Next Article in Journal
Helicobacter pylori and Its Role in Gastric Cancer
Next Article in Special Issue
Detection of Viable but Non-Culturable (VBNC)-Campylobacter in the Environment of Broiler Farms: Innovative Insights Delivered by Propidium Monoazide (PMA)-v-qPCR Analysis
Previous Article in Journal
Bovine Udder Health: From Standard Diagnostic Methods to New Approaches—A Practical Investigation of Various Udder Health Parameters in Combination with 16S rRNA Sequencing
Previous Article in Special Issue
Campylobacter Colonization and Diversity in Young Turkeys in the Context of Gastrointestinal Distress and Antimicrobial Treatment
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 11
Issue 5
10.3390/microorganisms11051313
microorganisms-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:
Article

Comparison of the Diagnostic Accuracy of Three Real-Time PCR Assays for the Detection of Arcobacter butzleri in Human Stool Samples Targeting Different Genes in a Test Comparison without a Reference Standard

by
Ramona Binder
1,
Andreas Hahn
2,
Kirsten Alexandra Eberhardt
3,4,
Ralf Matthias Hagen
5,
Holger Rohde
6,
Ulrike Loderstädt
7,
Torsten Feldt
8,
Fred Stephen Sarfo
9,10,
Veronica Di Cristanziano
11,
Sascha Kahlfuss
12,13,14,15,
Hagen Frickmann
2,16,† and
Andreas Erich Zautner
12,13,*,†
1
Laboratory Department, Bundeswehr Hospital Hamburg, 20359 Hamburg, Germany
2
Institute for Medical Microbiology, Virology and Hygiene, University Medicine Rostock, 18057 Rostock, Germany
3
Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine & I. Department of Medicine, University Medical Center Hamburg-Eppendorf, 20359 Hamburg, Germany
4
Division of Hygiene and Infectious Diseases, Institute of Hygiene and Environment, 20539 Hamburg, Germany
5
Department of Microbiology and Hospital Hygiene, Bundeswehr Central Hospital Koblenz, 56070 Koblenz, Germany
6
Institute of Medical Microbiology, Virology and Hygiene, University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
7
Department of Hospital Hygiene & Infectious Diseases, University Medicine Göttingen, 37075 Göttingen, Germany
8
Department of Gastroenterology, Hepatology and Infectious Diseases, University Medical Center Düsseldorf, 40225 Düsseldorf, Germany
9
Kwame Nkrumah University of Science and Technology, Kumasi 00233, Ghana
10
Department of Medicine, Komfo Anokye Teaching Hospital, Kumasi 00233, Ghana
11
Institute of Virology, Faculty of Medicine, University Hospital of Cologne, University of Cologne, 50935 Cologne, Germany
12
Institute of Medical Microbiology and Hospital Hygiene, Medical Faculty, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
13
CHaMP—Center for Health and Medical Prevention, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
14
Institute of Molecular and Clinical Immunology, Medical Faculty, Otto-von-Guericke University Magdeburg, 39104 Magdeburg, Germany
15
Health Campus Immunology, Infectiology and Inflammation (GCI), Medical Faculty, Otto-von-Guericke University Magdeburg, 39104 Magdeburg, Germany
16
Department of Microbiology and Hospital Hygiene, Bundeswehr Hospital Hamburg, 20359 Hamburg, Germany
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microorganisms 2023, 11(5), 1313; https://doi.org/10.3390/microorganisms11051313
Submission received: 3 April 2023 / Revised: 13 May 2023 / Accepted: 15 May 2023 / Published: 17 May 2023
(This article belongs to the Special Issue Advances in Campylobacter: Molecular Epidemiology, Virulence Factors, Immune Response and Drug Resistance)
Review Reports Versions Notes

Abstract

:
Potential etiological relevance for gastroenteric disorders including diarrhea has been assigned to Arcobacter butzleri. However, standard routine diagnostic algorithms for stool samples of patients with diarrhea are rarely adapted to the detection of this pathogen and so, A. butzleri is likely to go undetected unless it is specifically addressed, e.g., by applying pathogen-specific molecular diagnostic approaches. In the study presented here, we compared three real-time PCR assays targeting the genes hsp60, rpoB/C (both hybridization probe assays) and gyrA (fluorescence resonance energy transfer assay) of A. butzleri in a test comparison without a reference standard using a stool sample collection with a high pretest probability from the Ghanaian endemicity setting. Latent class analysis was applied with the PCR results obtained with a collection of 1495 stool samples showing no signs of PCR inhibition to assess the real-time PCR assays’ diagnostic accuracy. Calculated sensitivity and specificity were 93.0% and 96.9% for the hsp60-PCR, 100% and 98.2% for the rpoB/C-PCR, as well as 12.7% and 99.8% for the gyrA-PCR, respectively. The calculated A. butzleri prevalence within the assessed Ghanaian population was 14.7%. As indicated by test results obtained with high-titer spiked samples, cross-reactions of the hsp60-assay and rpoB/C-assay with phylogenetically related species such as A. cryaerophilus can occur but are less likely with phylogenetically more distant species like, e.g., A. lanthieri. In conclusion, the rpoB/C-assay showed the most promising performance characteristics as the only assay with sensitivity >95%, albeit associated with a broad 95%-confidence interval. In addition, this assay showed still-acceptable specificity of >98% in spite of the known cross-reactivity with phylogenetically closely related species such as A. cryaerophilus. If higher certainty is desired, the gyrA-assay with specificity close to 100% can be applied for confirmation testing with samples showing positive rpoB/C-PCR results. However, in case of a negative result in the gyrA-assay, this cannot reliably exclude the detection of A. butzleri in the rpoB/C-assay due to the gyrA-assay’s very low sensitivity.

1. Introduction

Arcobacter butzleri (homotypic synonym “Aliarcobacter butzleri”), formerly addressed as Campylobacter butzleri and first described in 1991, is a facultatively pathogenic bacterium from the order Campylobacterales and the family Arcobacteraceae [1,2,3,4,5]. Moreover, in 1991, the genus name Arcobacter was proposed [6]. Arcobacter spp. are phylogenetically closely related to Campylobacter spp. [7]. Nevertheless, the phenotypic characteristics of the genera are partly different [8]. In human patients, A. butzleri has been associated with partly severe infections comprising diarrhea including travelers’ diarrhea, enteritis, gangrenous appendicitis, peritonitis, endocarditis and bacteremia and accordingly, it has been regarded as an emerging pathogen since 2002 [9,10,11,12,13]. Nosocomial transmission has been reported [14].
Next to A. butzleri, the likely importance of A. cryaerophilus, A. skirrowii and other species for human enteric disease has been suggested, mostly due to epidemiological associations or based on case reports [12,15,16,17,18]. Other species, such as, e.g., A. lanthieri, have been primarily associated with livestock [19], although virulence factors with likely relevance for human disease have been found in their genomes as well [20,21,22]. Altogether, the etiological relevance of A. butzleri is considered to be the best established [15,16]. Cell invasion, induction of immune responses and toxin production have been associated with its pathogenic potential [16,18,23]. As expected, due to phylogenetic relatedness, various virulence genes are homologous to those in Campylobacter spp. [24]. In particular, invasive and adhesive properties have been demonstrated [25]. Selection of the microorganism under antibiotic pressure can be facilitated by its pronounced resistance to antimicrobial drugs or even multidrug-resistance [16,26]. In a recent meta-analysis on Arcobacter spp. [27], high minimum inhibitory concentrations, suggestive of a lack of antimicrobial susceptibility, were recorded, particularly for beta-lactams with up-to 99.2% and 97.4% for penicillin derivates and cephalosporines, respectively, followed by macrolides with up-to 39.8%, fluoroquinolones with up-to 14%, aminoglycosides with up-to 12.9% and tetracyclines with up-to 7.1%. Further, sequencing approaches have shown a high diversity of sequence types, mostly without clear-cut associations to specific hosts or geographic regions [24].
Livestock, including poultry and pigs, has been identified as a reservoir of A. butzleri, and raw meat products, as well as contaminated water, are of relevance for the pathogen’s transmission [15,24,28,29,30,31]. Accordingly, A. butzleri-associated disease is considered zoonotic [32]. Meat contamination is assumed to be caused by spillage of gastrointestinal fluids from animals during the slaughtering process [12]. Adverse environmental conditions such as food processing and storage can be survived by the microorganism, supporting its spread to human individuals [16].
Culture-based isolation and differentiation of A. butzleri is still not a standardized routine procedure in diagnostic laboratories assessing human sample materials [15,32]. The lack of standardized diagnosis has also been blamed for the lack of reports of A. butzleri-associated disease in regions where the microorganisms are known to be highly prevalent like, e.g., in Nigeria [33]. Though cultural growth of Arcobacter spp. in microaerobic or aerobic atmosphere is feasible, and—other than Campylobacter spp.—the microorganisms even grow at low temperature of 15 °C, common growth protocols suggest enrichment steps, i.e., use of selective broths and agars to suppress concomitant bacterial flora, as well as incubation times of 4–5 days [13,23,34]. A. butzleri also grows on standard agars such as blood agar, chocolate agar and MacConkey agar under standard conditions such as a temperature of 37 °C and an atmosphere enriched with 5% CO2, with colonies showing positive results in cytochrome oxidase and motility testing [13]. Biochemical differentiation is challenging [12,13], and the reliability of matrix-assisted laser-desorption-ionization time-of-flight mass spectrometry (MALDI) largely depends on the quality of the underlying database as reported elsewhere [18]. Accordingly, A. butzleri-associated gastroenteritis is assumed to frequently go undetected, making an estimation of the role of this microorganism in infectious gastroenteritis challenging [12]. In line with this, a previously reported association of Arcobacter spp.-isolations with 0.11% to 1.25% of diarrhea cases might considerably underestimate the microorganisms’ true prevalence [18].
Considering the challenges of traditional culture-based Arcobacter spp.-detection and identification in the diagnostic routine setting [12,13,15,18,23,32,33,34], molecular diagnostic approaches based on PCR or real-time PCR with or without subsequent sequence analysis were established [13,35]. Of note, however, early Arcobacter spp.-specific PCR assays targeting genes such as gyrA or the 16S and 23S ribosomal RNA genes showed restricted diagnostic accuracy including cross-reactions with non-target species [35]. For various Arcobacter spp., including A. butzleri, fluorescence resonance energy transfer (FRET)-based and hybridization probe-based real-time PCR assays have been introduced, established and applied for human diagnostic, agricultural and environmental use [17,36,37,38,39,40,41,42,43,44,45,46,47]; however, the validation studies were usually based on quite limited sample counts.
In the study presented here, the aim was to contribute to available evidence on the diagnostic accuracy of selected A. butzleri-specific real-time PCR assays [36,37,38] based on a test comparison without a reference standard [48], applying latent class analysis [49] and using a sample collection with unknown A. butzleri prevalence but expected high pretest probability due to a known high local abundance of this microorganism [26].

2. Materials and Methods

2.1. Residual Volumes of Sample Materials Used for the Test Comparison, Inclusion and Exclusion Criteria

The test comparison of the three assessed real-time PCR assays for the detection of A. butzleri in human stool samples was conducted in two steps. In a first step, 65 stool samples were spiked to high final concentrations of about 107 colony-forming-units (cfu) per µL with clinical and environmental A. butzleri strains (n = 30), A. cryaerophilus strains (n = 22), livestock-associated A. lanthieri strains (n = 12) [19] and a single Campylobacter coli strain as an outstander from a strain collection at the University of Magdeburg, Germany. Those spiked samples with high pathogen density were used to assess the general applicability of the compared assays, applying samples with known expected results in the first but superficial proof-of-principle. Dilution steps were not included in the proof-of-principle spiking experiments; respective dilution series to identify the applied real-time PCRs’ technical detection thresholds were performed with standardized positive control plasmids as described below instead.
In a second step, residual volumes of nucleic acid extraction eluates of stool samples taken from Ghanaian individuals with an expected high pretest probability of being colonized or infected with A. butzleri were included in a test comparison without a reference standard. The high pretest probability was assumed due to a known high local abundance of the microorganism in Western Africa [26,33]. Results of other diagnostic tests targeting this parameter, such as, e.g., cultural growth, were not available for these materials. The sample collection comprised a total of 1570 samples collected in the course of both a study on Ghanaian HIV patients [50,51] and an assessment of the epidemiology of gastroenteric pathogens in Ghanian children <2 years of age about 10 years in the past (unpublished).
All samples containing sufficient material for all compared test assays were included in the assessment. Samples showing sample inhibition in the inhibition control PCR as detailed below were subsequently excluded from the calculation of diagnostic accuracy parameters. In line with the ethical requirements for this test assessment as detailed below, complete anonymization of the samples was required for the analysis; thus, patient specific details such as age, sex or clinical symptoms cannot be provided for this study. This is an admitted deviation from the Standards of Reporting Diagnostic Accuracy (STARD) criteria [52].

2.2. Nucleic Acid Extraction and Storage

Nucleic acids had been extracted, applying the QIAamp stool DNA mini kit (Qiagen, Hilden, Germany) as described by the manufacturer and stored at −80 °C until application for the test comparison.

2.3. Real-Time PCR Assays Comparatively Applied for the Detection of Arcobacter butzleri

The compared assays comprised two hybridization probe-based real-time PCRs targeting the rpoB/C gene [37] and the hsp60 gene [38] of A. butzleri as well as a FRET-based real-time PCR designed to target the gyrA gene of A. butzleri, A. cryaerophilus, Arcobacter cibarius and Arcobacter nitrofigilis at different distinguishable melting temperatures [36]. The protocols were adapted to be run on Corbett Q cyclers (Qiagen, Hilden, Germany), resulting in minor modifications from the originally published run conditions [36,37,38]. Details on the applied oligonucleotides, reaction chemistry and run protocols are provided in Appendix A, Table A1, Table A2 and Table A3. A PCR-grade water-based negative control and a plasmid-based positive control were included in each run. The sequence insert of the positive control plasmid, which had been inserted in a pEX A128 vector backbone (eurofins Genomics, Luxemburg), is shown in Appendix A, Table A3. Based on dilution series of the positive control plasmid, technical detection thresholds of 37.1 copies per µL for the hsp60 and the gyrA PCR as well as of 370.5 copies per µL for the rpoB/C PCR were estimated, applying the internet-based software “Calculator for determining the number of copies of a template” (URI Genomics and Sequencing Center, https://cels.uri.edu/gsc/cndna.html, last accessed on 8 March 2023). Sample inhibition was confirmed or ruled out, applying a previously described real-time PCR targeting a Phocid herpes virus (PhHV) sequence as described elsewhere [53].

2.4. Diagnostic Accuracy Estimation, Agreement and Comparison of Obtained Cycle Threshold (Ct) Values

In the first step of the evaluation, the spiked stool samples were used to assess the general applicability of the compared real-time PCR assays. Accordingly, obtained cycle threshold (Ct) values were just descriptively recorded and assessed. Next to this, this step was conducted to identify the A. butzleri- and A. cryaerophilus-specific melting temperature for the adapted run conditions of the gyrA-PCR, in addition to the use of the positive control plasmid containing an A. butzleri-specific gyrA-sequence for this purpose.
In the second step, latent class analysis (LCA) [48,49,54] was applied to estimate the diagnostic accuracy—i.e., sensitivity and specificity for the detection of A. butzerli—of the compared real-time PCR assays. In short, LCA is a variant of structural equation models. Thereby, a latent non-observable variable, i.e., the “true” infection or colonization status of the tested individuals with A. butzleri, is estimated over directly observed variables, i.e., the recorded test results for different A. butzleri-specific target sequences [48,49,54]. Thereby, only real-time PCR signals in the gyrA-PCR with A. butzleri-specific melting temperature were counted as A. butzleri positive in this assay, while gyrA-PCR signals with melting temperatures suggestive of A. cryaerophilus or other microorganisms were considered as gyrA-negative for the target pathogen A. butzleri. As the gyrA sequence is highly under mutation pressure both naturally and under quinolone treatment, a melting temperature deviation from the expected melting temperature within the ±1 °C range was still accepted as species specific. In addition, LCA was applied to perform a diagnostic accuracy-adjusted prevalence estimation for A. butzleri in the assessed sample population. In line with the suggestions by Landis and Koch [55], Fleiss’ kappa was calculated for the agreement between the compared real-time PCR assays and interpreted as described in [55]. In addition, measured cycle threshold (Ct) values were recorded and descriptively compared. Typical sigmoid-shaped real-time amplification curves were considered as positive signals without specific Ct value cut-offs for this assessment. All statistical assessments were conducted with the software Stata/IC 15.1 for Mac 64-bit Intel (College Station, TX, USA).

2.5. Ethics

Ethical clearance for the anonymized use of residual volumes of sample materials for test comparison purposes was provided by the medical association of Hamburg, Germany, (reference number: WF-011/19, obtained on 11 March 2019) without further requirement for informed consent.

3. Results

3.1. Quantitative and Qualitative Results Obtained with Spiked Sample Materials

All applied A. butzleri-specific real-time PCRs correctly identified all 30 stool samples spiked with A. butzleri at high titer as described above. Cross-reactions with high-titer A. cryaerophilus-spiked stool samples were observed in 6 out of 22 cases for the hsp60-PCR and in 16 out of 22 cases for the rpoB/C-assay. For the hsp60-PCR, the mean Ct-value difference between A. butzleri-spiked samples and A. cryaerophilus-spiked samples was 14.9; for the rpoB/C-PCR, it was 4.5 Ct steps. The gyrA-assay showed an expected reaction with 21 out of 22 A. cryaerophilus-spiked samples, albeit with a clearly discriminable melting-temperature compared to A. butzleri-spiked samples. Of note, a moderate Ct-value difference of 7.3 was seen for the gyrA-PCR in comparison of A. butzleri- and A. cryaerophilus-spiked reference samples as well. All 12 assessed samples spiked with A. lanthieri showed negative results in the three assessed real-time PCR assays. Details are provided in Table 1 and its footnote. The single included “outstander” sample spiked with C. coli showed a signal in the rpoB/C-PCR only at a very high Ct value of 35.0, i.e., 18.6 Ct steps after the mean value of the samples spiked with A. butzleri.

3.2. Sensitivity and Specificity of the Assays as Calculated Based on Latent Class Analysis (LCA), Agreement between the Compared Assays and Accuracy-Adjusted Prevalence Estimations

For the LCA-based assessment of diagnostic accuracy, 75 samples showing PCR inhibition were excluded, resulting in a total number of n = 1495 included residual volumes of Ghanaian stool sample materials. As detailed in Table 2, the number of positive PCR signals within these included samples ranged from n = 30, as recorded with the gyrA-PCR, to n = 245, as recorded with the hsp60-PCR. Focusing on diagnostic accuracy, calculated sensitivity showed a broad spectrum ranging from 12.7% to 100% with rpoB/C-PCR, hsp60-PCR and gyrA-PCR in declining order of sensitivity. Calculated sensitivity >95% was observed for the rpoB/C-PCR only, albeit with a broad 95%-confidence interval. The calculated specificity values were much closer together, ranging from 96.8% to 98.8% with gyrA-PCR, rpoB/C-PCR and hsp60-PCR in declining order of specificity. The agreement kappa calculated for the results of all three compared PCRs was only moderate, as shown in Table 2, and mainly driven by the influence of the gyrA-PCR results. If only the hsp60-PCR and the rpoB/C-PCR were compared, the agreement kappa would rise to almost perfect with a value (95%-confidence interval) of 0.812 (0.771–0.852) (Table 2). Concordance and discordance between the individual real-time PCR assays are visualized in Table 3. Based on the LCA-assessments, a prevalence of 14.8% colonization or infection with A. butzleri was calculated for the assessed Ghanaian stool sample collection (Table 2).

3.3. Comparison of the Recorded Cycle Threshold (Ct) Values

Recorded median cycle threshold values ranged from 32 to 40.5 over all three assessed real-time PCRs for the 1495 samples without recorded PCR inhibition included in the LCA assessment and excluding the above-mentioned gyrA-PCR results with melting temperatures non-indicative for A. butzleri. Thereby, hsp60-PCR, rpoB/C-PCR and gyrA-PCR were arranged in inclining order of median Ct values (Table 4). While the measured median Ct values of hsp60-PCR and rpoB/C-PCR were quite similar, the values obtained with the gyrA-PCR were on average 6 to 9 Ct steps higher.

4. Discussion

Considering the yet-poor standardization of the diagnosis of Arcobacter butzleri in human stool samples in the routine diagnostic setting, the study was conducted to comparatively assess the diagnostic accuracy of three published real-time PCR assays with different target genes in a latent class analysis (LCA)-based test comparison without a reference standard using a sample collection with high pretest probability. The assessment led to a number of results.
First, the initial assessment with stool samples spiked at high titers with either A. butzleri or the phylogenetically closely related A. cryaerophilus indicated that all assessed real-time PCR assays are not perfectly specific for A. butzleri but have the potential of cross-reaction with phylogenetically related Arcobacter species. The lack of positive real-time PCR signals with samples spiked with 12 A. lanthieri strains at least indicates that such cross-reactions do not necessarily occur with all representatives of the genus. The observed cross-reaction of the rpoB-assay with the single “outstander” sample, which was high titer-spiked with Campylobacter coli, was considered as hardly relevant for the diagnostic situation due to the extraordinarily large associated Ct value shift. While the reaction with A. cryaerophilus was expected for the gyrA-assay due to its design [36] and could be easily discriminated from reactions with A. butzleri due to distinct melting temperatures, such a discrimination was unfeasible for the hybridization probe-based rpoB/C-assay and hsp60-assay. However, Ct-values shifts of 14.9 when comparing A. butzleri-spiked samples and A. cryaerophilus-spiked samples in the hsp60-PCR, and of 4.5 when comparing them in the rpoB/C-PCR, indicated base-mismatching-associated reduced likeliness of cross-reaction with A. cryaerophilus. This might result in lower susceptibility of the hybridization probe assays to non-specific reactions in case of use with clinical samples without spiking-associated exorbitantly high concentrations of pathogen DNA. Nevertheless, the results confirm the previously described challenges regarding the design of PCR assays with reliable selectivity for A. butzleri [35].
Second, LCA-based prevalence estimation of 14.7% A. butzleri-positive cases among the assessed Ghanaian stool samples confirmed the study’s assumption that the chosen specimen collection was associated with high pre-test probability. This finding matches previous reports on A. butzleri prevalence rates in Ghanian livestock being even higher than Campylobacter spp.-prevalence rates [56] next to reports on high enteric infection and colonization rates with bacterial pathogens in Ghanaian individuals [57,58] and on high A. butzleri-prevalence rates in Western African Nigeria [33]. In line with the ethical clearance of the here-presented study, the residual volumes of the samples were completely anonymously assessed; thus, no statements on epidemiological features such as age, sex and symptom associations can be provided. As stated above, this design is an admitted deviation from STARD criteria [52]. However, it is nevertheless in line with diagnostic routine conditions, despite the long storage of extracted DNA, because as-good-as-possible diagnostic accuracy for the detection of the target pathogen is required irrespective of the epidemiological background.
Third, and in line with previous findings [35], the LCA-assessment indicated considerable difference regarding the diagnostic accuracy of the assessed three published real-time PCR assays for the detection of A. butzleri [36,37,38]. The FRET-assay targeting the gyrA gene [36] showed the best specificity of close to 100%, albeit for the price of a very low sensitivity of less than 15%. Considering the high Ct values measured for the gyrA-PCR-positive Ghanian sample materials, it is likely that target DNA quantities close to the PCR’s detection threshold have been the reason for the poor sensitivity result. A 10-fold higher detection threshold observed with the dilution series of the positive control plasmid compared to the hsp60-assay and the rpoB/C-assay as described above also speaks in favor of this conclusion. The hybridization probe-based hsp60-assay showed intermediate sensitivity of only slightly less than 95% but the comparably worst specificity of less than 97%. The latter is also the reason for the calculated sensitivity being lower than the calculated sensitivity of the rpoB/C-assay, although more positive results were obtained with the hsp60-assay from the sample collection. Focusing on the observed higher likeliness of the rpoB/C-assay to cross-react with A. cryaerophilus-spiked samples compared to the hsp60-assay, the latter’s worse specificity might be considered surprising. However, the melting curve analysis of the non-A. butzleri-associated positive results in the gyrA-assay indicated a very low prevalence of only six samples positive for A. cryaerophilus, of which four showed negative reactions in the hsp60-assay. Due to the quantitatively low relevance of A. cryaerophilus as a potentially cross-reacting agent in the Ghanaian sample collection, its effect on the diagnostic accuracy estimations has most certainly been negligible. The rpoB/C-assay was the only assay with a calculated sensitivity of more than 95%, at least if the uncertainty due to the broad 95%-confidence interval is accepted, associated with a still-acceptable specificity of slightly more than 98%. In spite of the large confidence interval of the rpoB/C-PCR’s estimated sensitivity, the true sensitivity of the assay is nevertheless likely to be actually high due to the observed combination of (a) a high absolute number of positive PCR signals; (b) the almost perfect agreement of its results with the results of the hsp60-assay, which showed sensitivity only slightly lower than 95%; and (c) the quite-acceptable specificity. The rpoB/C-assay’s relatively good specificity may seem surprising considering the high rate of observed matching between positive rpoB/C-PCR results and positive gyrA-PCR results, with melting curves indicative of non-A. butzleri DNA. However, the observed non-A. butzleri-specific melting curves may have been due to co-colonization of the stool donators’ gut with both A. butzleri and microorganisms with DNA more readily reacting with the gyrA-assay and so, it cannot be assumed for certain that the associated rpoB/C-PCR results must have been false positive in all these instances. The same applies to positive hsp60-PCR results in concordance with non-A. butzleri-specific positive gyrA-PCR signals. This co-colonization hypothesis is supported by the finding of quite-similar mean Ct values in hsp60-PCR and rpoB/C-PCR, respectively, irrespective of the observation of A. butzleri-specific or non-A. butzleri-specific melting temperatures in the gyrA-RCR. In contrast, the broad spectrum of observed melting temperatures in the case of non-A. butzleri-specific positive gyrA-PCR signals indicates that several phylogenetically related non-target microorganisms potentially associated with cross-reactivity were abundant in the sample collection.
This study has a number of limitations. First, a culture-based reference standard for the test comparison was not available. Respective attempts were unfeasible due to the retrospective design of the study. However, due to the lack of standardization regarding the cultural growth-based diagnosis of A. butzleri, uncertainties regarding the sensitivity and specificity of such an approach, even from fresh sample materials in the case of a prospective study design, would have limited its value as a reliable reference standard for the test comparison. Second, the choice of microorganisms used for the initial spiking experiments was restricted by the availability within the research group. However, the spiking approach was considered as no more than an initial proof-of-principle, while the main study was based upon the LCA approach. Third, lacking funding of this investigator-initiated investigation made sequencing-based confirmatory testing from all obtained PCR amplicons unfeasible; thus, LCA was applied for the diagnostic accuracy estimation. Due to this lack of a sequencing option, unfortunately, gyrA PCR signals with melting temperatures different from the expected values for A. butzleri and A. cryaerophilus could not be resolved. Fourth, and as repeatedly stated above, the ethical requirement of thorough anonymization for the test comparison made any comparison of A. butzleri detections, both qualitatively and with focus on recorded Ct values, with clinical symptoms of the assessed individuals unfeasible. Future studies should be conducted in order to address this highly relevant issue and to estimate the clinical relevance of such real-time PCR findings. Fifth, and as an intrinsic limitation of the chosen mathematic approach, the principle of LCA implies that the calculated diagnostic accuracy does not necessarily refer specifically to A. butzleri but to a meta-structure sharing genetic elements detected by the three compared assays. This might as well mean a combination of A. butzleri and other phylogenetically closely related microorganisms abundant in the assessed Ghanaian stool samples. The observation of potential cross-reactivity within the Arcobacter/“Aliarcobacter” genus described in this study makes this option rather likely. Accordingly, LCA can help to estimate prevalence rates on a population level but cannot decide on the correctness of an individual PCR result.

5. Conclusions

In conclusion, imperfect diagnostic accuracy was observed for all assessed real-time PCR assays for the detection of A. butzleri. In comparison, the rpoB/C-assay showed the best performance characteristics, with sensitivity >95%, if residual uncertainty due to an observed broad 95% confidence interval is accepted, and with specificity >98%. Of note, the assay might be less reliable in settings where the discrimination of A. butzleri and A. cryaerophilus is of importance, e.g., due to relevant prevalence of the latter. For confirmation testing in cases of positive results obtained with the rpoB/C-based screening assay, the application of the highly specific gyrA-assay may be considered. In the case of a matching positive result with an A. butzleri-specific melting temperature, the diagnosis of A. butzleri can be considered as confirmed with high reliability. In the case of a negative gyrA-PCR result, however, the diagnosis of A. butzleri is not excluded due to the gyrA-assay’s low sensitivity. Finally, due to the observed high prevalence of A. butzleri in Ghanaian stool samples, future studies on its etiological relevance in the Ghanaian population seem advisable because the present study’s exclusive focus on technical aspects does not allow us to answer this.

Author Contributions

Conceptualization, H.F., R.M.H., H.R. and U.L.; methodology, R.B., A.H., A.E.Z. and H.F.; software, R.B. and A.H.; validation, R.B. and A.H.; formal analysis, A.H.; investigation, R.B. and A.H.; resources, K.A.E., A.E.Z., T.F., F.S.S., V.D.C., H.F., U.L. and S.K.; data curation, R.B. and A.H.; writing—original draft preparation, R.B. and H.F.; writing—review and editing, R.B., A.H., K.A.E., R.M.H., H.R., S.K., A.E.Z., T.F., F.S.S., V.D.C., H.F. and U.L.; visualization, A.H. and H.F.; supervision, R.M.H., H.R. and H.F.; project administration, H.F.; funding acquisition, S.K. and A.E.Z. All authors have read and agreed to the published version of the manuscript.

Funding

We acknowledge support by the Open Access Publication Funds of the Magdeburg University and funding by the “Deutsche Forschungsgemeinschaft” (fund number ZA 697/6-1).

Institutional Review Board Statement

Ethical clearance allowing the use of anonymized residual volumes of sample materials for test comparison purposes without requirement of informed consent was obtained from the medical association of Hamburg, Germany (reference number: WF-011/19, provided on 11 March 2019), according to national German laws. The study was performed in line with the Declaration of Helsinki and its amendments.

Informed Consent Statement

Not applicable.

Data Availability Statement

All relevant data are presented in the article. Raw data can be provided on reasonable request.

Acknowledgments

Simone Priesnitz and Annett Michel are gratefully acknowledged for excellent technical assistance.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Table
Table A1. Oligonucleotides applied for the compared target-specific real-time PCR assays.
Table A1. Oligonucleotides applied for the compared target-specific real-time PCR assays.
Target PathogenTarget GeneForward PrimerReverse PrimerProbe(s) as Well as Modifications at the 5′- and 3′-EndsReference
A(lia)rcobacter spp. *gyrA5′-ATCTTTAGTATTCTTTACAAGAAATGG-3′5′-AACTGTTGTTCGTTTTCCA-3′5′-LC640-ATCAAGGAAGAAGTACAAGAGGTGTAAG-3′ & 5′-AGTCTTGGTCAATGTATTAGATTTGAACTTGAAAAAACAAG-Alexa488-3′[36]
A. butzlerirpoB/C5′-GCCACACCAGTGACAATATC-3′ & 5′-AAAAAATACTTTCTTGGTCTTGTGGTGTA-3′5′-AACAACACCTTTGTATCTCATTTTTTTG-3′5′-HEX-TTGGACCAGTAAAAGATTATGAGTGTCTTTGTGGTAAA-BHQ1-3′[37]
A. butzlerihsp605′-CTCTTCATTAAAAGAGATGTTACCAATTTT-3′5′-CACCATCTACATCTTCWGCAATAATTACT-3′5′-FAM-CTTCCTGATTGATTTACTGATT-MBG-NFQ-3′[38]
* Designed for the detection and melting curve-based discrimination of Arcobacter butzleri, Arcobacter cibarius, Arcobacter cryaerophilus and Arcobacter nitrofigilis.
Table
Table A2. Details of the run conditions of the compared real-time PCR assays.
Table A2. Details of the run conditions of the compared real-time PCR assays.
gyrA AssayrpoB/C Assayhsp60 AssayPhHV DNA-Based Inhibition Control Assay
Reaction chemistry
Master MixHotStarTaq (Qiagen)HotStarTaq (Qiagen)HotStarTaq (Qiagen)HotStarTaq (Qiagen)
Reaction volume (µL)20 µL20 µL20 µL20 µL
Forward primer concentration (nM)700 nM300 nM (each)300 nM300 nM
Reverse primer concentration (nM)700 nM300 nM300 nM300 nM
Probe concentration (nM)200 nM (each)100 nM100 nM100 nM
Final Mg2+ concentration (nM)2.0 mM4.5 mM4.5 mM4.5 mM
Bovine serum albumin (ng/µL)none5 ng/µL5 ng/µL5 ng/µL
Run conditions
Initial denaturation95 °C, 15 min.95 °C, 15 min.95 °C, 15 min.95 °C, 15 min.
Cycle numbers50404040
Denaturation95 °C, 10 s95 °C, 15 s95 °C, 15 s95 °C, 15 s
Annealing60 °C, 10 s60 °C, 1 min.60 °C, 1 min.60 °C, 1 min.
Amplification72 °C, 25 sIdentical with annealing stepIdentical with annealing stepIdentical with annealing step
Hold95 °C, 1 min. followed by 45 °C, 50 s before melting, finally 40 °C, 30 s after melting40 °C, 20 s40 °C, 20 s40 °C, 20 s
Melting conditionsRamp from 45 °C to 80 °C with 5 cycles touchdown, rising of 1 °C each step, waiting for 90 s of pre-melt conditioning on first step, waiting for 4 s of each step afterwards, acquiring on green (Alexa 488) channelNot applicableNot applicableNot applicable
Table
Table A3. Sequence insert of the positive control plasmid.
Table A3. Sequence insert of the positive control plasmid.
Positive Control Insert Based on A. butzleri Sequences according to the NCBI Accession Numbers AB104481.1, AY628390.1 and DQ464331.1
5′-ACAAAAAAATCTCTTCATTAAAAGAGATGTTACCAATTTTAGAATCAGTAAATCAATCAGGAAGACCTTTAGTAATTATTGCTGAAGATGTAGATGGTGAAGCATTAGCGAATTCGCAAGTCCAGAAAAAATACTTTCTTGGTCTTGTGGTGAAGTTAAAAAACCTGAAACAATTAATTATAGAACATTAAAACCAGAAAGAGATGGATTATTTTGTGCTAAAATTTTTGGACCAGTAAAAGATTATGAGTGTCTTTGTGGTAAATACAAAAAAATGAGATACAAAGGTGTTGTTTGCGAAAGAATTCACGAATCTAAATCTTTAGTATTCTTTACAAGAAATGGAATTATAAAAAGAACATCATTAAATGAATTCTCAAATATTAGAAGTAATGGTGTAAGAGCTATTGTTTTAGATGATGCAGATGAGATTGTAACAGCAAAAATTGCTGATGTAGAAACACAATACATTATGATATTTACAAGTCTTGGTCAATGTATTAGATTTGAACTTGAAAAAACAAGAGATCAAGGAAGAAGTACAAGAGGTGTAAGAGGTATTAAATTTAAAATTGATACAGACTTCGTTGTAGATGCTGATGTTATTAGTAGTGAAGATCAAGAAATATTAACAGTTTCAGAAAAAGGAATTGGAAAACGAACAACAGTTGAAGAGTATA-3′

References

  1. Kiehlbauch, J.A.; Brenner, D.J.; Nicholson, M.A.; Baker, C.N.; Patton, C.M.; Steigerwalt, A.G.; Wachsmuth, I.K. Campylobacter butzleri sp. nov. isolated from humans and animals with diarrheal illness. J. Clin. Microbiol. 1991, 29, 376–385. [Google Scholar] [CrossRef] [PubMed]
  2. Vandamme, P.; Vancanneyt, M.; Pot, B.; Mels, L.; Hoste, B.; Dewettinck, D.; Vlaes, L.; van den Borre, C.; Higgins, R.; Hommez, J.; et al. Polyphasic taxonomic study of the emended genus Arcobacter with Arcobacter butzleri comb. nov. and Arcobacter skirrowii sp. nov., an aerotolerant bacterium isolated from veterinary specimens. Int. J. Syst. Bacteriol. 1992, 42, 344–356. [Google Scholar] [CrossRef] [PubMed]
  3. 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] [PubMed]
  4. Pérez-Cataluña, A.; Salas-Massó, N.; Diéguez, A.L.; Balboa, S.; Lema, A.; Romalde, J.L.; Figueras, M.J. Corrigendum (2): Revisiting the Taxonomy of the Genus Arcobacter: Getting Order from the Chaos. Front. Microbiol. 2019, 10, 2253. [Google Scholar] [CrossRef] [PubMed]
  5. Vandamme, P.; Goossens, H. Taxonomy of Campylobacter, Arcobacter, and Helicobacter: A review. Zentralbl. Bakteriol. 1992, 276, 447–472. [Google Scholar] [CrossRef]
  6. Vandamme, P.; Falsen, E.; Rossau, R.; Hoste, B.; Segers, P.; Tytgat, R.; De Ley, J. Revision of Campylobacter, Helicobacter, and Wolinella taxonomy: Emendation of generic descriptions and proposal of Arcobacter gen. nov. Int. J. Syst. Bacteriol. 1991, 41, 88–103. [Google Scholar] [CrossRef]
  7. Snelling, W.J.; Matsuda, M.; Moore, J.E.; Dooley, J.S. Under the microscope: Arcobacter. Lett. Appl. Microbiol. 2006, 42, 7–14. [Google Scholar] [CrossRef]
  8. Cervenka, L. Survival and inactivation of Arcobacter spp., a current status and future prospect. Crit. Rev. Microbiol. 2007, 33, 101–108. [Google Scholar] [CrossRef]
  9. Chieffi, D.; Fanelli, F.; Fusco, V. Arcobacter butzleri: Up-to-date taxonomy, ecology, and pathogenicity of an emerging pathogen. Compr. Rev. Food Sci. Food Saf. 2020, 19, 2071–2109. [Google Scholar] [CrossRef]
  10. Shange, N.; Gouws, P.; Hoffman, L.C. Campylobacter and Arcobacter species in food-producing animals: Prevalence at primary production and during slaughter. World J. Microbiol. Biotechnol. 2019, 35, 146. [Google Scholar] [CrossRef]
  11. Lόpez-Vélez, R.; Lebens, M.; Bundy, L.; Barriga, J.; Steffen, R. Bacterial travellers’ diarrhoea: A narrative review of literature published over the past 10 years. Travel Med. Infect. Dis. 2022, 47, 102293. [Google Scholar] [CrossRef]
  12. Ramees, T.P.; Dhama, K.; Karthik, K.; Rathore, R.S.; Kumar, A.; Saminathan, M.; Tiwari, R.; Malik, Y.S.; Singh, R.K. Arcobacter: An emerging food-borne zoonotic pathogen, its public health concerns and advances in diagnosis and control—A comprehensive review. Vet. Q. 2017, 37, 136–161. [Google Scholar] [CrossRef]
  13. Lau, S.K.; Woo, P.C.; Teng, J.L.; Leung, K.W.; Yuen, K.Y. Identification by 16S ribosomal RNA gene sequencing of Arcobacter butzleri bacteraemia in a patient with acute gangrenous appendicitis. Mol. Pathol. 2002, 55, 182–185. [Google Scholar] [CrossRef]
  14. Minaeva, N.Z.; Minaev, V.I.; Sokolov, A.A.; Avilova, N.D.; Mitrokhin, S.D. Bacteria of the genus Arcobacter, a new etiological factor of nosocomial infections. Antibiot. Khimioter. 2006, 51, 18–22. [Google Scholar]
  15. Hänel, I.; Tomaso, H.; Neubauer, H. Arcobacter—An underestimated zoonotic pathogen? Bundesgesundheitsblatt Gesundh. Gesundh. 2016, 59, 789–794. [Google Scholar] [CrossRef]
  16. 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]
  17. Miltenburg, M.G.; Cloutier, M.; Craiovan, E.; Lapen, D.R.; Wilkes, G.; Topp, E.; Khan, I.U.H. 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]
  18. Figueras, M.J.; Levican, A.; Pujol, I.; Ballester, F.; Rabada Quilez, M.J.; Gomez-Bertomeu, F. A severe case of persistent diarrhoea associated with Arcobacter cryaerophilus but attributed to Campylobacter sp. and a review of the clinical incidence of Arcobacter spp. New Microbes New Infect. 2014, 2, 31–37. [Google Scholar] [CrossRef]
  19. Whiteduck-Léveillée, K.; Whiteduck-Léveillée, J.; Cloutier, M.; Tambong, J.T.; Xu, R.; Topp, E.; Arts, M.T.; Chao, J.; Adam, Z.; André Lévesque, C.; et al. Arcobacter lanthieri sp. nov., isolated from pig and dairy cattle manure. Int. J. Syst. Evol. Microbiol. 2015, 65, 2709–2716. [Google Scholar] [CrossRef]
  20. Chuan, J.; Belov, A.; Cloutier, M.; Li, X.; Khan, I.U.H.; Chen, W. Comparative genomics analysis and virulence-related factors in novel Aliarcobacter faecis and Aliarcobacter lanthieri species identified as potential opportunistic pathogens. BMC Genom. 2022, 23, 471. [Google Scholar] [CrossRef]
  21. 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. 2020, 39, 100829. [Google Scholar] [CrossRef] [PubMed]
  22. Zambri, M.; Cloutier, M.; Adam, Z.; Lapen, D.R.; Wilkes, G.; Sunohara, M.; Topp, E.; Talbot, G.; Khan, I.U.H. Novel virulence, antibiotic resistance and toxin gene-specific PCR-based assays for rapid pathogenicity assessment of Arcobacter faecis and Arcobacter lanthieri. BMC Microbiol. 2019, 19, 11. [Google Scholar] [CrossRef] [PubMed]
  23. Kim, N.H.; Park, S.M.; Kim, H.W.; Cho, T.J.; Kim, S.H.; Choi, C.; Rhee, M.S. Prevalence of pathogenic Arcobacter species in South Korea: Comparison of two protocols for isolating the bacteria from foods and examination of nine putative virulence genes. Food Microbiol. 2019, 78, 18–24. [Google Scholar] [CrossRef] [PubMed]
  24. 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]
  25. Ho, H.T.; Lipman, L.J.; Gaastra, W. Arcobacter, what is known and unknown about a potential foodborne zoonotic agent! Vet. Microbiol. 2006, 115, 1–13. [Google Scholar] [CrossRef]
  26. Zautner, A.E.; Riedel, T.; Bunk, B.; Spröer, C.; Boahen, K.G.; Akenten, C.W.; Dreyer, A.; Färber, J.; Kaasch, A.J.; Overmann, J.; et al. Molecular characterization of Arcobacter butzleri isolates from poultry in rural Ghana. Front. Cell. Infect. Microbiol. 2023, 13, 1094067. [Google Scholar] [CrossRef]
  27. Ferreira, S.; Luís, Â.; Oleastro, M.; Pereira, L.; Domingues, F.C. A meta-analytic perspective on Arcobacter spp. antibiotic resistance. J. Glob. Antimicrob. Resist. 2019, 16, 130–139. [Google Scholar] [CrossRef]
  28. Bell, R.L.; Kase, J.A.; Harrison, L.M.; Balan, K.V.; Babu, U.; Chen, Y.; Macarisin, D.; Kwon, H.J.; Zheng, J.; Stevens, E.L.; et al. The Persistence of Bacterial Pathogens in Surface Water and Its Impact on Global Food Safety. Pathogens 2021, 10, 1391. [Google Scholar] [CrossRef]
  29. Iwu, C.D.; Ekundayo, T.C.; Okoh, A.I. A Systematic Analysis of Research on Arcobacter: Public Health Implications from a Food-Environment Interphase Perspective. Foods 2021, 10, 1673. [Google Scholar] [CrossRef]
  30. Meng, J.; Doyle, M.P. Emerging issues in microbiological food safety. Annu. Rev. Nutr. 1997, 17, 255–275. [Google Scholar] [CrossRef]
  31. Hsu, T.T.; Lee, J. Global Distribution and Prevalence of Arcobacter in Food and Water. Zoonoses Public Health 2015, 62, 579–589. [Google Scholar] [CrossRef]
  32. Calvo, G.; Arias, M.L.; Fernández, H. Arcobacter: A foodborne emerging pathogen. Arch. Latinoam. Nutr. 2013, 63, 164–172. [Google Scholar]
  33. Adesiji, Y.O.; Oloke, J.K.; Emikpe, B.O.; Coker, A.O. Arcobacter, an emerging opportunistic food borne pathogen—A review. Afr. J. Med. Med. Sci. 2014, 43, 5–11. [Google Scholar]
  34. Lehner, A.; Tasara, T.; Stephan, R. Relevant aspects of Arcobacter spp. as potential foodborne pathogen. Int. J. Food Microbiol. 2005, 102, 127–135. [Google Scholar] [CrossRef]
  35. Levican, A.; Figueras, M.J. Performance of five molecular methods for monitoring Arcobacter spp. BMC Microbiol. 2013, 13, 220. [Google Scholar] [CrossRef]
  36. Abdelbaqi, K.; Buissonnière, A.; Prouzet-Mauleon, V.; Gresser, J.; Wesley, I.; Mégraud, F.; Ménard, A. Development of a real-time fluorescence resonance energy transfer PCR to detect Arcobacter species. J. Clin. Microbiol. 2007, 45, 3015–3021. [Google Scholar] [CrossRef]
  37. Brightwell, G.; Mowat, E.; Clemens, R.; Boerema, J.; Pulford, D.J.; On, S.L. Development of a multiplex and real time PCR assay for the specific detection of Arcobacter butzleri and Arcobacter cryaerophilus. J. Microbiol. Methods 2007, 68, 318–325. [Google Scholar] [CrossRef]
  38. de Boer, R.F.; Ott, A.; Güren, P.; van Zanten, E.; van Belkum, A.; Kooistra-Smid, A.M. Detection of Campylobacter species and Arcobacter butzleri in stool samples by use of real-time multiplex PCR. J. Clin. Microbiol. 2013, 51, 253–259. [Google Scholar] [CrossRef]
  39. Liu, L.; Cloutier, M.; Craiovan, E.; Edwards, M.; Frey, S.K.; Gottschall, N.; Lapen, D.R.; Sunohara, M.; Topp, E.; Khan, I.U.H. Quantitative real-time PCR-based assessment of tile drainage management influences on bacterial pathogens in tile drainage and groundwater. Sci. Total Environ. 2018, 624, 1586–1597. [Google Scholar] [CrossRef]
  40. González, A.; Suski, J.; Ferrús, 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]
  41. 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]
  42. 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] [PubMed]
  43. Caruso, M.; Latorre, L.; Santagada, G.; Fraccalvieri, R.; Difato, L.M.; Miccolupo, A.; Capozzi, L.; Bonerba, E.; Mottola, A.; Parisi, A. Arcobacter spp. in bovine milk: An emerging pathogen with potential zoonotic risk. Ital. J. Food Saf. 2019, 7, 7685. [Google Scholar] [CrossRef] [PubMed]
  44. Marta, C.; Giovanni, N.; Angela, M.; Loredana, C.; Elisabetta, B.; Laura, D.; Anna, M.; Angela, D.P.; Gianfranco, S.; Antonio, P. Large genetic diversity of Arcobacter butzleri isolated from raw milk in Southern Italy. Food Microbiol. 2020, 89, 103403. [Google Scholar] [CrossRef]
  45. González, A.; Ferrús, M.A. Study of Arcobacter spp. contamination in fresh lettuces detected by different cultural and molecular methods. Int. J. Food Microbiol. 2011, 145, 311–314. [Google Scholar] [CrossRef]
  46. Ferreira, S.; Júlio, C.; Queiroz, J.A.; Domingues, F.C.; Oleastro, M. Molecular diagnosis of Arcobacter and Campylobacter in diarrhoeal samples among Portuguese patients. Diagn. Microbiol. Infect. Dis. 2014, 78, 220–225. [Google Scholar] [CrossRef]
  47. 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]
  48. Hahn, A.; Podbielski, A.; Meyer, T.; Zautner, A.E.; Loderstädt, U.; Schwarz, N.G.; Krüger, A.; Cadar, D.; Frickmann, H. On detection thresholds—A review on diagnostic approaches in the infectious disease laboratory and the interpretation of their results. Acta Trop. 2020, 205, 105377. [Google Scholar] [CrossRef]
  49. Qu, Y.; Tan, M.; Kutner, M.H. Random effects models in latent class analysis for evaluating accuracy of diagnostic tests. Biometrics 1996, 52, 797–810. [Google Scholar] [CrossRef]
  50. Eberhardt, K.A.; Sarfo, F.S.; Dompreh, A.; Kuffour, E.O.; Geldmacher, C.; Soltau, M.; Schachscheider, M.; Drexler, J.F.; Eis-Hübinger, A.M.; Häussinger, D.; et al. Helicobacter pylori Coinfection Is Associated With Decreased Markers of Immune Activation in ART-Naive HIV-Positive and in HIV-Negative Individuals in Ghana. Clin. Infect. Dis. 2015, 61, 1615–1623. [Google Scholar] [CrossRef]
  51. Sarfo, F.S.; Eberhardt, K.A.; Dompreh, A.; Kuffour, E.O.; Soltau, M.; Schachscheider, M.; Drexler, J.F.; Eis-Hübinger, A.M.; Häussinger, D.; Oteng-Seifah, E.E.; et al. Helicobacter pylori Infection Is Associated with Higher CD4 T Cell Counts and Lower HIV-1 Viral Loads in ART-Naïve HIV-Positive Patients in Ghana. PLoS ONE 2015, 10, e0143388. [Google Scholar] [CrossRef]
  52. Bossuyt, P.M.; Reitsma, J.B.; Bruns, D.E.; Gatsonis, C.A.; Glasziou, P.P.; Irwig, L.; Lijmer, J.G.; Moher, D.; Rennie, D.; de Vet, H.C.; et al. STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies. BMJ 2015, 351, h5527. [Google Scholar] [CrossRef]
  53. Niesters, H.G. Quantitation of viral load using real-time amplification techniques. Methods 2001, 25, 419–429. [Google Scholar] [CrossRef]
  54. Goodman, L.A. Latent class analysis: The empirical study of latent types, latent variables, and latent structures. In Applied Latent Class Analysis; Hagenaars, J.A., McCutcheon, A.L., Eds.; Cambridge University Press: Cambridge, UK, 2002; pp. 3–55. [Google Scholar]
  55. Landis, J.R.; Koch, G.G. The measurement of observer agreement for categorical data. Biometrics 1977, 33, 159–174. [Google Scholar] [CrossRef]
  56. Dekker, D.; Eibach, D.; Boahen, K.G.; Akenten, C.W.; Pfeifer, Y.; Zautner, A.E.; Mertens, E.; Krumkamp, R.; Jaeger, A.; Flieger, A.; et al. Fluoroquinolone-Resistant Salmonella enterica, Campylobacter spp., and Arcobacter butzleri from Local and Imported Poultry Meat in Kumasi, Ghana. Foodborne Pathog. Dis. 2019, 16, 352–358. [Google Scholar] [CrossRef]
  57. Krumkamp, R.; Sarpong, N.; Schwarz, N.G.; Adlkofer, J.; Loag, W.; Eibach, D.; Hagen, R.M.; Adu-Sarkodie, Y.; Tannich, E.; May, J. Gastrointestinal infections and diarrheal disease in Ghanaian infants and children: An outpatient case-control study. PLoS Negl. Trop. Dis. 2015, 9, e0003568. [Google Scholar]
  58. Eibach, D.; Krumkamp, R.; Hahn, A.; Sarpong, N.; Adu-Sarkodie, Y.; Leva, A.; Käsmaier, J.; Panning, M.; May, J.; Tannich, E. Application of a multiplex PCR assay for the detection of gastrointestinal pathogens in a rural African setting. BMC Infect. Dis. 2016, 16, 150. [Google Scholar] [CrossRef]
Table
Table 1. Positive real-time PCR signals in stool samples spiked with either A. butzleri, A. cryaerophilus or A. lanthieri.
Table 1. Positive real-time PCR signals in stool samples spiked with either A. butzleri, A. cryaerophilus or A. lanthieri.
rReal-Time PCR TargetgyrArpoB/Chsp60
Number and percentage of positive signals with samples spiked with A. butzleri, n/n (%)30/30 (100%)30/30 (100%)30/30 (100%)
Ct values measured with samples spiked with A. butzleri, mean (±SD)22.2 (±4.6)16.4 (±3.8)16.6 (±3.4)
Melting temperature in °C (±SD) with A. butzleri65.7 (±<0.1)n.a.n.a.
Number and percentage of positive signals with samples spiked with A. cryaerophilus, n/n (%)0/22 (0%) °16/22 (72.7%)6/22 (27.3%)
Ct values measured with samples spiked with A. cryaerophilus, mean (±SD)n.a.20.9 (±2.7)31.5 (±0.6)
Melting temperature in °C (±SD) with A. cryaerophilus60.5 (±0.3)n.a.n.a.
Number and percentage of positive signals with samples spiked with A. lanthieri, n/n (%)0/12 (0%)0/12 (0%)0/12 (0%)
Ct values measured with samples spiked with A. lanthieri, mean (±SD)n.a.n.a.n.a.
Melting temperature in °C (±SD) with A. lanthierin.a.n.a.n.a.
Significance level P for differences of the measured Ct values of A. butzleri and A. cryaerophilus *n.e.0.0003<0.0001
n = number; % = percent; Ct = cycle threshold; n.e. = not estimable; n.a. = not applicable; SD = standard deviation. * Calculation based on Mann–Whitney U testing. ° Twenty-one out of twenty-two (95.5%) samples were positive with clearly distinguishable melting temperature (see main text) and a significance (p = <0.0001) for higher Ct values of 29.5 (±4.1) compared to the samples spiked with A. butzleri.
Table
Table 2. Agreement kappa between the three compared real-time PCR assays targeting A. butzleri as well as sensitivity, specificity and accuracy-adjusted prevalence as calculated with latent class analysis (LCA) based on the assessment of 1495 samples, not showing PCR inhibition with high pre-test probability.
Table 2. Agreement kappa between the three compared real-time PCR assays targeting A. butzleri as well as sensitivity, specificity and accuracy-adjusted prevalence as calculated with latent class analysis (LCA) based on the assessment of 1495 samples, not showing PCR inhibition with high pre-test probability.
AssayTotal Number (n) of Included SamplesPositives (%)Sensitivity (0.95 CI)Specificity (0.95 CI)Kappa (0.95 CI)
gyrA149530 (1.91)0.1267 (0.0876, 0.1797)0.9984 (0.9936, 0.9996)0.436
(0.403, 0.472)
rpoB/C1495244 (16.32)1 (0, 1)0.9818 (0.9499, 0.9935)
hsp601495245 (16.39)0.9298 (0.7513, 0.9831)0.9688 (0.9576, 0.9771)
Prevalence (0.95 CI)0.1477 (0.1258, 0.1726)
CI = confidence interval.
Table
Table 3. Cross-table detailing mismatches between the real-time PCR assays targeting A. butzleri.
Table 3. Cross-table detailing mismatches between the real-time PCR assays targeting A. butzleri.
gyrArpoB/Chsp60
NegativePositiveNegativePositiveNegativePositive
gyrANegative1465
Positive030
rpoB/CNegative124921251
Positive216280244
hsp60Negative124641212381250
Positive21926392060245
Green = matching results; Red = mismatching results; Black = not filled in to avoid repetition.
Table
Table 4. Recorded cycle threshold (Ct) values of the real-time PCRs targeting A. butzleri.
Table 4. Recorded cycle threshold (Ct) values of the real-time PCRs targeting A. butzleri.
nMean (SD)Median (q25, q75)
gyrA3039.83 (2.41)40.5 (39, 41)
rpoB/C24433.55 (1.77)34 (33, 35)
hsp6024531.86 (1.58)32 (31, 33)
SD = standard deviation; q25 = 25%-quartile; q75 = 75%-quartile.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Binder, R.; Hahn, A.; Eberhardt, K.A.; Hagen, R.M.; Rohde, H.; Loderstädt, U.; Feldt, T.; Sarfo, F.S.; Di Cristanziano, V.; Kahlfuss, S.; et al. Comparison of the Diagnostic Accuracy of Three Real-Time PCR Assays for the Detection of Arcobacter butzleri in Human Stool Samples Targeting Different Genes in a Test Comparison without a Reference Standard. Microorganisms 2023, 11, 1313. https://doi.org/10.3390/microorganisms11051313

AMA Style

Binder R, Hahn A, Eberhardt KA, Hagen RM, Rohde H, Loderstädt U, Feldt T, Sarfo FS, Di Cristanziano V, Kahlfuss S, et al. Comparison of the Diagnostic Accuracy of Three Real-Time PCR Assays for the Detection of Arcobacter butzleri in Human Stool Samples Targeting Different Genes in a Test Comparison without a Reference Standard. Microorganisms. 2023; 11(5):1313. https://doi.org/10.3390/microorganisms11051313

Chicago/Turabian Style

Binder, Ramona, Andreas Hahn, Kirsten Alexandra Eberhardt, Ralf Matthias Hagen, Holger Rohde, Ulrike Loderstädt, Torsten Feldt, Fred Stephen Sarfo, Veronica Di Cristanziano, Sascha Kahlfuss, and et al. 2023. "Comparison of the Diagnostic Accuracy of Three Real-Time PCR Assays for the Detection of Arcobacter butzleri in Human Stool Samples Targeting Different Genes in a Test Comparison without a Reference Standard" Microorganisms 11, no. 5: 1313. https://doi.org/10.3390/microorganisms11051313

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