an array of compounds, such as biocides, heavy metals, pharmaceuticals, and personal care products, which play crucial roles in enhancing the selection of ARGs and other resistomes within microbial communities ( Uluseker et al., 2021 ). These resistomes have been reported to be transferred to human pathogens via horizontal gene transfer, resulting in the possibility of eliciting prominent public health risks ( Foxman et al., 2024 ). In many low-resource countries of the world, there is weak or poor enforcement of regulations guiding wastewater treatment; thus, the problem of AMR and its resistomes has been significantly exacerbated ( Mateo-Sagasta et al., 2018 ; Fouz et al., 2020 ). The major focus has been on the aftermath of human health implications of clinically associated AMR, with little or no concern for its spread and environmental reservoirs such as urban rivers ( Reddy et al., 2022 ). The importance of the impact of industrial wastewater on the microbiome and resistome of these rivers has recently been elucidated ( Donchev et al., 2024 ). In most industrialized continents such as Europe, South America, and Asia, which are hotspots of antibiotic-resistant bacteria due to their highly industrialized and urbanized nature, coinciding with inadequate wastewater management, there has been an increase in ARGs to a fold of 100 to 1000 in regions of close proximity to industrial effluents ( Attrah et al., 2024 ). Metagenomic sequence analysis was used to track the resistome profiles of urban rivers found in Sao Paulo (Brazil) and Guangzhou province in China to examine the impact of wastewater effluents, revealing that beta-lactamase genes were prevalent, with the abundance of ARGs being immensely increased downstream of the wastewater treatment plant ( Lira et al., 2021 ; Zhuang et al., 2021 ). Michaelis and Grohmann (2023) also recognized the primary role of horizontal gene transfer between bacterial species caused by biofilms associated with industrial wastewater carrying high levels of antibiotics such as quinolones and tetracyclines. Wastewater treatment plants (WWTPs) play a significant role in mitigating pollution; however, their current capacity to eliminate antibiotic resistance genes (ARGs) remains insufficient, posing significant risks to both river ecosystems and public health ( Hong et al., 2018 ; Sabar et al., 2023 ). The release of unchecked industrial effluents into water bodies has created hotspots for microbial adaptation, facilitating the growth and dissemination of ARGs ( Bobate et al., 2023 ). Pollutants such as antibiotics, heavy metals, and other xenobiotics released from industrial activities have been identified as key drivers of the emergence of multidrug-resistant bacterial strains, exacerbating the global antimicrobial resistance (AMR) crisis ( Anand et al., 2021 ). To address this threat, it is imperative to understand the impact of industrial effluents on the microbiome and resistome of aquatic ecosystems ( Zambrano, 2023 ). A systematic evaluation of the relationship between microbial communities and industrial pollutants can provide critical insights into the mechanisms driving the development of resistance. Such an understanding is essential for informing policy decisions and promoting sustainable waste-management practices. This systematic review aimed to assess the impact of industrial effluents on the microbiome and resistome of water bodies, contributing to a broader effort to mitigate the environmental and public health risks associated with AMR. 2. Methods 2.1 Search strategies A comprehensive literature search of published articles on the impact of industrial wastewater on the microbiome and resistome of waterbodies was carried out using the Web of Science, Scopus, and PubMed databases on the 13 th October 13, 2024. These three databases provide a robust foundation for capturing the most relevant and reliable literature for this review. The following search terms were used: “industrial”, “wastewater”, “microbiome”, “resistome”, and “water bodies”. Delimiters such as Boolean operators (AND/OR), quotation marks, parentheses wildcards, and asterisks (*) were used to combine the search terms ( Fasogbon et al., 2022 , 2023 , 2024 ) to attach the search strategy presented in Table 1 . To achieve a robust search outcome, the search field was limited to “Title, abstract, and keywords” in the databases. The records identified by the search strategy were downloaded for screening using pre-set inclusion/exclusion criteria ( Table 2 ). A systematic article selection process comprising title, abstract, and full-text screening was sequentially performed by two independent screeners in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guideline ( Page et al., 2021 ; Mustapha et al., 2022 ). Table 1. Keywords used in the WoS, Scopus and PubMed databases. Database Search strategy Search field WoS (industr*) AND (“waste water” OR waste-water OR wastewater OR effluent) AND (microbiome OR resistome) AND (river OR stream OR freshwater OR dam OR “water bod*”) Topic Scopus (industr*) AND (“waste water” OR waste-water OR wastewater OR effluent) AND (microbiome OR resistome) AND (river OR stream OR freshwater OR dam OR “water bod*”) Title, Abstract PubMed ((industr*[title/abstract]) AND ((wastewater [title/abstract]) OR waste water [title/abstract]) OR waste-water [title/abstract])) AND ((microbiome [title/abstract]) OR (resistome [title/abstract])) AND ((river [title/abstract]) OR (stream [title/abstract]) OR (freshwater [title/abstract]) OR (dam [title/abstract]) OR (“water bod*”[title/abstract])) Title/Abstract Table 2. Inclusion and exclusion criteria. Inclusion criteria Exclusion criteria Studies focusing on water bodies (rivers, lakes, ponds, estuaries) receiving industrial effluents or wastewater discharge. Studies on protected water bodies that are not impacted by industrial effluent. What bodies that are safe guarded from human or anthropogenic activities Studies that focus on water contamination by industrial effluent. Studies that focus on water contamination by agricultural runoff, urban sewage, or natural factors, without any industrial effluent component were excluded. Research assessing changes in microbial community composition (microbiome) and presence of antibiotic resistance genes (resistome) in response to industrial effluents. Studies that do not assess microbial communities or antibiotic resistance, e.g., those focusing solely on chemical contaminants, without considering biological effects . Peer-reviewed primary research studies, observational studies, and experimental research that use molecular, metagenomic, or microbial ecology techniques to analyze the microbiome and resistome. Systematic reviews, meta-analyses, opinion papers, and editorials that do not present new, original research subjects were excluded. Studies published in English. Studies published in languages other than English. 2.2 Data extraction All essential data from the eligible studies were extracted and analyzed. The extracted data included publication information (author and year of publication), geographical location, type of water body, type of industry, effluent composition, microbial community composition, microbial diversity, technique(s) used, resistome (ARG, MGEs) type, ARG abundance, mechanism of resistance, water quality, and outcome (ecological impact and health implication) ( Tables 3 , 4 , 5 ). Table 3. Effluent Composition by the type of water body. S/N Geographical location Water body Type of industry Effluent composition Reference 1. Austria,Czech Republic, France, Germany,Hungary, Italy,Norway,Poland, Romania, Slovakia, Spain, Sweden, Switzerland 274 lakes Not specified Heavy metals, antibiotics (tetracyclines, cephalosporins, quinolones, sulfonamides), and other pollutants Spänig et al., 2021 2. China Freshwater lake Printing, dyeing, pharmaceutical, mechanical processing, food and beverage industries Heavy metals (e.g., Pb), antibiotics (e.g., aminoglycosides), environmental stressors Ding et al., 2020 3. Croatia Creek sediment Pharmaceuticals, plant protection products Trimethoprim (up to 5.08 mg/kg), heavy metals (cadmium, chromium, copper, lead, nickel, zinc), nitrate, ammonia, phosphorus, azithromycin (up to 0.39 mg/kg), sulfonamides (up to 1.17 mg/kg) Milaković et al., 2019 4. China WWTP treating pharmaceutical effluent Pharmaceutical Antibiotics, particularly beta-lactam antibiotics Jiang et al., 2019 5. Bolivia Lake Clothing factories, tanneries, car-washing facilities, untreated domestic sewage Heavy metals (sodium, manganese, lead, cadmium, zinc); total organic nitrogen, orthophosphate, carbonate, bicarbonate, chloride, high BOD5 and COD Quillaguamán et al., 2021 6. India River Ganga, India Not specified Pollutants and toxic substances altering microbial dynamics Rout et al., 2023 7. Bulgaria Freshwater river Municipal WWTP High levels of antibiotic resistance genes (macrolide and tetracycline resistance genes, sulfonamide resistance genes, carbapenem resistance genes) Donchev et al., 2024 8. USA Lakes, rivers, and WWTP influent samples Wastewater treatment facilities Detected ARGs including genes resistant to beta-lactams, tetracyclines, sulfonamides, macrolides, others Stedtfeld et al., 2016 9. Not specified River Pharmaceutical Antibiotic residues, metals, organic pollutants; contributes to antibiotic resistance Wang et al., 2024 10. Croatia Not specified Pharmaceutical & Food industry High concentrations of macrolide antibiotics, heavy metals, nutrients Milaković et al., 2019 11. Nigeria Freshwater stream Textile industry Heavy metals (Hg, As, Pb, Cu), organic pollutants, high COD, BOD, salinity Odubanjo et al., 2021 12. India Synthetic textile effluent Textile industry Contains Azo dyes, harmful by-products (aromatic carcinogenic and mutagenic amines) Krishnaswamy et al., 2020 13. USA Creek Poultry Processing Plant Antibiotics (macrolides, tetracyclines), nitrogen compounds (ammonia, nitrate) Semedo et al., 2023 14. Italy Lake and River Wastewater treatment plants (WWTPs) Varied, involving chlorination, peracetic acid, and UV radiation disinfection treatments Corno et al., 2019 15. Croatia River & Stream Pharmaceutical industries High concentrations of antibiotics (macrolides, sulfonamides, tetracyclines, fluoroquinolones) González-Plaza et al., 2018 Table 4. Mechanism of resistance of ARG & MGEs. S/N Geographical location ARG MGEs types ARG abundance Mechanism of Resistance Microbial community composition Threats to environment and public health Reference 1. Austria, Czech Republic, France, Germany, Hungary, Italy, Norway, Poland, Romania, Slovakia, Spain, Sweden, Switzerland ARGs for tetracyclines, cephalosporins, quinolones, sulfonamides Not specified Drug efflux and gene mutations (e.g., gyrA, gyrB for fluoroquinolone resistance) Mycobacterium, Acinetobacter, Pseudomonas, Staphylococcus microbial community structures in lakes, with potential impacts on ecological balance. potential health risks posed by resistant bacteria in water bodies Spänig et al., 2021 2. China Aminoglycoside, beta-lactamases, multidrug resistance genes; MGEs (intI-1 integrons) High levels in ISTP influents Antibiotic deactivation, efflux pumps, cellular protection mechanisms Proteobacteria, Bacteroidetes, Chloroflexi present shifts in microbial community structure and reduced overall biodiversity. The persistence of antibiotic resistance genes in effluents poses potential risks to human and animal health. Ding et al., 2020 3. Croatia Macrolide-resistance genes (mph, mef, msr, erm); class 1 integrons Increased near discharge points (DW0) Both intrinsic and acquired resistance genes, enhanced by HGT under antibiotic and heavy metal pressures Proteobacteria, Bacteroidetes, Firmicutes, Epsilonbacteraeota; genera: Trichococcus, Thauera, Arcobacter, Pseudomonas Industrial effluents had a significant impact on the microbial community effect. Human and Animal Health potential risks of exposure to multi-resistant pathogens in environments affected by pharmaceutical effluent discharges Milaković et al., 2019 4. China Beta-lactam resistance (bla_CTX−M); aminoglycoside resistance (aadA1, aac(6′)-lb-cr); sulfonamide resistance (sul2, sul1) High ARG presence in E. coli, linked to mobile genetic elements Beta-lactamase production, efflux pumps, plasmid-based resistance ESBL-producing E. coli strains resistant to multiple antibiotics antibiotic-resistant bacteria and ARGs, impacting microbial ecology by promoting antibiotic resistance within microbial communities. The presence of virulence and antibiotic resistance genes in E. coli from the WWTP presents a potential risk to human health, Jiang et al., 2019 5. Bolivia Resistance genes: fluoroquinolones, tetracyclines, macrolides, beta-lactams, phenicols, rifamycin High resistance: 277 ARGs in water, 150 ARGs in sediment Efflux pumps, plasmid-mediated resistance, multidrug-resistance genes Proteobacteria dominant in water and sediment; notable strains: Pseudomonas aeruginosa, Acinetobacter spp. Stenotrophomonas maltophilia, and Nocardia spp.. Ecological Shifts in microbial communities due to pollution, with pathogenic and antibiotic-resistant impacting ecosystem health and biodiversity. Quillaguamán et al., 2021 6. India Aminoglycoside, Streptomycin, Beta-lactam, and various others such as cephalosporin and carbapenem resistance genes High variability across regions, with significant resistance in Rasulabad Ghat and Triveni Sangam sites Multiple mechanisms identified, including genes like acrB associated with multidrug resistance and various beta-lactamase genes Proteobacteria were the dominant phylum, with notable species like Flavobacterium spp. and Pseudomonas spp. in specific areas significant changes in microbial composition due to pollution, with implications for nutrient cycling and microbial diversity​. High levels of antibiotic resistance genes pose risks for public health, Rout et al., 2023 7. Bulgaria Resistance genes: fluoroquinolones, tetracyclines, macrolides, beta-lactams, phenicols, rifamycin High resistance: 277 ARGs in water, 150 ARGs in sediment Efflux pumps, plasmid-mediated resistance, multidrug-resistance genes Flavobacterium, Acidovorax, Polynucleobacter, and Limnohabitans. Polynucleobacter more abundant downstream and Flavobacterium declining over time. The effluent influenced microbial community structure, favoring ARG-enriched taxa near the WWTP, including non-native genera linked to human microbiota (e.g., Prevotella, Blautia). The presence of ARGs like blaOXA-58, commonly found in clinical settings, poses potential risks to human health Donchev et al., 2024 8. USA ARGs include beta-lactamase genes (blaOXA, blaCTX), tetracycline resistance genes (tetA, tetM), and mobile genetic elements (intI1). Elevated ARG abundance in wastewater influent, with genes like intI1 associated with higher ARG concentrations Resistance mechanisms detected include efflux pumps, enzymatic degradation, and target modification Not specified Increased ARGs in surface water linked to anthropogenic pollution, posing ecological risks by spreading resistance in natural microbial communities. ARG presence indicates potential health risks due to the dissemination of resistance genes from treated wastewater into natural water bodies. Stedtfeld et al., 2016 9. Not specified Primarily ARGs related to antibiotic resistance commonly found in wastewater, though exact types like specific gene families are not detailed. The abundance of ARGs and their proliferation in response to pollution is observed, particularly due to WWTP effluents, but lacks quantitative data. The study may cover mechanisms like efflux pumps, enzymatic degradation, and target alteration, typical of ARG studies, though not specified microbial diversity changes associated with antibiotic resistance, especially highlighting the presence of ARG-bearing microbes and potential pathogens. significant ecological impacts, including disruptions to natural microbial ecosystems in affected rivers, but lacks specific impact metrics. spread of ARGs into human pathogens, representing an increasing risk to public health through enhanced antibiotic resistance Wang et al., 2024 10. Croatia Primarily ARGs related to antibiotic resistance commonly found in wastewater, though exact types like specific gene families are not detailed. The abundance of ARGs and their proliferation in response to pollution is observed, particularly due to WWTP effluents, but lacks quantitative data. efflux pumps, enzymatic degradation, and target alteration, typical of ARG studies, though not specified Proteobacteria, Bacteroidetes, Firmicutes, and Epsilonbacteraeota. Notable genera include Trichococcus, Thauera, Arcobacter, Pseudomonas, and Comamonas​​​. Effluent discharge altered the bacterial community structure and enriched specific ARGs in river sediments, impacting sediment quality and biodiversity​. Potential risks to human and environmental health due to antibiotic resistance Milaković et al., 2019 11. Nigeria Detected macrolide-resistance genes include mph, mef, msr, and erm. Class 1 integrons were also found in effluent-impacted sediments Increased relative abundance of macrolide-resistance genes and class 1 integrons near discharge points (DW0) Presence of both intrinsic and acquired resistance genes, facilitated by horizontal gene transfer under environmental pressures from antibiotics and heavy metals Bathyarchaeota and Euryarchaeota among Archaea, Proteobacteria, Bacteroidetes among Bacteria, and Eukarya, Fungi Significant shifts in microbial taxa composition due to high heavy metal concentrations and organic pollutants. Potential risks from bioaccumulation of heavy metals in the ecosystem, affecting human and environmental health Odubanjo et al., 2021 12. India Genes for mobile elements, transposable elements, tRNA genes 16% of total genes linked to mobile genetic elements and resistance Transposable elements, xenobiotic degradation pathways for microbial adaptation Proteobacteria, Actinobacteria, Terrabacteria; significant order: Burkholderiales microbial degradation of pollutants in textile effluent, contributing to ecosystem detoxification​. Potential health risks that could affect human and animal health if released untreated. Krishnaswamy et al., 2020 13. USA Tetracycline (classes A, B, C, D, E, G, H, O), macrolide (MacA, MacB), streptogramin, beta-lactam, and aminoglycoside resistance genes; class 1 integron (intI1) used as a contamination marker Higher ARG abundance in impacted creek, particularly for genes conferring resistance to macrolides and tetracyclines Efflux pumps (MacAB transporter for macrolides), acetyltransferase (streptogramin resistance) Elevated levels of antibiotic-resistant bacteria in the impacted creek, with notable ARG types associated with antibiotics used in poultry production . Higher nitrogen in impacted creek, which may contribute to eutrophication. Increased ARGs and mobile genetic elements (MGEs) raise concerns over potential antibiotic resistance posing risks to environmental and human health. Semedo et al., 2023 14. Italy Beta-lactams (blaTEM, blaOXA), aminoglycosides (aac, aad, aph), macrolides (ermB, vatA), chloramphenicol (cat), tetracyclines (tetA), quinolones (qnrS) 350 ARGs; ARGs increased in WWTP effluent, co-selected with heavy metals Efflux pumps, enzymatic degradation, target alteration Aeromonas, Arcobacter, Deinococcus; receiving water taxa: Pseudomonas, Limnohabitans High WWTP effluent concentration led to a stabilized resistome in natural freshwater, potentially enhancing ARG propagation. Persistent ARGs pose indirect risks to human health, emphasizing the need for wastewater treatment improvements targeting resistome reduction. Corno et al., 2019 15 Croatia Macrolide, sulfonamide, tetracycline, beta-lactam, trimethoprim resistance; ARGs like blaGES-1, blaVEB-9 High ARG abundance in sediments influenced by antibiotic effluents Efflux pumps, ribosomal protection proteins, antibiotic-inactivating enzymes Macrolide-Resistant Bacteria: Colwellia chuchiensis, Klebsiella pneumoniae and Acinetobacter baumannii, A. baumannii and K. pneumoniae, Emergencia timonensis The effluent discharge contributes to environmental reservoirs of ARGs in sediments, with a potential spread of resistance through aquatic ecosystems. The environment impacted by antibiotic discharges could serve as a reservoir for ARGs that may transfer to human pathogens, raising public health concerns González-Plaza et al., 2018 Table 5. Techniques used for analysing Microbial communities and Resistomes (ARG & MGEs). S/N Geographical location Technique (s) used References 1. Austria, Czech Republic, France, Germany, Hungary, Italy, Norway, Poland, Romania, Slovakia, Spain, Sweden, Switzerland 16S rRNA, Short gun Metagenomic sequencing Spänig et al., 2021 2. China High-throughput qPCR (Resistome) and high-throughput 22 Illumina sequencing (Bacterial community structure) Ding et al., 2020 3. Croatia 16S rRNA gene amplicon sequencing, Quantitative PCR (qPCR) for ARG quantification, Network analysis to reveal ARG-bacterial host correlations Milaković et al., 2019a 4. China Whole Genome Sequencing and metagenomic sequencing using Illumina HiSeq for microbial and resistome analysis Jiang et al., 2019 5. Bolivia Shotgun metagenome sequencing and 16S rRNA gene analysis. Quillaguamán et al., 2021 6. India Metagenomic analysis, including taxonomic profiling with Kraken2 and Pavian, and functional analysis with PROKKA, CARD, and other bioinformatics tools Rout et al., 2023 7. Bulgaria shotgun metagenomics, employing Kraken2/Bracken for taxonomic classification, differential abundance analysis via ANCOM-BC, and additional diversity metrics analysis using tools such as ResistoXplorer and Qiime 2 Donchev et al., 2024 8. USA qPCR arrays targeting 296 ARG primers, metagenomic sequencing, and network analysis. Stedtfeld et al., 2016 9. Not specified Metagenomics sequencing and analysis for ARG and microbial community Wang et al., 2024 10. Croatia Amplicon sequencing of 16S rRNA genes (Microbial community), redundancy analysis (RDA), and Mantel tests to evaluate community shifts and correlations with environmental factors and QPCR for genes Milaković et al., 2019b 11. Nigeria Illumina MiSeq platform, OTU clustering, and diversity indices calculations (e.g., Shannon and Simpson indexes) Odubanjo et al., 2021 12. India Metagenomic profiling using nanopore sequencing, 16S rRNA gene primers, and bioinformatics analysis with Kaiju web server for taxonomic classification. Krishnaswamy et al., 2020 13. USA Metagenomic sequencing with Illumina HiSeq, qPCR for ARGs and integron markers Semedo et al., 2023 14. Italy Metagenomics, qPCR, and flow cytometry Corno et al., 2019 15. Croatia Functional metagenomics, culturing of sediment bacteria, and metagenomic library screening González-Plaza et al., 2018 2.3 Risk of bias analysis The Risk of Bias Visualization [Robvis] tool was used in this systematic review to methodically assess and display any potential bias in the included papers. 3.0 Results 3.1 Search results The Web of Science search returned 23 articles, Scopus search returned 22 articles, and PubMed returned six articles, totaling 51 articles from the three databases. The results from each database were exported and uploaded to Rayyan ( Ouzzani et al., 2016 ), a platform for a systematic review methodology, to screen articles based on the inclusion and exclusion criteria. Twenty-one (21) articles were deleted because of duplicate entries from the databases. The thirty (30) records were initially screened by their titles and abstracts (six articles were screened out). A more rigorous full-text screening identified nine (9) other articles that did not meet the inclusion criteria. A total of fifteen (15) articles were excluded in accordance with the eligibility criteria, and fifteen (15) articles that met the eligibility criteria were reviewed in the study ( Figure 1 ). Figure 1. PRISMA study selection flow chart. 3.2 Risk of bias analysis The results of the analysis of the assessment of the risk of bias of all the articles reviewed are shown in the plot below: 3.3 Summary of the characteristics of the included studies 3.3.1 Publication information (Authors and years of publications) Fifty-one articles were retrieved from the search for this study were 51, of which 15 were included in this study, which followed all the necessary inclusion and exclusion criteria. The articles selected based on the inclusion and exclusion criteria included in this study were research conducted by the authors, span between year 2016-2024 ( Figure 2 ). Figure 2. Risk of bias analysis of the included articles. 3.3.2 The publication trajectory The graph shows modest output between 2016 and 2018, whereas there was a progression in the number of publications in 2019 and 2021, as shown in the graph. This increase in the number of publications depicts an increase in research output, with significant peaks recorded in 2019 and 2021. In 2019, the highest peak was recorded with a research output of 4 ( Milaković et al., 2019a , b ; Corno et al., 2019 ; Jiang et al., 2019 ) followed by three ( Quillaguamán et al. (2021) , Spänig et al. (2021) , and Odubanjo et al. (2021) ) publications in 2021. This peak could be a result of interest in the research topic, scientific advancement, and increased awareness or funding. The other years recorded modest results with single publications in 2016 and 2018, whereas two were recorded for 2023 and 2024 ( Figure 3 ). Figure 3. Number of articles per year per author. 3.3.3 Type of water bodies analysed There were different industrial wastewater effluents involved, which were either directly from the industry’s wastewater treatment plant or through municipal wastewater treatment plant eluffents, which were chanelled into different freshwater bodies (rivers, creeks, and lakes), while others examined the studied parameters directly from the water bodies. The studies that explicitly included wastewater from industries made up of 10/15 included large studies ( Table 3 ) conducted by Spänig et al. (2021) , examining 274 water bodies across 13 European countries. The remaining studies were those from municipal wastewater treatment plants 3/15, river 1/15, which both comprise industrial effluents chaneled into them. 3.4 Geographical location of included studies The studies included for this review were as follows: Europe: (n= 7) Seven out of the 15 studies included in this review were from 15 European countries, of which 13 (Austria, Czech Republic, France, Germany, Hungary, Italy, Norway, Poland, Romania, Slovakia, Spain, Sweden, and Switzerland) and 274 lakes were examined by Spänig et al. (2021) , while creek and freshwater rivers were examined in Croatia and Bulgaria by Semedo and Song (2023) and Donchev et al. (2024) , respectively ( Table 5 ). North and South America (n= 3) Two out of the 15 studies were from North America (USA) and one out of 15 studies from South America (Bolivia) examined lake, river, and wastewater treatment plant influent samples ( Stedtfeld et al., 2016 ); Creek ( Semedo and Song, 2023 ); and Lake ( Quillaguamán et al., 2021 ) respectively ( Table 5 ). East Asia: (n = 4) Four studies from Asia, two from China, and two from India examined freshwater lakes ( Ding et al., 2020 ); wastewater treatment plants effulent of pharmaceuticals ( Jiang et al., 2019 ) and rivers ( Rout et al., 2023 ; Wang et al., 2024 ) ( Table 5 ). Sub-Saharan Africa (n= 1) One study from Africa was from Nigeria, and the freshwater stream that received wastewater treatment plant effluents from the textile industry was examined by Odubanjo et al. (2021) ( Table 5 ). 3.5 Microbiome and resistome community Studies on the impact of industrial wastewater on the microbiome and resistome communities of water bodies were published between 2016-2024 ( Figure 3 ). The largest sample sizes were reported by Spänig et al. (2021) , which were conducted in 13 European countries with 274 lakes. Of all the arrays of microorganisms discovered from the 15 studies of different industrial wastewater, municipal wastewater, and lakes, eight studies identified Proteobacteria, Bacteroidetes as the most dominant microbial taxa among the array of genera found within the microbial communities ( Table 4 ). Spänig et al. (2021) reported the presence of Mycobacterium, Acinetobacter, Pseudomonas, and Staphylococcus genera in 274 lakes sampled in 13 European countries, representing the largest of all the water bodies examined. Microbial diversity varied as measured by the Shannon and Simpson indices. Ding et al. (2020) reported that industrial influents have shown a higher diversity, whereas in the industrial sewage treatment plants lower region, compared to municipal Sewage Treatment Plants, Milaković et al. (2019a) reported no significant effects on the overall bacterial diversity based on Shannon-Wiener for alpha diversity but significant alteration in the bacterial structure based on Bray-Curtis dissimilarity between the UP, DW0, and DW3000 sites, lower microbial diversity with a focus on Extended Spectrum Betalactamase strains due to industrial contaminants ( Jiang et al., 2019 ), observable shifts in taxa relative to pollution levels ( Quillaguamán et al., 2021 ), diverse bacterial taxa with regional variation, particularly influenced by human and environmental factors ( Rout et al., 2023 ), increased microbial richness and evenness with no significant difference observed between the upstream and downstream over a period of time ( Donchev et al., 2024 ). The microbial diversity from other studies showed a variation that ranged from stable, decreased, to increased diversity, while no record was given for Spänig et al. (2021) and Stedtfeld et al. (2016) ( Table 4 ). 3.6 Mechanism of resistance Varied mechanisms of reactions were reported in the 15 studies included for this review, with 10 studies reporting efflux pump mechanism of drug resistance, in combination with other mechanisms like mutations in genes like gyrA and gyrB (fluoroquinolone resistance) ( Spänig et al., 2021 ), antibiotic deactivation, and cellular protection mechanisms ( Ding et al., 2020 ), plasmid-mediated resistance ( Milaković et al., 2019a ), beta-lactamase production, efflux pumps, and resistance genes on plasmids ( Jiang et al., 2019 ), plasmid-mediated resistance ( Quillaguamán et al., 2021 ; Donchev et al., 2024 ) coupled with multiresistance mechanisms ( Quillaguamán et al., 2021 ), enzymatic degradation, and target modification ( Stedtfeld et al., 2016 ), enzymatic degradation, and target alteration ( Wang et al., 2024 ), acetyltransferase (streptogramin resistance) ( Semedo and Song, 2023 ), ribosomal protection proteins, and antibiotic-inactivating enzymes (e.g., macrolide phosphotransferases) ( González-Plaza et al., 2018 ). Other reported mechanisms of resistance include multidrug resistance, horizontal gene transfer ( Milaković et al., 2019b ), heavy metal natural attenuation and sequestration mechanisms ( Odubanjo et al., 2021 ), transposable elements and pathways for the degradation of xenobiotic mechanisms ( Krishnaswamy et al., 2020 ), beta-lactam resistance, and co-selection of genes and pollutants ( Corno et al., 2019 ) ( Table 4 ). 3.7 Analysis of microbiome and resistome community The microbiomes were examined using genotypic methods (n=14) and one conventional cultural method of culture of sediment of water bodies (n=1), 16S rRNA sequencing, 16S rRNA gene amplicon sequencing, short gun metagenomic sequencing, and Whole Genome Sequencing (WGS), with the exception of González-Plaza et al. (2018) , where the microbial communities of the sediment water were analyzed using conventional methods ( Table 5 ). Similarly, many studies have analyzed resistomes using metagenomic analytical tools: high-throughput qPCR (Quantitative Polymerase Chain Reaction and ARG profiles, quantitative PCR (qPCR) for ARG quantification, and metagenomic sequencing using Illumina HiSeq for microbial and resistome analysis ( Table 5 ). 3.8 Identified microbiome and resistomes Of all the 15 studies included, 14 studies reported different ARGs, while one ( Odubanjo et al., 2021 ) suspected the presence of ARG but not precisely about it, five studies indicated the presence of MGEs ( Ding et al., 2020 ; Milaković et al., 2019a ; Stedtfeld et al., 2016 ; Semedo and Song, 2023 ) while Krishnaswamy et al. (2020) were not precise about the MGE found in the reported study. Spänig et al. (2021) represented the largest fraction of all studies; ARGs related to tetracyclines, cephalosporins, quinolones, and sulfonamides were identified in 274 lakes examined across 13 European countries. The ARGs identified in the 15 studies included teracyclines, cephalosporins, quinolones, sulfonamides, aminoglycosides, beta-lactamases, Multidrug resistance genes, macrolides, fluoroquinolones, chloramphenicol, phenicols, rifamycin, Multidrug efflux pumps, Streptomycin, Carbapenem, and tRNA, which are the predominant antibiotic resistance genes examined in synthetic textile effluent samples from India, genes coding for MGEs, transposable elements ( Krishnaswamy et al. 2020 ), and clinically significant carbapenem resistance genes blax OXA-58 and bla1MP-like genes ( Table 4 ). The MGEs that were also identified alongside the ARGs in five of the studies were Int-1 Integron ( Ding et al., 2020 ; Milaković et al., 2019a , b ; Stedtfeld et al., 2016 ; Semedo and Song, 2023 ), IncP-1 plasmids ( Milaković et al., 2019a ). The Int-1 integron and IncP-1 plasmids identified by Milaković et al. (2019a) were linked with ARG transfer, and in the study by Milaković et al. (2019b) Int-1 Integrons were found in the effulent sample of the sediment of the river in Croatia, while Semedo and Song (2023) reported Int-1 Integrons were used as contaminant markers to identify the presence of contaminants from anthropogenic sources. The abundance of Antibiotic Resistance Genes (ARGs) was identified in 13 studies, while two studies ( Spänig et al., 2021 and Odubanjo et al., 2021 ) had no reports of ARGs or MGEs either existing singly or co-existing. Six of the 13 studies identified the abundance of ARGs in the presence of MGEs in various water bodies examined. There was a high abundance of multiple ARGs with Eschericia coli strains co-existing with MGEs, such as plasmids and transposons, which enhanced the possibility of horizontal gene transfer, thus facilitating antimicrobial resistance ( Jiang et al., 2019 ), In samples collected closer to the wastewater treatment plants, tet(Q), tex(w), ErmF, and mph(E) were enriched closer to the effulent co-existing with MGEs (ISAlw 25 and Tn6082). In the location nearer to the WWTP, the relative abundance of the genes erm(B), erm(F), mph(E), msr(E) (macrolides), tet(C), tet(O), tet(W), tet(Q), and tet(X) (tetracyclines); sul1 and sul2 (sulfonamides); and cfxA3 and cfxA6 (beta-lactams) in days of susceptible sampling of the water samples, it is noteworthy that carbapenems blaOXA-58 and blaIMP-33-like genes were also identified ( Donchev et al., 2024 ), increased abundance of higher concentrations of ARGs that co-exist with IntI 1 integrin in wastewater influent ( Stedtfeld et al., 2016 ), an increase in the relative abundance of macrolide resistance genes co-existing with class 1 integrons was observed at the discharge point of the wastewater effluents of the pharmaceutical and food industries in Croatia ( Milaković et al., 2019b ), and less than 20% of the total ARGs co-existing with MGEs were reported as the abundance level associated with synthetic textile effluents treated with activated sludge from India ( Krishnaswamy et al., 2020 ). Other studies have reported that the abundance are: high in aminoglycoside and multidrug resistance genes found mainly in Industrial Sewage Treatment Plant (ISTP) influents ( Ding et al., 2020 ), higher ARG levels in sediments during the warm season, temperature being an influential factor enhancing ARGs persistence in Croatia ( Milaković et al., 2019a ), 277 ARGs in water and 150 ARGs in sediment from Bolivia ( Quillaguamán et al., 2021 ), high variability across regions with significant resistance in rivers in Rasulabad Ghat and Triveni Sangam sites in India ( Rout et al., 2023 ), pollution caused an increased in the ARGs abundance and proliferation which was significantly caused by WWTP effulents channeled into the river ( Wang et al., 2024 ), impacted creeks in USA had higher Abundance of ARGs specifically for macrolides and tetracycline resistant genes ( Semedo and Song, 2023 ), there was high ARG stability accompanied by an increase in the wastewater treatment plants effulent, 350 ARGs that are site-specific and shared genes cuts across locations in Italy ( Corno et al., 2019 ) ( Table 4 ). 3.9 Threats to environment and public health For all the studies considered in this review, there were changes in the microbial composition, structure, and diversity of the respective ecological communities, which in turn had a great impact on public health ( Table 4 ). Spänig et al. (2021) reported potential public health that arose from the contamination of water bodies examined in 13 European countries. In addition to these environmental changes, the persistence of ARGs in wastewater effluents poses a potential health risk to humans and animals ( Ding et al., 2020 ). Furthermore, in Croatia, it has been reported that there are increased risks associated with multidrug antibiotic resistance due to pharmaceutical industrial wastewater effluents ( Milaković et al., 2019a , b ), In another study conducted in the same country, it was discovered that the environment that was impacted by antibiotic resistance genes served as a reservoir for the genes that may transfer to human pathogens and become a public health treatment ( González-Plaza et al., 2018 ). Similarly, in China, it was reported that Escherichia coli associated with the wastewater treatment plant of the pharmaceutical industry poses a potential public health concern as the virulent antibiotic resistant gene associated with this microbe spreads to humans via environmental exposure because the WWTP of the Pharmaceutical industry is a significant reservoir for these antibiotic-resistant bacteria and their ARGs ( Jiang et al., 2019 ). In another study conducted in South America, Bolivia evidence suggested that the lake was the main reservoir for ARGs and pathogenic bacteria, which have an immense impact on the ecosystem and public health to the population in the environs, and it was reported that the key bacterial strain is associated with high-priority health treatment owing to the fact that they are potential contaminants of indigenous resources and pathways for human exposure ( Quillaguamán et al., 2021 ). In India, contamination associated with industrial wastewater effluents greatly impacts the ecosystem and public health owing to antibiotic-resistant genes that aggravate antimicrobial resistance menace ( Rout et al., 2023 ). In another similar study in the same country, pollutants from textile wastewater effluents impacted the ecosystems, and the presence of the high-risk pathogen Burkholderia pseudomallei was reported to pose a severe health threat to both humans and animals if released untreated into water bodies, thus constituting a public health threat to society ( Krishnaswamy et al., 2020 ). Similarly, it was reported that in Bulgaria, municipal wastewater altered the microbial diversity and ARG proliferation within the river ecosystem, and the presence of ARGs such as blac OXA-58, commonly associated with the clinical environment, poses potential risks to public health ( Donchev et al., 2024 ). In the USA, increased surface water ARGs are associated with anthropogenic activities, which are of great ecological risk, spreading resistance in natural microbial communities, and in turn greatly impacting public health due to the dissemination of these resistance genes from the treated water into other natural water bodies ( Stedtfeld et al., 2016 ). In another similar study conducted in the same country, there was higher retention of nitrogen, which led to eutrophication in the aquatic ecosystem and increased microbial resistome, which aided the propagation of antimicrobial resistance, thus impacting public health ( Semedo and Song, 2023 ). In an unspecified location, it was similarly reported that the ecosystem was affected by disruptions in the natural microbiome of the affected rivers, thus leading to the spread of ARGs into human pathogens, which is a potential risk factor to the public health of the community ( Wang et al., 2024 ). In Nigeria, it was reported that waterwater effluents from a textile industry heavily impacted by heavy metals and organic pollutants cause the bioaccumulation of heavy metals in the ecosystem, thus affecting human and environmental health due to the progressive discharge of untreated textile wastewater into the ecosystem ( Odubanjo et al., 2021 ). In a study conducted in italics, it was noted that a high WWTP effluent concentration aided the propagation of ArgG in fresh water due to the stabilized nature of this resistome, which further led to indirect risks to human health ( Corno et al., 2019 ) ( Table 4 ). 4. Discussion The findings of this study provide critical insights into the impact of industrial wastewater on the microbiomes and resistomes of aquatic ecosystems. By combining our results with the existing literature, we highlight the significant role of industrial effluents as sources of antibiotic resistance genes (ARGs) and their ability to disrupt microbial community structures. Our results showed that Proteobacteria and Bacteroidetes dominated the polluted water systems, reflecting similar findings by Zhang et al. (2021) , who observed significant alterations in microbial diversity across different industrial pollution types. This supports the notion that industrial pollutants impose selective pressure on microbial communities, thereby driving compositional shifts. Niu et al. (2022) further emphasized that long-term stress from industrial effluents forces microbial communities to adapt, potentially leading to ecological imbalances. The prevalence of ARGs, particularly beta-lactamases, macrolides, and sulfonamides, in downstream environments aligns with the findings of Sabar et al. (2023) , who demonstrated increased ARG abundance during combined sewer overflow (CSO) events. These findings highlight the role of untreated and inadequately treated wastewater in propagating clinically significant ARGs in aquatic ecosystems. Moreover, the coupling of ARGs with mobile genetic elements (MGEs), such as integrons and plasmids, is consistent with observations by Niu et al. (2022) and Attrah et al. (2024) , who emphasized the critical role of horizontal gene transfer (HGT) in facilitating ARG dissemination under pollution-induced stress. This indicates that ARG propagation is not only influenced by the composition of wastewater, but also by the genetic mobility of resistance determinants, which amplifies their environmental spread. Wastewater treatment plants (WWTPs) have emerged as a focal point in our findings and the broader literature. Although WWTPs play a critical role in mitigating pollution, their limited capacity to remove ARGs remains a significant challenge. Drane et al. (2024) highlighted the variability in ARG removal efficiency across treatment levels, with advanced methods, such as ultrafiltration, showing promise. Similarly, Su et al. (2024) reported that conventional treatment methods often failed to address the complexity of ARGs, particularly in industrial effluents. Attrah et al. (2024) also noted that although some ARG classes, such as sulfonamides, are reduced with advanced treatments, the performance of WWTPs remains inconsistent, necessitating localized assessments and tailored solutions. Environmental and public health risks associated with ARG dissemination are substantial. Our study identified ARGs linked to mobile genetic elements and high-risk pathogens, reflecting broader concerns raised by Niu et al. (2022) and Zhang et al. (2021) . These findings highlight the dual risks of ecological disruption and the public health threats posed by industrial effluents. Su et al. (2023) further emphasized the alarming presence of polymyxin resistance genes ( mcr-4 and mcr-5 ) in industrial effluent-receiving areas, underscoring the potential for these environments to become hotspots for the selection and spread of high-risk resistance genes. Addressing these challenges requires a multipronged approach. Improved wastewater treatment technologies such as membrane bioreactors and ultrafiltration can enhance ARG removal. However, as Drane et al. (2024) and Su et al. (2024) suggested, these solutions must be coupled with stringent regulatory frameworks to limit ARG release at the source. Promoting a One Health approach, as proposed by Niu et al. (2022) , encourages interdisciplinary collaboration across the environmental, public health, and industrial sectors to holistically manage ARG risks. Furthermore, reducing antibiotic and chemical inputs at their origin is essential to minimize the selective pressures that drive ARG proliferation, as emphasized by Zhang et al. (2021) and Su et al. (2023) . 4.1 Strengths The strength of this review lies in the following area: • The review consists of comprehensive literature that ensures the use of the PRISMA guidelines and multiple databases that ensure rigor and transparency. • This an interdisciplinary approach that integrates environmental microbiology, public health, and wastewater management for a holistic understanding. • Its focus is on ARGs and MGEs, which provides critical insights into the mechanisms driving antimicrobial resistance in polluted environments. • The geographical diversity of the studies offers a global perspective on this issue, enhancing the generalizability of the findings. • Practical recommendations, such as advanced treatment technologies and a one-health approach, can guide future interventions and policymaking. 4.2 Limitations This review shares some of its limitations as: • Limited scope in less industrialized or rural regions may reduce the applicability of findings. • Heterogeneity in study designs and methodologies complicates direct comparisons and generalization. • Temporal limitations and lack of long-term data may miss emerging trends or historical contexts. • Potential biases in data reporting and lack of quantitative synthesis limit statistical robustness. • A narrow focus on industrial effluents overlooks contributions from other pollution sources such as agricultural runoff or urban sewage. 5. Conclusion This study contributes to the growing body of evidence that industrial wastewater significantly affects microbial diversity and ARG dissemination. By integrating my findings with the existing literature, I highlight the urgent need for targeted interventions, including regulatory action, technological advancements, and interdisciplinary collaboration, to mitigate these impacts and safeguard both environmental and public health. This study underscores the critical importance of addressing industrial wastewater pollution as a central component of global efforts to combat antibiotic resistance. To effectively manage the impact of wastewater on the ecosystem to ensure public health safety, advanced wastewater treatments such as MBRs, ultrafiltration, ozonation, AOPs, and UV disinfection to remove ARGs, MGEs, and pollutants, while strengthening regulations with stricter limits on ARGs, heavy metals, and antibiotics should be adopted to promote one health approach, foster interdisciplinary collaboration to address AMR risks, and develop integrated policies for environmental, animal, and human health impacts. This study underscores the critical importance of addressing industrial wastewater pollution as a central component of global efforts to combat antibiotic resistance. Disclosure statement Ethics, Consent to Participate, and Consent to Publish declarations Ethical approval and consent were not required. Data availability statement Open Science Framework (OSF): Impact of Industrial Wastewater on the Microbiome and Resistome of Waterbodies: A Systematic Review, 10.17605/OSF.IO/P57RZ ( Ibitoye et al., 2025 ). This project contains the following underlying data: • Ibitoye olukayode Review paper (Data Extraction) 2.xlsx Extended data Open Science Framework (OSF): Impact of Industrial Wastewater on The Microbiome and Resistome of Waterbodies: A Systematic Review, 10.17605/OSF.IO/P57RZ ( Ibitoye et al., 2025 ). This project contains the following underlying data: • Figure 2 Risk of bias analysis of the included articles. PNG • Figure 3 Number of articles per year per author. PNG • Impact Manuscript.docx • Search Strategy.pdf Reporting guidelines Open Science Framework: PRISMA checklist and flowchart for Impact of Industrial Wastewater on the Microbiome and Resistome of Waterbodies: a systematic review” https://osf.io/p57rz/ . 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Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 19 Jun 2025 ADD YOUR COMMENT Comment Author details Author details 1 Microbiology and Immunology, Kampala International University - Western Campus, Bushenyi, Western Region, Uganda 2 Biochemistry, Kampala International University - Western Campus, Bushenyi, Western Region, Uganda 3 Discipline of Life Science, University of KwaZulu-Natal - Westville Campus, Durban, KwaZulu-Natal, South Africa 4 Microbiology and Parasitology, University of Rwanda School of Medicine and Pharmacy, Kigali, Kigali City, Rwanda 5 Department of Microbiology & Parasitology, Faculty of Medicine, St. Francis University College of Health and Allied Science, Ifakara, Tanzania Olukayode Adebola Ibitoye Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Abdulganiy Babatunde Agbaje Roles: Supervision, Writing – Review & Editing Chinyere Anyanwu Roles: Supervision, Writing – Review & Editing Ilemobayo Victor Fasogbon Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Reuben Samson Dangana Roles: Data Curation, Formal Analysis, Investigation Saheed Adekunle Akinola Roles: Supervision, Writing – Review & Editing Reuben S. Magehembe Roles: Conceptualization, Supervision Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 19 Jun 2025, 14:603 https://doi.org/10.12688/f1000research.164918.1 Copyright © 2025 Ibitoye OA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Ibitoye OA, Agbaje AB, Anyanwu C et al. 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