Abstract
Background:
Multidrug-resistant extended-spectrum β-lactamase (ESBL)-producing Enterobacterales is regarded as a critical health issue, yet, surveillance in the water-plant-food interface remains low, especially in Africa.
Objectives:
The objective of the study was to elucidate the distribution and prevalence of antimicrobial resistance in clinically significant members of the Enterobacterales order isolated from the water-plant-food interface in Africa.
Methods:
A literature search was conducted using six online databases according to the PRISMA guidelines. All available published studies involving phenotypic and genotypic characterization of ESBL-producing Enterobacterales from water, fresh produce or soil in Africa were considered eligible. Identification and characterization methods used as well as a network analysis according to the isolation source and publication year were summarized. Analysis of Escherichia coli, Salmonella spp. and Klebsiella pneumoniae included the calculation of the multiple antibiotic resistance (MAR) index according to isolation sources and statistical analysis was performed using RStudio.
Results:
Overall, 51 studies were included for further investigation. Twelve African countries were represented, with environmental AMR surveillance studies predominantly conducted in South Africa. In 76.47% of the studies, occurrence of antimicrobial resistant bacteria was investigated in irrigation water samples, while 50.98% of the studies included fresh produce samples. Analysis of bacterial phenotypic antimicrobial resistance profiles were reported in 94.12% of the studies, with the disk diffusion method predominantly used. When investigating the MAR indexes of the characterized Escherichia coli, Klebsiella pneumoniae and Salmonella spp., from different sources (water, fresh produce or soil), no significant differences were seen across the countries. The only genetic determinant identified using PCR detection in all the studies was the blaCTX − M resistance gene. Only four studies used whole genome sequence analysis for molecular isolate characterization.
Discussion:
Globally, AMR surveillance programmes recognize ESBL- and carbapenemase-producing Enterobacterales as vectors of great importance in AMR gene dissemination. However, in low- and middle-income countries, such as those in Africa, challenges to implementing effective and sustainable AMR surveillance programmes remain. This review emphasizes the need for improved surveillance, standardized methods and documentation of resistance gene dissemination across the farm-to-fork continuum in Africa.
1. Introduction
Antimicrobial resistance (AMR) is regarded as one of the top ten threats to global health (WHO, 2020). This follows as the emergence and spread of drug-resistant pathogens that have acquired new resistance mechanisms continue to threaten the effectiveness of clinically important antibiotics to treat common infections (Koutsoumanis et al., ; Rahman et al., ). Globally, major organizations including the World Health Organization (WHO), Food and Agriculture Organization of the United Nations (FAO), World Organization of Animal Health (OIE) and the European Commission (EC) have recognized the need for further investigation and a multidisciplinary approach to combatting AMR (Koutsoumanis et al., ). However, in low and middle income countries (LMICs), such as those in Africa, challenges to implementing effective and sustainable AMR surveillance programmes remain (Elton et al., ). This follows as LMICs often lack the necessary infrastructural and institutional capacities and effective reporting systems to roll out sustainable surveillance programmes (Elton et al., ).
Many different sources and routes for human acquisition of antimicrobial resistant bacteria are recognized, including human-to-human transmission, direct contact with food-producing animals and pets, foodborne transmission as well as the environment (Koutsoumanis et al., ). In LMICs, the main driver of AMR is reported to be transmission and not antimicrobial use (Koutsoumanis et al., ). Non-human sources of pathogens such as extended-spectrum β-lactamase (ESBL)-producing Escherichia coli and plasmid mediated AmpC (pAmpC)-producing E. coli has been reported, with the need for longitudinal studies and continuous monitoring (Mughini-gras et al., ). Furthermore, the Centers for Disease Control and Prevention (CDC) recently reported that urgent AMR threats in the United States (US) include carbapenem-resistant Enterobacterales, while ESBL-producing Enterobacterales, drug-resistant nontyphoidal Salmonella, and drug-resistant Salmonella serotype Typhi are regarded as serious threats, among others (CDC, ). The Enterobacterales form part of the normal epiphytic microflora of fruit and vegetables, and include members ubiquitous in terrestrial and aquatic environments, as well as human foodborne pathogens including pathogenic E. coli and Salmonella spp. (Rajwar et al., ). Moreover, Enterobacterales are adapted to sharing genetic material and often clinically significant resistance genes through carriage on mobile genetic elements (Partridge, ). Recently, a comprehensive assessment of the global burden of AMR stated that the six leading pathogens for deaths associated with resistance included E. coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa (Murray et al., ).
In the last decade, an increased emphasis on the role of the environment in dissemination of AMR has been reported (WHO, 2015; Koutsoumanis et al., ). Furthermore, authors have reported on the importance of an integrated One Health approach for developing and implementing mitigation strategies in combatting AMR (White and Hughes, 2019; Ikhimiukor et al., ). The One Health concept in AMR mitigation strategies recognizes that humans, animals (including wildlife), environments, and ecosystems are key priorities (White and Hughes, 2019). To date, most AMR surveillance studies, especially in LMICs in Africa, have focussed on humans and animals. As an example, from 901 studies in LMIC-based studies in 2000-2018, the rapid increasing trends of AMR in the food-animal sector for common indicator pathogens such as E. coli, Campylobacter spp., Salmonella spp., and S. aureus have been reported (Ikhimiukor et al., ). The main objective of the current study was to elucidate the distribution and prevalence of AMR in clinically significant members of the Enterobacterales family isolated from the water-plant-food nexus in Africa.
2. Materials and methods
The review and meta-analysis of occurrence included published articles from January 2010 – December 2022 and was compiled according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (Page et al., ).
2.1. Search strategy
Information on published articles from all countries on the African continent as defined by the African Union (African Union Commission, ) that reported on the occurrence of multidrug resistant Enterobacterales in the water-plant-food interface were included. Two authors independently performed comprehensive literature searches using five online databases: Google Scholar, PubMed Database, EBSCOhost Online Research Databases, Science Direct and Semantic Scholar. Boolean operators (“AND”, “OR”) were applied to search the articles and only English publications were included. The predefined search terms “(extended-spectrum beta-lactamase or extended-spectrum or beta-lactamase or ESBL or ESBL-producing) AND (Enterobacteriaceae or Enterobacterales) AND Africa AND (water or irrigation water or vegetables or fruit or fresh produce or produce or soil)” were used to retrieve relevant articles published within the chosen timeframe. The WHO Global Action Plan to tackle AMR (GAP-AMR) as well as the Global Antimicrobial Resistance and Use Surveillance System (GLASS) was established in 2015 and national action plans developed in individual countries subsequently followed (WHO, 2020; Willemsen et al., 2022). For the current review, the authors chose a timeframe that included environmental AMR surveillance studies 5 years prior to the launch of GLASS up to the most recent articles available in 2022, to provide an overview of current analysis of environmental AMR profiles in African countries over at least a decade.
2.2. Study inclusion and exclusion criteria
Publications were independently reviewed by two authors (LR and ED) to determine eligibility and duplicate entries were identified by considering the title, authors and the year of publication. Inclusion criteria comprised of all available full text articles involving phenotypic and genotypic characterization of ESBL-producing Enterobacterales from water, fresh produce or soil in Africa. More specifically, publications that described the occurrence of antimicrobial resistant bacteria in fresh produce production, including soil, harvested produce and associated irrigation water, as well as fresh produce at retail were considered eligible. Given the diversity of plant-associated bacteria, only members within the Enterobacterales order were included in the current review. This follows as a dramatic escalation in AMR among bacteria, especially members of the Enterobacteriaceae have been noted globally, resulting in ESBL-producing Enerobacteriaceae forming part of the WHO list of critical priority pathogens that pose the greatest threat to human health (WHO, 2017a; Lynch and Clark, ). Studies that focused on wastewater, human- or animal health and studies that did not identify the bacterial organisms to at least genus level were excluded (Figure 1). Furthermore, studies that focused on transmission of AMR or multidrug resistant bacteria through non-plant food sources (e.g. dairy, aquaculture or meat products) were not considered eligible for the current review. All published articles available on the selected databases at the time of data extraction (n = 922) were individually reviewed and those not meeting the pre-defined inclusion criteria were excluded from the final articles for analysis.
Figure 1
2.3. Data synthesis, analysis and reporting
Overall, 51 studies were included for further investigation. The publication year, isolation source (water, soil or fresh produce), percentage bacterial occurrence, and identification and characterization methods used were summarized for all publications (Supplementary Table 1). Network analysis was carried out using UCINET® 6 for Windows (Borgatti et al.,
For selected bacterial species (E. coli, K. pneumoniae and Salmonella spp.) isolated from the different sources, where the information was not already included in the published results and possible to calculate, the multiple antibiotic resistance (MAR) indexes were calculated for analysis of the potential health risk (data not shown). The MAR index for each bacterial species in the respective studies was calculated as x/(y.z) where x represents the aggregate resistance of antibiotics to all isolates, while y represents the total number of antibiotics and z the number of isolates from the isolation source (Riaz et al.,
2.4. Statistical analysis
Data were analyzed using RStudio (RStudio Team, 2020). The Shapiro-Wilk test was performed on the standardized residuals to test for deviations from normality (Shapiro and Wilk, 1965). ANOVA was used to test for significant differences between the MAR indexes of the characterized E. coli, K. pneumoniae and Salmonella spp. per country and publication year, respectively. Student's protected t-LSD (Least significant difference) were calculated at a 5% significance level to compare significant source effects of the MAR indexes for the characterized isolates (Snedecor and Cochran, 1980).
3. Results
3.1. General overview
Based on the eligibility criteria (Figure 1), a total of 51 articles were included in the systematic review. The included studies represented 12 African countries (Figure 2). The majority of the studies were conducted in South Africa (n = 20), followed by Nigeria (n = 10), Tunisia (n = 6), Algeria (n = 4), Benin (n = 2), Ghana (n = 2), Morocco (n = 2), Egypt (=1), Tanzania (n = 1), Sudan (n = 1), Kenya (n = 1), and Democratic Republic of the Congo (n = 1).
Figure 2

African countries (n = 12) represented by the data analyzed across studies (n = 51) related to antimicrobial resistance.
The occurrence of antimicrobial resistant bacteria in irrigation water samples were evaluated in the majority (76.47%) of the studies (Figure 3). Where indicated, the type of irrigation water included predominantly surface water (river) sources, followed by borehole, ponds, wells, streams and/or canals. In nine studies, irrigation water in conjunction with associated irrigated fresh produce were analyzed, while another eight studies focussed on bacterial isolation and characterization from water and soil in the agricultural environments (Figure 3). Six studies included analysis of irrigation water, soil and associated fresh produce, while eleven studies focussed on isolation and characterization of bacteria from fresh produce only and one focussed on soil analysis only (Figure 3). Overall, the studies that included water, soil and/or fresh produce samples were predominantly conducted in South Africa (n = 13), while studies that focussed on fresh produce or water samples only, were conducted mostly in Nigeria (n = 8) (Figure 3). The majority of the studies were published in 2020 (n = 13), followed by 2015 and 2022 (n = 7, respectively) and 2021 (n = 5).
Figure 3

Network analysis of studies done in Africa on the occurrence and characterization of antimicrobial resistant Enterobacterales isolated from the water-soil-plant environment from 2010 to 2022. The numbers represent the articles (n = 51) included in the systematic review, while the colors represent the publication year and shapes the different countries.
3.2. Identification and characterization of bacterial isolates
Enterobacterales from 15 different genera were isolated and characterized for antimicrobial resistance, either phenotypically, genotypically, or both, in 12 African countries. Bacterial isolate identification was performed using principally three methods, alone or in combination, that included biochemical tests, PCR and/or mass spectrometry (Supplementary Table 1). Matrix assisted laser desorption ionization time of flight (MALDI-ToF) mass spectrometry identification appeared to be the gold standard for isolate identification in South African studies, used in 60% (n = 12), followed by 16S rDNA PCR identification (n = 5), biochemical tests (n = 2) and one study that used the OmniLog system for identification. In the other African countries, biochemical tests [analytical profile index (API) or indole testing] were predominantly used for isolate identification (n = 18), followed by MALDI-ToF (n = 7) and 16S rDNA PCR identification (n = 4). One study in the Democratic Republic of the Congo used only selective media for isolate identification, while the Phoenix 100 phenotyping system and a combination of biochemical tests and PCR was used in two studies in Tunisia, respectively.
The 51 studies included in the current review reported on isolation and characterization of 15 different Enterobacterales genera from irrigation water sources, soil and fresh produce (Supplementary Table 1). In total, 20 (39.22%) articles focussed on E. coli only, three (5.88%) on Klebsiella pneumoniae, two (3.92%) on Salmonella spp., and one each on Citrobacter spp., and Enterobacter spp. In the remaining 24 articles that focused on the Enterobacterales family, the most frequently reported bacteria were Klebsiella spp. (43.14%), followed by Citrobacter spp. (35.29%), Enterobacter spp. and E. coli (33.33% each), Serratia spp. (15.69%) and Salmonella spp. and Proteus spp. (11.76% each).
Analysis of phenotypic antimicrobial resistance profiles of the isolates were reported in 94.12% of the studies, with the disk diffusion method predominantly used (Supplementary Table 1). Phenotypic results analysis mainly relied on the interpretive criteria of the Clinical and Laboratory Standards Institute (CLSI, including NCCLS, n = 31), followed by the European Committee of Antimicrobial Susceptibility Testing (EUCAST, n = 3) and the Antibiogram Committee of the French Society of Microbiology (n = 4). Furthermore, in seven studies, both the CLSI and EUCAST criteria were used for results interpretation. Additionally, 72.55% of the studies (n = 37) included PCR detection of the resistance genes, with DNA sequencing as a complementary test to the PCR included in 31.37% of the studies. Of the 31.37%, only 7.84% (n = 4) used the whole genome sequencing (WGS) technique for further characterization.
3.3. Shared resistance genes in potential human pathogens within the water-plant-food interface
The 37 studies that included PCR analysis of resistance genes were predominantly in South Africa (n = 13), followed by Tunisia and Nigeria (n = 6 each), Benin, Morocco and Algeria (n = 2 each) and one study each in Kenya, Sudan, Ghana, Egypt and the Democratic Republic of Congo. Overall, the greatest number of resistance genes were identified in isolates from water samples, however, this comes with a caveat that more studies focussed on water sample analysis (n = 39) alone or in combination with fresh produce and/or soil (Figure 3). In South Africa, Nigeria, Algeria and Tunisia, the blaCTX − M ESBL resistance gene was identified in isolates from water, fresh produce and/or soil. Furthermore, the blaCTX − M resistance gene was the only genetic determinant identified in most of the studies that included PCR analysis, with the exception of studies in Ghana, Morocco and Egypt where the blaTEM gene was predominantly identified. Additionally, beta-lactamase genes including blaSHV, blaTEM, blaOXA, as well as AmpC resistance genetic determinants (blaFOX, blaMOX, blaCIT), sulfonamides and tetracyclines were identified in isolates from water, fresh produce and soil in the South African studies. Isolates from water samples in Nigeria predominantly harbored genes which contributed to resistance against tetracyclines, followed by aminoglycosides (aminoglycoside kinase, aph). In water sample isolates from both South Africa and Tunisia, the blaVIM and blaIMP carbapenem resistance genes were identified. Additionally, the blaKPC and blaNDM carbapenem resistance genes were identified in isolates from water samples, while the blaGES carbapenem resistance gene was present in isolates from water and fresh produce, and the mcr gene was detected from isolates in soil samples, all in studies conducted in South Africa. The NDM carbapenem resistance gene was also identified in isolates from soil samples in Nigeria and water samples from Egypt.
3.4. Further analysis of selected potential human pathogens from the water-soil-fresh produce environment
3.4.1. Escherichia coli
In studies from all nine countries where E. coli was isolated, water samples predominantly included river water used for fresh produce irrigation in urban areas. River water was reported to be impacted by anthropogenic activities (agricultural, industrial and/or domestic) in all the included studies that investigated the presence of multidrug resistant Enterobacterales. The fresh produce samples were purchased at open air markets or formal retailers and farm fresh produce and soil samples included soil from the field where fresh produce was harvested. At least nine classes of antibiotics were included for phenotypic antimicrobial resistance screening in most of the studies that focussed on characterization of E. coli. The dominant resistance patterns of the E. coli isolates included resistance to antibiotics within the tetracycline, penicillin and sulfonamide antibiotic classes, followed by aminoglycosides and quinolones. For the studies where calculations were possible, isolated E. coli had multiple antimicrobial resistance (MAR) indexes of ≥ 0.2, except for two studies in South Africa in 2014 and 2016 (Supplementary Table 2). These studies were conducted in Tunisia, Nigeria, Algeria, Morocco, Sudan, Ghana or South Africa, with significant differences in the MAR indexes between certain countries (p = 0.006) (Figure 4). With a one-way ANOVA, sufficient evidence was given that the MAR indexes of characterized E. coli per publication year did not differ significantly (p = 0.18). Furthermore, no significant differences were seen in the overall MAR indexes of the characterized E. coli from different sources being either water (p = 0.215), fresh produce (p = 0.435) or soil (p = 0.471) samples throughout the study period. Overall, 17 studies that included PCR analysis (either presence/absence detection or further sequencing), screened for resistance genes in E. coli isolated from water, soil or fresh produce samples. The greatest diversity of β-lactamase genes was found in E. coli isolated from samples analyzed in South Africa (Figure 5). In isolates from Tunisia, South Africa, Nigeria, Kenya, Algeria, Sudan, Morocco and Benin, both the blaTEM and blaCTX − M genetic determinants were found (Figure 5). Where sequencing was done, the blaTEM genetic determinants included TEM-1, TEM-2, TEM-3 and TEM-215 in E. coli isolates from South African studies and TEM-15 in isolates from Tunisia. The blaCTX − M genetic determinants included CTX-M-15 (Tunisia, South Africa, Sudan, Algeria and Nigeria), CTX-M-55 (Tunisia and South Africa), CTX-M-14 (Morocco) and CTX-M-1, CTX-M-3, CTX-M-2, CTX-M-14, CTX-M-8/25, CTX-M-27, CTX-M-9 (South Africa).
Figure 4

The mean differences in multiple antimicrobial resistance (MAR) index values of Escherichia coli(A) and Klebsiella pneumoniae(B) between different countries with corresponding 95% confidence intervals in the individual studies (E. coli: p = 0.01 and K. pneumoniae: p = 0.04) systematically reviewed across African countries (2010–2022).
Figure 5

Beta-lactamase genes detected in Escherichia coli, Klebsiella pneumoniae and Salmonella spp. isolated from water, soil and/or fresh produce samples in different African countries between 2010 and 2022. The numbers in the middle of each circle indicates the number of studies where the genes were detected. In total, 21 different β-lactamase genes were detected across the different countries, indicated with different colors or patterns on the same circle.
3.4.2. Klebsiella pneumoniae
The samples where K. pneumoniae were isolated included mainly water, followed by fresh produce and soil. The soil sampled in the included studies were either close to food vending sites or where fresh produce were harvested in the field. The articles that focussed on characterization of K. pneumoniae only and included phenotypic characterization (Mouss et al.,
3.4.3. Salmonella spp.
The studies where Salmonella spp. were detected were all conducted in Nigeria or South Africa. From the studies in Nigeria, samples that tested positive for Salmonella spp. predominantly included irrigation water, followed by fresh produce and soil. The Salmonella spp. positive samples from studies conducted in South Africa predominantly water samples, followed by soil samples from the fields in fresh produce production in selected studies. All samples came from urban areas where the river water used for irrigation. The three Salmonella spp. focussed articles were conducted in Nigeria and South Africa between 2011 and 2015 (Akinyemi et al.,
4. Discussion
The role of the environment in dissemination of AMR is increasingly being reported (Koutsoumanis et al.,
It is well known that the environment contains a natural antimicrobial resistance gene pool as well as resistance genes resulting from anthropogenic activities (Manaia,
The current review also elucidated that water was mainly investigated in environmental AMR studies in Africa between 2010 and 2022. These studies had heterogeneous study locations and methods used, with E. coli predominantly isolated and characterized. The WHO recently published an integrated global surveillance protocol on ESBL-producing E. coli as an indicator (WHO, 2021). The procedures described were specifically designed to be conducted in a harmonized manner to provide the opportunity to increase capacities and to build national integrated surveillance systems for AMR within a One Health approach (WHO, 2021). The environmental aspect of this protocol proposes to detect and quantify ESBL-producing E. coli in contamination hotspot sources including surface water such as rivers that receive wastewater (WHO, 2021). However, the current metadata analysis showed that the methodology, including the frequency and number of samples analyzed, isolation, identification and characterization methods used (even within countries) differed, which contrasts the WHO proposed protocol to conduct research in a harmonized manner.
The results from the current review showed that for studies in South Africa and Algeria, MALDI-ToF was predominantly used for identification of the potential pathogens, while countries including Nigeria, Tunisia, Ghana, Egypt, Morocco and Kenya among others, predominantly used biochemical tests such as API strips for isolate identification. Recently, the reproducibility and accuracy of MALDI-ToF mass spectrometry was evaluated through comprehensive comparison studies in the clinical field (Hou et al.,
Across all countries from the current meta-analysis, the Kirby-Bauer disk diffusion method was mainly used for phenotypic AMR analysis, with the CLSI criteria predominantly followed. However, the antibiotics included in analysis differed across studies and countries, therefore, no conclusion regarding phenotypic resistance patterns of potential pathogens isolated from the different matrices could be reported. Of note is that multidrug resistance (MDR), which is defined as resistance to more than one antibiotic class (Magiorakos et al.,
The blaCTX − M ESBL resistance gene was identified in all the studies that included PCR analysis in the current review. Similarly, Muthupandian et al. (
Class A carbapenemases, which include KPC and GES, are plasmid-encoded and frequently detected in clinically significant Klebsiella spp. and Pseudomonas aeruginosa (Sawa et al., 2020). In the current review, these genes were present in E. coli and K. pneumoniae isolates in selected studies conducted in South Africa. Perovic et al. (
Class B carbapenemases are typically encoded on a plasmid, transposon, integron, or chromosome and include the IMP, VIM and NDM genetic determinants (Sawa et al., 2020), supporting rapid gene dissemination across the different one health domains. Interestingly, studies in South Africa from the current review only reported on presence of the VIM and IMP genetic determinants in E. coli isolates, while the same genetic determinants were reported in K. pneumoniae only, in similar studies in Tunisia. Additionally, the NDM carbapenem resistance gene was detected in soil K. pneumoniae isolates from Nigeria, water E. coli isolates from South Africa and fresh produce K. pneumoniae isolates from Egypt. The results from the current review reiterates that clinically significant antibiotic resistant bacteria are no longer restricted to hospital settings, supporting the WHO (2017c) findings that global research and development strategies should include antibiotics active against more common community bacteria.
Similar to Ragheb et al. (
The inclusion of WGS has been reported as a promising tool for estimation of ARB in a one health context (Aslam et al.,
5. Conclusion
The available data on occurrence of multidrug-resistant Enterobacterales in environmental settings in Africa emphasizes the need for improved surveillance and documentation of resistance gene dissemination across the farm-to-fork continuum globally. This follows as clinically significant bacterial isolates were found in various water sources and fresh produce. Furthermore, these human pathogenic microbes harbored resistance traits that corresponded to antibiotics often used in clinical settings as well as animal husbandry. The information obtained from the current review could however not be used to determine the extent of the human health risk in consumption of fresh produce where ESBL-producing potential pathogens were present. In addition to a need for harmonized methodology, the cost-effectiveness of One Health AMR surveillance systems, especially in LMICs, should also be considered and further investigated to inform the development and effective and efficient systems in Africa. The results further posed a challenge in comparative studies, as standardized methods were not utilized across the board. Although limited environmental AMR surveillance studies were found in comparison to published data on AMR surveillance in human and animal health, this review showed the vital importance of including information from the water-plant-food nexus in food safety surveillance programs related to AMR in a One Health context. It was further highlighted that comparable indicators to monitor AMR in food crop value chains is necessary. Establishing WGS as a surveillance tool in addition to phenotypic data in AMR surveillance studies will provide comprehensive information to inform comparable national and international actions plans against AMR.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
LR, ED, SD, and LK contributed to the conceptualization of the study. LR and ED performed the data extraction. LR summarized the data, prepared all figures, and performed the statistical analysis. All authors contributed to the manuscript revision, read, and approved the submitted version.
Acknowledgments
The authors would like to acknowledge the support of Ms. Liesl Morey from the Agricultural Research Council of South Africa's Biometry Unit as well as Dr. J Gokul for support through the statistical analysis.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2023.1106082/full#supplementary-material
References
1
AbakpaG. O.UmohV. J.AmehJ. B.YakubuS. E.KwagaJ. K. P.KamaruzamanS.et al. (2015). Diversity and antimicrobial resistance of Salmonella enterica isolated from fresh produce and environmental samples. Environ. Nanotechnol. Monit. Manag. 3, 38–46. 10.1016/j.enmm.2014.11.004
2
AdelowoO. O.IkhimiukorO. O.KnechtC.VollmersJ.BhatiaM.KasterA. K.et al. (2020). A survey of extended-spectrum betalactamase-producing Enterobacteriaceae in urban wetlands in southwestern Nigeria as a step towards generating prevalence maps of antimicrobial resistance. PLoS ONE15, 1–19. 10.1371/journal.pone.0229451
3
African Union Commission. African Union Handbook 2022: A Guide for Those Working With and Within the African Union. Ministry of Foreign Affairs and Trade (2022).
4
AkinolaT.OnyeaghasiriO.OlurantiF. U. O. O.Oladapo ElutadeO. (2022). NDM Assessment of well water as a reservoir for extended-spectrum β-lactamases (ESBL) and carbapenem resistant Enterobacteriaceae from Iwo, Osun state, Nigeria. J. Microbiol.14, 351–61. 10.18502/ijm.v14i3.9772
5
AkinyemiK. O.IwalokunB. A.FoliF.OshodiK.CokerA. O. (2011). Prevalence of multiple drug resistance and screening of enterotoxin (stn) gene in Salmonella enterica serovars from water sources in Lagos, Nigeria. Public Health125, 65–71. 10.1016/j.puhe.2010.11.010
6
AlabiM. E.EssackS. Y. (2022). Antimicrobial Resistance Antibiotic prescribing amongst South African general practitioners in private practice : an analysis of a health insurance database. JAC-Antimicrobial Resist. 4, 1–8. 10.1093/jacamr/dlac101
7
AltaybH. N.ElbadawiH. S.AlzahraniF. A.BaothmanO.KazmiI.NadeemM. S.et al. (2022). Co-Occurrence of β-Lactam and Aminoglycoside Resistance Determinants among Clinical and Environmental Isolates of Klebsiella pneumoniae and Escherichia coli: A Genomic Approach. Pharmaceuticals15, 8. 10.3390/ph15081011
8
AslamB.KhurshidM.ArshadM. I.MuzammilS.RasoolM. H.ShahidA.et al. (2021). Antibiotic resistance: one health one world outlook. Front. Cell. Infect. Microbiol. 11, 1–20. 10.3389/fcimb.2021.771510
9
BanachJ. L.Van Der Fels-KlerxH. J. (2020). Microbiological reduction strategies of irrigation water for fresh produce. J. Food Prot. 83, 1072–1087. 10.4315/JFP-19-466
10
BlaakH.van HoekA. H. A. M.VeenmanC.Docters van LeeuwenA. E.LynchG. (2014). Extended spectrum Beta-lactamase- and contitutively AmpC-producing Enterobacteriaceae on fresh procuce and in the agricultural environment. Int. J. Food Microbiol. 8, 168–169. 10.1016/j.ijfoodmicro.2013.10.006
11
BorgattiS. P.EverettM. G.FreemanL. C. (2002). Ucinet 6 for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies.
12
BrownE.DessaiU.McgarryS.Gerner-SmidtP. (2019). Use of whole-genome sequencing for food safety and public health in the United States. Foodborne Pathog. Dis. 16, 441–450. 10.1089/fpd.2019.2662
13
BrunnA.Kadri-AlabiZ.MoodleyA.GuardabassiL.TaylorP.MateusA.et al. (2022). Characteristics and global occurrence of human pathogens harboring antimicrobial resistance in food crops: a scoping review. Front. Sustain. Food Syst. 6, 1–19. 10.3389/fsufs.2022.824714
14
CDC (2019). Antibiotic Resistance Threats in the United States. Atlanta. GA: CDC.
15
ChengK.ChuiH.DomishL.HernandezD.WangG. (2016). Recent development of mass spectrometry and proteomics applications in identification and typing of bacteria. Proteomics Clin. Appl. 10, 346–357. 10.1002/prca.201500086
16
ElbehiryA.MarzoukE.HamadaM.Al-DubaibM.AlyamaniE.MoussaI. M.et al. (2017). Application of MALDI-TOF MS fingerprinting as a quick tool for identification and clustering of foodborne pathogens isolated from food products. New Microbiol. 40, 269–278.
17
EltonL.ThomasonM. J.TemboJ.VelavanT. P.PallerlaS. R.ArrudaL. B.et al. (2020). Antimicrobial resistance preparedness in sub-Saharan African countries. Antimicrob. Resis9, 1–11. 10.1186/s13756-020-00800-y
18
FadareF. T.AdefisoyeM. A.OkohA. I. (2020). Occurrence, identification, and antibiogram signatures of selected Enterobacteriaceae from Tsomo and Tyhume rivers in the Eastern Cape Province, Republic of South Africa. PLoS ONE15, 1–27. 10.1371/journal.pone.0238084
19
FAO (2018). Antimicrobial Resistance and Foods of Plant Origin. Available online at: http://www.fao.org/3/BU657en/bu657en.pdf (accessed November 12, 2022).
20
HouT. Y.Chiang-NiC.TengS. H. (2019). Current status of MALDI-TOF mass spectrometry in clinical microbiology. J. Food Drug Anal. 27, 404–414. 10.1016/j.jfda.2019.01.001
21
IkhimiukorO. O.OdihE. E.Donado-godoyP.OkekeI. N. (2022). A bottom-up view of antimicrobial resistance transmission in developing countries. Nat. Microbiol. 7, 24. 10.1038/s41564-022-01124-w
22
IwuC. D.PlessisE. M. d KorstenL.NontonganaN.OkohA. I. (2020). Antibiogram signatures of some enterobacteria recovered from irrigation water and agricultural soil in two district municipalities of south africa. Microorganisms8, 1–19. 10.3390/microorganisms8081206
23
Jones-DiasD.ManageiroV.FerreiraE.BarreiroP.VieiraL.MouraI. B.et al. (2016). Architecture of class 1, 2. and 3 integrons from gram negative bacteria recovered among fruits and vegetables. Front. Microbiol.7, 1–13. 10.3389/fmicb.2016.01400
24
KoutsoumanisK.AllendeA.Álvarez-OrdóñezA.BoltonD.Bover-CidS.ChemalyM.et al. (2021). Role played by the environment in the emergence and spread of antimicrobial resistance (AMR) through the food chain. EFSA J. 19, 6651. 10.2903/j.efsa.2021.6651
25
KrumpermanP. H. (1983). Multiple antibiotic resistance indexing of Escherichia coli to identify high-risk sources contamination of foods. Appl. Environ. Microbiol. 46, 165–170. 10.1128/aem.46.1.165-170.1983
26
Le TerrierC.MasseronA.UwaezuokeN. S.EdwinC. P.EkumaA. E.OlugbeminiyiF.et al. (2020). Wide spread of carbapenemase-producing bacterial isolates in a Nigerian environment. J. Glob. Antimicrob. Resist. 21, 321–323. 10.1016/j.jgar.2019.10.014
27
LiguoriK.KeenumI.DavisB. C.CalarcoJ.MilliganE.HarwoodV. J.et al. (2022). Antimicrobial resistance monitoring of water environments: a framework for standardized methods and quality control. Environ. Sci. Technol. 56, 9149–9160. 10.1021/acs.est.1c08918
28
LorenzM.AischG.KokkelinkD. (2012). Datawrapper: Create Charts and Maps. Available online at: https://www.datawrapper.de/.
29
LynchJ. P.ClarkN. M. (2021). Escalating antimicrobial resistance among Enterobacteriaceae: focus on carbapenemases. Expert Opinion Pharmacother.22, 1455–1473. 10.1080/14656566.2021.1904891
30
MagiorakosA. P.SrinivasanA.CareyR. B.CarmeliY.FalagasM. E.GiskeC. G.et al. (2012). Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin. Microbiol. Infect. 18, 268–281. 10.1111/j.1469-0691.2011.03570.x
31
ManaiaC. M. (2017). Assessing the risk of antibiotic resistance transmission from the environment to humans: non-direct proportionality between abundance and risk. Trends Microbiol. 25, 173–181. 10.1016/j.tim.2016.11.014
32
MousséW.NoumavoP. A.ChabiN. W.SinaH.TohoyessouM. G.AhoyoT. A.et al. (2016). Phenotypic and genotypic characterization of extended spectrum β -lactamase klebsiella pneumoniae and phenotypic and genotypic characterization of extended spectrum β -lactamase klebsiella pneumoniae and fluorescent pseudomonas spp. Strains Mark. 7, 192–204. 10.4236/fns.2016.73021
33
Mughini-grasL.Dorado-garcíaA.DuijkerenE.Van BuntG.DierikxC. M.BontenM. J. M. (2019). Articles Attributable sources of community-acquired carriage of Escherichia coli containing β-lactam antibiotic resistance genes: a population-based modelling study. Lancet3, 357–369. 10.1016/S2542-5196(19)30130-5
34
MurrayC. J.IkutaK. S.ShararaF.SwetschinskiL.Robles AguilarG.GrayA.et al. (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet399, 629–655. 10.1016/S0140-6736(21)02724-0
35
MuthupandianS.RamachandranB.BarabadiH. (2018). The prevalence and drug resistance pattern of extended spectrum β-lactamases (ESBLs) producing Enterobacteriaceae in Africa. Microb. Pathog. 114, 180–192. 10.1016/j.micpath.2017.11.061
36
OdigieI. E.AkinboB. D.AtereA. D.IdemudiaN. L.AsuquoA. (2013). Antimicrobial susceptibility of some members of enterobacteriaceae isolated from salad vegetables in calabar. J. Chem. Inf. Model. 53, 1689–1699. Available online at: https://www.semanticscholar.org/paper/Antimicrobial-Susceptibility-Of-Some-Members-Of-In-Odigie-Akinbo/8b894fad3d89cc77e94bfe0f894677bb48667fb2
37
OkoroR. N.NmekaC.ErahP. O. (2019). Antibiotics prescription pattern and determinants of utilization in the national health insurance scheme at a tertiary hospital in Nigeria. Afr. Health Sci. 19, 2356–2364. 10.4314/ahs.v19i3.8
38
PageM. J.McKenzieJ. E.BossuytP. M.BoutronI.HoffmannT. C.MulrowC. D.et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int. J. Surg. 88, 1–11. 10.1016/j.ijsu.2021.105906
39
PartridgeS. R. (2015). Resistance mechanisms in Enterobacteriaceae. Pathology47, 276–284. 10.1097/PAT.0000000000000237
40
PerovicO.BritzE.ChettyV.Singh-MoodleyA. (2016). Molecular detection of carbapenemase-producing genes in referral enterobacteriaceae in South Africa: a short report. South African. Med. J. 106, 975–977. 10.7196/SAMJ.2016.v106i10.11300
41
PopovćN. T.KepecS.KazazićS. P.Strunjak-PerovićI.BojanićK.RakovacR.et al. (2022). Identification of environmental aquatic bacteria by mass spectrometry supported by biochemical differentiation. PLoS ONE17, 1–13. 10.1371/journal.pone.0269423
42
RaghebS. M.GovindenU.SekyereJ. O. (2022). Genetic support of carbapenemases : a One Health systematic review and meta-analysis of current trends in Africa. Ann. N. Y. Acad. Sci. 1509, 50–73. 10.1111/nyas.14703
43
RahmanM.AlamM. U.LuiesS. K.KamalA.FerdousS.LinA.et al. (2022). Contamination of fresh produce with antibiotic-resistant bacteria and associated risks to human health: a scoping review. Int. J. Environ. Res. Public Health19, 1–15. 10.3390/ijerph19010360
44
RajwarA.SrivastavaP.SahgalM. (2015). Microbiology of fresh produce: route of contamination, detection methods and remedy. Crit. Rev. Food Sci. Nutr. 56, 2383–2390. 10.1080/10408398.2013.841119
45
RasealaC. M.EkwanzalaM. D.MombaM. N. B. (2020). Shared extended-spectrum β-lactamase-producing Salmonella serovars between agricultural and aquatic environments revealed through inva amplicon sequencing. Microorganisms8, 1–18. 10.3390/microorganisms8121898
46
RiazS.FaisalM.HasnainS. (2011). Antibiotic susceptibility pattern and multiple antibiotic resistances (MAR) calculation of extended spectrum β- lactamase (ESBL) producing Escherichia coli and klebsiella species in Pakistan. Afr. J. Biotechnol. 10, 6325–6331. 10.5897/AJB11.2449
47
RichterL.du PlessisE. M.DuvenageS.AllamM.IsmailA.KorstenL.et al. (2021). Whole genome sequencing of extended-spectrum- and AmpC- β-Lactamase-positive enterobacterales isolated from spinach production in Gauteng Province, South Africa. Front. Microbiol. 12, 734649. 10.3389/fmicb.2021.734649
48
RichterL.Du PlessisE. M.DuvenageS.KorstenL. (2019). Occurrence, identification, and antimicrobial resistance profiles of extended-spectrum and ampc β-lactamase-producing enterobacteriaceae from fresh vegetables retailed in Gauteng Province, South Africa. Foodborne Pathog. Dis. 16, 421–427. 10.1089/fpd.2018.2558
49
RichterL.Du PlessisE. M.DuvenageS.KorstenL. (2020). Occurrence, phenotypic and molecular characterization of extended-spectrum- and AmpC- β-lactamase producing enterobacteriaceae isolated from selected commercial spinach supply chains in South Africa. Front. Microbiol. 11, 1–10. 10.3389/fmicb.2020.00638
50
RStudio Team (2020). RStudio: Integrated Development Environment for R. Available online at: http://www.rstudio.com/ (accessed February 21, 2023).
51
SawaT.KooguchiK.MoriyamaK. (2020). Molecular diversity of extended-spectrum β-lactamases and carbapenemases, and antimicrobial resistance. J. Intensive Care8, 1–13. 10.1186/s40560-020-0429-6
52
ShapiroS. S.WilkM. B. (1965). An analysis of variance test for normality (complete samples). Biometrika52, 591–611. 10.1093/biomet/52.3-4.591
53
SheuC. C.ChangY. T.LinS. Y.ChenY. H.HsuehP. R. (2019). Infections caused by carbapenem-resistant Enterobacteriaceae: an update on therapeutic options. Front. Microbiol. 10, 80. 10.3389/fmicb.2019.00080
54
SnedecorG. W.CochranW. G. (1980). Statistical Methods. 7th Edn. Iowa: Iowa State University Press.
55
VelooY.ThahirS. S. A.RajendiranS.HockL. K.AhmadN.MuthuV.et al. (2022). Multidrug-resistant gram-negative bacteria and extended-spectrum ß-lactamase-producing klebsiella pneumoniae from the poultry farm environment. Am. J. Vet. Res. 48, 186–242. 10.1128/spectrum.02694-21
56
VitalP. G.ZaraE. S.ParaoanC. E. M.DimasupilM. A. Z.AbelloJ. J. M.SantosI. T. G.et al. (2018). Antibiotic resistance and extended-spectrum beta-lactamase production of Escherichia coli isolated from irrigation waters in selected urban farms in Metro Manila. Philippines. 20, 1–11. 10.3390/w10050548
57
WhiteA.HughesJ. M. (2019). Critical importance of a one health approach to antimicrobial resistance. Ecohealth16, 404–409. 10.1007/s10393-019-01415-5
58
WHO (2014). GLOBAL Report on Surveillance of Antimicrobial Resistance. Geneva, Switzerland.
59
WHO (2015). Global Antimicrobial Resistance Surveillance System. Geneva, Switzerland.
60
WHO (2017a). Global Priority List of Antibiotic-Resistant Bacteria to Guide Research, Discovery, and Development of New Antibiotics. Geneva, Switzerland.
61
WHO (2017b). Integrated Surveillance of Antimicrobial Resistance in Foodborne Bacteria: Application of a One Health Approach. Available online at: https://apps.who.int/iris/bitstream/handle/10665/255747/9789241512411-eng.pdf;jsessionid=1710DEAA0E355CBCB99450559C0DD0C8?sequence=1%0Ahttp://apps.who.int/iris/bitstream/10665/255747/1/9789241512411-eng.pdf?ua=1%0Ahttp://apps.who.int/iris/bitstream/10665/ (accessed November 18, 2022).
62
WHO (2017c). Prioritization of Pathogens to Guide Discovery, Research and Development of New Antibiotics for Drug-Resistant Bacterial Infections, Including Tuberculosis. Geneva, Switzerland.
63
WHO (2020). GLASS Whole-Genome Sequencing for Surveillance of Antimicrobial Resistance: Global Antimicrobial Resistance and Use Surveillance System (GLASS). Available online at: https://www.who.int/health-topics/antimicrobial-resistance (accessed November 18, 2022).
64
WHO (2021). Global Tricycle Surveillance – ESBL E.coli - Integrated Global Surveillance on ESBL-producing E. coli Using a “One Health” Approach: Implementation and Opportunities. Available online at: http://apps.who.int/bookorders (accessed November 18, 2022).
65
WillemsenA.ReidS.AssefaY. (2022). A review of national action plans on antimicrobial resistance: strengths and weaknesses. Antimicrob. Resist. Infect. Control.11:1-13. 10.1186/s13756-022-01130-x
66
YeQ.WuQ.ZhangS.ZhangJ.YangG.WangJ.et al. (2017). Characterization of extended-spectrum β-lactamase-producing enterobacteriaceae from retail food in China. Front. Microbiol. 9, 1709. 10.3389/fmicb.2018.01709
67
ZekarF. M.GranierS. A.TouatiA. (2020). Occurrence of third-generation cephalosporins-resistant klebsiella pneumoniae in fresh fruits and vegetables purchased at markets in Algeria. Microbial Drug Resist.26, 353–359. 10.1089/mdr.2019.0249
Summary
Keywords
multidrug resistance (MDR), ESBLs, environmental AMR surveillance, foodborne pathogens, low and middle-income countries (LMICs), meta-analysis, Enterobacterales
Citation
Richter L, Du Plessis EM, Duvenage S and Korsten L (2023) Prevalence of extended-spectrum β-lactamase producing Enterobacterales in Africa's water-plant-food interface: A meta-analysis (2010–2022). Front. Sustain. Food Syst. 7:1106082. doi: 10.3389/fsufs.2023.1106082
Received
23 November 2022
Accepted
06 March 2023
Published
28 March 2023
Volume
7 - 2023
Edited by
Barbara Häsler, Royal Veterinary College (RVC), United Kingdom
Reviewed by
Abdelaziz Ed-Dra, Sultan Moulay Slimane University, Morocco; Kalmia Kniel, University of Delaware, United States
Updates

Check for updates
Copyright
© 2023 Richter, Du Plessis, Duvenage and Korsten.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Erika M. Du Plessis erika.duplessis@up.ac.za
This article was submitted to Agro-Food Safety, a section of the journal Frontiers in Sustainable Food Systems
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.