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).

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).

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.

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).

The results of the analysis of the assessment of the risk of bias of all the articles reviewed are shown in the plot below:

3.3.1 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).

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).

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.

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).

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).

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).

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).

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).

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).

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 review shares some of its limitations as: •

Limited scope in less industrialized or rural regions may reduce the applicability of findings.