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Review

Shiga Toxin-Producing Escherichia coli in Aquaculture: A Decade (2015–2025)-Long Global Retrospective Outlook

by
Ayesha Sarwar
1,
Bilal Aslam
2,* and
Sulaiman F. Aljasir
2,*
1
Institute of Microbiology, Government College University Faisalabad, Faisalabad 38000, Pakistan
2
Department of Veterinary Preventive Medicine, College of Veterinary Medicine, Qassim University, Buraydah 52571, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Vet. Sci. 2026, 13(8), 779; https://doi.org/10.3390/vetsci13080779
Submission received: 1 July 2026 / Revised: 26 July 2026 / Accepted: 30 July 2026 / Published: 4 August 2026
(This article belongs to the Special Issue Veterinary Perspectives on Animal-Derived Food Systems: Bridging Safety, Quality and One Health)
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Simple Summary

Aquaculture environments have emerged as a potential source of Shiga toxin-producing Escherichia coli (STEC), posing a severe threat to global biosecurity. Driven by horizontal gene transfer, diverse and antibiotic-resistant non-O157 serotypes are rising alongside persistent, traditional O157:H7 lineages. Researchers utilize advanced phenotypic and molecular assays along with genome sequencing to map these virulence and resistance profiles. The survival of these robust pathogens throughout the supply chain demands the urgent implementation of “One Health” surveillance frameworks, which will be beneficial for global food safety.

Abstract

Shiga toxin-producing Escherichia coli (STEC) contamination and proliferation in aquaculture and aquatic systems is worrisome for global food safety, as well as veterinary and public health. Traditionally linked with terrestrial ruminant reservoirs, aquaculture matrices, including farmed finfish, shellfish, culture water, and benthic organisms, are increasingly acknowledged as potential conduits for the dissemination of STEC. Herein, the review documented data concerning the prevalence, genomic composition, and ecological dynamics of STEC across various aquaculture environments across different regions of the globe. As a result, the persistence of virulence-associated genes (VAGs) and antibiotic-resistant genes (ARGs) in STEC within the aquaculture supply chain presents a considerable risk to global food security and public health, highlighting the urgent necessity for comprehensive “One Health” surveillance frameworks aimed at alleviating aquatic biosecurity challenges.

1. Introduction

The role of aquaculture as a major food-producing sector worldwide is illustrated by its progressively increasing share of global fish production. As forecasts suggest, the world aquaculture output could surpass 104 million tons by 2025 [1]. Aquaculture is useful in providing protein for the increasing human population, but there is a limit to this expansion due to the environmental degradation of ecosystems. However, intensive aquaculture that is based on overcrowding causes aquatic ecosystems to deteriorate, making aquaculture facilities prone to microbial contamination. That is why it becomes very important that the link between environmental sustainability and biosecurity is well taken care of to reduce the risk of enteropathogen, i.e., E. coli, contamination in the production of aquatic food–water supply chain [2].
The resilience of E. coli in aquaculture settings presents a challenge that has been further compounded by various environmental factors that increase the possibility of bacterial proliferation and lateral gene transfer processes. Changes in hydrological dynamics and warming of aquatic bodies have been found to contribute significantly to the shift in E. coli from being merely commensal to an active pathogen that can cause serious tissue destruction and even death in fish populations [3]. This pathogenic activity involves the construction of biofilm and release of toxins that affect the mucosa of the host’s intestines [4]. Pathogen strains differ in terms of their genomic organization, particularly in the presence of virulence determinants such as intimin, hemolysin, and invasions [5]. Thus, the aquaculture ecosystem plays a crucial role in transferring these pathogens to humans through the food web [6].
STEC is a leading zoonotic pathogen with a notable feature of being able to induce disease with a small infectious dose. The appearance of STEC as a major public concern in the aquaculture industry reveals another important area of connection between food safety and environmental safety. While STEC was first isolated from terrestrial livestock animals, this pathogen showed an impressive ability to adapt to life in the aquatic environment by forming a resistant biofilm [7]. The pathological condition caused by STEC depends mainly on the presence of cytotoxic toxins stx1 and stx2, which result in varying severities of disease, including gastroenteritis and Hemolytic Uremic Syndrome (HUS), which is a major cause of acute renal failure in children and consists of microangiopathic hemolytic anemia, thrombocytopenia, and acute kidney injury [8]. The pathogen was usually linked to contaminated cattle products; however, STEC now causes more diseases via contaminated water and blue foods due to its considerable environmental adaptation. Highly toxic strains of the pathogen have the ability to create resistant biofilms in water bodies, and thus strict control is required for the prevention of HUS in the aquaculture chain [9].
The growing rate of AMR in aquaculture is emerging as a public health issue, mainly due to the misuse of antibiotics both in the prevention and treatment of ailments in intensive fish farming. These fish farms become environmental niches of MDR E. coli, which affects the health of teleost fishes and leads to zoonotic transmission of resistance genes through the food–water route [10]. Another important factor affecting the spread of AMR among fishes is climatic warming, as it enhances selective pressure and horizontal gene transfer (HGT) of resistance determinants [11]. One of the most concerning aspects of the matter is the occurrence of ESBL genes like blaCTX-M, blaTEM, blaSHV, and blaOXA-1, as well as carbapenemase resistance genes such as blaNDM, blaKPC, blaOXA-48, and blaVIM [12]. Importantly, carbapenemase makes carbapenems useless in fighting MDR Gram-negative infections [13].
STEC infiltration pathways into aquaculture present a complex biosecurity issue that stems from watershed pollution. Although livestock manure-laden runoff continues to be the most common source of pathogen infection, urban wastewater dumping adds an extra layer of danger to this process, especially in highly populated agricultural areas. Aside from anthropogenic pollution sources, wildlife becomes an additional mobile factor in the distribution of STEC across open waterways, creating new routes of contamination that go beyond the scope of traditional land-based prevention techniques [14]. Poor sanitary practices in rural watersheds only aggravate the situation. In this way, the aquatic ponds become bioenvironmental receptors within a dynamic system of microbial exchange, in which the presence of Shiga toxin-producing strains connects environmental damage directly to foodborne illness [15]. The spatial and temporal dynamics of STEC in aquatic environments are characterized by a complex combination of adaptive mechanisms, which allow STEC to withstand the changing physicochemical conditions. Contrary to the case of commensal types of bacteria, STEC strains often acquire a viable, not culturable (VBNC) state in response to various environmental stresses, such as salt concentration, temperature changes, and exposure to ultraviolet light. One of the key strategies adopted for the survival of STEC in water ecosystems involves the synthesis of extracellular polymeric substance (EPS) for forming biofilms on aquaculture equipment, including pond-lining material and filters [16]. Biofilms protect from oxidation and disinfection with traditional methods. Aquatic sediments may also serve as reservoirs for STEC, which can persist within them until random factors cause them to re-enter the water, such as resuspension during harvesting or rainfall [17].
Despite the known hazards associated with STEC in international food systems, a critical knowledge gap has been noted regarding its ecology in the context of the aquaculture industry. Although livestock farming and horticulture have historically been the focus of research, the temporal and spatial characteristics of STEC in fishery settings have yet to be explored sufficiently [18]. Moreover, it should be noted that the scientific community lacks information on the effects of weather phenomena, from seasonal floods to ocean warming, on the incidence of hyper-virulent STEC serotypes. Such geographical biases can be observed in the lack of genotypic data on LEE-negative strains of STEC in tropical aquaculture centers, which have become leaders in international production volumes. Therefore, present monitoring efforts tend to ignore the One Health perspective on the interaction between sediments and wildlife agents in water ecosystems, raising concerns about ensuring the safety of the emerging blue economy [19].
The current research will bridge this significant data gap in understanding STEC persistence in aquaculture by studying its distribution along the entire continuum of aquatic environments. We will ponder the significance of sediment and biofilm communities as microbial sanctuaries, and investigate the presence of STEC and its associated properties in aquaculture systems and the associated environment, focusing specifically on local environmental stressors [18]. The key aspects under study consist of seasonal microbial variation, the persistence of the main VAGs (stx1, stx2, eaeA), and the relevant ARGs associated with them. The results obtained are expected to form the basis for implementing biosecurity practices and protecting the blue economy from both anthropogenic contamination and evolving pathogenic niches [19].

2. Materials and Methods

A systematic literature review was conducted to evaluate laboratory-confirmed STEC infections. Comprehensive electronic searches were performed through three major databases, PubMed, Web of Science, and Google Scholar, targeting the publication period from 2015 to 2025. To avoid the effects of publication bias and to get a wider epidemiological picture, beyond the peer-reviewed literature search, an evaluation of the gray literature and public health surveillance data was also done. The entire process of searching the databases, literature screening, and data extraction was accomplished from 19 February to 1 November 2024. Various steps involved in the implementation of the search strategy include identification, screening eligibility, and final article selection. This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The PRISMA Checklist (File S1) was followed for each section of the meta-analysis (https://www.prisma-statement.org/). This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO), under the identifier CRD420261443874 dated 7 July 2026.

2.1. Inclusion Criteria

The search terms used in these databases included Medical Subject Heading (MeSH) terms (https://www.nlm.nih.gov/mesh/meshhome.html, accessed on 19 April 2024). These included “Verotoxigenic”, “Verotoxigenic Escherichia coli”, “Verocytotoxin Producing Escherichia coli”, “Verotoxin-Producing Escherichia coli”, “VTEC”, “Shiga Toxin-Producing Escherichia coli”, “STEC”, “Enterohemorrhagic Escherichia coli”, and “EHEC”, along with the names of developing countries classified by the United Nations in the World Economic Situation and Prospects 2023 report (https://unctad.org/system/files/official-document/wesp2023_en.pdf, accessed on 1 January 2024). While VTEC and EHEC were valid search terms for finding relevant papers in this review, they are now generally considered obsolete or less precise in the scientific community.

2.2. Exclusion Criteria

To maintain a focus on novel epidemiological and observational data, we excluded laboratory-centric studies (focusing on isolation or diagnosis), microbiological characterizations of redundant strain sets, and clinical treatment reviews. After an individual assessment of each record against these predefined exclusion criteria, the final dataset comprised 108 peer-reviewed papers.

2.3. Systemic Literature Screening

Adopting the widely accepted STEC nomenclature, we performed a systematic screening of 315 non-duplicate, English-language articles. After the initial screening, selected publications were examined to see if they reported the presence of non-sorbitol-fermenting E. coli O157 or non-O157 strains that contain the stx genes or produce the Shiga toxin. Studies had to verify STEC in human or animal feces by molecular methods like PCR or by phenotypic tests like ELISA or cell cytotoxicity to be considered eligible for this study. Articles dealing with asymptomatic carriage or urinary tract infections were omitted (Figure 1).

2.4. Statistical Analysis

Information from selected papers was combined into a Microsoft Excel 2019 spreadsheet; each entry included study metadata (DOI, year, country) and virulence markers (serotypes, stx1/stx2, stx subtypes, eae). Results were then divided by source: human, animal (e.g., ruminants, pigs, and birds), and food (fish farming and its environment). Moreover, data from various continents was subjected to one-way analysis of variance (ANOVA) to compare the means of independent variables, i.e., STEC.

3. Results

In this study, after reviewing (n = 315) papers and applying different exclusion criteria, (n = 270) articles were retrieved, which were evaluated by passing through various filters to get (n = 235) articles. After employing final inclusion on the basis of VAG and ARG detection in aquaculture, (n = 108) articles were included (Figure 1). Of these 108 articles across six continents, the largest share originated from Asia (53 papers), followed by Africa (30 papers), transcontinental Asia/Europe (5 papers), South America (10 papers), Europe (7 papers), and the lowest number was from North America (3 papers). A total of 42 papers from different countries showed the presence of VAGs, where Shiga toxin genes (stx1, stx2) and their respective subtypes and the intimin gene (eae) were collected from 1419 out of 4082 isolates, while 3055 ARGs were reported among various aquaculture and associated samples (Figure 2).

3.1. Distribution of STEC Isolates

3.1.1. Continent/Country-Wise

Among all the 4082 STEC isolates reported worldwide within various aquatic ecosystems (Figure 3), Asia has the largest number of 2283 isolates (approximately 55.92%), owing to sampling in coastal and aquaculture regions of India, along with major contributions from Thailand, Malaysia, Jordan, and Lebanon. Second on the list is the continent of Africa, which accounts for 1009 isolates (approximately 24.33%), mostly from Egypt (the largest contributor within Africa, based on research on fish farms, shrimp, and seafood), with smaller contributions from Zambia, Nigeria, Ethiopia, and Tanzania. Turkey, which is considered to fall between both Asia and Europe, contributes 261 isolates (estimated 5.29%). In comparison, Europe itself contributes very low numbers of 180 isolates (approximately 4.34%), mostly from Italy, with no records found in Germany, Norway, or Denmark. Finally, North and South America only contribute low numbers of STEC isolates, with almost no (0.14%) contributions from Canada and the United States, while South America contributes 343 isolates (approximately 8.27%), mainly from Brazil, and less from Chile and Peru (through oysters and fish farms) (Table 1).

3.1.2. Aquaculture Sample Type

The STEC isolation rate by individual aquaculture sample type shows great disparity, indicating the presence of significant localized threats. In Asia, the maximum percentage of positive STEC samples is found in Malaysia, where oysters (Crassostrea iredalei) achieved the peak positivity of 86.67% (104/120) and downstream fishing villages 84.57% (148/175), followed by rainbow trout in Lebanon achieving 85.93% (116/135). India presents another example of high positivity in certain sectors, including shrimps at 77.45% (79/102) and pre-processing plants for seafood at 76.39% (110/144); however, the largest Nile Tilapia investigation in Thailand, which achieved only 19.08%, is considered moderate while having the largest number (828 isolates) among all studies. In Africa (Egypt), shrimp samples, finfish (Nile Tilapia) and O. niloticus cultivated in fish farms exhibit very high susceptibility, with 52.66% and 42.75% positivity, respectively. On the other hand, Bangladesh, South Korea, China (duck fish and grass carp), and India and Pakistan (fish, fish markets, clams, and shrimps) provide many sample types that have a negligible % of STEC isolates; thus, the STEC threat can be said to vary regionally and sector-specifically (Table 2).

3.2. Distribution of VAGs Among Aquaculture and the Associated Environment

Out of 4082 reported isolates from developing countries between 2015 and 2025, data on VAGs are available for 1419 cases (34.76%). Significant global heterogeneity indicates that certain aquaculture settings serve as critical reservoirs for highly virulent strains. In Africa, aquaculture environments exhibit significant heterogeneity, serving as critical reservoirs for highly virulent strains due to varying waste management and water sources. In Egypt, profiling reveals an unstable VAG distribution rate (4% to 54%) dominated by EHEC strains carrying classical Shiga toxins (stx1, stx2), intimin (eaeA), and hemolytic proteins (OmpA, hlyA) [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]. On the other hand, researchers who have done work in sub-Saharan Africa report a prevalence that is quite a bit lower. For example, river basins in Nigeria show an 11% distribution rate, mainly with stx2 and hlyA as a characterizing feature [40], whereas Tanzanian groups have a very low 5% to 7% prevalence dominated almost entirely by the stx2 gene [44]. At the same time, country and ecosystem-specific virulence gene patterns show sharp differences in Asia’s aquaculture hotspots, which are also the area’s most densely packed with commercial activities. India has highly scattered contamination levels (0% to 100%) in coastal brines, freshwater ponds, and retail shops, with isolates generally carrying multi-virulent stx1, stx2, and eaeA gene combinations [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74]. Iran’s atypical typing levels are widely diverse (2% to 86%), acting as a special reservoir of certain allele versions such as stx2c and stx2d, as well as being a host for eaeA and enterohemolysin ehxA [75,76]. In the same region, baseline monitoring figures differ greatly: Thailand ranges from 24% to 100% (stx1, stx2) [94,95,96], Malaysia reports an 86% level (hylA) [85], and Pakistan shows a very localized 19% incidence featuring a typical stx1, stx2, and eaeA profile [89]. However, thorough testing initiatives in China and South Korea have led only to baseline or unclassified results (Table S1).
Alternatively, European, North American (USA), and South American regions show a great difference in STEC levels, as Asian and Northeast African regions are hotspots while they are mostly below the detection level. Although regular monitoring in Germany, Switzerland, and some parts of Italy depicts a 0% baseline (or unclassified status), regional vulnerabilities still exist [109,111]. In fact, a 14% presence rate of stx1 and stx2 mixtures has been found in Italian shellfish and related environmental samples [110]. An equal 14% occurrence is found in Brazilian aquatic farming and fish systems, where molecular typing reveals a stable pathogen opportunity mainly characterized by the very toxic stx2 gene together with ehxA [121,122,124,125] (Table S1).

3.3. Shiga Toxins and Intimin Genes

Among 1419/1175 aquaculture isolates studied, stx1 and stx2 were the major virulence genes, whereas eae clusters were found in 827 isolates, which were mostly from wastewater sewerage systems. The VAG profiles differed when the data were broken down by commodity type. For example, out of 517 finfish isolates, 26.5% contained either stx1 or stx2, while 15.9% (n = 82) had eae co-expression—a profile mostly isolated from Iranian, Egyptian, and Indian retail markets [52,72]. Furthermore, the 406 shellfish and seafood isolates exhibited high pathogenic features; 22.4% contained stx1 or stx2, and 11.6% (n = 47) showed co-expression of multi-virulent stx1/stx2/eae. Interestingly, 28.6% of these shellfish isolates showed unique non-stx adherence profiles, mainly hlyA or eae alone, which were mostly found in Malaysian and Brazilian production sectors (Table S1).

3.4. Detection of ARGs in Aquaculture and Associated Environments

Of the 4082 total cases recorded in fisheries and related habitats worldwide from 2015 to 2025, around 3055 (74.84%) ARGs were identified across different continents. In Northeast Africa, the intensive sectors of Egypt’s aquaculture contributed a major share of this resistance, with local strains of beta-lactamase and tetracycline resistance at high levels. In 90 cases, the fish species Nile Tilapia (Oreochromis niloticus) consisted of a complicated arrangement of genes for tetA and AmpC, and at the same time a separate group of 103 Tilapia isolates from earthen ponds revealed different ARG profiles with blaCTX-M, blaCTX-M-15, blaSHV, blaAMP, and blaOXA-2.
High gene density per positive sample was a signature characteristic of Asian aquaculture hubs. For instance, in India, two critical cases of specialized finfish and shellfish sectors displayed extreme multidrug resistance (MDR) capacity, resulting in 52 unique ARGs only in two isolates [52]. These isolates contain sophisticated resistance mechanisms like the carbapenemase gene blaNDM and tetG, tetO, tetW, qnrA, and qnrB. Similarly, the monitoring of Pakistan’s carp fish farming and surface water and sediments found a combined total of 79 ARG detections, dominated by blaCTX-M and blaTEM and also containing tetA, sul1, sul2, qnrA, and qnrB [87,88,89,90]. In a similar way, Thailand’s coastal aquaculture environments in the SEA region displayed a heavy resistance footprint; 67 positive cases in oysters and estuarine waters accounted for a shockingly high 158 total ARG detections, almost entirely driven by blaTEM, blaCTX-M, and tetA combinations [94,95,96,97] (Table S2).
In the Middle East, fish sold in the Iranian retail sector displayed 127 structural resistance markers over 49 cases, which reveals a highly diverse profiling mix of tetA, tetM, sul1, sul2, qnrA, qnrB, dfrA14, and blaOXA [75,78,79]. In the Americas, Brazil showcased a well-established MDR scheme where 65 total cases covering fish, shrimp, oysters, and aquaculture water revealed 57 certain ARGs including blaTEM, blaCTX-M, blaCMY-2, qnrA, qnrB, and sul1. Interestingly, a particular group of 23 oyster samples featured clear distributions of blaTEM, blaSHV, and the very important carbapenem resistance marker blaKPC [118,120,125]. Then again, routine monitoring in Bangladesh, South Korea, Germany, Denmark, Kosovo, Chile, and Peru did not find any relevant ARG presence in aquaculture systems or their surrounding environments (Table S2).

3.5. Serogroups

Across 3052 VAG-based isolates, 1118 (36.63%) aquaculture samples were heavily dominated by serogroups O157, O26, and O111. In 1577 cases (95.4%), O157:H7 was tracked in 25 cases (1.5%) [20,26,36,37,38,87]. Serological testing on 547 finfish isolated different serogroups; O157, O26, and O111 dominated a total of 291 cases (53.2%) [80,86]. The individual high-risk serotypes O157:H7 and O118 (or O111:H8 variants) made up 44 (8.0%) and 90 (16.5%) cases, respectively [27,35]. In contrast, among 464 shellfish and seafood isolates, serogroups O157, O26, and O111 were found in 242 cases (52.2%). Interestingly, the dangerous O157:H7 serotype was more common in the shellfish group, making up 110 cases (23.7%), whereas the specialized O113:H21 serotype came next in 65 cases (14.0%) (Table S2).

4. Discussion

A worldwide meta-analysis of combined data shows that even though STEC has extensively spread over aquaculture systems and related environments, its prevalence is still very uneven. The isolation of STEC from the world’s major aquaculture producers, like Malaysia, India, Brazil, and Egypt, highlights the cross-continental aspect of this biosecurity threat [128]. An important result from this data is the big difference between the total number of samples and the number of positive samples obtained. This massive difference between the sampling denominator and the positive numerator proves that STEC contamination is not evenly spread but is found in clusters, which points to particular ecological niches that can be targeted for regulatory intervention.
One of the main factors determining the environmental persistence of STEC in aquaculture systems is its capacity to colonize different types of substrates. The pathogen’s higher recovery rates in benthic sediments, processing machinery, and effluent runoff versus open water bodies imply that stagnant hydraulic conditions and organic enrichment enhance the survival of these enteric organisms [129]. In fact, sediments are not only a part of the environment; on the contrary, they offer bacterial populations protection from solar ultraviolet radiation and predatory microbes, leading to their long-term survival and to their resuspension into the water column during heavy rains or harvesting operations. Beyond substrate colonization, STEC spread in aquaculture is driven by favorable fluctuations in water temperature and pH, reduced competition from native microbial communities, and the formation of sanitizer-resistant biofilms on processing equipment [17]. However, the health and animal threat that aquaculture-related STEC may cause is mainly regulated by the detailed characterization of its toxins. The gathered data indicate a very sparse distribution of the main pathogenicity factors, i.e., the Shiga toxin variants (stx1 and stx2) and eae (Table 3).
The relative proportions of stx1 to stx2 locally exhibited marked geographical differences among the isolates examined. So, stx1 was the most common toxin type in several African and Asian aquaculture-related datasets, whereas in the European and South American ones, stx2 showed either a higher or a similar prevalence as stx1. This probably occurred because integrated farming practices introduce animal manure and agricultural runoff into aquaculture environments. As a result, E. coli strains in these waterways reflect local animal shedding, with small ruminants and wildlife driving a high prevalence of stx1-carrying strains in the water, sediment, and seafood. From an epidemiological viewpoint, this difference is quite important; human STEC infections with strains harboring stx2 are generally connected to very severe clinical symptoms such as hemorrhagic colitis (HC) and even life-threatening HUS; in contrast, those strains carrying stx1 usually cause self-limited, mild diarrhea [130]. So, the identification of stx2-positive strains in commercial food-producing aquatic environments points to a significant zoonotic threat for consumers. The stx2 gene plays a crucial role in zoonotic outbreaks because it encodes Shiga toxin type 2, which has a significantly higher binding affinity for human endothelial receptors than stx1. This allows the toxin to effectively halt host protein synthesis, causing severe clinical complications such as HC and life-threatening HUS. This is because it has an exceptionally low infectious dose and is easily shed from animal reservoirs into food chains [128]. In 2019, a multi-state US outbreak linked to raw oysters imported from Mexico resulted in 16 confirmed illnesses, with non-O157 STEC identified as a critical co-infecting pathogen alongside Vibrio and Shigella species [116].
The presence of eae provides valuable context regarding how these environmental isolates colonize hosts. Strains carrying the eae gene represent typical Shiga toxin-producing STEC pathotypes, which can produce characteristic attaching-and-effacing (A/E) histopathological lesions on intestinal epithelial tissue [131]. However, a notable proportion of the strains evaluated in this literature review harbored core stx genes but lacked the eae marker. These atypical STEC variants are common in environmental samples. Their presence in aquaculture systems points to two likely scenarios: either these strains utilize alternative chromosomal or plasmid-encoded adhesins such as saa or iha to adhere to cells, or these production systems are experiencing temporary biological inputs from agricultural or wildlife runoff, where eae-negative populations are widespread. Beyond these virulence profiles, a highly concerning finding from our meta-analysis is the widespread co-carriage of ARGs within these environmental STEC populations. This evidence strongly indicates that aquaculture infrastructure acts as an active, critical reservoir that preserves and facilitates the horizontal gene transfer of AMR [132].
Among the ARGs found in the literature, genes that provide resistance to old antimicrobials predominantly consist of tetracyclines (tetA, tetB), β-lactams (blaTEM), and sulfonamides (sul1, sul2), which showed a high level of baseline presence. These highly prevalent resistances represent the effects of prolonged and repeated selective pressures due to the historical overuse or misuse of these drug classes as prophylactics or growth promoters, not just in aquatic environments under intensive production but also in terrestrial animal husbandry [131]. Yet, what is more alarming from a clinical perspective is the rise in the detection of genes that confer resistance against major human drugs, like the genes for ESBLs (blaCTX-M), those that give resistance to quinolones in plasmid form (qnrS), and even mcr-1. Furthermore, driven by veterinary interventions and heavy metal runoff, aquaculture environments exert continuous co-selection pressure that actively selects for bacteria carrying critical ARGs. Concurrently, the protective biofilms formed by STEC on pond sediments and processing machinery trap these microorganisms in close proximity. This localized, high-density environment accelerates HGT, allowing native aquatic bacteria to readily transfer mobile plasmids carrying last-resort resistance genes like blaNDM and mcr-1 directly to STEC [89]. The finding of very dangerous resistance genes like blaNDM or mcr-1 that compromise the main food production chains is a catastrophic event for the modern clinical medicine, as it could render ineffective the drug combinations that are vital in human medicine for the treatment of the most serious system-wide infections [133].
The VAGs and ARGs are found together in single bacteria, highlighting the huge evolutionary benefits of these environmental pathogens. In water bodies, long-term exposure to very low levels of stress factors such as leftover antibiotics, heavy metals, and disinfectant residues continuously pushes organisms to develop MDR [133]. Since many ARGs (blaTEM, tet, sul, qnrS) and certain atypical virulence characters are located on the same mobile genetic elements (MGEs) like conjugative plasmids, transposons, and class 1 integrons, giving selective pressure by only one antimicrobial agent might cause the simultaneous co-selection and stabilization of the entire virulence-resistance genetic cassette. Sediments and microbial biofilms at the bottom of the fish-farming ponds act as high-density cellular matrices, which greatly enhance the rate of horizontal gene transfer (HGT) [131]. So, harmless commensal E. coli or other aquatic microbiota can easily take in these mobile genes; that is, it turns the aquaculture ecosystem into a genetic chamber from which new, hyper-virulent, and pan-resistant pathotypes constantly evolve [134].
Monitoring STEC and its related VAGs and ARGs in aquaculture systems has revealed external anthropogenic and agricultural inputs as the main culprits for environmental contamination. Since E. coli is an enteric organism and not a natural aquatic pathogen, its presence in production ponds, water inflows, and farm equipment is a clear indication of fecal contamination [133]. Municipal wastewater discharges, untreated agricultural runoff from adjacent livestock operations, and the conventional use of raw animal manure for pond fertilization (integrated livestock–fish aquaculture) are the main transmission vehicles [135]. After introduction into the environment, the pathogen shows remarkable ecological plasticity, allowing it to efficiently adapt to the aquatic habitat. The evidence lies in the collected data that demonstrate recovery of the pathogen not only within the primary water column but also on processing equipment, workers’ hands, and internal anatomical sites (including the abdominal cavity and branchial filaments) of cultured fish species. Such widespread presence along the post-harvest supply chain indicates that the aquatic environment is more than just a passive vehicle for the transport of pathogens. It is, in fact, an active matrix where pathogens survive, build up, and horizontally exchange resistance traits [136].
The rise in toxigenic STEC strains with crucial virulence factors and complicated antimicrobial resistance profiles in aquatic farming environments is posing a complex risk to global public health. The contamination of seafood, mainly when the fish is caught from highly polluted areas with sediment or water, is a direct yet frequently overlooked route to transmission. The isolation of MDR-STEC from the hands of workers and processing tools at fish farms indicates a direct pathway for local cross-contamination within the post-harvest supply chain. Most importantly, therapeutic choices would be very limited for treating clinically infected individuals in foodborne outbreaks attributed to these pathotypes due to their acquired ARG profiles [137].
Though the present paper describes a decade-long overview, some limitations still need to be studied in future like the literature’s bias towards commercial aquaculture and standard culturing techniques, which fails to take into account unculturable STEC, thus distorting the actual prevalence of genes. Additionally, the lack of longitudinal studies in original scientific papers makes it difficult to distinguish between temporary runoff from terrestrial sites and permanent ARG integration into the aquatic microbiome. Moreover, current studies are biased towards basic molecular methods of identifying ARGs and VAGs without being able to prove their co-localization on mobile genetic elements, i.e., plasmids, transposons, etc. These outcomes highlight the necessity for an integrated “One Health” approach that aligns regular molecular monitoring of VAGs and ARGs with strict cleaning requirements and upstream fecal management methods to maintain both food safety and clinical effectiveness.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/vetsci13080779/s1, File S1: PRISMA 2020 checklist [138]; Table S1: Distribution of VAGs in Shiga-toxin producing E. coli from Aquaculture and its associated Environment; Table S2: Continent wise distribution of ARGs in STEC from Aquaculture and its associated Environments.

Author Contributions

Conceptualization, B.A. and A.S.; methodology, A.S.; software, A.S.; validation, B.A., A.S. and S.F.A.; formal analysis, B.A.; investigation, A.S.; resources, S.F.A.; data curation, A.S. and B.A.; writing—original draft preparation, A.S.; writing—review and editing, S.F.A. and B.A.; visualization, supervision, B.A.; project administration, funding acquisition, S.F.A. All authors have read and agreed to the published version of the manuscript.

Funding

The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University (https://www.qu.edu.sa) for financial support (QU-APC-2026).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
STECShiga toxin-producing E. coli
ARGsAntibiotic resistance genes
VAGsVirulence-associated genes
MDRMultidrug Resistance
HGTHorizontal gene transfer
AMRAntimicrobial resistance
EPSExtracellular polymeric substance
HUSHemolytic uremic syndrome

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Vetsci 13 00779 g001
Figure 1. A systemic literature review employed in the form of a flowchart describing the various selection criteria (including different exclusion and inclusion parameters, evaluation approaches and final selection) of STEC distribution from aquaculture and associated environments.
Figure 1. A systemic literature review employed in the form of a flowchart describing the various selection criteria (including different exclusion and inclusion parameters, evaluation approaches and final selection) of STEC distribution from aquaculture and associated environments.
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Vetsci 13 00779 g002
Figure 2. Illustration of the overall distribution of research articles reviewed. (A) Summary of total article count (where 315 papers were initially reviewed, which reduced to 270 papers after applying exclusion criteria and 235 after applying multiple filters, leading to a final paper count of 108. (B) Geographical representation of the paper count by continent. These blue bars indicate the number of articles reviewed from different continents. The highest number of articles included was from Asia (53) followed by Africa (30), Europe (12), South America (10) and finally the lowest number of articles reviewed was from North America (3).
Figure 2. Illustration of the overall distribution of research articles reviewed. (A) Summary of total article count (where 315 papers were initially reviewed, which reduced to 270 papers after applying exclusion criteria and 235 after applying multiple filters, leading to a final paper count of 108. (B) Geographical representation of the paper count by continent. These blue bars indicate the number of articles reviewed from different continents. The highest number of articles included was from Asia (53) followed by Africa (30), Europe (12), South America (10) and finally the lowest number of articles reviewed was from North America (3).
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Vetsci 13 00779 g003
Figure 3. Scattered bubble plot; the size of the bubble represents the number of STEC isolates (×100) in different continents, along with total isolates among continents (x-axis), and occurrence of ARGs among isolates (y-axis).
Figure 3. Scattered bubble plot; the size of the bubble represents the number of STEC isolates (×100) in different continents, along with total isolates among continents (x-axis), and occurrence of ARGs among isolates (y-axis).
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Table 1. Total distribution of STEC isolated from different aquaculture sources.
Table 1. Total distribution of STEC isolated from different aquaculture sources.
ContinentTotal No. of IsolatesSTEC IsolatesVAGsARGs
Africa56551009270657
Asia843722839261938
Asia/Europe15792617124
Europe171418093184
North America3756ND55
South America542634312397
23,186408214193055
ANOVA
Source SSDfMSFp-valueF crit
Between Groups“52,285,211”3“17,428,403.6”6.2736986730.003551 *3.098391
Within Groups“55,560,219”20“2,778,010.95”
* alpha = 0.05; ND = not detected.
Table 2. Within-continent prevalence of various VAGs and ARGs in Shiga toxin-producing E. coli from aquaculture and its associated environments.
Table 2. Within-continent prevalence of various VAGs and ARGs in Shiga toxin-producing E. coli from aquaculture and its associated environments.
ContinentCountrySourceTotal No. of IsolatesSTEC IsolatesVAGsARGsReference
Africa EgyptFish farm water (fish, O. niloticus, S. pilchardus32876stx1, stx2, eaeA, st, lt, bfpAblaTEM, blaCTX-M, blaOXA-48[20]
Clarias gariepinus catfish5010stx1, stx2, ND[21]
Oysters3327sfa, exhA, eaeA, estA, aer, stx1, stx2, rfc, hlyA, etrA, cnf1, sepA, blaTEM, blaSHV, blaCTX−M, blaOXA−1[22]
Finfish (Tilapia, mullet)15079stx1, stx2MDR gene [23]
Mullet meat10020SLT, LTaaC2 gene[24]
Gandolfi, Ruditapes decussatus2020stx1, phoA, stx2ND[25]
Shrimp (L. vannamei)8211NDblaTEM, tetA, blaOXA, sul1, aadA, ermB[26]
Seafood13557stx1, stx2, eaeA,ND[27]
Seafood samples19210stx1, stx2 and eaeAblaTEM, tetA[28]
Migratory birds3494stx1, stx2 and eaeAMDR gene [29]
Fish15010stx1, stx2, and eaeA ND[30]
Fish samples from gills, musculature2009stx1, stx2 and eaeAND[31]
Earthen pond-cultured O. niloticus14411ND blaTEM, blaCTX-M, blaSHV, blaAMP blaOXA-2[32]
Tilapia (O. niloticus), Clarias gariepine400171stx1, stx2 and eaeAtetA, AmpC[33]
Fresh Nile Tilapia fish21824stx2, eaeAtetA, blaTEM, sul1, Sul 2, aadA2[34]
EthiopiaFish 4508stx1, stx2, eaeA,blaTEM-1B, tetA, fos7, qnrS, gyrA[35]
Fish4101stx1, stx2ND[36]
Fish 12141eae, hlyA1 [37]
Fish32862NilND[38]
Ivory CoastFish, crab, water sediment629stx1, stx2, eae, AggRelt, est[39]
NigeriaMiscellaneous retail fish25628NDND[40]
Fish, shellfish7211stx2, hlyAblaSHV, blaTEM, mcr genes[41]
Nile Tilapia, fish farms50-NDND[42]
Clarias gariepinus (catfish and aquaculture)475NDND[43]
TanzaniaManure, vegetables, and fish362Stx2ND[44]
West AfricaFish farm15699NDND[45]
KenyaNile Tilapia fish6824NDtetA, blaTEM, sul1, Sul 2, aadA2[46]
ZambiaFish41856stx1, stx2 and eaeAblaSHV, blaTEM, blaCTX-M[47]
ZimbabweWater sample
(natural lake)		600120NDND[48]
Fish304NDND[49]
AsiaBangladeshFish312139NDND[50]
Shrimps
(water, basket, mat)		5829NDND[51]
IndiaShrimp farms
(pond water)		10279stx2blaTEM, blaCTX-M, blaSHV, AmpC, sul1[52]
Fish (freshwater pond sites, retail)24318NDblaTEM, blaCTX-M-15, blaOXA-1, tetA, sul1, qnrS, cat1, cat2[53]
Seafood samples6334NDND[54]
Seafood samples13060NDblaCTX-M, blaSHV, blaTEM, AmpC[55]
Carp, aquaculture23249stx1, stx2MDR gene, enteric genes[56]
Seafood pre-processing plants144110stx1, stx2, eaeAuid A gene[57]
Seafood4321NDRfb[58]
Fish supply chain9121NDSul1, blaCTX-M, tetD, dfrA, AmpC[59]
Fish7028NDND[60]
Shrimps14418stx1, stx2, eaeA, hlyAuid A gene[61]
Salmon sashimi (fish)3015NDND[62]
Food fish795NDblaTEM, blaSHV, blaOXA, tetA, StrA, Sul1, Sul2, oqxA, qnrB[63]
Fish1006NDND[64]
Finfish, shellfish1342papAH, papC, sfa/focDE, iutA, kpsMT-IIblaTEM, blaSHV, blaCTXM, blaNDM, tetA, tetB, aphA2, strA, sul1, sul2, dhfrIa[65]
Fish20076NDblaTEM, blaSHV, blaCTX-M[66]
Carp from integrated aquaculture ponds301stx1, stx2, eae, ehlyA, rfbO157Rfb[67]
Fresh seafood1009stx1d, stx2, eaeA, hlyAND[68]
Carps339stx1, stx2, eae, ehlyA, rfbO157ND[69]
Fish market waste351NDND[70]
Fish23828NDND[71]
Freshwater fish (Catla catla)10418stx1, stx2, eaeA, hlyAND[72]
Clams and shrimps16012NDblaVIM[73]
Shrimp farms2619viz., iutA, entB, mrkDblaTEM, tetA, tetB, Sul1, Sul2, catA, CmlA, qnrS[74]
IranFish (retail store)16665stx1a, stx1c, stx1d, stx2c, stx2d, stx2e stx2fblaTEM, qnrA, tetB, sul2, blaSHV, sul, blaCTX-M, blaOXA[75]
River and sewage water7014stx1, eaeND[76]
Cold-smoked salted fish2005NDND[77]
Aquaculture water15027NDtetA, Cmla, acc-IV, qnrA, sul1, sul2[78]
Cold water fish10014stx1, stx2, eaeMDR genes[79]
IraqCultured fish (Labeo rohita, Cyprinus)12011NDblaTEM, blaCTX-M[80]
Water, sediment, feed, tools23040NDESBL producing isolates[81]
Fish farms, fish markets15040NDMDR gene[82]
JordanRecreational lake water262130NDMDR genes[83]
LebanonFish, rainbow trout, aquaculture135106NDmcr-1[84]
MalaysiaOyster (Crassostrea iredalei)120104HlyAND[85]
Downstream of a fishing village175148NDMDR gene[86]
PakistanFreshwater fish-farming sites10155NDqnrA, qnrB, qnrS[87]
Fish (Channa marulius)48029NDtetA, sul3, gyrB, blaTEM[88]
Fish, shrimp, aquaculture water22565stx1, stx2, eaeAblaNDM-1, mcr-1[89]
Cerus (Labeo rohita, Catla catla)21625NDblaSHV, blaTEM, blaCTX-M, tetA, tetB, ermA, ermB, ermC, vgaA, strA, strB, aadA1, aac3-I[90]
SingaporeAquaculture farms3123stx, intLblaSHV, blaOXA, blaCTX-M, qnrA[91]
South KoreaCoastal aquaculture2222NDstrA, strB, aadA1, aac3-I[92]
Fish (gizzard shad, halibut, rockfish)20649NDND[93]
ThailandNile Tilapia828158stx, eaeA,blaTEM, tetA, qnrS[94]
Oysters and estuarine water4098stx1, stx2blaTEM, tetA[95]
Oysters, estuarine water10959stx1, stx2, eaeblaTEM, tet A, blaSHV, stn, sul2[96]
Aquatic environment, coastal waters38484NDblaTEM, blaCTX-M-15, blaOXA-1, tetA, sul1, qnrS[97]
Fish4222scnf2s, LTIs or vt2eND[98]
ChinaFish (carp)208NDND[99]
Aquaculture farms, soil sediment9028NDblaTEM, blaNDM, floR, OptrA, cmlA, aphA1, Sul2, oqxA, qnrS[100]
Duck fish250143NDmcr-1[101]
Grass carp fish109NDblaCTX-M 27, blaCTX-M15[102]
Asia/EuropeTürkiyeFish (Engraulis encrasicolus, Merlangu)300157stx1, stx2ND[103]
Raw mussels712 Sul3, tetB, qnrA, qnrB, sul1, fimH, floR[104]
Fish1402stx1, stx2ND[105]
Mussels, sea bass823100NDblaTEM, tetA[106]
Fish245-NDND[107]
EuropeDenmarkZebra fish30067NDblaTEM, Sul2, qnrS, mcr-1[108]
GermanyFish in Baltic sea11641NDND[109]
ItalyClams and shrimps62014stx1, stx2, eaeA, hlyAblaVIM, acc, blaSHV[110]
Carpet shells (Cerastoderma spp.)4020stx1, stx2ND[111]
KosovoSalmon fish542NDND[112]
NorwayMarine bivalve mollusks54010NDblaCTX-M[113]
SwitzerlandShellfish
(oysters, shrimps)		4426NDND[114]
North AmericaCanadaSeafood2772NDblaKPC, blaOXA, blaNDM[115]
USASeawater, shellfish (oysters)84NDND[116]
Nile Tilapia90-NDblaTEM, blaCTX-M, blaSHV, ampC, sul1[117]
South AmericaBrazilFish, shrimp, oysters, aquaculture water22565NDblaTEM, blaCTX-M, blaCMY-2, tetA, tetB, sul1, sul2, qnrA, qnrB, qnrS, aadA, floR, mcr-1[118]
Culture farms (fish, shrimp, oysters)6929NDND[119]
Aquaculture farms (fish, shrimp, oysters, seafood)65NDblaTEM, blaCTX-M, blaCMY-2, tetA, sul1, qnrS[120]
Mussels2121stx1, elT, bfpAND[121]
Shellfish1501phoA, eaeND[122]
Frozen Tilapia filets (processing units)10050NDND[123]
Fish, fish ponds11513eaeA, stx1, stx2ND[124]
Oysters94023eaeA, stx1, stx2, Ial, STa, STb, LTblaTEM, blaSHV, blaKPC[125]
ChileFresh seafood33018hylA, stx2, ipfApmrB, qnrB[126]
PeruFish500118NDND[127]
ND = not detected.
Table 3. Global overview of microbial contamination, aquaculture pathogens, and detection methodologies across sampled continents.
Table 3. Global overview of microbial contamination, aquaculture pathogens, and detection methodologies across sampled continents.
ContinentNo. of StudiesRepresentative
Sources Sampled		Human Risk
Categories
Detected		Animal Fecal/Tissue
Contamination Markers		Primary Aquaculture Pathogens (Strains)Methodologies &
Techniques Employed
Asia53Cultured fish Labeo rohita, Cyprinus carpio, fish, freshwater fish-farming sitesPotential public health risk, hand swabsFecal contamination, skin, gills, intestinesSTEC, E. coli O157:H7, STEC, EPECPCR, culture, genotyping, MALDI-TOF
Africa30Nile Tilapia O. niloticus, shrimp L. vannamei, dish farm waterN/A, zoonotic risk identifiedYes—92.68%, tissueE. coli O157:H7, STEC O157, STECCulture, 16S rRNA PCR, PCR, pathogenicity trial
Europe12Fish Engraulis encrasicolus, Merlangius euxmus, Trachurus mediterraneus, Mullus barbutus, Tinca tinca, Cyprinus carpio and Oncorchynhus mykiss, raw mussels, fishN/ABeef, meatE. coli O157:H7, E. coli, STEC, APECmPCR, E-test method, Electrophoresis, RT-PCR
South America10Fish, shrimp, oysters, aquaculture water, aquaculture farm fish, shrimp, oysters, aquaculture pond water, seafood, meat, vegetables and ready-to-eat street-vended foodYesFecal contaminationSTEC, EPEC, ETEC, STEC, E. coli O157:H7, STECPCR, AMR profiling, RT-PCR, qPCR
North America3Seawater, shellfish, oysters, seafood, fish fecesFecal contaminationFecal contaminationSTEC O157, E. coli, CRE, E. coli O157:H7PCR, qPCR, Multiplex PCR
Total Studies108
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Sarwar, A.; Aslam, B.; Aljasir, S.F. Shiga Toxin-Producing Escherichia coli in Aquaculture: A Decade (2015–2025)-Long Global Retrospective Outlook. Vet. Sci. 2026, 13, 779. https://doi.org/10.3390/vetsci13080779

AMA Style

Sarwar A, Aslam B, Aljasir SF. Shiga Toxin-Producing Escherichia coli in Aquaculture: A Decade (2015–2025)-Long Global Retrospective Outlook. Veterinary Sciences. 2026; 13(8):779. https://doi.org/10.3390/vetsci13080779

Chicago/Turabian Style

Sarwar, Ayesha, Bilal Aslam, and Sulaiman F. Aljasir. 2026. "Shiga Toxin-Producing Escherichia coli in Aquaculture: A Decade (2015–2025)-Long Global Retrospective Outlook" Veterinary Sciences 13, no. 8: 779. https://doi.org/10.3390/vetsci13080779

APA Style

Sarwar, A., Aslam, B., & Aljasir, S. F. (2026). Shiga Toxin-Producing Escherichia coli in Aquaculture: A Decade (2015–2025)-Long Global Retrospective Outlook. Veterinary Sciences, 13(8), 779. https://doi.org/10.3390/vetsci13080779

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