1. Introduction
Shiga toxin-producing Escherichia coli (STEC) is a major cause of intestinal infections and foodborne outbreaks worldwide [1,2]. In the European Union, STEC is the third most common foodborne pathogen, contributing to an estimated 2.8 million cases of enteric diseases annually [3]. Domestic animals, especially ruminants, play a key role as reservoirs for STEC infections, with transmission occurring primarily through direct contact or consumption of contaminated food [4].
The pathogenicity of STEC is primarily attributed to the production of Shiga toxins (Stx), which are classified into two main types, Stx1 and Stx2, based on sequence similarity, antigenic differences, and key biochemical characteristics [5,6]. These toxins are encoded by the stx1 and stx2 genes. The stx1 gene has four subtypes—stx1a, stx1c, stx1d, and stx1e—while the stx2 gene is more diverse, encompassing currently 15 known subtypes, from stx2a to stx2o [7,8]. Studies have shown that strains producing stx2, particularly the stx2a subtype, are more commonly associated with severe diseases like hemolytic uremic syndrome (HUS) [7,8,9]. This has led to the identification of STEC pathotypes based on stx profiles, which can inform both clinical management and public health strategies. Although this approach is not yet officially integrated into clinical practice, enhancing the use of stx subtyping—especially in regions with a high burden of STEC infections—could improve risk assessment, guide treatment decisions, and strengthen outbreak response measures by allowing more accurate identification of high-virulence strains. Another critical virulence factor in STEC is intimin, a protein encoded by the eae gene, which facilitates the intimate attachment of STEC to intestinal epithelial cells [10]. Strains that are intimin-positive are often linked to serious conditions, including hemorrhagic colitis (HC) and HUS [11,12,13,14]. Over 20 intimins mediating the attachment to intestinal cells have currently been described [12,13,15,16,17,18]. The combination of eae subtype γ1 and stx2a is more associated with severe disease [12,19]. It is also important to note that disease severity is multifactorial, influenced by host factors, microbiota, and environmental conditions, and cannot be inferred solely from the STEC strain characteristics [20,21,22,23,24,25,26,27]. STEC includes over 400 serotypes, but severe human illness is usually caused by a relative small subset of serogroups, including non-O157 STEC serogroups O26, O45, O103, O111, O121, O145, referred to as the “Big Six”, which along with the globally dominant O157:H7, form the “Top Seven” [15,28,29].
Although the significance of STEC is well documented in developed countries, the situation in developing nations remains largely unclear. Many of these countries lack comprehensive data on key serogroups and serotypes, making it difficult to track outbreaks and identify sources of infection. Contributing factors may include limited financial resources, a shortage of trained personnel, inadequate laboratory infrastructure, and challenges related to sample collection and transportation. This review aims to fill this gap by reporting the distribution and virulence traits of STEC strains isolated from various sources across developing countries for the first time. Furthermore, this review seeks to highlight the worldwide distribution and public health impact of major STEC serogroups and serotypes, emphasizing the common prevalence patterns of dominant strains in both developed and developing nations and the importance of strengthening detection, surveillance, and control strategies, especially in food safety.
2. Materials and Methods
Data for this review were collected from online databases including Google Scholar, PubMed, and Web of Science. We searched peer-reviewed literature, gray literature (e.g., preprints, surveillance data), and publicly available notifiable disease data (e.g., nationally reported, laboratory-confirmed STEC infections). The search was limited to studies published between January 2014 and October 2024, accessed between 19 February and 1 November 2024. We followed the Prisma guidelines (
The selected papers were evaluated based on the following criteria: isolation of non-sorbitol fermenting E. coli O157, isolation of non-O157 E. coli carrying stx genes or producing Shiga toxin, detection of stx genes in human and animal stool by polymerase chain reaction (PCR) or other molecular methods, and detection of Shiga toxin in clinical stool by enzyme-linked immunosorbent assay or cell cytotoxicity assay. Urinary and asymptomatic infections were excluded (Figure 1). The data were organized using Microsoft Excel 2019 (Microsoft Corporation), categorized by study DOI, year, country, serogroup, serotype, Shiga toxin-encoding genes (stx1 and stx2), stx subtypes, and intimin-encoding gene (eae). The data were further grouped into three categories: humans, animals (cattle, sheep, goats, pigs, and pigeons), and foods (meat, milk, and vegetables).
3. Results
We reviewed 250 papers that provided data on STEC in 39 developing countries across Africa (17 countries), Asia (15 countries), and Latin America and the Caribbean (7 countries). These papers covered 11 regions, including East Africa (19 papers), West Africa (32 papers), North Africa (77 papers), Central Africa (1 paper), Southern Africa (9 papers), East Asia (28 papers), South Asia (46 papers), Western Asia (24 papers), the Caribbean (1 paper), Mexico and Central America (5 papers), and South America (8 papers). Information on STEC serogroups (O), serotypes (O:H), Shiga toxin genes (stx1, stx2) and respective subtypes, and the intimin gene (eae) was collected for 8986 cases, obtained from humans (1053 cases), animals (5292 cases), and foods (2641 cases). The animal isolates included cattle (2629), sheep (1047), goats (998), pigs (539), and pigeons (79). The food isolates came from meat (1386), milk (769), and vegetables (486).
3.1. Serogroups and Serotypes
Out of 8986 reported Shiga toxin-positive (stx-positive) isolates from developing countries between 2014 and 2024, data on STEC serogroups (O) and serotypes (O:H) were available for 5026 cases (55.9%) (Table 1 and Figure 2). These comprise 389, 2936, and 1701 isolates from humans, animals and food, respectively. Among the 389 human isolates, the 15 most frequently reported serogroups/serotypes were O111 (9.5%, 37/389), O157 (9.5%, 37/389), O26 (8.7%, 34/389), O26:H11 (8.2%, 32/389), O55 (7.1%, 28/389), O157:H7 (6.4%, 25/389), O128 (4.1%, 16/389), O111:H8 (3.8%, 15/389), O91:H14 (3.5%, 14/389), O104 (3.3%, 13/389), O91 (2.8%, 11/389), O145 (2.8%, 11/389), O128:H2 (2.5%, 10/389), O118 (1.7%, 7/389), and O151:H2 (1.7%, 7/389) (Table 1 and Figure 2).
Out of the 2936 reported animal isolates, the top 15 serogroups/serotypes were O26 (10.9%, 321/2936), O157:H7 (8.7%, 257/2936), O157 (5.6%, 166/2936), O103 (5.2%, 154/2936), O3 (3.3%, 99/2936), O103:H8 (3.3%, 98/2936), O3:H21 (3.2%, 94/2936), O45 (2.9%, 86/2936), O111 (2.7%, 82/2936), O43 (2.6%, 78/2936), O43:H2 (2.5%, 76/2936), O145 (2.1%, 64/2936), O8 (2.0%, 61/2936), O76:H19 (2.0%, 60/2936), and O76 (1.8%, 54/2936) (Table 1 and Figure 2).
For the 1701 food isolates, the 15 most detected serogroups/serotypes were O157 (14.7%, 251/1701), O157:H7 (14.2%, 242/1701), O111 (12.2%, 208/1701), O26 (8.5%, 146/1701), O26:H11 (6.6%, 113/1701), O128 (3.8%, 65/1701), O55 (3.2%, 55/1701), O103 (2.9%, 50/1701), O91 (2.8%, 48/1701), O121 (2.5%, 43/1701), O128:H2 (2.5%, 43/1701), O145 (2.4%, 41/1701), O45 (1.8%, 32/1701), O113:H21 (1.4%, 24/1701), and O146 (1.4%, 24/1701) (Table 1 and Figure 2).
3.2. Shiga Toxins and Intimin Genes
In 1053 isolates from humans, stx1 was reported in 427 cases (40.5%), stx2 in 394 cases (37.4%), and stx1 and stx2 in 232 cases (22.0%). Among 5292 animal isolates, stx1 was detected in 1821 cases (34.4%), stx2 in 2234 cases (42.2%), and both subtypes in 1237 cases (23.3%). In the case of 2641 STEC isolates from food sources, stx1 was present in 1083 cases (41.0%), stx2 in 990 cases (37.4%), and both stx1 and stx2 in 568 cases (21.5%). The intimin-coding gene eae was present in 43.3% of human cases (457/1053), 18.1% of animal isolates (959/5292), and 32.4% of food-sourced samples (858/2641) (Table 2 and Figure 2).
3.3. Stx Gene Subtypes
Among strains with reported subtyping data, the most common stx subtypes in human isolates were stx1a (67.0%, 57/85), stx2a (14.1%, 12/85), and stx2c (7.0%, 6/85). In animal isolates, the most prevalent stx subtypes were stx2e (27.7%, 269/971), stx2k (17.5%, 170/971), and stx1c (16.3%, 159/971). For food isolates, the most frequently detected stx subtypes were stx2c (34.5%, 37/107), stx2e (26.1%, 28/107), and stx1c (12.1%, 13/107) (Table 3 and Figure 3).
4. Discussion
Shiga toxin-producing Escherichia coli or STEC is a major cause of foodborne outbreaks worldwide [280,281]. It produces Shiga toxin, also known as verocytotoxin, which is structurally and functionally similar to the toxin produced by Shigella dysenteriae [7]. STEC has been historically categorized into different serogroups based on the O antigen in its cell wall and into serotypes based on both the O antigen and the flagellar H antigen [282]. This review provides the first comprehensive description of key serogroups/serotypes and Shiga toxin genes found in STEC isolates from human, animal, and food samples across developing countries, over a 10-year period (2014–2024).
The data reviewed here highlight the global distribution and public health significance of various STEC serogroups and serotypes, underscoring also a shared pattern of predominant STEC in both developed and developing countries (Figure 4). The data collected from developing countries we surveyed indicate that the serogroups O111, O157, and O26, along with the serotype O26:H11, are most frequently reported in human STEC cases, together accounting for 35.9% of all confirmed cases with known serogroups and serotypes. These serogroups are associated with severe human illnesses, including hemorrhagic colitis and HUS, and have been widely studied for their public health significance [29,283]. Serogroup O157, in particular, is a major cause of foodborne outbreaks, contributing to about 36% of all STEC infections in the United States, and has been linked to multiple large-scale outbreaks, including two involving ground beef in 2022 [284,285,286,287]. In Europe, O157 remains the most frequently detected serogroup, and in countries such as the United Kingdom, Australia, and Japan, human infections with O157 strains are consistently reported [3,288,289,290]. O111 is included in the USDA-FSIS’s list of “Big Six” non-O157 serogroups requiring monitoring in meat production and has also been implicated in clinical cases worldwide [291,292]. Similarly, O26:H11, another “Big Six” serotype, was responsible for a major outbreak in France in 2022, with 50 reported cases of HUS [3]. Moreover, O26 is the major non-O157 serogroup isolated from clinical samples reported in Japan and Canada [290,293]. As part of the USDA-FSIS “Big Six”, O26:H11′s prevalence in outbreaks in Europe reinforces its status as a high-risk pathogen. These trends illustrate the global significance of these serogroups in STEC infections, indicating a need for continued attention to their detection, monitoring, and control, especially in food safety practices.
The serogroups O26, O157, and O103, along with the serotype O157:H7, were the most frequently detected STEC strains in animal isolates across the regions surveyed. These findings highlight the presence of these serogroups with animal reservoirs, particularly cattle, where they persist in the intestinal tract and can contaminate meat during slaughter and processing [294]. The prevalence of O157:H7 in animals is particularly concerning due to its association with severe outbreaks worldwide, and as such, remains the most studied and monitored STEC lineage [1,5]. The “Big Six” serogroup O103 has also emerged as a significant pathogen. In Europe, it was ranked as the third most frequent STEC serogroup in human isolates between 2007 and 2022 [3]. In the United States, O103 is recognized as one of the most common non-O157 serogroups linked to foodborne illness [285]. In Japan and Canada, it is, after O26, the most frequently reported non-O157 STEC serogroup in clinical samples [290,293,295]. The frequent detection of these STEC strains in animal isolates underscores the importance of ongoing surveillance in livestock populations to better understand the dynamics of transmission from animals to humans via contaminated meat and dairy products.
In food isolates, O157, O157:H7, and O111 were most commonly detected, consistent with global trends [296]. These findings indicate that, similar to other parts of the world, the most commonly identified STEC strains in food sources in developing countries are those with a high pathogenic potential, such as O157 and O111.
Interestingly, O145, which is commonly reported in the U.S. and Europe, was notably absent from the ‘’Top Seven’’ serogroups in the developing countries surveyed. This discrepancy could be attributed to differences in detection capabilities, where less comprehensive or more targeted diagnostic methods in developing regions might focus on the better-known, high-risk serogroups [297]. Indeed, in developed countries, sequencing is also used, which allows the in silico determination of serogroup and serotype [298]. This advanced technology enables the detection of a broader range of serogroups, such as O145, that are not as readily identified in developing regions.
The ongoing global monitoring of these serogroups is crucial, as they are included in food safety guidelines such as the USDA-FSIS’s “Big Six”, and continue to be linked to outbreaks worldwide, reinforcing the need for heightened surveillance and control measures, especially in food production and processing environments. Figure 4 shows the three most frequently reported non-O157 STEC serogroups in different countries and regions, based on data accessed in October 2024 from public agencies and reviewed literature. The data cover both developed and developing nations, including Canada (O26, O103, O111), the United States (O26, O103, O111), Europe (O26, O103, O146), the United Kingdom (O26, O146, O91), Australia (O111, O26, O113), Japan (O26, O103, O111), Africa (O111, O26, O55), Asia (O104, O26, O112), and Latin America and the Caribbean (O118, O111, O123).
Among the human, animal, and food isolates, the highest rate of eae+ strains was observed in human samples (43.3%). The 2022 European surveillance report indicated that 44.8% of STEC strains isolated from severe human cases, including HUS and bloody diarrhea, were positive for both stx2 and eae, further emphasizing the pathogenic potential of strains with these virulence traits in clinical settings [3,4]. In line with these findings, the survey also showed that stx1a and stx2a are carried by more than 81% of human isolates. In Europe, similarly, stx2a and stx2d were the most prevalent subtypes linked to severe human cases in 2022 [3]. stx2, particularly stx2a and stx2d, have been associated with elevated cytotoxicity and severe human illness, such as HUS [299,300,301,302] compared to the morbidity caused by stx1a+ STEC [303,304,305,306]. While stx2 is often associated with more severe disease outcomes, the increased cytotoxicity of stx1 in Vero cells, however, suggests that strains producing this toxin could also pose significant risks [300,307,308]. These findings underscore the critical role of these particular Shiga toxin suballeles in disease severity and clinical outcomes of STEC infections globally.
In animal isolates, stx2e was found to be the most prevalent Shiga toxin subtype. This observation might, however, be biased by the high number of stx2e-positive isolates reported from China, where stx2e-producing strains, primarily from pigs, have been frequently identified [169,170] (Table 3). While stx2e is clearly prevalent in animal populations [309], it has not been documented as a major culprit of human infections in Europe and the United States [3,7,283,285]. In food samples, stx2c was the most commonly reported Shiga toxin subtype in developing countries, and it was also the fourth most prevalent subtype of human infection in Europe in 2022, following stx2a, stx1a, and stx2b [3], which highlights the ongoing risk of foodborne transmission of stx2c+ strains.
Food products play a crucial role in the transmission via the ingestion of contaminated food, though they are not considered true reservoirs for STEC [4,5]. Overall, the high prevalence of stx2a in human infections and the increasing detection of stx2c in food isolates in developing countries emphasize the need for enhanced food safety measures and global surveillance efforts.
The data from our review highlight notable regional disparities in the reporting of STEC prevalence, with a clear gap observed in regions such as Central Africa, the Caribbean, Mexico, and Central America, where reports were less frequent compared to North Africa, South Asia, and West Africa (Table 4). This discrepancy may be linked to the economic conditions of low-income countries, which often face limitations in research funding and infrastructure, hindering comprehensive surveillance of STEC. Additionally, we observed regional variations in the major serogroups and serotypes reported (Table 4). These differences may be influenced by factors such as human-animal interactions (e.g., close contact with cattle, goats, and sheep), food production practices (e.g., traditional food preparation methods like raw milk consumption and fresh produce handling), and dietary habits (e.g., consuming raw or undercooked meat, seafood, and fresh vegetables) [310,311,312,313]. Recognizing these regional patterns is essential for developing targeted interventions to reduce the burden of STEC infections worldwide.
Among developing countries that reported STEC cases, only 55.9% included information on defined serogroups. This reflects limited surveillance capacity, with resources may be preferentially directed toward a small subset of well-known STEC serogroups, or a tendency to stop at identifying the STEC pathotype without further serogroup characterization, largely due to restricted diagnostic capabilities, particularly in molecular analyses and sequencing. Addressing these gaps is essential to improving STEC surveillance and strengthening our global understanding of its epidemiology.
5. Conclusions
Over the past two decades, STEC has been responsible for large-scale outbreaks, affecting thousands of people worldwide. Monitoring systems for STEC are well-established in countries like the United States, the United Kingdom, Europe, Australia, Japan, and Canada; however, there is a notable lack of data from developing countries, primarily due to the limited availability of organized molecular diagnostic networks and surveillance systems. The integration of genomics sequencing in STEC surveillance has significantly enhanced the ability to characterize STEC isolates [314,315]. However, many developing countries lack the infrastructure and resources for genomic-based and molecular biosurveillance. This review aims to fill that gap by providing an overview of STEC prevalence in regions with limited surveillance. To address this disparity, we recommend the establishment of reference laboratories for STEC in developing countries, similar to those operating in Argentina and Brazil. Such laboratories would enhance data quality, improve monitoring and tracking capabilities, and ultimately help reduce STEC infection. Furthermore, these reference laboratories could synergistically collaborate with existing laboratories, enabling the creation of more comprehensive global data on STEC prevalence in support of more effective public health strategies and interventions.
A.N. and M.E. designed the study, supervised the project, revised the data analyses, and drafted the manuscript. Z.K., S.S.K., A.D., A.S.M.S., H.J., and M.H. conducted the literature search and collected the data. M.A.B., F.G., L.F.d.S., and K.N. revised the data analyses and edited the manuscript. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
The authors declare no conflicts of interest.
Footnotes
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Figure 1 Flow chart of papers selection, exclusion, evaluation, and inclusion scheme.
Figure 2 The ‘’Top Seven’’ most prevalent STEC serogroups/serotypes in humans (A), animals (B), and foods (C), and major Shiga toxin gene alleles in humans (D), animals (E), and foods (F), based on data collected in developing countries between 2014 and 2024.
Figure 3 Prevalence of stx gene subtypes based on data reported for humans (A), animals (B), and foods (C) in developing countries from 2014 to 2024.
Figure 4 Global prevalence of the three most common non-O157 STEC serogroups. Countries with no available data in the surveyed databases are shown in gray (accessed October 2024).
Data on the prevalence of Shiga toxin-producing Escherichia coli (STEC) serogroups (O) and serotypes (O:H) from humans, animals, and foods collected from online databases in developing countries from 2014 to 2024.
| Source Country 1,2 | Serotype 3 | Year | ||
|---|---|---|---|---|
| Humans | Animals 4 | Foods 5 | ||
| Ethiopia | O157:ND (6) 6 | O157:ND (72), O157:H7 (45) | O157:ND (8), O157:H7 (29) | 2017–2023 |
| South Sudan | O26:H11 (3), O111:H8 (2) | - | - | 2023 |
| Tanzania | - | O113:H21 (2), O157:H7 (4) | - | 2014 |
| Benin | - | O157:ND (2) | O157:ND (2) | 2016–2020 |
| Nigeria | O26:ND (2), O103:ND (1), O128:ND (1), O145:ND (1), O157:ND (10) | O26:ND (22), O91:ND (18), O103:ND (6), O111:ND (3), O157:ND (20), O157:H7 (8) | O26:ND (18), O45:ND (5), O91:ND (4), O103:ND (7), O111:ND (15), O121:ND (1), O128:ND (5), O138:ND (3), O145:ND (5), O157:ND (19), O157:H7 (11) | 2014–2023 |
| Senegal | - | O26:ND (2), O111:ND (1), O145:ND (1) | O111:ND (1) | 2023 |
| Algeria | - | O26:ND (8), O111:ND (5), O157:ND (2) | O26:ND (3), O111:ND (2), O157:ND (30) | 2014–2020 |
| Egypt | O3:ND (1), O6:ND (5), O26:ND (26), O26:H11 (14), O45:ND (5), O55:ND (28), O75:ND (1), O91:ND (10), O103:ND (2), O103:H2 (1), O104:ND (6), O111:ND (33), O111:H8 (6), O113:ND (2), O113:H4 (1), O117:ND (4), O121:ND (5), O128:ND (15), O128:H2 (10), O145:ND (6), O146:ND (1), O146:H21 (1), O157:ND (19), O157:H7 (11) | O6:ND (1), O26:ND (97), O26:H11 (18), O45:ND (45), O55:ND (7), O63:ND (1), O75:ND (3), O91:ND (13), O103:ND (12), O103:H2 (2), O111:ND (60), O113:ND (3), O113:H4 (1), O121:ND (14), O128:ND (23), O128:H2 (8), O145:ND (20), O145:H28 (3), O146:ND (3), O146:H21 (1), O157:ND (7), O157:H7 (9) | O26:ND (82), O26:H11 (111), O45:ND (3), O55:ND (53), O76:ND (1), O91:ND (37), O103:ND (13), O103:H2 (9), O104:ND (3), O111:ND (148), O111:H8 (12), O113:ND (3), O113:H4 (1), O113:H21 (1), O117:ND (16), O121:ND (31), O128:ND (52), O128:H2 (32), O145:ND (5), O146:ND (24), O146:H21 (10), O157:ND (54), O157:H7 (80), O166:H28 (21) | 2014–2023 |
| Sudan | O26:H11 (3), O111:H8 (2), O157:H7 (5) | - | - | 2023 |
| Tunisia | - | O45:ND (3), O103:ND (13), O145:ND (14), O157:H7 (10) | O26:ND (9), O45:ND (2), O91:ND (2), O103:ND (20), O121:ND (2), O145:ND (14), O157:ND (23) | 2022–2024 |
| Namibia | - | O103:ND (5), O145:ND (5), O157:H7 (10) | O26:ND (5), O45:ND (5), O103:ND (1), O111:ND (2), O121:ND (1), O145:ND (1), O157:ND (15) | 2016–2020 |
| South Africa | - | O2:ND (1), O3:ND (97), O3:H21 (94), O4:ND (1), O5:ND (3), O5:H19(3), O6:ND (15),O7:ND (1), O8:ND (42), O10:ND (3), O16:ND (1), O22:ND (3), O26:ND (157), O26:H11 (19),O43:ND (75), O43:H2 (73), O45:ND (37), O49:ND (1), O54:ND (2), O64:ND (1), O71:ND (3), O75:ND (37), O75:H8 (37), O76:ND (52), O76:H19 (50), O79:ND (1), O102:ND (1), O103:ND (101), O103:H2 (1), O103:H8 (98), O107:H7 (3), O108:ND (13), O111:ND (2), O111:H8 (8), O113:ND (8), O117:ND (7), O121:ND (10), O125:ND (1), O132:ND (1), O145:ND (23), O145:H8 (4), O146:ND (31), O146:H21 (31), O157:ND (44), O157:H7 (44), O159:ND (2), O162:ND (1), O163:ND (2), O168:ND (1), O174:ND (16), O175:ND (3), O176:ND (5), O185:ND (4) | - | 2015–2022 |
| China | O5:ND (2), O8:ND (1), O21:H25 (1), O26:H11 (5), O43:H2 (1), O84:ND (1), O91:ND (1), O112:ND (4), O157:H7 (2), O178:ND (1) | O2:ND (11), O5:ND (1), O5:H19 (4), O6:ND (1), O6:H10 (1), O8:ND (11), O8:H19 (4), O8:H21 (2), O8:H25 (5), O9:ND (4), O15:ND (1), O16:ND (8), O20:ND (24), O21:ND (1), O21:H25 (2), O22:H8 (12), O22:H16 (5), O44:ND (1), O48:H21 (1), O66:ND (2), O74:ND (2), O76:ND (1), O76:H19 (4), O81:ND (2), O84:ND (23), O86:ND (3), O87:ND (5), O93:ND (52), O100:ND (8), O100:H19 (3), O104:ND (1), O108:ND (4), O112:ND (1), O113:H4 (1), O114:ND (1), O116:ND (7), O128:H2 (1), O129:ND (1), O130:ND (1), O133:ND (11), O140:ND (1), O142:ND (1), O143:ND (3), O157:H7 (50), O159:ND (4), O172:ND (3), O174:ND (13), O177:ND (8), O184:H19 (17) | O5:ND (1), O5:H9 (1), O5:H19 (1), O8:ND (2), O8:H9 (3), O8:H19 (3), O8:H30 (2), O12:ND (2), O20:H21 (1), O21:H25 (2), O22:ND (2), O26:H11 (1), O40:H8 (2), O45:ND (5), O57:H21 (1), O76:H19 (2), O76:H21 (1), O84:ND (1), O91:ND (1), O91:H4 (4), O96:H19 (1), O98:ND (4), O100:H19 (3), O103:H25 (2), O104:H7 (3), O110:ND (2), O111:ND (1), O112:ND (2), O113:H4 (5), O113:H7 (1), O116:H21 (1), O118:ND (1), O120:ND (2), O121:ND (1), O128:H2 (10), O141:H29 (2), O145:ND (1), O150:ND (2), O157:ND (53), O157:H7 (76), O161:H19 (1), O172:ND (1), O174:ND (1), O176:H4 (5) | 2014–2023 |
| Malaysia | - | O130:H26 (1), O139:H1 (5), O157:H7 (32), O168:H21 (1) | - | 2013–2015 |
| South Korea | O26:H21 (1), O91:H14 (13), O103:H2 (1), O121:H19 (1) | O8:H19 (3), O8:H21 (1), O26:ND (3), O104:ND (1), O111:ND (1), O157:ND (5) | O1:ND (2), O6:ND (1), O18:ND (1), | 2014–2024 |
| Thailand | - | O157:H7 (1) | - | 2022 |
| Bangladesh | - | O43:H2 (3), O57:ND (40), O76:H19 (6), O87:H16 (3), O110:H16 (1), O152:H8 (2) | O157:ND (7) | 2015–2023 |
| India | O28:ND (1), O59:ND (2), O64:ND (1), O69:ND (1), O85:ND (2), O95:ND (1), O102:ND (1), O103:ND (1), O157:H7 (1), O168:ND (1) | O2:ND (9), O3:ND (1), O4:ND (2), O5:ND (9), O7:ND (7), O8:ND (8), O9:ND (19), O10:ND (1), O11:ND (12), O12:ND (1), O17:ND (1), | O2:ND (1), O5:ND (2), O7:ND (7), | 2014–2023 |
| Iran | O26:ND (2), O45:ND (1), O121:ND (1), O157:ND (2), O157:H7 (1) | O5:ND (16), O5:H19 (3), O26:ND (29), | O26:ND (25), O26:H11 (1), O45:ND (9), O91:ND (3), O103:ND (6), O111:ND (8), O113:ND (5), O121:ND (7), O121:H7(1), O128:ND (7), O128:H2 (1), O145:ND(5), O157:ND (34) | 2014–2023 |
| Pakistan | - | - | O9:H9 (2), O9:H30 (2), O82:H4 (3), | 2017–2024 |
| Iraq | - | - | O157:H7 (9) | 2019–2023 |
| Israel | O26:H11 (6), O71:H8 (1), O157:H7 (1) | O171:H21 (1), O171:H25 (1), O171:H29 (1) | - | 2017 |
| Saudi Arabia | - | - | O22:H8 (6), O111:ND (20), O113:H21 (12), O157:H7 (30) | 2015 |
| Turkey | O26:ND (4), O26:H6 (1), O26:H12 (1), O45:H2 (1), O103:H2 (2), O104:ND (7), O111:ND (1), O145:ND (3), O157:H7 (3), O174:H21 (2), O181:H4 (1) | O26:ND (1), O45:ND (1), O121:ND (22), | O21:ND (2), O21:H25 (2), O45:ND (3), O103:ND (2), O111:ND (11), O145:ND (10), O157:ND (6), O157:H7 (3) | 2016–2023 |
| United Arab Emirates | - | O157:H7 (5) | - | 2020 |
| Costa Rica | O25:H4 (1), O71:H11 (2), O103:H2 (2), O111:H8 (3), O118:ND (7), O119:H2 (2), O137:H4 (1), O145:H28 (1), O151:H2 (7), O174:H21 (2) | - | - | 2024 |
| Mexico | - | O3:ND (1), O8:H7 (2), O8:H8 (2), O9:H19 (5), O15:ND (1), O39:H21 (1), O48:ND (3), | - | 2016–2023 |
| Argentina | - | O1:H21 (1), O113:H21 (1), O130:ND (1), | O8:H16 (4), O8:H19 (2), O22:H8 (3), | 2017–2023 |
| Brazil | O8:H19 (1), O24:H4 (1), O26:H11 (1), O71:H8 (1), O91:H14 (1), O100:ND (1), O103:ND (1), O111:ND (3), O111:H8 (2), O111:H11 (1), O118:H16 (1), | O104:ND (2), O113:H2 (2), O157:H7 (5) | O8:H21 (1), O21:H19 (1), O22:H16 (1), O26:ND (3), O73:H45 (1), O79:H7 (3), O83:H19 (1), O113:H21 (3), O117:H7 (1), O132:H21 (1) | 2018–2023 |
| Uruguay | - | O8:H16 (2), O8:H19 (2), O20:H7 (1), O22:H8 (5), O74:H42 (1), O88:H25 (1), O99:H19 (1), O113:H21 (1), O116:H49 (1), O120:H7 (1), O156:H10 (2), O157:H7 (1), O159:H28 (1), O171:H2 (1), O174:H21 (2), O174:H28 (3), O178:H19 (2), O185:H25 (1) | - | 2023 |
1 Countries were classified as developing based on the World Economic Situation and Prospects 2023 report by the United Nations (
Data on the distribution of Shiga toxin-coding genes stx1, stx2, and intimin-coding gene eae in Shiga toxin-producing Escherichia coli (STEC) gathered from online databases covering humans, animals, and foods in developing countries from 2014 to 2024.
| Source Country 1 | Shiga toxin/Intimin | Year | Author | ||
|---|---|---|---|---|---|
| Humans | Animals 2 | Foods 3 | |||
| Ethiopia | stx1 (29), stx2 (28), stx1+stx2 (30)/eae (42) | stx1 (40), stx2 (113), stx1+stx2 (30)/eae (156) | stx1 (3), stx2 (15), stx1+stx2 (5)/eae (13) | 2017–2023 | [ |
| Kenya | stx2 (6), stx1+stx2 (13)/eae (9) | - | - | 2018 | [ |
| South Sudan | stx2 (5), stx1+stx2 (5) | - | - | 2023 | [ |
| Tanzania | - | stx2 (5), stx1+stx2 (2)/eae (4) | - | 2014 | [ |
| Benin | - | stx1 (2)/eae (2) | stx1 (12), stx2 (104)/eae (1) | 2016–2020 | [ |
| Burkina Faso | - | stx1 (2), stx2 (2), stx1+stx2 (1)/eae (1) | stx2 (6)/eae (3) | 2016–2017 | [ |
| Nigeria | stx1 (101), stx2 (88), stx1+stx2 (71)/eae (78) | stx1 (249), stx2 (232), stx1+stx2 (17)/eae (14) | stx1 (257), stx2 (166), stx1+stx2 (69)/eae (139) | 2014–2023 | [ |
| Senegal | - | stx1+stx2 (7)/eae (8) | stx1+stx2 (4)/eae (2) | 2023 | [ |
| Algeria | - | stx1 (168), stx2 (22), stx1+stx2 (7)/eae (68) | stx1 (7), stx2 (21), stx1+stx2 (6)/eae (18) | 2014–2020 | [ |
| Egypt | stx1 (97), stx2 (75), stx1+stx2 (41)/eae (169) | stx1 (45), stx2 (63), stx1+stx2 (52)/eae (93) | stx1 (225), stx2 (205), stx1+stx2 (236)/eae (308) | 2014–2023 | [ |
| Sudan | stx2 (5), stx1+stx2 (5) | - | - | 2023 | [ |
| Tunisia | - | stx1 (16), stx2 (13), stx1+stx2 (11), eae (8) | stx1 (61), stx2 (17), stx1+stx2 (1), eae (28) | 2022–2024 | [ |
| Cameroon | - | - | stx1 (4), stx2 (4), stx1+stx2 (4), eae (6) | 2017 | [ |
| Namibia | - | - | stx1 (73), stx2 (2), stx1+stx2 (10), eae (84) | 2016–2020 | [ |
| South Africa | - | stx1 (223), stx2 (384), stx1+stx2 (380), eae (181) | - | 2015–2022 | [ |
| Zambia | - | stx1 (4), stx2 (15), stx1+stx2 (22) | - | 2016 | [ |
| Zimbabwe | - | - | stx2 (7), eae (1) | 2020 | [ |
| China | stx1 (6), stx2 (3), stx1+stx2 (5), eae (11) | stx1 (215), stx2 (485), stx1+stx2 (97), eae (91) | stx1 (47), stx2 (165), stx1+stx2 (85), eae (11) | 2014–2023 | [ |
| Indonesia | - | stx2 (1) | stx1 (1) | 2016 | [ |
| Malaysia | - | stx1 (1), stx2 (32), stx1+stx2 (1), eae (32) | - | 2013–2015 | [ |
| South Korea | stx1 (14), stx2 (20), stx1+stx2 (22), eae (3) | stx1 (17), stx2 (87), stx1+stx2 (52) | stx1 (18), stx2 (8), stx1+stx2 (9), eae (3) | 2014–2024 | [ |
| Thailand | - | stx2 (1), eae (1) | - | 2022 | [ |
| Vietnam | - | stx2 (50) | - | 2019 | [ |
| Bangladesh | - | stx1 (25), stx2 (58), stx1+stx2 (15)/eae (6) | stx1 (1), stx2 (11) | 2015–2023 | [ |
| India | stx1 (26), stx2 (12), stx1+stx2 (9)/eae (5) | stx1 (236), stx2 (224), stx1+stx2 (230)/eae (63) | stx1 (101), stx2 (88), stx1+stx2 (71)/eae (78) | 2014–2023 | [ |
| Iran | stx1 (66), stx2 (80), stx1+stx2 (18)/eae (33) | stx1 (115), stx2 (132), stx1+stx2 (50)/eae (127) | stx1 (101), stx2 (23), stx1+stx2 (12)/eae (67) | 2014–2024 | [ |
| Pakistan | - | - | stx1 (144), stx2 (13), stx1+stx2 (25)/eae (9) | 2017–2024 | [ |
| Iraq | stx1 (13), stx2 (18)/eae (33) | stx1 (13), stx2 (17), stx1+stx2 (62)/eae (27) | stx1 (12), stx2 (15), stx1+stx2 (2)/eae (6) | 2019–2023 | [ |
| Israel | stx2 (6), stx1+stx2 (3) | stx2 (2), stx1+stx2 (1) | - | 2017 | [ |
| Saudi Arabia | - | - | stx1 (1), stx2 (74), stx1+stx2 (8)/eae (51) | 2015 | [ |
| Turkey | stx1 (10), stx2 (19), stx1+stx2 (4)/eae (14) | stx1 (181), stx2 (123), stx1+stx2 (84)/eae (61) | stx1 (13), stx2 (14), stx1+stx2 (13)/eae (29) | 2016–2023 | [ |
| United Arab Emirates | - | stx2 (12), eae (8) | - | 2020 | [ |
| Trinidad and Tobago | - | stx1 (9), stx2 (10), stx1+stx2 (4)/eae (1) | - | 2016 | [ |
| Costa Rica | stx1 (23), stx2 (4), stx1+stx2 (1)/eae (22) | - | - | 2024 | [ |
| Guatemala | stx1 (23), stx2 (19)/eae (18) | - | - | 2022 | [ |
| Mexico | - | stx1 (42), stx2 (46), stx1+stx2 (25)/eae (4) | - | 2016–2023 | [ |
| Argentina | - | stx1 (5), stx2 (50), stx1+stx2 (17)/eae (1) | stx1 (1), stx2 (20), stx1+stx2 (3) | 2017–2023 | [ |
| Brazil | stx1 (19), stx2 (6), stx1+stx2 (5)/eae (20) | stx1 (207), stx2 (38), stx1+stx2 (54) | stx1 (1), stx2 (12), stx1+stx2 (5)/eae (1) | 2018–2023 | [ |
| Uruguay | - | stx1 (6), stx2 (17), stx1+stx2 (16)/eae (2) | - | 2023 | [ |
| Total | stx1 (427), stx2 (394), stx1+stx2 (232)/eae (457) | stx1 (1821), stx2 (2234), stx1+stx2 (1237)/eae (959) | stx1 (1083), stx2 (990), stx1+stx2 (568)/eae (858) | 2014–2024 | - |
1 Countries for which there were no data in the surveyed databases (PubMed, Google Scholar, Web of Science) were excluded from this table (accessed in October 2024). 2 Cattle, Sheep, Goats, Pigs, and Pigeons. 3 Meat, Milk, and Vegetables.
Data on the frequency of stx gene subtypes in Shiga toxin-producing Escherichia coli (STEC) strains retrieved from online databases, focusing on humans, animals, and foods in developing countries from 2014 to 2024.
| Source Country 1 | Shiga toxin subtypes | Year | ||
|---|---|---|---|---|
| Humans | Animals 2 | Foods 3 | ||
| Ethiopia | stx2a (1), stx2c (3) | stx2a (6), stx2c (53) | stx2a (2), stx2c (4) | 2017–2023 |
| Tanzania | - | stx2c (4) | - | 2014 |
| Egypt | stx1a (1) | stx2a (4), stx2b (1), stx2c (4), stx2d (2), stx2e (1) | - | 2014–2023 |
| South Africa | - | stx2c (10), stx2d (10) | - | 2015–2022 |
| China | stx1a (3), stx1c (3), stx2d (1), stx2e (1) | stx1a (79), stx1c (133), stx2a (4), stx2b (13), stx2c (23), stx2e (216), stx2g (13), stx2k (170) | stx1a (12), stx1c (13), stx1d (1), stx2a (5), stx2b (2), stx2c (31), stx2d (1), stx2e (26), stx2k (3) | 2014–2023 |
| Malaysia | - | stx1a (1), stx2c (32) | - | 2013–2015 |
| South Korea | stx1a (2), stx2a (1) | - | stx2c (2), stx2e (2) | 2014–2024 |
| Vietnam | - | stx2e (50) | - | 2019 |
| Bangladesh | - | stx2d (1) | stx2d (1) | 2015–2023 |
| Iran | stx1a (6), stx1d (1) | - | - | 2014–2024 |
| Israel | stx2a (5) | stx2c (1), stx2d (1) | - | 2017 |
| Turkey | stx1a (4), stx2a (1) | stx1a (4), stx2a (2), stx2b (9), stx2c (11), stx2d (6), stx2e (2), stx2f (12), stx2h (3), stx2i (3), stx2j (2) | stx2a (2) | 2016–2023 |
| Costa Rica | stx1a (23), stx2a (4) | - | - | 2024 |
| Mexico | - | stx1b (9), stx1c (26), stx1d (1) | - | 2016–2023 |
| Argentina | - | stx1a (4), stx2a (24) | - | 2017–2023 |
| Brazil | stx1a (18), stx1d (1), stx2c (3), stx2d (2), | - | - | 2018–2023 |
| Uruguay | - | stx1a (2), stx1d (4), stx2a (4), stx2c (7), | - | 2023 |
| Total | stx1a (57), stx1c (3), stx1d (2), stx2a (12), stx2c (6), stx2d (3), stx2e (2) | stx1a (90), stx1b (9), stx1c (159), stx1d (5), stx2a (44), stx2b (23), stx2c (145), stx2d (24), stx2e (269), stx2f (12), stx2g (13), stx2h (3), stx2i (3), stx2j (2), stx2k (170) | stx1a (12), stx1c (13), stx1d (1), stx2a (9), stx2b (2), stx2c (37), stx2d (2), stx2e (28), stx2k (3) | 2014–2024 |
1 Countries for which there were no data in the surveyed databases (PubMed, Google Scholar, Web of Science) were excluded from this table (accessed in October 2024). 2 Cattle, Sheep, Goats, Pigs, and Pigeons. 3 Meat, Milk, and Vegetables.
The main reported serogroup (O), serotype (O:H), Shiga toxin (Stx), and stx gene subtype of humans, animals, and foods STEC strains in developing countries based on region, from 2014 to 2024.
| Region 1 | Serogroup, Serotype, Shiga Toxin | No. Papers 4 | ||
|---|---|---|---|---|
| Humans | Animals 2 | Foods 3 | ||
| East Africa | O157, O26:H11, stx1+stx2, stx2c | O157, O157:H7, stx2, stx2c | O157, O157:H7, stx2, stx2c | 19 |
| West Africa | O157, stx1 | O26, O157:H7, stx1 | O157, O157:H7, stx2 | 32 |
| North Africa | O111, O26:H11, stx1, stx1a | O26, O157:H7, stx1, stx2a, stx2c | O111, O26:H11, stx1 | 77 |
| Central Africa | - | - | stx1, stx2, stx1+stx2 | 1 |
| Southern Africa | - | O26, O103:H8, stx1+stx2, stx2c, stx2d | O157, stx1 | 9 |
| East Asia | O112, O91:H14, stx1+stx2, stx1a | O93, O157:H7, stx2, stx2e | O157, O157:H7, stx2, stx2c | 28 |
| South Asia | O26, O59, O85, O157, O157:H7, stx1, stx2, stx1a | O57, O76:H19, stx2, stx2d | O157, O82:H4, stx1, stx2d | 46 |
| Western Asia | O104, O26:H11, stx2, stx2a | O121, O157:H7, stx1, stx2c, stx2f | O111, O157:H7, stx2, stx2a | 24 |
| Caribbean | - | stx2 | - | 1 |
| Mexico and Central America | O118, O151:H2, stx1, stx1a | O75, O157:H7, stx2, stx1c | - | 5 |
| South America | O111, O123, O111:H8, stx1, stx1a | O104, O175:H19, O178:H19, stx1, stx2a | O26, O113:H21, stx2 | 8 |
1 Regions were classified by the United Nations (UN) in the World Economic Situation and Prospects 2023 report (
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Details
; Dadvar Ali 2
; Eppinger, Mark 3 ; Karimpour Zohreh 1 ; Saberi Kakhki Soroush 1 ; Sabeti Moghaddam Sabzevar Alireza 1
; Badouei Mahdi Askari 1
; Gigliucci Federica 4
; Santos Luis Fernando dos 5
; Nakamura, Keiji 6
; Javidi Hooman 1
; Hafiz Maryam 1 1 Department of Pathobiology, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran; [email protected] (A.N.); [email protected] (Z.K.); [email protected] (S.S.K.); [email protected] (A.S.M.S.); [email protected] (M.A.B.); [email protected] (H.J.); [email protected] (M.H.)
2 Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, SE-171 77 Stockholm, Sweden; [email protected]
3 Department of Molecular Microbiology & Immunology, University of Texas at San Antonio, San Antonio, TX 78249-0600, USA, South Texas Center for Emerging Infectious Diseases (STCEID), San Antonio, TX 78249-0600, USA
4 European Union Reference Laboratory (EURL) for
5 Center of Bacteriology (National Reference Laboratory for STEC Infections and HUS), Instituto Adolfo Lutz, São Paulo 01246-000, SP, Brazil; [email protected]
6 Department of Bacteriology, Faculty of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; [email protected]