Next Article in Journal
Was It Chikungunya? Laboratorial and Clinical Investigations of Cases Occurred during a Triple Arboviruses’ Outbreak in Rio de Janeiro, Brazil
Previous Article in Journal
Factors Associated with HBsAg Seropositivity among Pregnant Women Receiving Antenatal Care at 10 Community Health Centers in Freetown, Sierra Leone: A Cross-Sectional Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pathogens
Volume 11
Issue 2
10.3390/pathogens11020244
pathogens-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

The Prevalence of Arcobacteraceae in Aquatic Environments: A Systematic Review and Meta-Analysis

by
Igor Venâncio
1,
Ângelo Luís
1,2,
Fernanda Domingues
1,
Mónica Oleastro
3,
Luísa Pereira
2,4,5 and
Susana Ferreira
1,*
1
CICS-UBI-Health Sciences Research Centre, University of Beira Interior, 6200-506 Covilhã, Portugal
2
Grupo de Revisões Sistemáticas (GRUBI), Faculdade de Ciências da Saúde, Universidade da Beira Interior, 6200-506 Covilhã, Portugal
3
National Reference Laboratory for Gastrointestinal Infections, Department of Infectious Diseases, National Institute of Health Dr. Ricardo Jorge, 1649-016 Lisbon, Portugal
4
CMA-UBI-Centro de Matemática e Aplicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
5
C4-UBI, Cloud Computing Competence Centre, University of Beira Interior, 6200-284 Covilhã, Portugal
*
Author to whom correspondence should be addressed.
Pathogens 2022, 11(2), 244; https://doi.org/10.3390/pathogens11020244
Submission received: 4 January 2022 / Revised: 31 January 2022 / Accepted: 10 February 2022 / Published: 13 February 2022
Browse Figures
Versions Notes

Abstract

:
Members of the family Arcobacteraceae are distributed widely in aquatic environments, and some of its species have been associated with human and animal illness. However, information about the diversity and distribution of Arcobacteraceae in different water bodies is still limited. In order to better characterize the health risk posed by members in the family Arcobacteraceae, a systematic review and meta-analysis-based method was used to investigate the prevalence of Arcobacteraceae species in aquatic environments based on available data published worldwide. The database search was performed using related keywords and considering studies up to February 2021. The pooled prevalence in aquatic environments was 69.2%, ranging from 0.6 to 99.9%. These bacteria have a wide geographical distribution, being found in diverse aquatic environments with the highest prevalence found in raw sewage and wastewater treatment plants (WWTP), followed by seawater, surface water, ground water, processing water from food processing plants and water for human consumption. Assessing the effectiveness of treatments in WWTP in eliminating this contamination, it was found that the wastewater treatment may not be efficient in the removal of Arcobacteraceae. Among the analyzed Arcobacteraceae species, Al. butzleri was the most frequently found species. These results highlight the high prevalence and distribution of Arcobacteraceae in different aquatic environments, suggesting a risk to human health. Further, it exposes the importance of identifying and managing the sources of contamination and taking preventive actions to reduce the burden of members of the Arcobacteraceae family.

1. Introduction

Proposed in 1991, the genus Arcobacter was included in the family Campylobacteracea, which comprised two more genera, Campylobacter and Sulfurospirillum [1,2]. Over the years, this genus has been expanded to include more species, currently comprising 34 species, of which 30 are validly published [3,4]. Since the proposal for the creation of the Arcobacter genus, it has been subjected to changes, and its taxonomical organization remains controversial. In 2017, after a comparative genomic analysis of the class Epsilonproteobacteria, a reclassification of the historically denominated Arcobacter genus as a new family denominated Arcobacteraceae was proposed to be included in the class Campylobacteria [5]. More recently, through phylogenetic and genomic analyses, Pérez-Cataluña et al., (2018) have suggested the reassessment of the taxonomy of genus in order to clarify the relationships among its species. The authors suggested the division of the genus Arcobacter in six genera and one candidate [6]. However, despite the reclassification in different genera having been validated [7,8], it has been refuted by considering Arcobacter as a taxon phenotypically-, phylogenetically- and genomically-coherent, while accepting the proposal of the family Arcobacteraceae as reasonable [9,10]. Arcobacteraceae is a family found in diverse habitats, environments and hosts, including animals, humans, foods and food-processing facilities, environmental and water sources such as underground water, surface water, sewage and sea water [11,12,13]. In fact, most of the species from this family have been first isolated from aquatic environments [14]. Few species from this family have been associated with animal and human disease, among which Aliarcobacter butzleri and Aliarcobacter cryaerophilus have been classified by the International Commission on Microbiological Specification for Food as a serious hazard to human [15]. Despite these two species being the most predominantly associated with human disease, infections with Aliarcobacter skirrowii, Aliarcobacter thereius, Malacobacter mytili and Aliarcobacter lanthieri have also been reported [16,17,18,19]. These species can cause intestinal diseases, with symptoms of diarrhea, abdominal pain, nausea, vomiting and fever, but also extraintestinal diseases, such as bacteraemia and peritonitis [20]. Arcobacters are described as water and food-borne bacteria, for which contaminated food or water are considered the probable route of transmission to human and animals [11]. Considering the wide distribution of Arcobacteraceae in environmental samples and water sources, the consumption or the direct contact of humans and animals with these bacteria may be seen as a relevant threat to public health. Thus, studies on Arcobacteraceae species in water sources can be useful to determine their role as a vehicle for the transmission of infectious agents, ecological characteristics and the potential zoonotic risk of these samples. Although studies on the prevalence of bacteria from this family in different aquatic environments can be found, there is no comprehensive data available on its prevalence to estimate the load. Therefore, the main aim of this work was to perform a systematic review followed by meta-analysis in order to investigate the prevalence of Arcobacteraceae in different water bodies based on data available worldwide.

2. Results and Discussion

2.1. Selection and Characteristics of Studies

After removing duplicate articles from the searches of the three selected databases, 613 articles were available for title and abstract screening. Of these, 117 were identified as potentially relevant, and 70 were eligible for inclusion after full-text review (Figure 1). The prevalence data was gathered from the articles considering the employment of molecular or cultural methods; when both methodologies were used, the overall value of prevalence was collected and applied for meta-analysis. When it was not possible to recover the full data required for the analysis, the work was not considered, and in the situation of examination of samples from different countries, the study was divided by country.

2.2. Meta-Analysis Results on Overall Prevalence

The global prevalence of Arcobacteraceae in aquatic environments was investigated considering 70 studies (Figure 2), from which the pooled prevalence was 69.2% (0.692; 95% CI: 0.609–0.765), ranging from 0.6 to 99.9%. The heterogeneity among the studies was found significant, as demonstrated by the values of statistics of the studies included in this meta-analysis (I2 = 91.927%; tau2 = 1.693; p-value < 0.001). The publication bias was assessed by applying a funnel plot generated for the outcome, considering the Trim and Fill adjustment. The adjustment of the funnel plot to the absence of publication bias can be achieved with the inclusion of 8 additional studies (Supplementary Figure S1). The presence of publication bias was further assessed by using Egger’s regression test (Supplementary Table S2). The results of this test showed that there is evidence to reject the null hypothesis (p-value < 0.001), indicating that there is asymmetry in the funnel plot. Consequently, apparent publication bias exists in the studies included in this meta-analysis, which can be justified by the relevance of the publication of articles with positive results regarding the presence of Arcobacteraceae in aquatic environment samples.

2.3. Subgroup Analysis by Geographical Distribution

A subgroup analysis based on the country and continent of origin was taken (Table 1). Arcobacteraceae in aquatic environments have been reported in 26 countries among the 70 included papers. However, a small number of surveys in water were conducted in each of the 26 different countries considered, usually with a low number of samples (from three to 780, with a median number of 24 samples). Country-level estimates showed that the highest pooled prevalence of Arcobacteraceae can be found in Denmark, followed by Brazil, Australia and Korea, while the lowest prevalence was observed for the Netherlands and Cameroon.
Regarding the prevalence of Arcobacteraceae among continents, South America showed the highest pooled prevalence with 96.2% (0.962; 95% CI: 0.350–0.999), followed by Oceania, North America, Europe, Asia and finally Africa, with the lowest pooled prevalence value of 19.2% (0.192; 95% CI: 0.047–0.536) (Table 1).
When analyzing prevalence data in subgroups categorized by the income level of the countries, the highest prevalence was presented by countries with a low-income level, at 90.0% (0.900; 95% CI: 0.145–0.998), followed by the countries of high-income level, at 79.0% (0.79; 95% CI: 0.702–0.858), of upper middle-income level, at 47.2% (0.472; 95% CI: 0.281–0.673) and lower middle, at 39.8% (0.398; 95% CI: 0.180–0.666). A high heterogeneity (I2 > 75%) was observed in the subgroup analysis by countries, except for Czech Republic and India, which showed a moderate heterogeneity. Additionally, the I2 statistics demonstrated a high heterogeneity (I2 > 75%) for all the continents. This parameter was not calculated when less than three studies were considered.
Industrial pollution load, poor water and sewage treatment facilities, inadequate water pollution control laws and rapid urbanization rates have contributed to the increasing degradation of the aquatic environment in many developed and developing countries [21], which in turn may potentiate the emergence of genus Arcobacteraceae. The observed scenario regarding the prevalence according to the geographical location and level of economic development must be analyzed carefully, given the high heterogeneity between studies. The estimated prevalence by geographical location is clearly affected by the type of samples analyzed in each study, as all types of aquatic samples were included in this analysis, namely from wastewater treatment plants. Nonetheless, this analysis shows the global distribution of members of the family Arcobacteraceae in aquatic environments worldwide. Furthermore, several other parameters may influence the observed trend, such as the methods of detection used among studies and even the distribution of studies analyzed by countries. Indeed, the highest prevalence was observed for South America and for the low-income country included that considered only one study, with a low number of samples analyzed (twelve and four samples, respectively). Nonetheless, in general, more studies are globally needed to understand the prevalence of Arcobacteraceae worldwide.

2.4. Subgroup Analysis by Parameters of Samples Analysis

As the volume of the sample analyzed is a parameter that may clearly influence the prevalence of Arcobacteraceae, we further performed a subgroup analysis considering the sample size. For that, the studies were divided into four groups, regarding the amount of sample analyzed (Volume of sample 0–200 mL; 201–500 mL; 1 L and >1 L). When it was not possible to clearly define the volume of sample used in the analysis, the studies were excluded. Through the analysis of the results, it was observed that when the volume of the samples was up to 200 mL, there was a lower prevalence of detection of Arcobacteraceae (58.7%), but if the volume exceeded 201 mL, the estimated prevalence increased to at least 82.0% (Table 2).
The amount of used sample is one of the factors that may influence the isolation or detection of bacteria. When a bacterium is present in a low number in environmental water samples, the straightforward way is to analyze larger sample volumes to increase the recovery; however, in turbid environmental water, for example, the high levels of background bacteria can interfere and prevent the isolation or detection of bacteria, such as described for thermotolerant campylobacters [22]. In the case of bacteria from the Arcobacteraceae family, the influence of the volume of sample has not been clarified, with only a limited number of the studies examining its presence using a quantitative approach.
In addition to the volume of sample analyzed, the laboratory detection technique used will likely influence the reported prevalence. Herein, data was divided and analyzed considering five subgroups (Table 3). Considering the results, studies using molecular methodologies presented a higher estimated prevalence when compared with culture techniques. Furthermore, similar prevalence values were found for direct and after enrichment isolation, or for direct or after enrichment molecular detection, when excluding the metagenomic studies. The use of molecular methods allows a faster and more sensitive detection of bacteria, being able to detect both viable and non-viable cells, as well as viable but not cultivable cells. Nonetheless, this methodology has some drawbacks as well, associated with the fact that some molecular methodologies do not allow to distinguish dead from live cells or to recover bacterial isolates that can be used for further studies [14,23,24,25]. Several culture methods are used and the recovery of bacteria from this family can be associated with various factors related with the sample, but also with the disparity in the sensitivity and specificity of isolation methods [25], pointing out the need for a standard protocol for the isolation of Arcobacteraceae species from diverse samples [11,23,25]. Also, the use of a selective supplements may lead to lower recovery rates in environmental water samples, due to stressed or injured cells, which may be affected by using these compounds leading to a reduced recovery rate [25,26]. Despite this, when data from culture methodologies with or without an enrichment step are examined, prevalence estimates are close to 43.3% (0.433; 95% CI: 0.348–0.521) and 48.7% (0.487; 95% CI: 0.274–0.705), respectively. Considering the results related to direct molecular detection, a subgroup analysis was performed, dividing data into detection by metagenomic sequencing methodologies and detection methods by PCR techniques or other methods of nucleic acids amplification. When comparing these methodologies, the highest percentage of the detection of Arcobacteraceae species was achieved through direct sequencing of the samples (96.0%) instead of using conventional PCR identification techniques (68.8%). This may be associated with the fact that most of PCR methods are directed for some species-specific detection, which has intrinsic limitations beyond the ones associated with the methodology used.
Considering the diversity of protocols used for the isolation, detection and identification of Arcobacteraceae members, these data must be interpreted with caution.

2.5. Subgroup Analysis by Aquatic Source

A subgroup analysis based on the type of sample examined was performed, taking into consideration the wastewater treatment plants (WWTP) at three distinct stages: influent, treatment at any point and effluent. The results showed that the pooled prevalence of samples collected from raw sewage and WWTP were the ones with the highest prevalence values, followed by samples of seawater, surface water, ground water, processing water from food processing plants and, lastly, water classified as for human consumption with a prevalence of 3.2% (Table 4). The high values of pooled prevalence found in seawater and surface water may be a concern due to its potential recreational use, but also due to its possible influence on the food chain. Further, surface and groundwater are usually used as a water source in developing countries for multiple purposes, increasing the potential health risk. In turn, the lower values of the estimated prevalence of Arcobacteraceae species in processing water and drinking water may be associated with the potential inactivation effect of these bacteria by the chlorination process of the water [27], which may be ineffective [28,29].
The presence of Arcobacteraceae in environmental waters indicates that it can survive and persist in those waters, which points to their potential to be waterborne pathogens. Furthermore, water can act as a contamination vehicle for these species, namely in the food chain [3].
Some studies suggest that fecal contamination may be responsible for introducing these bacteria into the water, being the presence of arcobacters correlated with a high level of fecal pollution [30]. In fact, among the outbreaks associated with arcobacters, some have suggested that the consumed water could have been contaminated by sewage [31,32]. However, the presence of high recovery rates of Arcobacteraceae in sea and surface waters may be due not only to the proximity of possible sources of fecal pollution, but also because several species have already been described as native to marine environments. In fact, many waterborne species of this family are found with high frequency in seawater or seafood from coastal waters [3,33].
The highest prevalence in this meta-analysis was found in the wastewater entering wastewater treatment plants (WWTP), which is in line with the reported prevalence in raw sewage. Thus, to assess the effectiveness of treatments in WWTP in eliminating this contamination, we followed with a subgroup analysis. Despite that a small decrease in the pooled prevalence of Arcobacteraceae through the WWTP was observed, a high prevalence continues to be observed, which could be seen as a potential health concern. Some authors suggest that these species are well suited to survive in adverse conditions, such as those in wastewater treatment plants, where their discharge into the environment is classified as a global problem [34]. Kristensen et al., 2020 described that the high relative abundance of arcobacters in the effluent may be associated with the removal of influent microorganisms in biological WWTPs. In the case of arcobacters, a large fraction of cells dispersed in the water phase prevails due to the reduced ability of these bacteria to flocculate and attach to the activated sludge flocs, preventing their effective removal [34]. This points to the need to reevaluate the treatment processes adopted or to even develop more effective methodologies to eliminate or potentially minimize the discharge of this emerging pathogen.

2.6. Subgroup Analysis by Arcobacteraceae Species

Considering that species from Arcobacteraceae family can be seen as waterborne pathogens, but also as naturally found in these environments, we proceeded with a subgroup analysis considering the different species. In this subgroup analysis, when a study presented prevalence data for each species determined by culture and molecular techniques, the global value or the highest value was collected for analysis. When evaluating the prevalence of the different species identified in the several categories of water samples, considering the ones that were identified in at least three studies, Al. butzleri was the species with the highest overall prevalence (58.3%), followed by Al. cryaerophilus (42.5%), Malaciobacter mytili (16.2%), Al. thereius (15.4%), Pseudarcobacter cloacae (14.8%), Pseudarcobacter defluvii (14.7%), Al. skirrowii (12.7%) and Arcobacter nitrofigilis (8.8%) (Table 5). Al. butzleri presented the highest pooled prevalence in most different water categories revealing the highest prevalence in seven of nine different water types, followed by Al. cryaerophilus, two of the species most associated with human diseases.
Çelik and Ünver (2005) suggested that Al. butzleri may present a stronger viability than other species in water, while presenting a competitive inhibitory effect in the population dynamic with other species [35]. Nonetheless, when analyzing these results, it should be considered that isolation and identification methods are needed for the analysis of the species considered, since the currently used methodologies may lead to an underestimation of the presence of some Arcobacteraceae species throughout the aquatic environment.
This systematic review and meta-analysis on the prevalence of species from the Arcobacteraceae family provides a comprehensive analysis on its occurrence and wide distribution worldwide. The use of meta-analytic techniques to assess the prevalence of pathogens in the environment, while allowing to overcome some flaws of the traditional review, also has the advantage of considering the relative weight with which each individual study contributes to the final result. The lack of defined criteria for carrying out the systematic review and meta-analysis outside clinical settings represents, however, one of its weaknesses. In addition, this study also includes some limitations: (a) there was a lack of studies in some regions across the world, (b) the confounding effect of using samples from different aquatic environments in the global estimate of prevalence, (c) the low number of samples analyzed in some studies or (d) the diversity of the detection and identification methods with different sensitivities and specificities.
Despite this, the results confirm that the species from this family have a wide geographical distribution, being present on diverse aquatic environments. The presence of the pathogenic species in these environments represents a public health risk, particularly when accessible to animals and humans. Thus, this study demonstrates the need for the monitoring and surveillance of water quality and safety, considering the presence of Arcobacteraceae species, as well as to assess to microbial risk. Further, some concern can be associated with Arcobacteraceae in wastewater plants effluents, highlighting the need for rapid action and review of bacterial elimination processes of this family, as effluents may eventually impact the receiving water body quality and in turn contaminate animals and food products that are easily accessible to humans.

3. Materials and Methods

3.1. Search Strategy and Study Selection

A comprehensive systematic literature search from databases ISI Web of Science, PubMed and Scopus were accessed for studies in February 2021 using the following search strategy: Arcobacter AND (Water OR groundwater OR seawater OR influent OR Effluent OR ambient OR sewage OR wastewater). This systematic review was performed following the PRISMA protocol. The recovered records were exported to Rayyan QCRI (https://rayyan.qcri.org/welcome (accessed on 31 January 2022)) for the initial screening. Thereafter, all the studies from the search were independently analyzed by the title, abstract and selected full-text by two reviewers, and a third reviewer arbitrated any divergence. Full-text articles published or in press were collected, while reviews, conference abstracts and chapter books were excluded. Only studies in English, Portuguese and Spanish were accessed for inclusion. Articles were considered for full-text review if (1) the full-text article could be retrieved, (2) it reported primary data or (3) the article reported isolation by culture or detection by molecular techniques of Arcobacteraceae or their species in water samples.

3.2. Data Extraction and Statistical Analyses

After a careful analysis, the following data were extracted and summarized from each included article: first author’s last name, year of publication, country, continent, income level, total analyzed samples, source of the samples, detection technique, volume of water used for analysis, species identified/detected and prevalence or number of positive samples. Meta-analysis of the prevalence of Arcobacteraceae was performed using Comprehensive Meta-Analysis Software v.2.0 (https://www.meta-analysis.com/ (accessed on 31 January 2022)). Forest plots were generated to show the study-specific effect sizes, with the pooled prevalence (PP) considered with a 95% confidence interval (CI), using the random-effects model. Heterogeneity among studies was measured by applying the I2 statistics. Values close to 0% indicate no heterogeneity, whilst values close to 25%, 50% and 75% correspond to a low, moderate and high heterogeneity, respectively. p-values correspond to the heterogeneities between studies from a Chi-squared test of the null hypothesis that there is no heterogeneity. The potential impact of publication bias on the present meta-analysis was assessed by three different analyses: funnel plot [36,37]; Egger’s regression test [38,39] and Duval and Tweedie’s Trim and Fill approach [40,41]. This allowed us to obtain the best estimate of the unbiased pooled effect size, creating a funnel plot including both the observed studies (shown as blue circles) and the necessary imputed studies (shown as red circles) to obtain the absence of bias. A sensitivity analysis was performed by removing each study at a time to evaluate the stability of the results. Subgroup analysis was performed on the outcome under the study per countries, continents (Turkey was included in Asia), income level, volume of analyzed water, laboratory detection technique, Arcobacteraceae species and water types.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/pathogens11020244/s1, Figure S1: Funnel plot of standard error by logit event rate (publication bias tests) for Arcobacteraceae prevalence in aquatic environments, Table S1: Main characteristics of the 70 included studies in this meta-analysis. and Table S2: Assessment of publication bias for the prevalence of Arcobacteraceae in aquatic environments using Egger’s regression test [24,26,28,30,31,33,34,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103].

Author Contributions

Conceptualization, S.F.; methodology, I.V., Â.L. and S.F.; statistical analysis, L.P.; formal analysis, I.V., Â.L., L.P. and S.F.; writing—original draft preparation, I.V.; writing—review and editing, I.V., Â.L., M.O., F.D., L.P. and S.F.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was developed within the scope of the CICS-UBI projects UIDB/00709/2020 and UIDP/00709/2020, financed by national funds through the Portuguese Foundation for Science and Technology/MCTES. This work was also supported by operation Centro-01-0145-FEDER-000019-C4-Centro de Competências em Cloud Computing, cofinanced by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica—Programas Integrados de IC&DT, which also funded the APC.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the text and the Supplementary Materials.

Acknowledgments

Susana Ferreira and Ângelo Luís acknowledge the contract of Scientific Employment under the scope of DL 57/2016.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Vandamme, P.; De Ley, J. Proposal for a New Family, Campylobacteraceae. Int. J. Syst. Bacteriol. 1991, 41, 451–455. [Google Scholar] [CrossRef]
  2. On, S.L.W. Taxonomy of Campylobacter, Arcobacter, Helicobacter and related bacteria: Current status, future prospects and immediate concerns. J. Appl. Microbiol. 2001, 90, 1S–15S. [Google Scholar] [CrossRef] [PubMed]
  3. Ferreira, S.; Oleastro, M.; Domingues, F. Current insights on Arcobacter butzleri in food chain. Curr. Opin. Food Sci. 2019, 26, 9–17. [Google Scholar] [CrossRef]
  4. Alonso, R.; Girbau, C.; Martinez-Malaxetxebarria, I.; Pérez-Cataluña, A.; Salas-Massó, N.; Romalde, J.L.; Figueras, M.J.; Fernandez-Astorga, A. Aliarcobacter vitoriensis sp. nov., isolated from carrot and urban wastewater. Syst. Appl. Microbiol. 2020, 43, 126091. [Google Scholar] [CrossRef] [PubMed]
  5. Waite, D.W.; Vanwonterghem, I.; Rinke, C.; Parks, D.H.; Zhang, Y.; Takai, K.; Sievert, S.M.; Simon, J.; Campbell, B.J.; Hanson, T.E.; et al. Comparative Genomic Analysis of the Class Epsilonproteobacteria and Proposed Reclassification to Epsilonbacteraeota (phyl. nov.). Front. Microbiol. 2017, 8, 682. [Google Scholar] [CrossRef]
  6. Pérez-Cataluña, A.; Salas-Massó, N.; Diéguez, A.L.; Balboa, S.; Lema, A.; Romalde, J.L.; Figueras, M.J. Revisiting the Taxonomy of the Genus Arcobacter: Getting Order from the Chaos. Front. Microbiol. 2018, 9, 2077. [Google Scholar] [CrossRef]
  7. Oren, A.; Garrity, G.M. List of new names and new combinations previously effectively, but not validly, published. Int. J. Syst. Evol. Microbiol. 2020, 70, 1–5. [Google Scholar] [CrossRef]
  8. Oren, A.; Garrity, G.M. List of new names and new combinations previously effectively, but not validly, published. Int. J. Syst. Evol. Microbiol. 2019, 69, 5–9. [Google Scholar] [CrossRef]
  9. On, S.L.W.; Miller, W.G.; Biggs, P.J.; Cornelius, A.J.; Vandamme, P. A critical rebuttal of the proposed division of the genus Arcobacter into six genera using comparative genomic, phylogenetic, and phenotypic criteria. Syst. Appl. Microbiol. 2020, 43, 126108. [Google Scholar] [CrossRef]
  10. Oren, A.; Garrity, G.M. List of new names and new combinations previously effectively, but not validly, published. Int. J. Syst. Evol. Microbiol. 2020, 70, 2960–2966. [Google Scholar] [CrossRef]
  11. Collado, L.; Figueras, M.J. Taxonomy, epidemiology, and clinical relevance of the genus Arcobacter. Clin. Microbiol. Rev. 2011, 24, 174–192. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Snelling, W.J.; Matsuda, M.; Moore, J.E.; Dooley, J.S.G. Under the microscope: Arcobacter. Lett. Appl. Microbiol. 2006, 42, 7–14. [Google Scholar] [CrossRef] [PubMed]
  13. Hsu, T.-T.D.; Lee, J. Global Distribution and Prevalence of Arcobacter in Food and Water. Zoonoses Public Health 2015, 62, 579–589. [Google Scholar] [CrossRef] [PubMed]
  14. Rahman, F.U.; Andree, K.B.; Salas-Massó, N.; Fernandez-Tejedor, M.; Sanjuan, A.; Figueras, M.J.; Furones, M.D. Improved culture enrichment broth for isolation of Arcobacter-like species from the marine environment. Sci. Rep. 2020, 10, 14547. [Google Scholar] [CrossRef] [PubMed]
  15. International Commission on Microbiological Specifications for Foods (ICMS). Microorganisms in Food 7—Microbiological Testing in Food Safety Management; Kuwer Academic/Plenum Publishers: New York, NY, USA, 2002; ISBN 0306472627. [Google Scholar]
  16. Wybo, I.; Breynaert, J.; Lauwers, S.; Lindenburg, F.; Houf, K. Isolation of Arcobacter skirrowii from a Patient with Chronic Diarrhea. J. Clin. Microbiol. 2004, 42, 1851–1852. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Van den Abeele, A.-M.; Vogelaers, D.; Van Hende, J.; Houf, K.; Van Den Abeele, A.; Vogelaers, D.; Van Hende, J.; Houf, K. Prevalence of Arcobacter Species among Humans, Belgium, 2008–2013. Emerg. Infect. Dis. 2014, 20, 1746–1749. [Google Scholar] [CrossRef] [Green Version]
  18. Vasiljevic, M.; Fenwick, A.J.; Nematollahi, S.; Gundareddy, V.P.; Romagnoli, M.; Zenilman, J.; Carroll, K.C. First Case Report of Human Bacteremia with Malacobacter (Arcobacter) mytili. Open Forum Infect. Dis. 2019, 6, ofz319. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Kerkhof, P.J.; Van den Abeele, A.M.; Strubbe, B.; Vogelaers, D.; Vandamme, P.; Houf, K. Diagnostic approach for detection and identification of emerging enteric pathogens revisited: The (Ali)arcobacter lanthieri case. New Microbes New Infect. 2021, 39, 100829. [Google Scholar] [CrossRef]
  20. Ferreira, S.; Queiroz, J.A.; Oleastro, M.; Domingues, F.C. Insights in the pathogenesis and resistance of Arcobacter: A review. Crit. Rev. Microbiol. 2016, 42, 364–383. [Google Scholar] [CrossRef] [PubMed]
  21. Raj Kannel, P.; Lee, S.; Lee, Y.; Kanel, S.; Pelletier, G. Application of automated QUAL2Kw for water quality modeling and management in the Bagmati River, Nepal. Ecol. Model. 2007, 202, 503–517. [Google Scholar] [CrossRef]
  22. Abulreesh, H.H.; Paget, T.A.; Goulder, R. Campylobacter in Waterfowl and Aquatic Environments:  Incidence and Methods of Detection. Environ. Sci. Technol. 2006, 40, 7122–7131. [Google Scholar] [CrossRef]
  23. Mateus, C.; Martins, R.; Luís, Â.; Oleastro, M.; Domingues, F.; Pereira, L.; Ferreira, S. Prevalence of Arcobacter: From farm to retail—A systematic review and meta-analysis. Food Control 2021, 128, 108177. [Google Scholar] [CrossRef]
  24. Fera, M.T.; Maugeri, T.L.; Gugliandolo, C.; Beninati, C.; La Camera, E.; Carbone, M.; Giannone, M. Detection of Arcobacter spp. in the Coastal Environment of the Mediterranean Sea. Appl. Environ. Microbiol. 2004, 70, 1271–1276. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Ferreira, S.; Oleastro, M.; Domingues, F. Arcobacter spp. in Food Chain—From Culture to Omics. In Food Borne Pathogens and Antibiotic Resistance; Singh, O.V., Ed.; Wiley-Blackwell: Hoboken, NJ, USA, 2017; pp. 73–118. [Google Scholar]
  26. Talay, F.; Molva, C.; Atabay, H.I. Isolation and identification of Arcobacter species from environmental and drinking water samples. Folia Microbiol. 2016, 61, 479–484. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Moreno, Y.; Alonso, J.L.; Botella, S.; Ferrús, M.A.; Hernández, J. Survival and injury of Arcobacter after artificial inoculation into drinking water. Res. Microbiol. 2004, 155, 726–730. [Google Scholar] [CrossRef] [PubMed]
  28. Ertas, N.; Dogruer, Y.; Gonulalan, Z.; Guner, A.; Ulger, I.; Nurhan, E.; Dogruer, Y.; Zafer, G.; Guner, A.; Ulger, I. Prevalence of Arcobacter species in drinking water, spring water, and raw milk as determined by multiplex PCR. J. Food Prot. 2010, 73, 2099–2102. [Google Scholar] [CrossRef]
  29. Zerva, I.; Remmas, N.; Kagalou, I.; Melidis, P.; Ariantsi, M.; Sylaios, G.; Ntougias, S. Effect of chlorination on microbiological quality of effluent of a full-scale wastewater treatment plant. Life 2021, 11, 68. [Google Scholar] [CrossRef] [PubMed]
  30. Collado, L.; Inza, I.; Guarro, J.; Figueras, M.J. Presence of Arcobacter spp. in environmental waters correlates with high levels of fecal pollution. Environ. Microbiol. 2008, 10, 1635–1640. [Google Scholar] [CrossRef]
  31. Fong, T.T.; Mansfield, L.S.; Wilson, D.L.; Schwab, D.J.; Molloy, S.L.; Rose, J.B. Massive microbiological groundwater contamination associated with a waterborne outbreak in Lake Erie, South Bass Island, Ohio. Environ. Health Perspect. 2007, 115, 856–864. [Google Scholar] [CrossRef] [Green Version]
  32. Kopilovi, B.; Ucakar, V.; Koren, N.; Krek, M.; Kraigher, A. Waterborne outbreak of acute gastroenteritis in a costal area in Slovenia in June and July 2008. EuroSurveillance 2008, 13, 18957. [Google Scholar] [CrossRef] [Green Version]
  33. Salas-Massó, N.; Figueras, M.J.; Andree, K.B.; Furones, M.D. Do the Escherichia coli European Union shellfish safety standards predict the presence of Arcobacter spp., a potential zoonotic pathogen? Sci. Total Environ. 2018, 624, 1171–1179. [Google Scholar] [CrossRef]
  34. Kristensen, J.M.; Nierychlo, M.; Albertsen, M.; Nielsen, P.H. Bacteria from the Genus Arcobacter Are Abundant in Effluent from Wastewater Treatment Plants. Appl. Environ. Microbiol. 2020, 86, e03044-19. [Google Scholar] [CrossRef] [PubMed]
  35. Çelik, E.; Ünver, A. Isolation and Identification of Arcobacter spp. by Multiplex PCR from Water Sources in Kars Region. Curr. Microbiol. 2015, 71, 546–550. [Google Scholar] [CrossRef] [PubMed]
  36. Light, R.J.; Pillemer, D.B. Summing Up: The Science of Reviewing Research; Harvard University Press: Cambridge, MA, USA, 1984. [Google Scholar]
  37. Light, R.J.; Singer, J.D.; Willett, J.B. The visual presentation and interpretation of meta-analyses. In The Handbook of Research Synthesis; Cooper, M., Hedges, L.V., Eds.; Russell Sage Foundation: New York, NY, USA, 1994. [Google Scholar]
  38. Borenstein, M.; Hedges, L.; Higgins, J. Introduction to Meta-Analysis; John Wiley & Sons: Chichester, UK, 2009. [Google Scholar]
  39. Egger, M.; Davey Smith, G.; Schneider, M.; Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997, 315, 629–634. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Duval, S.; Tweedie, R. A non parametric “Trim and Fill” method of accounting for publication bias in meta-analysis. J. Am. Stat. Assoc. 2000, 95, 89–98. [Google Scholar]
  41. Duval, S.; Tweedie, R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000, 56, 455–463. [Google Scholar] [CrossRef] [PubMed]
  42. Sorensen, J.P.R.; Lapworth, D.J.; Read, D.S.; Nkhuwa, D.C.W.; Bell, R.A.; Chibesa, M.; Chirwa, M.; Kabika, J.; Liemisa, M.; Pedley, S. Tracing enteric pathogen contamination in sub-Saharan African groundwater. Sci. Total Environ. 2015, 538, 888–895. [Google Scholar] [CrossRef] [Green Version]
  43. Hsu, T.-T.D.; Mitsch, W.J.; Martin, J.F.; Lee, J. Towards sustainable protection of public health: The role of an urban wetland as a frontline safeguard of pathogen and antibiotic resistance spread. Ecol. Eng. 2017, 108, 547–555. [Google Scholar] [CrossRef]
  44. Levican, A.; Collado, L.; Figueras, M.J. The use of two culturing methods in parallel reveals a high prevalence and diversity of Arcobacter spp. in a wastewater treatment plant. Biomed Res. Int. 2016, 2016, 8132058. [Google Scholar] [CrossRef] [Green Version]
  45. Diergaardt, S.M.; Venter, S.N.; Spreeth, A.; Theron, J.; Brözel, V.S. The occurrence of campylobacters in water sources in South Africa. Water Res. 2004, 38, 2589–2595. [Google Scholar] [CrossRef]
  46. Klase, G.; Lee, S.; Liang, S.; Kim, J.; Zo, Y.G.; Lee, J. The microbiome and antibiotic resistance in integrated fishfarm water: Implications of environmental public health. Sci. Total Environ. 2019, 649, 1491–1501. [Google Scholar] [CrossRef] [PubMed]
  47. Ho, H.T.; Lipman, L.J.; Gaastra, W. The introduction of Arcobacter spp. in poultry slaughterhouses. Int. J. Food Microbiol. 2008, 125, 223–229. [Google Scholar] [CrossRef] [PubMed]
  48. Rodriguez-Manzano, J.; Alonso, J.L.; Ferrús, M.A.; Moreno, Y.; Amorós, I.; Calgua, B.; Hundesa, A.; Guerrero-Latorre, L.; Carratala, A.; Rusiñol, M.; et al. Standard and new faecal indicators and pathogens in sewage treatment plants, microbiological parameters for improving the control of reclaimed water. Water Sci. Technol. 2012, 66, 2517–2523. [Google Scholar] [CrossRef]
  49. Moreno, Y.; Botella, S.; Alonso, J.L.; Ferrús, M.A.; Hernández, M.; Hernández, J. Specific detection of Arcobacter and Campylobacter strains in water and sewage by PCR and fluorescent in situ hybridization. Appl. Environ. Microbiol. 2003, 69, 1181–1186. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. De Smet, S.; De Zutter, L.; Houf, K. Small ruminants as carriers of the emerging foodborne pathogen Arcobacter on small and medium farms. Small Rumin. Res. 2011, 97, 124–129. [Google Scholar] [CrossRef]
  51. Chen, X.; Lang, X.L.; Xu, A.L.; Song, Z.W.; Yang, J.; Guo, M.Y. Seasonal variability in the microbial community and pathogens in wastewater final effluents. Water 2019, 11, 2586. [Google Scholar] [CrossRef] [Green Version]
  52. Suh, S.S.; Park, M.; Hwang, J.; Kil, E.J.; Jung, S.W.; Lee, S.; Lee, T.K. Seasonal dynamics of marine microbial community in the South Sea of Korea. PLoS ONE 2015, 10, e0131633. [Google Scholar] [CrossRef] [Green Version]
  53. Shrestha, R.G.; Tandukar, S.; Bhandari, D.; Sherchan, S.P.; Tanaka, Y.; Sherchand, J.B.; Haramoto, E. Prevalence of Arcobacter and other pathogenic bacteria in river water in Nepal. Water 2019, 11, 1416. [Google Scholar] [CrossRef] [Green Version]
  54. González, A.; Suski, J.; Ferrus, M.A. Rapid and accurate detection of Arcobacter contamination in commercial chicken products and wastewater samples by real-time polymerase chain reaction. Foodborne Pathog. Dis. 2010, 7, 327–338. [Google Scholar] [CrossRef]
  55. Chandra, S.; Saxena, T.; Nehra, S.; Mohan, M.K. Quality assessment of supplied drinking water in Jaipur city, India, using PCR-based approach. Environ. Earth Sci. 2016, 75, 153. [Google Scholar] [CrossRef]
  56. Shah, A.H.; Saleha, A.A.; Zunita, Z.; Cheah, Y.K.; Murugaiyah, M.; Korejo, N.A. Genetic characterization of Arcobacter isolates from various sources. Vet. Microbiol. 2012, 160, 355–361. [Google Scholar] [CrossRef] [PubMed]
  57. Andersen, M.M.; Wesley, I.V.; Nestor, E.; Trampel, D.W. Prevalence of Arcobacter species in market-weight commercial turkeys. Antonie Leeuwenhoek 2007, 92, 309–317. [Google Scholar] [CrossRef]
  58. Webb, A.L.; Taboada, E.N.; Selinger, L.B.; Boras, V.F.; Inglis, G.D. Prevalence and diversity of waterborne Arcobacter butzleri in southwestern Alberta, Canada. Can. J. Microbiol. 2017, 63, 330–340. [Google Scholar] [CrossRef]
  59. Pejchalová, M.; Dostalikova, E.; Slámová, M.; Brožková, I.; Vytřasová, J. Prevalence and diversity of Arcobacter spp. in the Czech Republic. J. Food Prot. 2008, 71, 719–727. [Google Scholar] [CrossRef]
  60. Aydin, F.; Gümüşsoy, K.S.; Atabay, H.I.; Iça, T.; Abay, S. Prevalence and distribution of Arcobacter species in various sources in Turkey and molecular analysis of isolated strains by ERIC-PCR. J. Appl. Microbiol. 2007, 103, 27–35. [Google Scholar] [CrossRef] [PubMed]
  61. Fisher, J.C.; Levican, A.; Figueras, M.J.; McLellan, S.L. Population dynamics and ecology of Arcobacter in sewage. Front. Microbiol. 2014, 5, 525. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Ferreira, S.; Oleastro, M.; Domingues, F.C. Occurrence, genetic diversity and antibiotic resistance of Arcobacter sp. in a dairy plant. J. Appl. Microbiol. 2017, 123, 1019–1026. [Google Scholar] [CrossRef] [PubMed]
  63. Serraino, A.; Giacometti, F. Occurrence of Arcobacter species in industrial dairy plants. J. Dairy Sci. 2014, 97, 2061–2065. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Khoshbakht, R.; Tabatabaei, M.; Shirzad Aski, H.; Seifi, S. Occurrence of Arcobacter in Iranian poultry and slaughterhouse samples implicates contamination by processing equipment and procedures. Brit. Poult. Sci. 2014, 55, 732–736. [Google Scholar] [CrossRef] [PubMed]
  65. Hausdorf, L.; Neumann, M.; Bergmann, I.; Sobiella, K.; Mundt, K.; Fröhling, A.; Schlüter, O.; Klocke, M. Occurrence and genetic diversity of Arcobacter spp. in a spinach-processing plant and evaluation of two Arcobacter-specific quantitative PCR assays. Syst. Appl. Microbiol. 2013, 36, 235–243. [Google Scholar] [CrossRef]
  66. Giacometti, F.; Lucchi, A.; Manfreda, G.; Florio, D.; Zanoni, R.G.; Serraino, A. Occurrence and genetic diversity of Arcobacter butzleri in an artisanal dairy plant in Italy. Appl. Environ. Microbiol. 2013, 79, 6665–6669. [Google Scholar] [CrossRef] [Green Version]
  67. Collado, L.; Kasimir, G.; Perez, U.; Bosch, A.; Pinto, R.; Saucedo, G.; Huguet, J.M.; Figueras, M.J. Occurrence and diversity of Arcobacter spp. along the Llobregat River catchment, at sewage effluents and in a drinking water treatment plant. Water Res. 2010, 44, 3696–3702. [Google Scholar] [CrossRef]
  68. Elmali, M.; Can, H.Y. Occurence and antimicrobial resistance of Arcobacter species in food and slaughterhouse samples. Food Sci. Technol. 2017, 37, 280–285. [Google Scholar] [CrossRef] [Green Version]
  69. Shrestha, R.G.; Tanaka, Y.; Malla, B.; Bhandari, D.; Tandukar, S.; Inoue, D.; Sei, K.; Sherchand, J.B.; Haramoto, E. Next-generation sequencing identification of pathogenic bacterial genes and their relationship with fecal indicator bacteria in different water sources in the Kathmandu Valley, Nepal. Sci. Total Environ. 2017, 601, 278–284. [Google Scholar] [CrossRef] [PubMed]
  70. Healy-Profitós, J.; Lee, S.; Mouhaman, A.; Garabed, R.; Moritz, M.; Piperata, B.; Lee, J. Neighborhood diversity of potentially pathogenic bacteria in drinking water from the city of Maroua, Cameroon. J. Water Health 2016, 14, 559–570. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Houf, K.; De Zutter, L.; Verbeke, B.; Van Hoof, J.; Vandamme, P. Molecular characterization of Arcobacter isolates collected in a poultry slaughterhouse. J. Food Prot. 2003, 66, 364–369. [Google Scholar] [CrossRef] [PubMed]
  72. Šilha, D.; Šilhová-Hrušková, L.; Vytřasová, J. Modified isolation method of Arcobacter spp. from different environmental and food samples. Folia Microbiol. 2015, 60, 515–521. [Google Scholar] [CrossRef] [PubMed]
  73. Vytřasová, J.; Pejchalova, M.; Harsova, K.; Bínová, Š. Isolation of Arcobacter butzleri and A. cryaerophilus in samples of meats and from meat-processing plants by a culture technique and detection by PCR. Folia Microbiol. 2003, 48, 227–232. [Google Scholar] [CrossRef]
  74. Morita, Y.; Maruyama, S.; Kabeya, H.; Boonmar, S.; Nimsuphan, B.; Nagai, A.; Kozawa, K.; Nakajima, T.; Mikami, T.; Kimura, H. Isolation and phylogenetic analysis of Arcobacter spp. in ground chicken meat and environmental water in Japan and Thailand. Microbiol. Immunol. 2013, 48, 527–533. [Google Scholar] [CrossRef]
  75. Shrestha, G.R.; Tanaka, Y.; Sherchand, J.B.; Haramoto, E. Identification of 16S rRNA and Virulence-Associated Genes of Arcobacter in Water Samples in the Kathmandu Valley, Nepal. Pathogens 2019, 8, 110. [Google Scholar] [CrossRef] [Green Version]
  76. Carney, R.L.; Brown, M.V.; Siboni, N.; Raina, J.B.; Kahlke, T.; Mitrovic, S.M.; Seymour, J.R. Highly heterogeneous temporal dynamics in the abundance and diversity of the emerging pathogens Arcobacter at an urban beach. Water Res. 2020, 171, 115405. [Google Scholar] [CrossRef] [PubMed]
  77. Fernandez-Cassi, X.; Silvera, C.; Cervero-Aragó, S.; Rusiñol, M.; Latif-Eugeni, F.; Bruguera-Casamada, C.; Civit, S.; Araujo, R.M.; Figueras, M.J.; Girones, R.; et al. Evaluation of the microbiological quality of reclaimed water produced from a lagooning system. Environ. Sci. Pollut. Res. 2016, 23, 16816–16833. [Google Scholar] [CrossRef] [PubMed]
  78. Salas-Massó, N.; Andree, K.B.; Furones, M.D.; Figueras, M.J. Enhanced recovery of Arcobacter spp. using NaCl in culture media and re-assessment of the traits of Arcobacter marinus and Arcobacter halophilus isolated from marine water and shellfish. Sci. Total Environ. 2016, 566, 1355–1361. [Google Scholar] [CrossRef]
  79. Cui, Q.; Huang, Y.; Wang, H.; Fang, T. Diversity and abundance of bacterial pathogens in urban rivers impacted by domestic sewage. Environ. Pollut. 2019, 249, 24–35. [Google Scholar] [CrossRef]
  80. Maugeri, T.L.; Irrera, G.P.; Lentini, V.; Carbone, M.; Fera, M.T.; Gugliandolo, C. Detection and enumeration of Arcobacter spp. in the coastal environment of the Straits of Messina (Italy). New Microbiol. 2005, 28, 177–182. [Google Scholar]
  81. González, A.; Botella, S.; Montes, R.M.; Moreno, Y.; Ferrus, M.A. Direct detection and identification of Arcobacter species by multiplex PCR in chicken and wastewater samples from Spain. J. Food Prot. 2007, 70, 341–347. [Google Scholar] [CrossRef] [PubMed]
  82. Shrestha, R.G.; Tanaka, Y.; Malla, B.; Tandukar, S.; Bhandari, D.; Inoue, D.; Sei, K.; Sherchand, J.B.; Haramoto, E. Development of a quantitative PCR assay for Arcobacter spp. and its application to environmental water samples. Microbes Environ. 2018, 33, 309–316. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Banihashemi, A.; Van Dyke, M.I.; Huck, P.M. Detection of viable bacterial pathogens in a drinking water source using propidium monoazide-quantitative PCR. J. Water Supply Res. Technol. 2015, 64, 139–148. [Google Scholar] [CrossRef]
  84. Chinivasagam, H.N.; Corney, B.G.; Wright, L.L.; Diallo, I.S.; Blackall, P.J. Detection of Arcobacter spp. in piggery effluent and effluent-irrigated soils in southeast Queensland. J. Appl. Microbiol. 2007, 103, 418–426. [Google Scholar] [CrossRef]
  85. Rathlavath, S.; Kumar, S.; Nayak, B.B. Comparative isolation and genetic diversity of Arcobacter sp. from fish and the coastal environment. Lett. Appl. Microbiol. 2017, 65, 42–49. [Google Scholar] [CrossRef] [PubMed]
  86. Hausdorf, L.; Mundt, K.; Winzer, M.; Cordes, C.; Fröhling, A.; Schlüter, O.; Klocke, M. Characterization of the cultivable microbial community in a spinach-processing plant using MALDI-TOF MS. Food Microbiol. 2013, 34, 406–411. [Google Scholar] [CrossRef] [PubMed]
  87. Lu, X.; Zhang, X.X.; Wang, Z.; Huang, K.; Wang, Y.; Liang, W.; Tan, Y.; Liu, B.; Tang, J. Bacterial pathogens and community composition in advanced sewage treatment systems revealed by metagenomics analysis based on high-throughput sequencing. PLoS ONE 2015, 10, e0125549. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Leight, A.K.; Crump, B.C.; Hood, R.R. Assessment of fecal indicator bacteria and potential pathogen co-occurrence at a shellfish growing area. Front. Microbiol. 2018, 9, 384. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Merga, J.Y.; Royden, A.; Pandey, A.K.; Williams, N.J. Arcobacter spp. isolated from untreated domestic effluent. Lett. Appl. Microbiol. 2014, 59, 122–126. [Google Scholar] [CrossRef] [PubMed]
  90. Lee, C.; Agidi, S.; Marion, J.W.; Lee, J. Arcobacter in Lake Erie beach waters: An emerging gastrointestinal pathogen linked with human-associated fecal contamination. Appl. Environ. Microbiol. 2012, 78, 5511–5519. [Google Scholar] [CrossRef] [Green Version]
  91. Giacometti, F.; Piva, S.; Vranckx, K.; De Bruyne, K.; Drigo, I.; Lucchi, A.; Manfreda, G.; Serraino, A. Application of MALDI-TOF MS for the subtyping of Arcobacter butzleri strains and comparison with their MLST and PFGE types. Int. J. Food Microbiol. 2018, 277, 50–57. [Google Scholar] [CrossRef]
  92. Newton, R.J.; Bootsma, M.J.; Morrison, H.G.; Sogin, M.L.; McLellan, S.L. A microbial signature approach to identify fecal pollution in the waters off an urbanized coast of Lake Michigan. Microb. Ecol. 2013, 65, 1011–1023. [Google Scholar] [CrossRef]
  93. Acharya, K.; Blackburn, A.; Mohammed, J.; Haile, A.T.; Hiruy, A.M.; Werner, D. Metagenomic water quality monitoring with a portable laboratory. Water Res. 2020, 184, 116112. [Google Scholar] [CrossRef]
  94. Niedermeyer, J.A.; Miller, W.G.; Yee, E.; Harris, A.; Emanuel, R.E.; Jass, T.; Nelson, N.; Kathariou, S. Search for Campylobacter spp. reveals high prevalence and pronounced genetic diversity of Arcobacter butzleri in floodwater samples associated with Hurricane Florence in North Carolina, USA. Appl. Environ. Microbiol. 2020, 86, e01118-20. [Google Scholar] [CrossRef]
  95. Kutilova, I.; Medvecky, M.; Leekitcharoenphon, P.; Munk, P.; Masarikova, M.; Davidova-Gerzova, L.; Jamborova, I.; Bortolaia, V.; Pamp, S.J.; Dolejska, M. Extended-spectrum beta-lactamase-producing Escherichia coli and antimicrobial resistance in municipal and hospital wastewaters in Czech Republic: Culture-based and metagenomic approaches. Environ. Res. 2021, 193, 110487. [Google Scholar] [CrossRef]
  96. Godoy, R.G.; Marcondes, M.A.; Pessôa, R.; Nascimento, A.; Victor, J.R.; da Silva Duarte, A.J.; Clissa, P.B.; Sanabani, S.S. Bacterial community composition and potential pathogens along the Pinheiros River in the southeast of Brazil. Sci. Rep. 2020, 10, 9331. [Google Scholar] [CrossRef] [PubMed]
  97. Zhang, L.; Cheng, Y.; Qian, C.; Lu, W. Bacterial community evolution along full-scale municipal wastewater treatment processes. J. Water Health 2020, 18, 665–680. [Google Scholar] [CrossRef] [PubMed]
  98. Miltenburg, M.G.; Cloutier, M.; Craiovan, E.; Lapen, D.R.; Wilkes, G.; Topp, E.; Khan, I.U. Real-time quantitative PCR assay development and application for assessment of agricultural surface water and various fecal matter for prevalence of Aliarcobacter faecis and Aliarcobacter lanthieri. BMC Microbiol. 2020, 20, 164. [Google Scholar] [CrossRef] [PubMed]
  99. Khan, I.U.H.; Becker, A.; Cloutier, M.; Plötz, M.; Lapen, D.R.; Wilkes, G.; Topp, E.; Abdulmawjood, A. Loop-mediated isothermal amplification: Development, validation and application of simple and rapid assays for quantitative detection of species of Arcobacteraceae family-and species-specific Aliarcobacter faecis and Aliarcobacter lanthieri. J. Appl. Microbiol. 2020, 131, 288–299. [Google Scholar] [CrossRef]
  100. Zhang, R.; Liu, W.C.; Liu, Y.; Zhang, H.L.; Zhao, Z.H.; Zou, L.Y.; Shen, Y.C.; Lan, W.S. Impacts of anthropogenic disturbances on microbial community of coastal waters in Shenzhen, South China. Ecotoxicology 2020, 30, 1652–1661. [Google Scholar] [CrossRef]
  101. Shrestha, R.G.; Sherchan, S.P.; Kitajima, M.; Tanaka, Y.; Gerba, C.P.; Haramoto, E. Reduction of Arcobacter at Two Conventional Wastewater Treatment Plants in Southern Arizona, USA. Pathogens 2019, 8, 175. [Google Scholar] [CrossRef] [Green Version]
  102. Shah, A.H.; Saleha, A.A.; Zunita, Z.; Murugaiyah, M.; Aliyu, A.B.; Jafri, N. Prevalence, Distribution and Antibiotic Re-sistance of Emergent Arcobacter spp. from Clinically Healthy Cattle and Goats. Transbound. Emerg. Dis. 2012, 60, 9–16. [Google Scholar] [CrossRef] [Green Version]
  103. Pejchalová, M.; Vytřasová, J.; Brožková, I.; Husková, Z.; Červenka, L. Occurrence of arcobacters in the Czech Republic and the influence of sample matrix on their detection using PCR. J. Food Nut Res. 2006, 45, 152–158. [Google Scholar]
Pathogens 11 00244 g001 550
Figure 1. Flow diagram of database search, study selection and articles included in the meta-analysis.
Figure 1. Flow diagram of database search, study selection and articles included in the meta-analysis.
Pathogens 11 00244 g001
Pathogens 11 00244 g002 550
Figure 2. Forest plot of the meta-analysis of prevalence of Arcobacteraceae in aquatic environments (in the references, 1 and 2 concern a division of the study by country).
Figure 2. Forest plot of the meta-analysis of prevalence of Arcobacteraceae in aquatic environments (in the references, 1 and 2 concern a division of the study by country).
Pathogens 11 00244 g002
Table
Table 1. Meta-analysis of the prevalence of Arcobacteraceae according to countries, continents and income level.
Table 1. Meta-analysis of the prevalence of Arcobacteraceae according to countries, continents and income level.
Countries/Continent/Income LevelnPooled Prevalence95% CIQ-ValueI2tau2p-Value
Lower LimitUpper Limit
Countries
Australia20.9460.6070.9950.2000.655
Belgium20.6470.150.9511.21591.0846.0690.001
Brazil10.9620.2980.9990001
Cameroon10.00600.2480001
Canada40.9190.6860.983157.86698.16.5330
China60.880.5820.97522.11277.3887.142<0.001
Czech Republic50.4950.1640.838.13750.8390.8860.087
Denmark10.9640.5710.9980001
Ethiopia10.90.1250.9980001
Germany20.4580.0660.914.99379.9724.8890.025
India20.1940.0210.7283.52371.6112.7170.061
Iran10.6330.080.9720001
Italy60.4960.1840.81128.09582.2033.702<0.001
Japan10.2350.0910.4860001
Korea10.9440.2210.9990001
Malaysia20.1110.0120.560001
Nepal40.780.4080.94829.84389.9472.843<0.001
Netherlands10.10.0020.8750001
Portugal10.1250.0020.9030001
South Africa10.3330.0230.9140001
Spain100.8940.7420.96163.89785.9151.383<0.001
Thailand10.9380.4610.9960001
Turkey40.1240.0280.41331.32890.4241.365<0.001
UK20.9350.4430.9960.07000.792
USA90.8570.6690.94798.42491.8721.3610
Zambia10.1140.0060.7360001
Continent
Africa40.1920.0470.53617.35182.712.322<0.001
Asia220.4990.3360.663149.35785.9391.1430
Europe300.7270.5960.828193.06284.9791.8460
North America130.8710.7490.939348.86996.562.160
Oceania20.9450.6540.9940.2000.655
South America10.9620.350.9990001
Income level
Low10.90.1450.9980001
Lower middle80.3980.180.66654.12387.0671.211<0.001
Upper middle160.4720.2810.673116.08987.0791.950
High470.790.7020.858644.84592.8671.9560
Table
Table 2. Meta-analysis of the prevalence of Arcobacteraceae according to the volume of sample analyzed.
Table 2. Meta-analysis of the prevalence of Arcobacteraceae according to the volume of sample analyzed.
Volume of Sample (mL)nPooled Prevalence95% CIQ-ValueI2tau2p-Value
Lower LimitUpper Limit
0–200440.5870.4790.687445.84890.3561.312<0.001
201–50040.8200.4800.95832.09590.65310.209<0.001
100030.9030.6840.9768.35876.0690.517<0.001
>100070.8580.6660.94872.32491.7044.86<0.001
Table
Table 3. Meta-analysis of the prevalence of Arcobacteraceae according to the used detection method.
Table 3. Meta-analysis of the prevalence of Arcobacteraceae according to the used detection method.
MethodsnPooled Prevalence95% CIQ-ValueI2tau2p-Value
Lower LimitUpper Limit
Culture—after enrichment370.4330.3480.521278.03887.0520.779<0.001
Culture—without enrichment40.4870.2740.7059.53568.5360.5430.023
Molecular after enrichment100.6310.4370.7939.80977.3920.924<0.001
Molecular direct300.8760.7690.937422.34993.1344.281<0.001
—metagenomic sequencing160.960.8910.9867.954000.926
—PCR and other amplification methods140.6880.4380.862277.22295.3114.058<0.001
Table
Table 4. Meta-analysis of the prevalence of Arcobacteraceae according to sample sources.
Table 4. Meta-analysis of the prevalence of Arcobacteraceae according to sample sources.
Sources SamplesnPooled Prevalence95% CIQ-ValueI2tau2p-Value
Lower LimitUpper Limit
Seawater110.7800.6000.89393.28389.2801.453<0.001
Surface water280.6450.4850.778368.52592.6732.406<0.001
Ground water70.3960.1980.63631.5981.0071.176<0.001
Raw sewage200.9060.7860.962120.60884.2463.325<0.001
Processing Water90.3430.1410.62433.62476.2071.942<0.001
Drinking water70.0320.0140.0692.791000.835
WWTP
Influent WWTP
Treatment WWTP		110.9640.930.9823.556000.965
70.9310.7520.98424.55975.5692.703<0.001
Effluent WWTP90.8760.7740.93612.72537.130.3660.122
Table
Table 5. Meta-analysis of the prevalence of Arcobacteraceae according to the sources of samples and species.
Table 5. Meta-analysis of the prevalence of Arcobacteraceae according to the sources of samples and species.
SpeciesDrinking Water, AnimalsDrinking Water, HumansSurface WaterSeawaterProcessing WaterRaw SewageInfluent WWTPTreatment WWTPEfluent WWTPOverall
nPooled Prevalence (95% CI)nPooled Prevalence (95% CI)nPooled Prevalence (95% CI)nPooled Prevalence (95% CI)nPooled Prevalence (95% CI)nPooled Prevalence (95% CI)nPooled Prevalence (95% CI)nPooled Prevalence (95% CI)nPooled Prevalence (95% CI)nPooled Prevalence (95% CI)
Aliarcobacter butzleri30.090
(0.019–0.342)		20.029
(0.004–0.195)		180.503 (0.347–0.659)50.704 (0.389–0.898)30.090 (0.019–0.342)130.696 (0.502–0.838)40.954 (0.776–0.992)30.832 (0.485–0.963)30.830 (0.49–0.961)560.583 (0.483–0.675)
Aliarcobacter skirrowii0̵̵̵̵̵̶-0̵̵̵̵̵̶-10.962 (0.597–0.998)10.042 (0.003–0.425)0̵̵̵̵̵̶-10.033 (0.062–0.366)10.125 (0.031–0.386)30.071 (0.014–0.288)10.071 (0.004–0.577)80.127 (0.057–0.258)
Aliarcobacter cryaerophilus20.079 (0.007–0.50)0̵̵̵̵̵̶-40.465 (0.120–0.847)40.207 (0.049–0.570)20.089 (0.01–0.479)90.500 (0.251–0.749)30.524 (0.135–0.885)40.770 (0.369–0.950)20.796 (0.237–0.98)300.425 (0.285–0.579)
Aliarcobacter thereius0̵̵̵̵̵̶-0̵̵̵̵̵̶-0̵̵̵̵̵̶-0̵̵̵̵̵̶-0̵̵̵̵̵̶-10.167 (0.023–0.631)10.167 (0.023–0.631)30.167 (0.055–0.409)10.071 (0.004–0.577)60.154 (0.068–0.311)
Arcobacter nitrofigilis0̵̵̵̵̵̶-0̵̵̵̵̵̶-0̵̵̵̵̵̶-10.042 (0.003–0.425)0̵̵̵̵̵̶-20.108 (0.027–0.346)10.167 (0.023–0.631)30.071 (0.014–0.288)30.071 (0.014–0.288)100.088 (0.041–0.178)
Malaciobacter mytili0̵̵̵̵̵̶-0̵̵̵̵̵̶-0̵̵̵̵̵̶-20.207 (0.062–0.506)0̵̵̵̵̵̶-10.033 (0.001–0.475)0̵̵̵̵̵̶-0̵̵̵̵̵̶-0̵̵̵̵̵̶-30.162 (0.052–0.405)
Pseudarcobactercloacae0̵̵̵̵̵̶-0̵̵̵̵̵̶-0̵̵̵̵̵̶-10.091 (0.013–0.439) ̵̵̵̵̵̶-20.197 (0.054–0.512)10.333 (0.084–0.732)30.071 (0.014–0.288)10.071 (0.004–0.577)80.148 (0.072–0.281)
Pseudarcobacter defluvii0̵̵̵̵̵̶-0̵̵̵̵̵̶-0̵̵̵̵̵̶-10.042 (0.003–0.425)0̵̵̵̵̵̶-20.150 (0.049–0.377)10.167 (0.023–0.631)30.167 (0.055–0.409)20.160 (0.031–0.530)90.147 (0.078–0.261)
̵̵̵̵̵̶-: Corresponds to no value.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Venâncio, I.; Luís, Â.; Domingues, F.; Oleastro, M.; Pereira, L.; Ferreira, S. The Prevalence of Arcobacteraceae in Aquatic Environments: A Systematic Review and Meta-Analysis. Pathogens 2022, 11, 244. https://doi.org/10.3390/pathogens11020244

AMA Style

Venâncio I, Luís Â, Domingues F, Oleastro M, Pereira L, Ferreira S. The Prevalence of Arcobacteraceae in Aquatic Environments: A Systematic Review and Meta-Analysis. Pathogens. 2022; 11(2):244. https://doi.org/10.3390/pathogens11020244

Chicago/Turabian Style

Venâncio, Igor, Ângelo Luís, Fernanda Domingues, Mónica Oleastro, Luísa Pereira, and Susana Ferreira. 2022. "The Prevalence of Arcobacteraceae in Aquatic Environments: A Systematic Review and Meta-Analysis" Pathogens 11, no. 2: 244. https://doi.org/10.3390/pathogens11020244

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop