Abstract
Water and food sources play a major role in the distribution and transfer of microsporidia infection to animals and humans. So, this systematic review and meta-analysis aimed to assess the status and genetic diversity of microsporidia infection in water, vegetables, fruits, milk, cheese, and meat. The standard protocol of Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines was followed. Scopus, PubMed, Web of Science, and Google Scholar were searched from 1 January 2000 and 1 February 2023. The point estimates and 95% confidence intervals (CIs) were calculated using a random-effects model. Of the 1,308 retrieved studies, 35 articles were included in the final meta-analysis. The pooled prevalence of microsporidia infection in mixed water, mixed fruits, mixed vegetables, and milk was 43.3% (95% CI, 33–54.2%; I2, 94.86%), 35.8% (95% CI, 5.3–84.8%; I2, 0), 12% (95% CI, 4.9–26.6%; I2, 96.43%), and 5.8% (95% CI, 2.7–12%; I2, 83.72%), respectively. Considering the genotypes, microsporidia with genotype D in water sources and genotype CD6 in vegetables/fruits were the highest reported genotypes. Given the relatively high prevalence of microsporidiosis (especially in water sources), designing strategies for control, and prevention of microsporidia infection in these sources should be recommended.
HIGHLIGHTS
Among the potential resources of microsporidia, water, and food sources play a major role in the distribution and transfer of microsporidia infection to animals and humans.
The pooled prevalence of microsporidia infection in mixed water, mixed vegetables, mixed fruits, and milk was 43.3, 35.8, 12, and 5.8%, respectively.
INTRODUCTION
Microsporidia are fungal-related protozoa known as spore-forming intracellular organisms; these worldwide distributed eukaryotes are the One Health matters (Taghipour et al. 2021a). Different species of microsporidia have been isolated from the environment, vertebrate/invertebrate hosts, etc. (Malysh et al. 2019; Han et al. 2021). In addition to the zoonotic importance of microsporidia, human anthropozoonotic, and enteropathogens can be obtained from contaminated food and water supplies, vegetables, milk, and dairy products such as cheese (Taghipour et al. 2021b). Human infections can range from self-limiting gastrointestinal complications in adequate immunity individuals to life-threatening problems in immunocompromised individuals and children as well as the elderly (Shabani et al. 2022; Taghipour et al. 2022a). Although complications such as myositis and hepatitis, ocular and renal even systematic forms are reported, the main clinical index of microsporidiosis is diarrhea; among the 200 identified genera, the well-known genus Enterocytozoon bieneusi and Encephalitozoon spp. are responsible for most of the human clinical manifestations (Taghipour et al. 2020a, 2022b).
There are numerous reports of contamination of environmental factors like soil, drinking water, dairy, and vegetable sources from all over the world, due to the need for processing in each of mentioned sources, untreated drinking water, pasteurization, sterilization, and/or coagulation steps of milk and cheese, as well as proper washing steps of vegetables, it reminds us of the challenges and hidden gaps in the process of these highly consumed substances (Hoch & Solter 2017; Yildirim et al. 2020; Vecková et al. 2021). Thick-walled spores tolerate harsh environmental conditions and in initially entering the human body orally, frequently settle in the intestinal tract (Steele & Bjørnson 2014; Hosseini Parsa et al. 2021). It seems that at least 15 of 1,400 identified fungi-sister microsporidia species have been associated with human infections (Mhaissen & Flynn 2018). Microscopic and polymerase chain reaction-based molecular approaches are used to detect and characterize the Microsporidia spp.; in the former, modified trichrome stain (chromotrope 2R) is used and in the latter, the SSU rRNA genes are targeted (Park & Poulin 2021). It should be noted that the identification, removal, and inactivation of microsporidia spores from water and vegetables use current technologies (Said 2012; Javanmard et al. 2018). Lack of attention to the contamination of the mentioned sources can cause emerging outbreaks, especially in low-income and non-developed communities (Wang et al. 2018). There are various sporadic reports on the contamination of water, vegetables, fruits, milk, cheese, and meat, but no comprehensive report is available on the level of contamination of these sources. Therefore, the present systematic and meta-analysis investigated the contamination of these sources in order to deepen the explorations into the unknown sources of microsporidia fields.
METHODS
Information sources and systematic search
The present meta-analysis study was systematically done consistent with the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement (Moher et al. 2009). Literature searches for published studies on the prevalence of microsporidia infection in water, vegetables, fruits, milk, cheese, and meat were retrieved via four international databases (Scopus, PubMed, Web of Science, and Google Scholar) searching, between 1 January 2000 and 1 February 2023. The searching process was accomplished using MeSH terms alone or in the following combination: (‘Microsporidiosis’ OR ‘Microsporidia’ OR ‘Microsporidium’ OR ‘Microspora’ OR ‘Enterocytozoon bieneusi’ OR ‘Encephalitozoon spp.’) AND (‘Prevalence’ OR ‘Epidemiology’) AND (‘Water’ OR ‘Vegetables’ OR ‘Fruits’ OR ‘Milk’ OR ‘Cheese’ OR ‘Meat’). Moreover, the bibliographic list of all selected records and their citations was searched manually to retrieve extra pertinent reports.
Inclusion/exclusion criteria, study selection, and data extraction
Initial screening was accomplished for eligible records by title and abstract. Duplicate studies were detected and removed with the EndNote X8 software (Thomson Reuters, New York, USA). Then, the full text of potential studies was retrieved and evaluated by two independent expert researchers (S.R. and S.B.). Thereupon, recovered studies data were extracted by another independent investigator (A.T.) and double-checked by others (S.R. and S.B.). Any doubt or disagreement in any of the mentioned processes was resolved by consensus of opinions and discussion. For meta-analysis, studies that met all of the following pre-established criteria were selected: (1) full-texts or abstracts published in English without geographical limitation; (2) peer-reviewed original research articles, short reports, or letters to the editors that studied the prevalence of microsporidia in water, vegetables, fruits, milk, cheese, and meat; (3) papers published online from the inception up to 1 February 2023; and (4) those papers that provided the exact total sample size and positive samples. Studies were excluded which, in addition to not having one or more inclusion criteria, had other factors such as: (1) the studies that did not clearly mention the total studied samples and/or positive cases and (2) all types of review articles, case reports, and case series, as well as local reports in a non-English language. Next, the finalized studies data and variables were collected, which included the last name of the first author, year of publication, geographical region (continent and country) diagnostic method, type of source, totally examined sample size, and positive (isolated) sample numbers.
Study quality assessment
In our meta-analysis, the standard quality assessment checklist, Joanna Briggs Institute (JBI) was used for the included studies’ quality evaluation (JB 2014). This 10-question checklist has four answering options: Yes, No, Unclear, and Not applicable. In summary, each study can be awarded a maximum of one star for each numbered item. The studies that were considered as moderate and strong (high quality) scored 4–6 and 7–10 points, respectively. Based on the obtained score, the authors have decided to include (4–10 points) and exclude (≤3 points) the papers due to the weak studies category.
Data synthesis and statistical analysis
For each included study, the point estimates and their respective 95% confidence intervals (CIs) were calculated using a random-effects model. It should be noted that the random-effects model allows for a distribution of true effect sizes between published studies. To minimize the biases, using sub-group analyses, the pooled prevalence of microsporidia infection was estimated according to the type of source (water, vegetables, fruits, and milk). Forest plot analysis was used to reveal possible heterogeneity among included studies. The I2 statistic was performed to assess the heterogeneity between studies and the values of <50%, 50–80%, and >80% were defined as low, moderate, and high heterogeneity, respectively (Higgins et al. 2003; Taghipour et al. 2020a, 2020b, 2021c). Moreover, small study effects and their publication bias were discerned by Egger's regression test (Egger et al. 1997; Zhang et al. 2017). All analytical functions were applied by comprehensive meta-analysis software (version 2, BIOSTAT, Englewood, NJ, USA) (Nasiri et al. 2015; Taghipour et al. 2020a).
RESULTS
Study characteristics
Characteristics of included studies on the prevalence of Microsporidia spp. in water sources
First author . | Publication year . | Type of sample . | Type of water . | Diagnostic method . | Country . | Continent . | Sample size . | Infected by Microsporia . | Species . | Genotypes . | QA . |
---|---|---|---|---|---|---|---|---|---|---|---|
Fournier et al. (2000) | 2000 | Water | Surface Water | Nested PCR | France | Europe | 25 | 16 | Microsporidia spp. | 7 | |
Fournier et al. (2002) | 2002 | Water | Swimming Pools | PCR | France | Europe | 48 | 1 | Endoreticulatus schubergi | 7 | |
Thurston-Enriquez et al. (2002) | 2002 | Water | Irrigation Water | PCR | USA | America | 25 | 7 | Encephalitozoon intestinalis and Pleistophora | 7 | |
Dowd et al. (2003) | 2003 | Water | Drinking Water | PCR | Guatemala | America | 12 | 6 | E. intestinalis | 7 | |
Coupe et al. (2006) | 2006 | Water | Surface Water, including recreational areas | PCR | France | Europe | 57 | 2 | Enterocytozoon bieneusi | 7 | |
Graczyk et al. (2007) | 2007 | Water | Recreational Bathing Water | Sugar-phenol flotation + Multiplexed fluorescence in situ hybridization | USA | America | 60 | 26 | E. bieneusi (26) and E. intestinalis (1) | 8 | |
Kwakye-Nuako et al. (2007) | 2007 | Water | Drinking Water | Modified Zielhl-Neelsen | Ghana | Africa | 27 | 14 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Water | Tap water | Magnesium Sulfate Flotation | Zimbabwe | Africa | 6 | 0 | _ | 6 | |
aIzquierdo et al. (2011) | 2011 | Water | Drinking Water Treatment Plants | Trichrome stain + PCR | Spain | Europe | 16 | 2 | Microsporidia spp. | 7 | |
Izquierdo et al. (2011) | 2011 | Water | Wastewater Treatment Plants | Trichrome stain + PCR | Spain | Europe | 16 | 5 | Microsporidia spp. | 7 | |
Izquierdo et al. (2011) | 2011 | Water | Recreational River Areas | Trichrome stain + PCR | Spain | Europe | 6 | 1 | E. intestinalis | 7 | |
Izquierdo et al. (2011) | 2011 | Water | Total (mixed) | Trichrome stain + PCR | Spain | Europe | 38 | 8 | Microsporidia spp. | 7 | |
aBen Ayed et al. (2012) | 2012 | Water | Raw Wastewater | PCR | Tunisia | Africa | 110 | 86 | E. bieneusi | Genotypes D and IV | 9 |
Ben Ayed et al. (2012) | 2012 | Water | Treated Wastewater | PCR | Tunisia | Africa | 110 | 49 | E. bieneusi | Genotypes D and IV | 9 |
Ben Ayed et al. (2012) | 2012 | Water | Total (mixed) | PCR | Tunisia | Africa | 220 | 135 | E. bieneusi | 9 | |
Li et al. (2012) | 2012 | Water | Raw Wastewater | PCR | China | Asia | 386 | 338 | E. bieneusi | Genotype D was the most prevalent, being found in 279 of 338 (82.5%) | 9 |
aGalván et al. (2013) | 2013 | Water | Drinking Water Treatment Plants | PCR | Spain | Europe | 63 | 17 | Microsporidia spp. | 7 | |
Galván et al. (2013) | 2013 | Water | Wastewater Treatment Plants | PCR | Spain | Europe | 112 | 68 | Microsporidia spp. | 7 | |
Galván et al. (2013) | 2013 | Water | Locations of Influence | PCR | Spain | Europe | 48 | 24 | Microsporidia spp. | 7 | |
Galván et al. (2013) | 2013 | Water | Total (mixed) | PCR | Spain | Europe | 223 | 109 | Microsporidia spp. | ||
Guo et al. (2014) | 2014 | Water | Stormwater | PCR | USA | America | 67 | 39 | E. bieneusi | W4 (33), W1 (15), W13 (14), W10 (12), W7 (12), W19 (5), W16 (4), W17 (2), W8 (2), W12 (2), W15 (2), W5 (1), and W11 (3) | 10 |
Hu et al. (2014) | 2014 | Water | River Water | PCR | China | Asia | 178 | 56 | E. bieneusi | EbpC, EbpA, D, CS-8, PtEb IX, Peru 8, Peru 11, PigEBITS4, EbpB, G, O, and RWSH1 to RWSH6 | 10 |
Ma et al. (2016) | 2016 | Water | Wastewater Treatment Plant | PCR | China | Asia | 50 | 35 | E. bieneusi | D, EbpC, PigEBITS7, Peru11, Peru8, EbpA, ESH-01 to ESH-05. The predominant genotype was D, being detected in 31 samples. | 10 |
Saad et al. (2016) | 2016 | Water | Wastewater | PCR | Egypt | Africa | 96 | 10 | E. bieneusi (10) and E. intestinalis (3) | 7 | |
aHuang et al. (2017) | 2017 | Water | Combined Sewer Overflow | PCR | China | Asia | 40 | 37 | E. bieneusi | D, PigEBITS7, Henan V, type IV, Peru 8, Peru 11, and one new genotype SHW2 | 9 |
Huang et al. (2017) | 2017 | Water | Raw Wastewater | PCR | China | Asia | 40 | 40 | E. bieneusi | D, PigEBITS7, Henan V, type IV, Peru 8, Peru 11, and two new genotypes SHW1 and SHW2 | 9 |
Huang et al. (2017) | 2017 | Water | Total (mixed) | PCR | China | Asia | 80 | 77 | E. bieneusi | 9 | |
Karaman et al. (2017) | 2017 | Water | Environmental Waters | Light microscopy | Turkey | Europe | 228 | 38 | Microsporidia spp. | 7 | |
aYamashiro et al. (2017) | 2017 | Water | Wastewater (Raw sewage) | Nested PCR | Brazil | America | 18 | 3 | E. bieneusi (3) | 7 | |
Yamashiro et al. (2017) | 2017 | Water | Wastewater (Treated effluent) | Nested PCR | Brazil | America | 18 | 2 | E. bieneusi (2) | 7 | |
Yamashiro et al. (2017) | 2017 | Water | Total (mixed) | Nested PCR | Brazil | America | 36 | 5 | E. bieneusi | 7 | |
Ye et al. (2017) | 2017 | Water | Raw Wastewater | PCR | China | Asia | 108 | 46 | E. bieneusi | D, BEB6, I, J, PigEbIX, PigEBITS5, EbpA, Peru6, Peru8, Type IV, HNWW1, HNWW2, HNWW3, HNWW4, and HNWW5 | 10 |
aChen et al. (2018) | 2018 | Water | Reservoirs | Nested PCR | Taiwan | Asia | 28 | 28 | Vittaforma-like | 7 | |
Chen et al. (2018) | 2018 | Water | Rivers | Nested PCR | Taiwan | Asia | 22 | 19 | Vittaforma-like | 7 | |
Chen et al. (2018) | 2018 | Water | Total (mixed) | Nested PCR | Taiwan | Asia | 50 | 47 | Vittaforma-like | 7 | |
Javanmard et al. (2018) | 2018 | Water | Treated Wastewater | PCR | Iran | Asia | 12 | 7 | E. bieneusi (7) and Encephalitozoon spp. (1) | Genotypes D and E | 9 |
Li et al. (2018) | 2018 | Water | Water Ponds | PCR | China | Asia | 8 | 2 | E. bieneusi | SC02 | 8 |
Chen et al. (2019) | 2019 | Water | Different water (Environmental, Artemia franciscana, Clinical, Curculionidae, Euproctis chrysorrhoea) | PCR | Taiwan | Asia | 19 | 9 | Vittaforma corneae, Microsporidium sp., Enterocytospora artemiae, Uncultured microsporidia clone, Unikaryonidae sp., and Endoreticulatus sp. | 8 | |
Gad et al. (2019) | 2019 | Water | Irrigation water samples (Ground and surface freshwater) | Microscopic examination | Egypt | Africa | 36 | 4 | Microsporidia spp. | _ | 7 |
Jiang et al. (2020) | 2020 | Water | Raw Wastewater | PCR | China | Asia | 164 | 122 | E. bieneusi | D (97), Peru11 (4), EbpC (6), PigEBITS7 (1), SHW3 (1), SHW5 (1), SHW6 (1), and SHW7 (1), HenanV (n 3), Peru11 (1), Peru8 (1), D + Peru8 (1), D + SHW4 (1), SHW4 (1), and D + Peru11 (2) | 10 |
aFan et al. (2021) | 2021 | Water | Wastewater Treatment Plants | Nested PCR | China | Asia | 238 | 134 | E. bieneusi | D (77), Type IV (30), Peru8 (10), Peru11 (2), EbpC (2), Peru6 (1), MWC-m1 (1), GZW1 (1), PtEb IX (1), Type IV + D (7), Type IV + Peru11 (1), and Peru8 + Type IV (1) | 10 |
Fan et al. (2021) | 2021 | Water | Sewer Samples | Nested PCR | China | Asia | 88 | 23 | E. bieneusi | D (11), Type IV (5), PtEb IX (2), Type IV + D (2), EbpC (1), GZW2 (1), and GZW3 (1) | 10 |
Fan et al. (2021) | 2021 | Water | Total (mixed) | Nested PCR | China | Asia | 326 | 157 | E. bieneusi | 10 |
First author . | Publication year . | Type of sample . | Type of water . | Diagnostic method . | Country . | Continent . | Sample size . | Infected by Microsporia . | Species . | Genotypes . | QA . |
---|---|---|---|---|---|---|---|---|---|---|---|
Fournier et al. (2000) | 2000 | Water | Surface Water | Nested PCR | France | Europe | 25 | 16 | Microsporidia spp. | 7 | |
Fournier et al. (2002) | 2002 | Water | Swimming Pools | PCR | France | Europe | 48 | 1 | Endoreticulatus schubergi | 7 | |
Thurston-Enriquez et al. (2002) | 2002 | Water | Irrigation Water | PCR | USA | America | 25 | 7 | Encephalitozoon intestinalis and Pleistophora | 7 | |
Dowd et al. (2003) | 2003 | Water | Drinking Water | PCR | Guatemala | America | 12 | 6 | E. intestinalis | 7 | |
Coupe et al. (2006) | 2006 | Water | Surface Water, including recreational areas | PCR | France | Europe | 57 | 2 | Enterocytozoon bieneusi | 7 | |
Graczyk et al. (2007) | 2007 | Water | Recreational Bathing Water | Sugar-phenol flotation + Multiplexed fluorescence in situ hybridization | USA | America | 60 | 26 | E. bieneusi (26) and E. intestinalis (1) | 8 | |
Kwakye-Nuako et al. (2007) | 2007 | Water | Drinking Water | Modified Zielhl-Neelsen | Ghana | Africa | 27 | 14 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Water | Tap water | Magnesium Sulfate Flotation | Zimbabwe | Africa | 6 | 0 | _ | 6 | |
aIzquierdo et al. (2011) | 2011 | Water | Drinking Water Treatment Plants | Trichrome stain + PCR | Spain | Europe | 16 | 2 | Microsporidia spp. | 7 | |
Izquierdo et al. (2011) | 2011 | Water | Wastewater Treatment Plants | Trichrome stain + PCR | Spain | Europe | 16 | 5 | Microsporidia spp. | 7 | |
Izquierdo et al. (2011) | 2011 | Water | Recreational River Areas | Trichrome stain + PCR | Spain | Europe | 6 | 1 | E. intestinalis | 7 | |
Izquierdo et al. (2011) | 2011 | Water | Total (mixed) | Trichrome stain + PCR | Spain | Europe | 38 | 8 | Microsporidia spp. | 7 | |
aBen Ayed et al. (2012) | 2012 | Water | Raw Wastewater | PCR | Tunisia | Africa | 110 | 86 | E. bieneusi | Genotypes D and IV | 9 |
Ben Ayed et al. (2012) | 2012 | Water | Treated Wastewater | PCR | Tunisia | Africa | 110 | 49 | E. bieneusi | Genotypes D and IV | 9 |
Ben Ayed et al. (2012) | 2012 | Water | Total (mixed) | PCR | Tunisia | Africa | 220 | 135 | E. bieneusi | 9 | |
Li et al. (2012) | 2012 | Water | Raw Wastewater | PCR | China | Asia | 386 | 338 | E. bieneusi | Genotype D was the most prevalent, being found in 279 of 338 (82.5%) | 9 |
aGalván et al. (2013) | 2013 | Water | Drinking Water Treatment Plants | PCR | Spain | Europe | 63 | 17 | Microsporidia spp. | 7 | |
Galván et al. (2013) | 2013 | Water | Wastewater Treatment Plants | PCR | Spain | Europe | 112 | 68 | Microsporidia spp. | 7 | |
Galván et al. (2013) | 2013 | Water | Locations of Influence | PCR | Spain | Europe | 48 | 24 | Microsporidia spp. | 7 | |
Galván et al. (2013) | 2013 | Water | Total (mixed) | PCR | Spain | Europe | 223 | 109 | Microsporidia spp. | ||
Guo et al. (2014) | 2014 | Water | Stormwater | PCR | USA | America | 67 | 39 | E. bieneusi | W4 (33), W1 (15), W13 (14), W10 (12), W7 (12), W19 (5), W16 (4), W17 (2), W8 (2), W12 (2), W15 (2), W5 (1), and W11 (3) | 10 |
Hu et al. (2014) | 2014 | Water | River Water | PCR | China | Asia | 178 | 56 | E. bieneusi | EbpC, EbpA, D, CS-8, PtEb IX, Peru 8, Peru 11, PigEBITS4, EbpB, G, O, and RWSH1 to RWSH6 | 10 |
Ma et al. (2016) | 2016 | Water | Wastewater Treatment Plant | PCR | China | Asia | 50 | 35 | E. bieneusi | D, EbpC, PigEBITS7, Peru11, Peru8, EbpA, ESH-01 to ESH-05. The predominant genotype was D, being detected in 31 samples. | 10 |
Saad et al. (2016) | 2016 | Water | Wastewater | PCR | Egypt | Africa | 96 | 10 | E. bieneusi (10) and E. intestinalis (3) | 7 | |
aHuang et al. (2017) | 2017 | Water | Combined Sewer Overflow | PCR | China | Asia | 40 | 37 | E. bieneusi | D, PigEBITS7, Henan V, type IV, Peru 8, Peru 11, and one new genotype SHW2 | 9 |
Huang et al. (2017) | 2017 | Water | Raw Wastewater | PCR | China | Asia | 40 | 40 | E. bieneusi | D, PigEBITS7, Henan V, type IV, Peru 8, Peru 11, and two new genotypes SHW1 and SHW2 | 9 |
Huang et al. (2017) | 2017 | Water | Total (mixed) | PCR | China | Asia | 80 | 77 | E. bieneusi | 9 | |
Karaman et al. (2017) | 2017 | Water | Environmental Waters | Light microscopy | Turkey | Europe | 228 | 38 | Microsporidia spp. | 7 | |
aYamashiro et al. (2017) | 2017 | Water | Wastewater (Raw sewage) | Nested PCR | Brazil | America | 18 | 3 | E. bieneusi (3) | 7 | |
Yamashiro et al. (2017) | 2017 | Water | Wastewater (Treated effluent) | Nested PCR | Brazil | America | 18 | 2 | E. bieneusi (2) | 7 | |
Yamashiro et al. (2017) | 2017 | Water | Total (mixed) | Nested PCR | Brazil | America | 36 | 5 | E. bieneusi | 7 | |
Ye et al. (2017) | 2017 | Water | Raw Wastewater | PCR | China | Asia | 108 | 46 | E. bieneusi | D, BEB6, I, J, PigEbIX, PigEBITS5, EbpA, Peru6, Peru8, Type IV, HNWW1, HNWW2, HNWW3, HNWW4, and HNWW5 | 10 |
aChen et al. (2018) | 2018 | Water | Reservoirs | Nested PCR | Taiwan | Asia | 28 | 28 | Vittaforma-like | 7 | |
Chen et al. (2018) | 2018 | Water | Rivers | Nested PCR | Taiwan | Asia | 22 | 19 | Vittaforma-like | 7 | |
Chen et al. (2018) | 2018 | Water | Total (mixed) | Nested PCR | Taiwan | Asia | 50 | 47 | Vittaforma-like | 7 | |
Javanmard et al. (2018) | 2018 | Water | Treated Wastewater | PCR | Iran | Asia | 12 | 7 | E. bieneusi (7) and Encephalitozoon spp. (1) | Genotypes D and E | 9 |
Li et al. (2018) | 2018 | Water | Water Ponds | PCR | China | Asia | 8 | 2 | E. bieneusi | SC02 | 8 |
Chen et al. (2019) | 2019 | Water | Different water (Environmental, Artemia franciscana, Clinical, Curculionidae, Euproctis chrysorrhoea) | PCR | Taiwan | Asia | 19 | 9 | Vittaforma corneae, Microsporidium sp., Enterocytospora artemiae, Uncultured microsporidia clone, Unikaryonidae sp., and Endoreticulatus sp. | 8 | |
Gad et al. (2019) | 2019 | Water | Irrigation water samples (Ground and surface freshwater) | Microscopic examination | Egypt | Africa | 36 | 4 | Microsporidia spp. | _ | 7 |
Jiang et al. (2020) | 2020 | Water | Raw Wastewater | PCR | China | Asia | 164 | 122 | E. bieneusi | D (97), Peru11 (4), EbpC (6), PigEBITS7 (1), SHW3 (1), SHW5 (1), SHW6 (1), and SHW7 (1), HenanV (n 3), Peru11 (1), Peru8 (1), D + Peru8 (1), D + SHW4 (1), SHW4 (1), and D + Peru11 (2) | 10 |
aFan et al. (2021) | 2021 | Water | Wastewater Treatment Plants | Nested PCR | China | Asia | 238 | 134 | E. bieneusi | D (77), Type IV (30), Peru8 (10), Peru11 (2), EbpC (2), Peru6 (1), MWC-m1 (1), GZW1 (1), PtEb IX (1), Type IV + D (7), Type IV + Peru11 (1), and Peru8 + Type IV (1) | 10 |
Fan et al. (2021) | 2021 | Water | Sewer Samples | Nested PCR | China | Asia | 88 | 23 | E. bieneusi | D (11), Type IV (5), PtEb IX (2), Type IV + D (2), EbpC (1), GZW2 (1), and GZW3 (1) | 10 |
Fan et al. (2021) | 2021 | Water | Total (mixed) | Nested PCR | China | Asia | 326 | 157 | E. bieneusi | 10 |
aA study that includes several datasets based on the type of water.
QA, quality assessment.
Characteristics of included studies on the prevalence of Microsporidia spp. in vegetables and fruits
First author . | Publication Year . | Type of sample . | Type of vegetables/fruits . | Diagnostic method . | Country . | Continent . | Sample size . | Infected by Microsporia . | Species . | Genotypes . | QA . |
---|---|---|---|---|---|---|---|---|---|---|---|
Javanmard et al. (2018) | 2018 | Vegetables | Total (mixed) | PCR | Iran | Asia | 12 | 5 | Enterocytozoon bieneusi (5), Encephalitozoon hellem (1) | Genotypes D (3) and E (2) for E. bieneusi | 9 |
aGad et al. (2020) | 2020 | Vegetables | Lettuce | Acid-fast trichrome stain | Egypt | Africa | 23 | 7 | Microsporidia spp. | _ | 7 |
Gad et al.(2020) | 2020 | Vegetables | Parsley | Acid-fast trichrome stain | Egypt | Africa | 25 | 6 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Watercress | Acid-fast trichrome stain | Egypt | Africa | 22 | 4 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Dill | Acid-fast trichrome stain | Egypt | Africa | 26 | 8 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | White radish | Acid-fast trichrome stain | Egypt | Africa | 23 | 1 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Green onion | Acid-fast trichrome stain | Egypt | Africa | 23 | 4 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Tomatoes | Acid-fast trichrome stain | Egypt | Africa | 28 | 6 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Carrot | Acid-fast trichrome stain | Egypt | Africa | 25 | 3 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Cucumber | Acid-fast trichrome stain | Egypt | Africa | 24 | 1 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Total (mixed) | Acid-fast trichrome stain | Egypt | Africa | 219 | 40 | Microsporidia spp. | 7 | |
aSaid (2012) | 2012 | Vegetables | Rocket | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 22 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Lettuce | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 25 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Parsley | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 10 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Leek | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 8 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Green onion | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 11 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Total (mixed) | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 300 | 76 | Microsporidia spp. | 7 | |
aLi et al. (2019a) | 2019 | Vegetables | Lettuce | PCR | China | Asia | 200 | 13 | E. bieneusi (13) | CM8 (2), CD6 (7), EbpA (3), and Henan-IV (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Coriander | PCR | China | Asia | 152 | 1 | E. bieneusi (1) | CM8 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Celery | PCR | China | Asia | 70 | 1 | E. bieneusi (1) | EbpA (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Baby bok choy | PCR | China | Asia | 59 | 1 | E. bieneusi (1) | CHV3 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Chinese cabbage | PCR | China | Asia | 47 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Leaf lettuce | PCR | China | Asia | 44 | 1 | E. bieneusi (1) | CHG19 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Water spinach | PCR | China | Asia | 28 | 3 | E. bieneusi (3) | CD6 (1), BEB8 (1), and CTS3 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Crown daisy | PCR | China | Asia | 27 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Fennel plant | PCR | China | Asia | 26 | 1 | E. bieneusi (1) | EbpC (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Endive | PCR | China | Asia | 25 | 1 | E. bieneusi (1) | Henan-IV (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Spinach | PCR | China | Asia | 20 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Schizonepeta | PCR | China | Asia | 20 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Cabbage | PCR | China | Asia | 18 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Leaf mustard | PCR | China | Asia | 11 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Chinese chive | PCR | China | Asia | 132 | 5 | E. bieneusi (5) | CD6 (1), EbpA (2), EbpC (1), and CHV1 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Chive | PCR | China | Asia | 128 | 4 | E. bieneusi (4) | CD6 (2), CHV2 (1), and CTS3 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Cucumber | PCR | China | Asia | 41 | 1 | E. bieneusi (1) | CD6 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Potato | PCR | China | Asia | 3 | 1 | E. bieneusi (1) | CHV4 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Bean | PCR | China | Asia | 28 | 4 | E. bieneusi (4) | CD6 (4) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Green chili | PCR | China | Asia | 5 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Total (mixed) | PCR | China | Asia | 1084 | 37 | E. bieneusi (37) | _ | 10 |
Li et al. (2019a) | 2019 | Fruits | Watermelon | PCR | China | Asia | 15 | 1 | E. bieneusi (1) | CD6 (1) for E. bieneusi | 10 |
aJedrzejewski et al. (2007) | 2007 | Fruits | Strawberries | Fluorescence in situ hybridization | Poland | Europe | 15 | 3 | E. intestinalis (3) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Fruits | Raspberries | Fluorescence in situ hybridization | Poland | Europe | 10 | 3 | E. intestinalis (1) and E. bieneusi (2) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Fruits | Total (mixed) | Fluorescence in situ hybridization | Poland | Europe | 25 | 6 | E. intestinalis (4) and E. bieneusi (2) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Mung bean | Fluorescence in situ hybridization | Poland | Europe | 5 | 1 | E. bieneusi (1) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Alfalfa | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Radishes | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Sprouts (mixed) | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Parsley leaves | Fluorescence in situ hybridization | Poland | Europe | 5 | 1 | E. cuniculi (1) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Arugula lettuce | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Curly lettuce | Fluorescence in situ hybridization | Poland | Europe | 5 | 1 | E. bieneusi (1) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Iceberg lettuce | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Red radish | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Leek | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Dill | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Total (mixed) | Fluorescence in situ hybridization | Poland | Europe | 55 | 3 | E. cuniculi (1) and E. bieneusi (2) | _ | 7 |
aSalamandane et al. (2021) | 2021 | Vegetables | Coriander | Nested PCR | Mozambique | Africa | 39 | 1 | E. bieneusi (1) | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Parsley | Nested PCR | Mozambique | Africa | 45 | 0 | _ | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Portuguese Cabbage | Nested PCR | Mozambique | Africa | 45 | 0 | _ | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Pointed White Cabbage | Nested PCR | Mozambique | Africa | 45 | 1 | E. bieneusi (1) | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Carrot | Nested PCR | Mozambique | Africa | 18 | 0 | _ | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Tomato | Nested PCR | Mozambique | Africa | 42 | 1 | E. bieneusi (1) | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Lettuce | Nested PCR | Mozambique | Africa | 45 | 1 | E. bieneusi (1) | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Green Pepper | Nested PCR | Mozambique | Africa | 42 | 0 | _ | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Total (mixed) | Nested PCR | Mozambique | Africa | 321 | 4 | E. bieneusi (4) | _ | 8 |
aMasungo et al. (2010) | 2010 | Fruits | Mangoes | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 7 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Fruits | Apples | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 2 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Fruits | Peaches | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 0 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Fruits | Guavas | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 0 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Fruits | Total (mixed) | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 40 | 9 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Black Nightshade | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 0 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Cabbage | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 5 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Rape | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 4 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Pumpkin Leaves | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 6 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Total (mixed) | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 40 | 15 | Microsporidia spp. | 7 |
First author . | Publication Year . | Type of sample . | Type of vegetables/fruits . | Diagnostic method . | Country . | Continent . | Sample size . | Infected by Microsporia . | Species . | Genotypes . | QA . |
---|---|---|---|---|---|---|---|---|---|---|---|
Javanmard et al. (2018) | 2018 | Vegetables | Total (mixed) | PCR | Iran | Asia | 12 | 5 | Enterocytozoon bieneusi (5), Encephalitozoon hellem (1) | Genotypes D (3) and E (2) for E. bieneusi | 9 |
aGad et al. (2020) | 2020 | Vegetables | Lettuce | Acid-fast trichrome stain | Egypt | Africa | 23 | 7 | Microsporidia spp. | _ | 7 |
Gad et al.(2020) | 2020 | Vegetables | Parsley | Acid-fast trichrome stain | Egypt | Africa | 25 | 6 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Watercress | Acid-fast trichrome stain | Egypt | Africa | 22 | 4 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Dill | Acid-fast trichrome stain | Egypt | Africa | 26 | 8 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | White radish | Acid-fast trichrome stain | Egypt | Africa | 23 | 1 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Green onion | Acid-fast trichrome stain | Egypt | Africa | 23 | 4 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Tomatoes | Acid-fast trichrome stain | Egypt | Africa | 28 | 6 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Carrot | Acid-fast trichrome stain | Egypt | Africa | 25 | 3 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Cucumber | Acid-fast trichrome stain | Egypt | Africa | 24 | 1 | Microsporidia spp. | 7 | |
Gad et al. (2020) | 2020 | Vegetables | Total (mixed) | Acid-fast trichrome stain | Egypt | Africa | 219 | 40 | Microsporidia spp. | 7 | |
aSaid (2012) | 2012 | Vegetables | Rocket | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 22 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Lettuce | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 25 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Parsley | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 10 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Leek | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 8 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Green onion | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 60 | 11 | Microsporidia spp. | 7 | |
Said (2012) | 2012 | Vegetables | Total (mixed) | Modified Zeihl–Neelsen stain and modified trichrome stain | Egypt | Africa | 300 | 76 | Microsporidia spp. | 7 | |
aLi et al. (2019a) | 2019 | Vegetables | Lettuce | PCR | China | Asia | 200 | 13 | E. bieneusi (13) | CM8 (2), CD6 (7), EbpA (3), and Henan-IV (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Coriander | PCR | China | Asia | 152 | 1 | E. bieneusi (1) | CM8 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Celery | PCR | China | Asia | 70 | 1 | E. bieneusi (1) | EbpA (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Baby bok choy | PCR | China | Asia | 59 | 1 | E. bieneusi (1) | CHV3 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Chinese cabbage | PCR | China | Asia | 47 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Leaf lettuce | PCR | China | Asia | 44 | 1 | E. bieneusi (1) | CHG19 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Water spinach | PCR | China | Asia | 28 | 3 | E. bieneusi (3) | CD6 (1), BEB8 (1), and CTS3 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Crown daisy | PCR | China | Asia | 27 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Fennel plant | PCR | China | Asia | 26 | 1 | E. bieneusi (1) | EbpC (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Endive | PCR | China | Asia | 25 | 1 | E. bieneusi (1) | Henan-IV (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Spinach | PCR | China | Asia | 20 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Schizonepeta | PCR | China | Asia | 20 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Cabbage | PCR | China | Asia | 18 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Leaf mustard | PCR | China | Asia | 11 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Chinese chive | PCR | China | Asia | 132 | 5 | E. bieneusi (5) | CD6 (1), EbpA (2), EbpC (1), and CHV1 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Chive | PCR | China | Asia | 128 | 4 | E. bieneusi (4) | CD6 (2), CHV2 (1), and CTS3 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Cucumber | PCR | China | Asia | 41 | 1 | E. bieneusi (1) | CD6 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Potato | PCR | China | Asia | 3 | 1 | E. bieneusi (1) | CHV4 (1) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Bean | PCR | China | Asia | 28 | 4 | E. bieneusi (4) | CD6 (4) for E. bieneusi | 10 |
Li et al. (2019a) | 2019 | Vegetables | Green chili | PCR | China | Asia | 5 | 0 | _ | _ | 10 |
Li et al. (2019a) | 2019 | Vegetables | Total (mixed) | PCR | China | Asia | 1084 | 37 | E. bieneusi (37) | _ | 10 |
Li et al. (2019a) | 2019 | Fruits | Watermelon | PCR | China | Asia | 15 | 1 | E. bieneusi (1) | CD6 (1) for E. bieneusi | 10 |
aJedrzejewski et al. (2007) | 2007 | Fruits | Strawberries | Fluorescence in situ hybridization | Poland | Europe | 15 | 3 | E. intestinalis (3) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Fruits | Raspberries | Fluorescence in situ hybridization | Poland | Europe | 10 | 3 | E. intestinalis (1) and E. bieneusi (2) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Fruits | Total (mixed) | Fluorescence in situ hybridization | Poland | Europe | 25 | 6 | E. intestinalis (4) and E. bieneusi (2) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Mung bean | Fluorescence in situ hybridization | Poland | Europe | 5 | 1 | E. bieneusi (1) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Alfalfa | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Radishes | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Sprouts (mixed) | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Parsley leaves | Fluorescence in situ hybridization | Poland | Europe | 5 | 1 | E. cuniculi (1) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Arugula lettuce | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Curly lettuce | Fluorescence in situ hybridization | Poland | Europe | 5 | 1 | E. bieneusi (1) | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Iceberg lettuce | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Red radish | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Leek | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Dill | Fluorescence in situ hybridization | Poland | Europe | 5 | 0 | _ | _ | 7 |
Jedrzejewski et al. (2007) | 2007 | Vegetables | Total (mixed) | Fluorescence in situ hybridization | Poland | Europe | 55 | 3 | E. cuniculi (1) and E. bieneusi (2) | _ | 7 |
aSalamandane et al. (2021) | 2021 | Vegetables | Coriander | Nested PCR | Mozambique | Africa | 39 | 1 | E. bieneusi (1) | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Parsley | Nested PCR | Mozambique | Africa | 45 | 0 | _ | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Portuguese Cabbage | Nested PCR | Mozambique | Africa | 45 | 0 | _ | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Pointed White Cabbage | Nested PCR | Mozambique | Africa | 45 | 1 | E. bieneusi (1) | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Carrot | Nested PCR | Mozambique | Africa | 18 | 0 | _ | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Tomato | Nested PCR | Mozambique | Africa | 42 | 1 | E. bieneusi (1) | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Lettuce | Nested PCR | Mozambique | Africa | 45 | 1 | E. bieneusi (1) | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Green Pepper | Nested PCR | Mozambique | Africa | 42 | 0 | _ | _ | 8 |
Salamandane et al. (2021) | 2021 | Vegetables | Total (mixed) | Nested PCR | Mozambique | Africa | 321 | 4 | E. bieneusi (4) | _ | 8 |
aMasungo et al. (2010) | 2010 | Fruits | Mangoes | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 7 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Fruits | Apples | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 2 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Fruits | Peaches | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 0 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Fruits | Guavas | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 0 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Fruits | Total (mixed) | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 40 | 9 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Black Nightshade | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 0 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Cabbage | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 5 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Rape | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 4 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Pumpkin Leaves | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 10 | 6 | Microsporidia spp. | 7 | |
Masungo et al. (2010) | 2010 | Vegetables | Total (mixed) | Magnesium Sulfate Flotation Technique | Zimbabwe | Africa | 40 | 15 | Microsporidia spp. | 7 |
aA study that includes several datasets based on the type of vegetables or fruits.
QA, quality assessment.
Characteristics of included studies on the prevalence of Microsporidia spp. in milk, cheese, and meat
First author . | Publication year . | Type of sample . | Type of animals . | Diagnostic method . | Country . | Continent . | Sample size . | Infected by Microsporia . | Species . | Genotypes . | QA . |
---|---|---|---|---|---|---|---|---|---|---|---|
Lee (2008) | 2008 | Milk | Cows | PCR | South Korea | Asia | 180 | 15 | E. bieneusi (15) | CEbB (1), CEbB (1), CEbA (2), CEbD (1), CEbA (1), CEbC (1), CMITS1 (1), CEbB (1), CEbA (1), CMITS1 (1), CEbA (1), CEbD (1), and CEbA (2) | 10 |
Kváč et al. (2016) | 2016 | Milk | Lactating Multiparous Holstein Cows | Nested PCR | Czech Republic | Europe | 50 | 1 | E. cuniculi (1) | _ | 7 |
aYildirim et al. (2020) | 2020 | Milk | Cattle | Nested PCR | Turkey | Asia | 200 | 9 | Enterocytozoon bieneusi (9) | ERUSS1 (2), BEB6 (2), TREb1 (1), ERUSS1 (2), BEB6 (1), and ERUSS1 (1) | 10 |
aYildirim et al. (2020) | 2020 | Milk | Sheep | Nested PCR | Turkey | Asia | 200 | 36 | E. bieneusi (36) | ERUSS1 (7), BEB6 (4), TREb2 (2), TREb3 (1), TREb4 (1), ERUSS1 (7), BEB6 (4), ERUSS1 (5), BEB6 (3), TREb3 (1), TREb5 (1) | 10 |
aYildirim et al. (2020) | 2020 | Milk | Water buffalo | Nested PCR | Turkey | Asia | 50 | 1 | E. bieneusi (1) | TREb6 (1) | 10 |
bVecková et al. (2021) | 2021 | Milk | Goat | Molecular detection | Czech Republic | Europe | 99 | 3 | E. cuniculi | Genotype II (3) | 9 |
bVecková et al. (2021) | 2021 | Cheese | Goat | Molecular detection | Czech Republic | Europe | 9 | 8 | Encephalitozoon cuniculi | Genotype I and II | 9 |
Sak et al. (2019) | 2019 | Meat (from the shoulder, belly, and ham) | Pigs | Nested PCR | Czech Republic | Europe | 50 | 2 | E. cuniculi (2) | Genotype II (2) | 9 |
First author . | Publication year . | Type of sample . | Type of animals . | Diagnostic method . | Country . | Continent . | Sample size . | Infected by Microsporia . | Species . | Genotypes . | QA . |
---|---|---|---|---|---|---|---|---|---|---|---|
Lee (2008) | 2008 | Milk | Cows | PCR | South Korea | Asia | 180 | 15 | E. bieneusi (15) | CEbB (1), CEbB (1), CEbA (2), CEbD (1), CEbA (1), CEbC (1), CMITS1 (1), CEbB (1), CEbA (1), CMITS1 (1), CEbA (1), CEbD (1), and CEbA (2) | 10 |
Kváč et al. (2016) | 2016 | Milk | Lactating Multiparous Holstein Cows | Nested PCR | Czech Republic | Europe | 50 | 1 | E. cuniculi (1) | _ | 7 |
aYildirim et al. (2020) | 2020 | Milk | Cattle | Nested PCR | Turkey | Asia | 200 | 9 | Enterocytozoon bieneusi (9) | ERUSS1 (2), BEB6 (2), TREb1 (1), ERUSS1 (2), BEB6 (1), and ERUSS1 (1) | 10 |
aYildirim et al. (2020) | 2020 | Milk | Sheep | Nested PCR | Turkey | Asia | 200 | 36 | E. bieneusi (36) | ERUSS1 (7), BEB6 (4), TREb2 (2), TREb3 (1), TREb4 (1), ERUSS1 (7), BEB6 (4), ERUSS1 (5), BEB6 (3), TREb3 (1), TREb5 (1) | 10 |
aYildirim et al. (2020) | 2020 | Milk | Water buffalo | Nested PCR | Turkey | Asia | 50 | 1 | E. bieneusi (1) | TREb6 (1) | 10 |
bVecková et al. (2021) | 2021 | Milk | Goat | Molecular detection | Czech Republic | Europe | 99 | 3 | E. cuniculi | Genotype II (3) | 9 |
bVecková et al. (2021) | 2021 | Cheese | Goat | Molecular detection | Czech Republic | Europe | 9 | 8 | Encephalitozoon cuniculi | Genotype I and II | 9 |
Sak et al. (2019) | 2019 | Meat (from the shoulder, belly, and ham) | Pigs | Nested PCR | Czech Republic | Europe | 50 | 2 | E. cuniculi (2) | Genotype II (2) | 9 |
aAn article with three datasets (classification of milk based on the type of animal).
bAn article with two datasets (divided by milk and cheese).
QA, quality assessment.
Status of microsporidia in water
The pooled prevalence of Microsporidia spp. in water sources based on the random-effects model.
The pooled prevalence of Microsporidia spp. in water sources based on the random-effects model.
Status of microsporidia in vegetables and fruits
The pooled prevalence of Microsporidia spp. in vegetables and fruits based on the random-effects model.
The pooled prevalence of Microsporidia spp. in vegetables and fruits based on the random-effects model.
Status of microsporidia in milk, cheese, and meat
The pooled prevalence of Microsporidia spp. in milk based on the random-effects model.
The pooled prevalence of Microsporidia spp. in milk based on the random-effects model.
DISCUSSION
Foodborne and waterborne diseases occur mainly through many classes of pathogens (i.e., bacteria, viruses, and parasites) excreted in the feces of animals and humans, which are the main cause of disease outbreaks worldwide (Bartlett 1996; Percival et al. 2004; Lynch et al. 2009; Bonadonna & La Rosa 2019). In recent years, a number of foodborne and waterborne epidemics caused by protozoa (i.e., giardiasis, cryptosporidiosis, cyclosporiasis, and toxoplasmosis) have been reported in the world (Mintz et al. 1993; Choi et al. 1997; Quiroz et al. 2000; Strausbaugh & Herwaldt 2000; Efstratiou et al. 2017; E Silva et al. 2019). Among the foodborne and waterborne pathogens, Microsporidia spp. is of special importance due to the spread of spores through the water and food, which can ignite serious adverse impacts on human and animal's health. This systematic review is the first that brings information to reveal the global status of Microsporidia spp. infection in water and food sources. These findings could be helpful for physicians and public health policymakers. Our results indicate that 43.3% mixed water, 35.8% mixed fruits, 12% mixed vegetables, and 5.8% milk were positive for Microsporidia spp. infection, respectively. In this review, most studies have been conducted on the water sources. While there are few studies regarding the contamination of vegetables/fruits (seven articles), milk (four articles), cheese (one study), and meat (one study) with Microsporidia spp., especially in developing countries. Hence, the need for further studies and more attention to Microsporidia spp. infection in these sources is tangible in these countries. Stray animals (cats and dogs) (Taghipour et al. 2020a, 2021c), farm animals (cattle, sheep, goat, pig, and boar) (Taghipour et al. 2021b, 2022a, 2022b), rodents (Taghipour et al. 2021a) and a broad range of birds' (Ruan et al. 2021) access to various environmental resources, including water and vegetable sources, are serious problems in controlling and preventing Microsporidia spp. waterborne and foodborne outbreaks. Thus, monitoring programs for Microsporidia spp. infection should be considered in these animals to prevent water and food contamination with the microsporidia spores. Considering the water contamination (Table 1), the sites contaminated by Microsporidia spp. belonged to surface water, drinking water, and wastewater.
The prevalence of microsporidia in water samples was surprisingly higher than expected, which can be a warning sign of environmental pollution because water, whether drinking or non-drinking water, is a potential source of contamination for humans. In addition to the risk of contaminated drinking water, accidental ingestion of pool water and other water can be considered a possible route of contamination. On the other hand, the aspect of water resource origin animal infections and playing the role of infection reservoir should not be neglected (Afzali et al. 2015; Stentiford et al. 2016). The high prevalence of microsporidia in water becomes even more alarming when we consider its lethality in immunocompromised individuals. Besides, the detection of Microsporidia spp. in drinking water could demonstrate that disinfection processes applied to this source of water are not adequate (Izquierdo et al. 2011). It seems that the sanitization of drinking water and water in close-to-human settlements is necessary, especially in areas with a low level of hygiene.
Contamination of soil and water sources with microsporidia spores probably increases the burden of vegetables/fruit contamination in public places (Dado et al. 2012; Kwok et al. 2013). Therefore, it seems that the cycle of water, fruits, and soil is important for the transmission of microsporidia spores and requires more attention to control and prevent the spread of this microorganism. The significant contamination of fruits and vegetables, directly and indirectly, refers to the contamination of the surrounding soil, as well as their irrigation system defect (Bartosova et al. 2021). Unfortunately, there is no global standard protocol for washing vegetables/fruits and it is diverse in different geographical areas. It seems that the use of natural substances such as concentrations of salt water (saturated), vinegar, and so on, that have the sporicidal effect of microsporidia and are more available can be included in preventive health measures and policies (Leiro et al. 2012; Rodríguez-García et al. 2022).
Consumption of unpasteurized and raw milk can be dangerous for humans because it is a potential source of microbial contamination (Mungai et al. 2015). Although the potential risk of unpasteurized milk and raw milk for transmission of several microbial agents is well known (Mungai et al. 2015), limited knowledge is available concerning the presence of Microsporidia spp. in milk and related risk factors for public health. The limited data analysis has made our interpretation of the actual extent of milk contamination ambiguous, however, at least it can be recommended to consume pasteurized/sterilized milk and dairy products in groups with insufficient immunity as well as infants/children who are fed with non-mother's milk (Firoozeh et al. 2017; Vecková et al. 2021). However, the presence of Microsporidia spp. in raw milk could arise from direct contamination from the environment of dairy farms, infected dairy animals, and food handlers. Also, few studies have been done on cheese and meat (one study for each), so it is suggested to do extensive studies on these products for a deeper understanding of this issue.
The most prevalent genotype which has been detected in humans and animals is genotype D (Li et al. 2019b; Shen et al. 2020). In this systematic review, along with genotype D (in water), genotype CD6 (in vegetables) was also reported; these genotypes have been found in humans and animals (Taghipour et al. 2021a, 2021b). Microsporidia can be considered a One Health issue, the contamination of water and food sources (fruits and vegetables), and livestock products are not only important in terms of human infections but also a potential indicator and representative of environmental contamination and/or animal infections (Taghipour et al. 2020a); therefore, this finding may suggest that a human source or an animal source can infect the environmental sources (e.g., water sources and vegetables).
It is suggested that future studies with more accurate diagnostic methods screen the health of water, milk, dairy products, fruits, and vegetables as well as meat in terms of contamination with resistant microsporidia spores. In measuring the contamination of fruits and vegetables and even water, studies should take into account the possibility of contamination with animal/human feces and improper disposal of wastewater, and in sampling, they should not take samples from places that have a higher probability of contamination.
This study has some limitations and the results presented here should be interpreted with regard to these limitations; we can mention the limited number of reports from the countries of the world, especially developed countries. Limited reporting makes the estimated prevalence an apparent prevalence rather than a true prevalence. Similarly, there were fewer studies and sample/sizes on milk, cheese, and meat than expected. The used methods with different sensitivity and specificity cause variation and make it more difficult to interpret the results. To solve this problem, it is suggested that future studies use standard and high sensitivity and specificity methods for diagnosis. Some studies were published in languages other than English (local languages) and we were unable to include their data; so, it is thought that the publication of articles/reports in English can make us more aware of the situation close to the reality of the prevalence in the world. Nevertheless, from a global perspective, we believe what we had reported here is close to true microsporidia prevalence in water, vegetable, and milk sources.
CONCLUSION
A relatively high prevalence was found in water sources, which were mainly according to the molecular methods from worldwide, that the filtration and sanitization of drinking water, as well as the disinfection of swimming pools with chlorine and the same substances, seem necessary. Next, the fruits and vegetables contamination probably indicate improper and/or unsanitary irrigation of these products, which seems to require apt washing and the use of a safe and appropriate protocol to eliminate the resistant form (spores) of parasites and other infectious organisms. In the present research, the milk, cheese, and meat contamination, although was estimated less than others, however, it is significant because the reports on these samples are very limited, and it seems that our information is like the tip of the iceberg. Therefore, it is recommended to consume pasteurized and/or sterilized dairy products and avoid raw or undercooked meat.
ACKNOWLEDGEMENT
The authors would like to thank all staff of the Department of Medical Parasitology, Jahrom University of Medical Sciences.
AUTHORS’ CONTRIBUTION
All authors contributed to the study design. A.T. and S.R. contributed to all parts of the study. A.A. and V.M. contributed to the study implementation. A.T. and S.R. collaborated in the analysis and interpretation of data. A.T. and S.B. collaborated on manuscript writing and revision. All the authors commented on the drafts of the manuscript and approved the final version of the article.
FUNDING
None.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.
REFERENCES
Author notes
These authors contributed equally to this work.