Systematic evaluation and optimization of TaqMan qPCR assays targeting F57, ISMAP02, and IS900 for multiplex detection of Mycobacterium avium subsp. paratuberculosis

ABSTRACT

This study aimed to develop a multiplex quantitative PCR (qPCR) assay for the detection of Mycobacterium avium subsp. paratuberculosis (MAP), the etiological agent of paratuberculosis disease, a chronic and endemic infectious disease affecting ruminant livestock worldwide. Infected animals may remain asymptomatic for years while intermittently shedding MAP into their environment through feces, contributing to ongoing transmission. To develop a robust multiplex qPCR assay, we reviewed all TaqMan qPCR studies published since 1990 and selected 18 primer-probe combinations targeting the MAP-specific F57 gene and the repetitive sequence elements ISMAP02 and IS900. In samples with moderate to high MAP levels, all combinations performed well, with only minor differences in analytical performance. However, in low-abundance samples, several TaqMan designs yielded unreliable results, indicating limited specificity in complex matrices. Among the evaluated assays, the IS900-Herthnek design demonstrated significantly higher diagnostic sensitivity, detecting MAP in 81% of low-abundance samples, compared to 0% and 3% for the IS900-Kim and IS900-Slana assays, respectively. For ISMAP02, only the ISMAP02-Sevilla assay produced reliable results. For F57, Herthnek provided the most consistent and accurate quantification. Similar trends were observed in environmental sample testing. Based on these findings, we recommend a multiplex qPCR assay incorporating the IS900-Herthnek, ISMAP02-Sevilla, and F57-Herthnek TaqMan designs for the detection of MAP in fecal and environmental samples. This combination offers high analytical sensitivity and specificity, making it a valuable and accurate tool for the diagnosis of paratuberculosis and for environmental surveillance on dairy farms to identify herds potentially harboring MAP-infected animals.

IMPORTANCE

Mycobacterium avium subsp. paratuberculosis (MAP) is the etiological agent of Johne’s disease (JD) in ruminant livestock industries and has been associated with Crohn’s disease in humans. Emerging scientific evidence also links MAP to other human conditions, including inflammatory bowel disease, autoimmune disorders, colorectal cancer, and Alzheimer’s disease. This potential public health threat has intensified interest in developing more sensitive diagnostic tools and effective control strategies to eradicate MAP from dairy herds. Infected ruminants typically remain in the subclinical stage of the disease for 2–5 years, during which they shed MAP in their feces and contaminate the environment. Diagnosis during this stage is particularly challenging, as the pathogen evades the host’s immune response, rendering serological tests insufficiently sensitive. In contrast, fecal PCR offers greater sensitivity than serum ELISA and traditional culture methods. Multiplex quantitative PCR is especially promising due to its high specificity and sensitivity for detecting MAP-infected animals and identifying herds with active shedders. Herd-level environmental screening, followed by individual animal testing, represents a robust national biosecurity strategy. This approach is a critical step toward reducing MAP transmission and improving herd health within the dairy industry.

INTRODUCTION

Mycobacterium avium subsp. paratuberculosis (MAP) is the etiological agent of Johne’s disease (JD), a chronic and debilitating condition affecting ruminant livestock worldwide. MAP has also been associated with Crohn’s disease in humans (1–4), and increasing scientific evidence also links MAP to other human conditions, including other inflammatory bowel diseases, autoimmune disorders, colorectal cancer, and Alzheimer’s disease (5, 6). This potential zoonotic threat has intensified efforts to develop more sensitive diagnostic tools and effective control strategies to eradicate MAP from dairy herds and to safeguard the food supply through enhanced biovigilance across the farm-to-fork continuum.

The diagnosis of paratuberculosis remains challenging due to the limited sensitivity of current diagnostic methods. Historically, fecal culture has been used as an ante-mortem diagnostic test. However, MAP is a slow-growing bacterium, requiring several weeks to months for detection. Furthermore, the decontamination steps necessary to suppress contaminating flora are also harsh on MAP, resulting in a 1–2 log (approximately 99%) reduction in viability (7, 8). In subclinical animals, where MAP shedding is low, culture methods often fail to detect infection (7, 9). Serological detection using ELISA is widely employed due to its low cost, high throughput, and rapid turnaround time. However, its sensitivity in cattle is low (~0.44), particularly in early and subclinical stages of infection, resulting in limited diagnostic accuracy (10, 11). Since the 1990s, the development of molecular methods targeting specific bacterial DNA sequences has enabled the rapid identification of fastidious organisms such as MAP. Among these, direct fecal PCR has shown the highest correlation with fecal culture compared to blood- or milk-based diagnostics (12, 13), and it yields fewer false negatives in subclinical animals (9, 14, 15). Quantitative PCR (qPCR) methods have gained popularity due to their speed, sensitivity, and ability to quantify bacterial load. However, commercial qPCR kits remain costly and often lack transparency, as the primer and probe sequences are typically proprietary.

This study aimed to develop and validate a multiplex qPCR assay for the detection of MAP, in accordance with the Minimum Information for Publication of Quantitative real-time PCR experiments (MIQE) guidelines (16). Multiplex PCR is a powerful tool for achieving robust and meaningful diagnostic results. The F57 gene is unique to MAP and is absent in other members of the Mycobacterium avium complex (MAC), making it a highly specific target for detecting MAP (17, 18). As F57 is present in only a single copy per genome, combining it with multicopy targets such as IS900 (17 copies) (19) and ISMAP02 (six copies) (20) enhances assay sensitivity.

We conducted a systematic review of published TaqMan qPCR assays since 1990 and identified 18 primer-probe multiplex combinations targeting IS900, ISMAP02, and F57. A comprehensive in silico analysis was performed to assess the specificity of each qPCR design. The selected assays were assessed for analytical sensitivity and then validated for diagnostic sensitivity using fecal samples from MAP-infected cows and environmental samples from herds with a history of confirmed clinical paratuberculosis. Including multiple targets in a single reaction not only reduces reagent costs and sample volume requirements but also increases diagnostic confidence by enabling the simultaneous detection of highly sensitive MAP-specific markers. This multiplexing strategy is particularly advantageous for large-scale surveillance and routine diagnostics in veterinary settings.

MATERIALS AND METHODS

Systematic review of published F57 and ISMAP02 assays

In a previous study, we performed a systematic review to identify the best-performing IS900 TaqMan assays (21). We used the same strategy to identify qPCR designs for F57 and ISMAP02. To identify publications reporting the use of F57 and ISMAP02 target sequences for the detection of MAP by qPCR, the Scopus, Agricola, Biological Abstracts (Ovid), CAB Abstracts (Ovid), FSTA (Ovid), and Medline (Ovid) databases were searched using a previously developed protocol (21). Search results published between 1990 and 8 May 2024 along with the keywords, index terms, and strings used are reported in Table S1A and B for F57 and ISMAP02, respectively. No distinction was made between qPCR assays designed to test milk, tissue, or fecal samples. Articles were excluded if they were (i) opinion articles, (ii) conference abstracts, (iii) theses, or (iv) not written in English or French. The librarians conducted the initial screening of the literature on MAP for the identification of F57 or ISMAP02 PCR-related reports, which were imported into EndNote 20 (Clarivate) for further analysis. Information specialists followed Cochrane guidelines for conducting the systematic review, using metadata (in RIS format) to remove duplicates (via EndNote’s “Find Duplicates” function) and organize publications by type. It is important to note that some databases contained metadata errors, such as inverted author names or incorrect publication types, which were identified and corrected during manual review.

Primer and probe sequences

The annotated F57 gene, located at positions 886,451–887,722 in the MAP K-10 genome (GenBank accession no. NC_002944.2), was used to align qPCR assay designs targeting this unique sequence. For ISMAP02 and IS900, which are present in 6 and 17 copies, respectively, consensus sequences were generated to facilitate visualization of primer and probe alignments across multiple loci. For ISMAP02, the consensus sequence was constructed using the six annotated genomic regions from the MAP K-10 genome (NC_002944.2) at the following positions: 363,072–361,399 (C1), 2,715,954–2,714,281 (C2), 2,809,770–2,811,443 (C3), 2,885,070–2,886,743 (C4), 3,729,378–3,727,705 (C5), and 3,853,724–3,852,051 (C6). The consensus sequence for IS900 was obtained from GenBank (accession no. PP417235). All TaqMan qPCR primer and probe sequences were mapped to their respective targets using the sequence visualization tool ApE v3.1.3 (22). To evaluate multiplexing efficiency, a total of 18 qPCR assay combinations were created using three designs each for IS900 and F57, and both published designs for ISMAP02 (3 × 3 × 2). All selected primers and probes used in these multiplex assays are listed in Table 1. As an internal amplification control (IAC) to monitor both DNA extraction and qPCR success, we designed an assay using the Integrated DNA Technologies (IDT) PrimerQuest tool (https://www.idtdna.com/PrimerQuest/Home/Index) targeting the reference β-actin gene of Bos taurus (ACTB; NCBI Gene ID 280979) using the following forward and reverse primers: 5′-ATCCTGACCCTCAAGTACCC-3′ and 5′-ACACGGAGCTCGTTGTAGA-3′, respectively, and 5′-CACCAACTGGGACGACATGGAGAA-3′ as a probe.

TABLE 1.

Characteristics of the primers and probe used in 18 multiplex assays

Referencea	Oligo nameb	Sequence 5′ – 3′	Positionc	Oligo length (nt)d	Spe	Size (bp)f
IS900
Herthnek et al. (23)	MPF	CCGCTAATTGAGAGATGCGATT	139–160	22	Low
	MPR	CCAGACAGGTTGTGCCACAA	253–234	20	–	115
	MPP	ACCTCCGTAACCGTCATTGTCCAGATCA	231–204	28	–
Slana et al. (24)	IS900qPCRF	GATGGCCGAAGGAGATTG	94–111	18	Low
	IS900qPCRR	CACAACCACCTCCGTAACC	238–220	19	–	145
	IS900qPCRTM	ATTGGATCGCTGTGTAAGGACACGT	158–182	25	–
Kim et al. (25)	F2	AATGACGGTTACGGAGGTGGT	214–234	21	–
	R2	GCAGTAATGGTCGGCCTTACC	289–269	21	–	76
	P2	TCCACGCCCGCCCAGACAGG	264–245	20	–
ISMAP02
Irenge et al. (26)	ISMAP02-for	CGCCAGGAACGCAAACAT	126–109	18	Low
	ISMAP02-rev	GTGCAGGGTCGCTCTGATG	31–49	19	Low	96
	ISMAP02-probe	ACTCCGCATCCAACAACTCACGCTG	76–52	25	Low
Sevilla et al. (27)	ISMap02-F	CGGCTGGACACGGAATG	1,209–1,225	17	Low
	ISMap02-R	CATGAGCGACAGTATCTTTCGAA	1,275–1,253	23	Low	67
	ISMap02-probe	ATCCGTCCCAGTGGCGGAGTCAC	1,229–1,251	23	Low
F57
Herthnek and Bolske (28)	DH3F	AACTAAGCGGATCGACAATTC	397–377	21	–
	DH3R	TGGTGTACCGAATGTTGTTG	318–337	20	–	80
	DH3 probe	TGCAACTCGAACACACCTGGGA	371–350	22	–
Irenge et al. (26)	F57-for	TTCATCGATACCCAAACTCAGAGA	462–439	24	–
	F57-rev	GTTCGCCGCTTGAATGGT	395–412	18	–	68
	F57-probe	TGCCAGCCGCCCACTCGTG	419–437	19	–
Ricchi et al. (29)	Forward	ATAGCTTTCCTCTCCTTCGTC	875–855	21	–
	Reverse	CAGGGCAACAACATATTCGG	736–755	20	–	140
	Probe	TCCAGGAACGCTTGGCACTCG	839–819	21	–

^a qPCR designs targeting the respective Mycobacterium avium subsp. paratuberculosis target sequence, namely IS900, ISMAP02, and F57.

^b The names of the primers and the probe respect those reported in the publication. In the absence of a name, the usual designation forward, reverse, and probe is given.

^c Position of primers and probes targeting IS900 according to the GenBank accession no. PP417235 sequence, targeting ISMAP02 (consensus sequence of 1,674 nt, Fig. 1), and targeting F57 according to the GenBank accession no. PP971135 sequence.

^d Length of primers and probes; nt, number of nucleotides.

^e Specificity of primer and probe sequences for MAP was evaluated using BLASTn (from NCBI) by filtering out the Mycobacterium avium subsp. paratuberculosis organism (taxid 1770); BLAST results are found in Table S3A through C, for F57, ISMAP02, and IS900, respectively; Sp, specificity; “–” indicates the absence of lack of specificity.

^f Size of the amplicon generated using the forward and reverse primers.

Evaluation of analytical specificity

The analytical specificity of ISMAP02 and F57 primer and probe sequences for MAP was evaluated in silico using the NCBI BLASTn tool. To ensure specificity, searches excluded MAP (TaxID: 1770) from the database. BLASTn results for F57, ISMAP02, and IS900 were compiled into an Excel file for further analysis. Hits with 100% query coverage and 100% identity were summarized in the first tab of the file. Potential secondary structures of primers and probes, which can impact PCR efficiency through self-folding or dimerization, were assessed using the OligoAnalyzer tool from IDT. The thermodynamic stability of hairpins and homodimers was evaluated based on Gibbs free energy (ΔG), with thresholds applied to identify sequences with minimal secondary structure interference. All TaqMan qPCR designs were further evaluated in silico using whole-genome sequences from MAP field isolates obtained from axenic cultures, as previously described (30). The genomic sequences of an additional 241 MAP field strains that we sequenced for this project (GenBank BioProject PRJNA925907) were deposited in the NCBI Sequence Read Archive (SRA) repository (accession numbers: SRA37985472–SRA37985606). Along with the previously deposited sequences (30, 31), this led to a total of 433 MAP field strains that were used to confirm the number of gene copies and the sequence identity of primers and probes relative to their target regions. In addition, in silico analyses of the other MAP strain types (Bison-type, a subcategory of C strain/Type II lineage strains, and the Telford S-type, subtype I identified in sheep or goats) were performed using the BLAST2seq tool and the respective reference genomes (CP033688.1 for Telford S-type and CP033911.1 for MAPK B-type).

qPCR conditions

The SensiFAST Probe no-ROX kit (Meridian Bioscience) was selected to enable the use of the fourth detection channel for monitoring DNA extraction and qPCR efficiency via an endogenous and internal control. For each assay, the qPCR mixture contained 12.5 µL of master mix, 400 nM of each primer (IDT), 100 nM of the IS900, ISMAP02, F57, and ACTB probes (Thermo Fisher Scientific), and 8 µL of DNA, for a final volume of 25 µL, in accordance with the manufacturer’s recommendations. Each probe was labeled with a distinct fluorophore at the 5′ end and a QSY quencher at the 3′ end. The custom TaqMan QSY quencher is compatible with FAM, VIC, ABY, and JUN dyes and is suitable for use with the 7500 Fast Real-Time PCR thermocycler (Applied Biosystems). To ensure clear spectral separation in the multiplex design, each of the four gene-specific probes was assigned a unique dye. The FAM dye, known for its high fluorescence intensity, was used for the single-copy gene F57. VIC and ABY were assigned to ISMAP02 and IS900, respectively, while JUN was used for the endogenous IAC, ACTB, which also served as an extraction control.

Biological samples were analyzed as duplicates, and each 96-well plate used for qPCR included both negative controls (no template) and positive controls containing MAP K10 genomic DNA (gDNA). The 18 selected multiplex qPCR combinations were compared using a standardized thermal cycling program on a single calibrated thermocycler. The program consisted of an initial denaturation and polymerase activation cycle at 95°C for 10 min, followed by 45 cycles of denaturation at 95°C for 15 s and annealing/extension at 60°C for 1 min, as recommended by the manufacturer. Amplification plots from all runs were visually inspected to confirm the presence and quality of amplification signals.

As a qPCR control, we used 100 genome equivalents (Ge) of DNA from the MAP K-10 reference strain (ATCC BAA-968) in all plates to monitor inter-run variability. The Ge concentration was calculated based on the molecular weight of MAP chromosomal DNA. The MAP K-10 genome is 4,829,781 bp in size (32). DNA from two Mycobacterium avium subsp. hominissuis (MAH) reference strains: ATCC 700898 and MAH 101 were included for specificity testing, which were kindly provided by Dr. Marcel Behr (McGill University, Canada). For comparison, the genome size of MAH, based on the MAV101 assembly, is approximately 5.5 Mb, with an estimated molecular weight of 3,575,000,000 g/mol. The number of genome copies per reaction was calculated by multiplying the DNA mass in ng by Avogadro’s number and dividing by the molecular weight in ng/mol (33).

Evaluation of analytical sensitivity

According to MIQE guidelines, it is essential to assess both the amplification efficiency (%Eff) and the limit of detection (LOD) of qPCR assays. For multiplex assays, these parameters must be determined for each target (16). The performance of each primer-probe design was evaluated across the 18 multiplex qPCR assays. The LOD was determined using serial dilutions corresponding to 0.1, 0.2, 0.5, 1, 10, 20, 100, 200, 1,000, 2,000, and 10,000 Ge. Given that stochastic amplification of low-copy DNA templates follows a Poisson distribution, a higher number of replicates (n = 7) was used for the lowest DNA concentrations (0.1, 0.2, 0.5, and 1 Ge) to improve statistical confidence. For 10 Ge, three replicates were included, and for concentrations ≥20 Ge, two replicates were used. An arbitrary quantification cycle (Cq) threshold of 38 was selected to define the LOD, in accordance with commonly accepted guidelines (34).

The %Eff of each primer-probe design was calculated for the 18 multiplex assays. The %Eff was calculated from the slope of the standard curve using the formula: Efficiency = 10 (−1/slope), and is expressed as percent efficiency: %Eff = (E − 1) × 100%. In practice, acceptable amplification efficiencies range from 90% to 110%, corresponding to slope values between −3.2 and −3.5 (35). For each multiplex qPCR design, %Eff was calculated from the slope of standard curves generated across three independent runs (i.e., three separate 96-well plate assays) using serial dilutions of MAP K10 DNA.

Animals and environmental samples

Herds selected for the study were part of a longitudinal investigation involving 22 dairy herds, recruited based on veterinary confirmation that at least one cow with JD had been detected on the farm within the previous year. All animals (>24 months) were sampled (blood and feces) twice a year (every 5–7 months) during a 3-year period by technicians or animal handlers under the supervision of the herd veterinarian and research scientist from Agriculture and Agri-Food Canada (AAFC), as previously described (36). Information about fecal consistency, presence of diarrhea, body condition score, and overall health status was collected on the farm by AAFC at each herd visit. Individual cow feces were collected directly from the rectum using a single-use veterinary glove. Concurrently, individual blood samples were collected in dry tubes (BD Biosciences) for serum separation. In total, 3,458 cows were sampled, yielding 12,087 blood and fecal samples for analysis.

All fecal and blood samples were analyzed using the USDA-licensed VetMAX-Gold MAP Detection Kit (VetMAX; Thermo Fisher Scientific) for fecal qPCR and the IDEXX MAP Ab Test Kit (IDEXX Laboratories) for serum ELISA, as previously described (9, 30, 36). Because the sensitivity and specificity of tests for the diagnosis of paratuberculosis vary significantly depending on the infection stage, it is recommended to include a consistent classification of the animal instead of using a case definition (10, 37). Therefore, we used the longitudinal profile of MAP-fecal excretion and serum ELISA results to classify cows (Fig. S1). We previously determined by culture that MAP-fecal excretion levels can be correlated with VetMAX fecal qPCR results, corresponding to low (Cq > 35, <10 cfu per tube), moderate (Cq ~ 32, 10–100 cfu per tube), and high (Cq ~ 20, too many to count) levels (9). A comparison of MAP shedding levels was performed between qPCR and culture using the same sample, which was processed according to protocols specifically adapted for either molecular (9) or culture (30) analysis, as previously described. Based on this classification, 21 cows were selected: 6 with low (F05–F10; Fig. S1A), 6 with moderate (F11–F16; Fig. S1B), and 9 with high MAP burden (F17–F25; Fig. S1D). Additionally, four cows negative by both fecal qPCR and serum ELISA were included as controls (F01–F04). Each cow’s fecal sample was used to compare all multiplex qPCR assay combinations.

Environmental samples from 22 herds were collected using a standardized description sheet and procedure guided by schematic farm maps for documentation. Briefly, 3–5 environmental samples at each visit were collected from manure storage areas (manure pit or pre-pit) and from lactation alleys (tie-stall) or scraping lines (free-stall). A total of 538 environmental samples were analyzed by qPCR using VetMAX. For the comparison of the 18 multiplex-qPCR combinations, two environmental samples each from four herds (n = 8) were used (farms A–D; Fig. S1D). For additional validation of problematic TaqMan designs, environmental samples (pool of 3–5 samples per herd) were prepared from each of the remaining 14 herds, as previously described (38).

To confirm that the multiplex format maintains the analytical sensitivity of each target, we evaluated the diagnostic performance of the final multiplex qPCR assay and compared it to the results obtained from individual singleplex qPCR assays targeting F57, ISMAP02, and IS900. Four samples were tested, with five replicates each, in both multiplex and singleplex conditions, to assess repeatability and concordance between the two techniques. The final version of the multiplex qPCR was validated using 2,100 cows and 463 environmental samples. All reactions were run in parallel on the same 7500 Fast Real-Time PCR thermocycler to ensure consistent amplification conditions. DNA from both fecal and environmental samples was extracted using the Quick-DNA Fecal/Soil Microbe Miniprep Kit (Zymo Research Corp.). The use of a bead beater and zirconium beads in the lysis buffer ensured MAP cell wall disruption. The protocol for DNA extraction, which had been optimized in a previous study (9), included the following steps: two 1-min bead beating periods with a 5-min break interval to prevent the sample from overheating. The purified DNA was finally recovered in 100 µL of elution buffer.

Statistical analysis

Data were analyzed through a one-way analysis of variance (Statistical Analysis System, Release 9.4, 2002–2012, SAS Institute Inc.) with the 18 selected multiplex qPCR combinations treated as a fixed effect in the model. The 17 degrees of freedom were further partitioned according to a 3 × 2 × 3 factorial design to evaluate the effect of each of the three factors (IS900 [three designs], ISMAP02 [two designs], and F57 [three designs]) and their interactions. For each gene target and within each MAP excretion level (low, medium, or high MAP abundance, or the environmental samples), the outcome variable was the adjusted mean value of Cq for every combination (3 × 2 × 3 for 18 combinations) with their estimated SE. Mean differences between designs (levels of the factors) corresponding to the gene target were evaluated using multiple comparisons with a Tukey adjustment (for the two factors with three levels). Results were considered statistically significant if P < 0.05. For the environmental samples and cows with low-MAP abundance excretion, detection of a signal (yes/no) was evaluated through a Chi-square association test. In all cases, a difference was considered significant if the P value for the Chi-square test was <0.05. The concordance correlation coefficient (r_c) was calculated to assess agreement on a repetitive measure obtained with singleplex and multiplex methods, or to evaluating alternative annealing temperatures of the pPCR program.

RESULTS

Selection of qPCR designs for F57, ISMAP02, and IS900

The three primary gene targets used for MAP detection, IS900, ISMAP02, and F57, were selected to develop a multiplex qPCR. We applied the same strategy previously used for IS900 TaqMan assays (21) to identify suitable designs for ISMAP02 and F57. The initial literature search yielded 19,154 records. After removing duplicate publications and excluding articles that did not involve PCR, as well as conference abstracts, reviews, and publications in languages other than English or French, 86 articles on F57 and 23 articles on ISMAP02 were retained. Further exclusion of studies that employed SYBR Green chemistry, commercial assays, or PCR assays designed for nested-PCR, loop-mediated-PCR, or standard PCR (e.g., agarose gel electrophoresis) led to the retention of 39 publications that used TaqMan assays for detecting MAP based on the F57 gene and four publications used ISMAP02 (Table S2A and B, respectively). Following additional screening to remove redundant citations using the same primer/probe sets, we identified seven unique TaqMan assay designs targeting F57 and two targeting ISMAP02 (Table S2A and B, respectively).

In silico analysis of the ISMAP02 designs

We mapped the TaqMan designs to the respective MAP gene sequence. For ISMAP02, we extracted the six repetitive sequences (C1–C6) from the MAP K-10 reference genome (GenBank accession No. NC_002944.2). Although the six ISMAP02 copies are similar, two DNA mismatches were identified, one at position 402 in C2, C3, and C4, and one genetic variation was detected at position 1,139 in C3 (Fig. 1). The two ISMAP02-Irenge (26) and ISMAP02-Sevilla designs (27) do not overlap these genetic variations (Fig. 1). Furthermore, no genetic variations were identified in the six ISMAP02 genomic copies in the 433 MAP strains we sequenced (30, 31), including 129 that were analyzed most recently (GenBank BioProject acc. No. PRJNA925907).

Fig 1.

Alignment of the six copies of ISMAP02 and location of primers and probe from each TaqMan assay design. The C1–C6 sequences were extracted from GenBank accession no. NC_002944.2, at genomic positions 363,072–361,399, 2,715,954–2,714,281, 2,809,770–2,811,443, 2,885,070–2,886,743, 3,729,378–3,727,705, and 3,853,724–3,852,051, respectively. Differences between copies are highlighted in red. Two mismatches were identified in ISMAP02, notably in position 402 for copies nos. C2, C3, and C4, and one additional genetic variation was detected in position 1,139 of C3. Primer (F for forward and R for reverse, as described in the original publication) and probe (P) annotations include directionality (>>> for sense and <<< for reverse strand). The GTG translation initiation codon (yellow box) of ISMAP02 is at positions 31–33. Regions showing similarity (blastn) to MAH strains 101 and 104 are boxed in black, and percentage of similarity is reported.

In silico BLAST analysis showed that while primers and probes of the two ISMAP02 TaqMan designs are specific to MAP, matches were found in 14 deposited genomes of MAH (including GenBank accessions AP020326.1, CP040247.1, CP040250.1, CP035744.1, CP018019.2, CP018363.1, AP012555.1, CP036220.2, CP040255.1, CP060405.1, CP018014.3, CP018020.3, CP016818.3, and CP083998.1) (Table S3A). A further screen of 190 MAH genomes, including 70 well-characterized isolates (39), had no similarity between the ISMAP02 gene and these MAH strains, except for the 14 deposited genomes reported above. Moreover, the limited similarity observed between ISMAP02 gene and MAH strains 101 or 104 was restricted to three short regions that did not overlap the primer-probe TaqMan binding regions (Fig. 1, black boxes). To further evaluate the analytical specificity of the ISMAP02 designs, we performed qPCR using DNA from two reference strains, MAH ATCC 700898 and MAH 101, using both ISMAP02-Irenge and ISMAP02-Sevilla designs, as well as the commercial USDA-licensed VetMAX qPCR assay. There was no detection of MAH with up to 1,000 Ge of MAH ATCC 700898 or MAH 101 DNA using both in-house ISMAP02 TaqMan designs and the VetMAX qPCR assay.

In silico analysis of the F57 designs

The F57 sequence (GenBank accession no. PP971135) corresponds to a unique locus (positions 886,451–887,722) within the MAP K-10 genome (GenBank accession no. NC_002944.2). This uniqueness was further validated through comparison with the whole-genome sequences from 433 field strains (data not shown). Seven published qPCR designs were mapped along the F57 gene (see Fig. 2), none of which overlapped with the only genetic variation identified among the field strains; a single-nucleotide polymorphism (A > G) at position 943 in strain AAFC_MAP_#822 (GenBank accession no. PP971134).

Fig 2.

Location of primers and probe from each TaqMan assay design on the GenBank accession no. PP971135 corresponding to the F57 gene at MAP genomic position 886,451–887,722 (GenBank accession no. NC_002944.2). Primers (F for forward or R for reverse, as described in the original publication) and probe (P) annotations include directionality (>>> for sense and <<< for reverse strand). Genetic variation identified at position 943 (GenBank accession no. PP971134) is represented by a red box. The GTG translation initiation codon (yellow box) of F57 is at positions 1–3.

An in silico BLAST analysis was performed for the seven F57 TaqMan probe designs, and a summary of the results is provided in Table S3B. Two designs were excluded: F57-Schönenbrücher (40) and F57-Singh (41). Both utilize short probes (14 and 15 nucleotides, respectively), which exhibited numerous non-specific alignments to microbial genomes, including Escherichia coli, a fecal commensal (Table S3B). Such non-specific interactions could impair assay performance by reducing analytical sensitivity (42). The F57-Slana design (43) generated the largest amplicon (147 nt, Table S3D). Its reverse primer (Slana_R) and probe (Slana_P) demonstrated low predicted specificity (Table S3B), and the 27-nt-long probe was prone to self-dimerization (ΔG = −10.24 kcal/mol, data not shown). The F57-Herthnek (28) and F57-Irenge (26) designs (primers and probe) showed the greatest specificity. They overlap the region of the F57-Slana amplicon while being both adjacent (Fig. 2) and were selected to develop the multiplex TaqMan qPCR assay. Conversely, the F57-Donaghy design (34) was excluded due to poor specificity of both the forward primer and probe (see Table S3B), and a strong tendency to form self-dimers (ΔG = −10.36 and −12.43 kcal/mol, respectively; data not shown). The third F57 design selected for multiplex testing was the F57-Ricchi design (29), which targets a distinct 3′ downstream region of the F57 gene. According to in silico analysis (Table S3B), the F57-Ricchi primer-probe sequences are specific to MAP. The three selected TaqMan probe designs, F57-Herthnek, F57-Irenge, and F57-Ricchi, were retained for inclusion in the development of multiplex qPCR assays in combination with other gene targets.

In silico analysis of IS900 designs

In a previous study (21), we identified the three most robust TaqMan qPCR designs for IS900 detection as IS900-Kim (25), IS900-Slana (24), and IS900-Herthnek (23). The in silico BLAST analysis for the three IS900 qPCR designs was updated (Table S3C) and includes the identification of a newly reported non-MAP species (Mycobacterium europaeum). Although isolated cases of perfect sequence identity were observed between individual assay components (either a primer or probe) and non-MAP species, none of the complete TaqMan designs (i.e., both primers and probe in combination) matched non-target organisms. Using both the reference MAP K-10 genome and the 433 MAP field strains we had previously sequenced, we confirmed in silico the presence of 17 copies of IS900 (data not shown). The characteristics of primers and probes of IS900-Kim, IS900-Slana, and IS900-Herthnek designs are described in Table 1, and their positions were mapped to the IS900 reference sequence (GenBank accession No. PP417235).

qPCR efficiency and detection limit

In total, 18 multiplex qPCR assay combinations (3 × 2 × 3) were tested using TaqMan probe qPCR designs including: three IS900: IS900-Kim (25), IS900-Slana (24), and IS900-Herthnek (23); two ISMAP02: ISMAP02-Irenge (26) and ISMAP02-Sevilla (27); and three F57 designs: F57-Herthnek (28), F57-Irenge (26), and F57-Ricchi (29). According to MIQE guidelines, it is mandatory to evaluate the amplification efficiency and the detection limit of qPCR assays. The %Eff of each gene TaqMan design (IS900, ISMAP02, and F57) was calculated for the 18 multiplex qPCR assays using the SensiFAST master mix. The %Eff of amplification of IS900 (P = 0.45), ISMAP02 (P = 0.47), or F57 (P = 0.18) was not affected by multiplexing (Table 2). The SensiFAST master mix provided an excellent %Eff over a broad range of MAP excretion and across all multiplexing qPCR assays, nearing 100% (Table 2). A second parameter to consider when evaluating PCR efficiency is the formation of secondary structures in pooled primers or probes during multiplexing. We calculated the ΔG values for potential primer/probe secondary structures (self-folding or dimerization) in the 18 different multiplex-qPCR combinations (Table S4). No substantial differences were observed among the 18 multiplex-qPCR combinations in terms of number of secondary structures or interaction stability (ΔG).

TABLE 2.

Efficiency of qPCR of the 18 multiplex qPCR designs^a

Design (IS900-ISMAP02-F57)	IS900 target	ISMAP02 target	F57 target
Herthnek-Irenge-Herthnek	100.0 ± 2.3a	98.2 ± 2.7a	98.8 ± 2.0a
Herthnek-Irenge-Irenge	98.4 ± 1.2a	98.7 ± 1.6a	103.9 ± 0.3a
Herthnek-Irenge-Ricchi	98.7 ± 2.7a	100.1 ± 1.9a	98.6 ± 3.9a
Herthnek-Sevilla-Herthnek	98.2 ± 3.3a	96.9 ± 1.2a	94.1 ± 0.8a
Herthnek-Sevilla-Irenge	97.2 ± 2.1a	98.5 ± 1.4a	95.5 ± 2.2a
Herthnek-Sevilla-Ricchi	99.6 ± 2.4a	100.7 ± 1.2a	98.7 ± 0.8a
Kim-Irenge-Herthnek	100.0 ± 1.4a	99.8 ± 0.7a	97.5 ± 1.7a
Kim-Irenge-Irenge	98.5 ± 1.3a	99.6 ± 0.9a	101.9 ± 0.5a
Kim-Irenge-Ricchi	100.6 ± 1.2a	99.2 ± 1.2a	100.4 ± 0.8a
Kim-Sevilla-Herthnek	98.2 ± 1.0a	98.9 ± 2.1a	94.6 ± 2.9a
Kim-Sevilla-Irenge	98.2 ± 0.9a	97.1 ± 1.0a	99.7 ± 2.4a
Kim-Sevilla-Ricchi	99.9 ± 2.3a	100.4 ± 2.5a	97.8 ± 0.6a
Slana-Irenge-Herthnek	98.1 ± 1.1a	99.4 ± 2.6a	98.3 ± 1.6a
Slana-Irenge-Irenge	97.9 ± 0.8a	97.8 ± 1.0a	97.5 ± 0.9a
Slana-Irenge-Ricchi	103.2 ± 2.6a	102.8 ± 1.7a	105.3 ± 3.2a
Slana-Sevilla-Herthnek	98.5 ± 1.7a	98.9 ± 1.6a	94.2 ± 3.1a
Slana-Sevilla-Irenge	97.8 ± 1.4a	97.6 ± 0.6a	95.7 ± 0.7a
Slana-Sevilla-Ricchi	102.5 ± 1.8a	100.9 ± 1.8a	105.2 ± 2.6a

^a Percentage of qPCR efficiency was calculated from the slope of a standard curve produced three times (distinct plate assays) for each multiplex design using serial dilutions of MAP K10 DNA. Results are presented as adjusted means ± SE, and mean differences between designs were evaluated using multiple comparisons with a Tukey adjustment; ^a–kvalues in the same column with different superscripts differ (P < 0.05).

Analytical sensitivity was evaluated by determining the LOD at 50%, 75%, and 95% probability (LOD₅₀, LOD₇₅, and LOD₉₅, respectively) for each assay. The SensiFAST master mix performed well with all multiplex TaqMan assays for detecting all genes (Table 3), and none of the tested combinations failed. The respective analytical sensitivity for the IS900, ISMAP02, and F57 genes corresponds to the number of copies of each gene in MAP. Based on the robust gDNA extraction protocol used, it is not surprising that 0.1 Ge of MAP was detected with IS900 and 0.2 Ge with ISMAP02, given that 17 copies of IS900 and 6 copies of ISMAP02 are present in the MAP genome. The next step was to evaluate their performance using field samples.

TABLE 3.

LOD of the 18 multiplex qPCR designs at 50 (LOD₅₀), 75 (LOD₇₅), and 95% (LOD₉₅)^a

	LOD50b			LOD75c			LOD95d
Design (IS900-ISMAP02-F57)	IS900 target	ISMAP02 target	F57 target	IS900 target	ISMAP02 target	F57 target	IS900 target	ISMAP02 target	F57 target
Herthnek-Irenge-Herthnek	0.5	0.2	2	0.5	0.5	2	1	0.5	10
Herthnek-Irenge-Irenge	0.2	0.2	0.5	0.2	0.5	10	0.5	2	10
Herthnek-Irenge-Ricchi	0.2	0.5	2	1	0.5	10	1	2	10
Herthnek-Sevilla-Herthnek	0.1	0.2	2	0.2	1	2	0.5	2	10
Herthnek-Sevilla-Irenge	0.2	0.5	2	0.5	0.5	2	1	2	10
Herthnek-Sevilla-Ricchi	0.2	0.2	1	0.2	0.5	1	0.5	2	10
Kim-Irenge-Herthnek	0.2	0.2	2	0.2	0.5	10	0.5	1	10
Kim-Irenge-Irenge	0.5	0.5	1	0.5	0.5	2	0.5	1	10
Kim-Irenge-Ricchi	0.2	0.2	0.5	0.2	1	10	0.5	1	10
Kim-Sevilla-Herthnek	0.2	0.2	1	0.2	1	10	0.2	1	10
Kim-Sevilla-Irenge	0.1	0.5	2	0.5	0.5	10	0.5	1	10
Kim-Sevilla-Ricchi	0.1	0.5	2	0.2	0.5	10	0.5	0.5	10
Slana-Irenge-Herthnek	0.2	0.2	2	0.5	0.5	10	0.5	2	10
Slana-Irenge-Irenge	0.2	0.5	10	0.2	0.5	10	0.5	1	10
Slana-Irenge-Ricchi	0.1	0.5	1	0.5	0.5	10	1	1	10
Slana-Sevilla-Herthnek	0.1	0.2	2	0.2	1	1	0.2	2	10
Slana-Sevilla-Irenge	0.1	0.5	2	0.2	0.5	1	1	1	10
Slana-Sevilla-Ricchi	0.1	0.2	1	0.5	0.5	10	0.5	1	10

^a The LOD was established using serial dilutions of MAP K-10 DNA. Seven replicates of 0.1, 0.2, 0.5, 1, and 2 Ge of MAP were used to evaluate the LOD. Three replicates of 10 Ge of MAP were also analyzed.

^b The LOD₅₀ is equivalent to positive detection of MAP in four replicates out of the seven. The lowest amount of MAP (Ge) yielding LOD₅₀ is reported for each gene target (IS900, ISMAP02, and F57) for the respective multiplex qPCR design.

^c The LOD₇₅ is equivalent to positive detection of MAP in six replicates out of the seven. The lowest amount of MAP (Ge) yielding LOD₇₅ is reported for each gene target (IS900, ISMAP02, and F57) for the respective multiplex qPCR design.

^d The LOD₉₅ is equivalent to positive detection of MAP in all replicates. The lowest amount of MAP (Ge) yielding LOD₉₅ is reported for each gene target (IS900, ISMAP02, and F57) for the respective multiplex qPCR design.

Evaluation of diagnostic sensitivity using field samples

To evaluate the analytical sensitivity of the 18-qPCR multiplex in complex samples, we included fecal samples with low (F05–F10), moderate (F11–F16), and high (F17–F25) levels of MAP (Fig. S1A through C, respectively), as well as two environmental samples from each of four herds (E01–E08, Fig. S1D). Cows classified as high MAP shedders (F17–F25) exhibited a mean Cq value of 22.21 ± 1.58 using the VetMAX assay (Fig. S1C). Significant differences in amplification efficiency were observed across designs targeting IS900, ISMAP02, or F57 (P ˂ 0.0001, Table 4). Multiple comparisons for the IS900 target revealed that the IS900-Kim demonstrated greater sensitivity compared to the IS900-Herthnek and IS900-Slana designs. No significant interaction effects were observed between IS900 and ISMAP02 (P = 0.1829) or F57 (P = 0.0620) designs. Representative qPCR results of fecal samples with high MAP levels (F17–F25) are provided in Fig. S2. For ISMAP02, ISMAP02-Irenge outperformed ISMAP02-Sevilla (P < 0.05), though VetMAX Cq values fell within the confidence intervals (CIs) of both designs (Table 4).

TABLE 4.

Results of the 18 qPCR multiplex assays for the detection of MAP in fecal samples from cows excreting high levels of MAP (F17–F25)^a

Design (IS900-ISMAP02-F57)	IS900 target		ISMAP02 targetb		F57 target
Herthnek-Irenge-Herthnek	20.66 ± 0.52cdefghi	(19.64, 21.68)	21.82 ± 0.59ef	(20.64, 22.99)	23.78 ± 0.52ef	(22.76, 24.80)
Herthnek-Irenge-Irenge	20.95 ± 0.58abc	(19.81, 22.09)	21.92 ± 0.56ef	(20.82, 23.02)	23.71 ± 0.51fgh	(22.69, 24.72)
Herthnek-Irenge-Ricchi	20.57 ± 0.56efghij	(19.46, 21.67)	21.79 ± 0.53f	(20.75, 22.84)	23.57 ± 0.53hi	(22.54, 24.61)
Herthnek-Sevilla-Herthnek	20.84 ± 0.59bcdeg	(19.67, 22.00)	22.13 ± 0.55abcd	(21.05, 23.21)	23.95 ± 0.54abcd	(22.88, 25.02)
Herthnek-Sevilla-Irenge	21.13 ± 0.55ab	(20.04, 22.22)	22.20 ± 0.55abc	(21.11, 23.29)	23.93 ± 0.53bc	(22.89, 24.97)
Herthnek-Sevilla-Ricchi	21.17 ± 0.57a	(20.05, 22.29)	22.14 ± 0.55abcd	(21.06, 23.21)	23.85 ± 0.54bcdef	(22.79, 24.90)
Kim-Irenge-Herthnek	20.32 ± 0.53j	(19.27, 21.36)	21.86 ± 0.52ef	(20.84, 22.88)	23.61 ± 0.50hi	(22.63, 24.60)
Kim-Irenge-Irenge	20.41 ± 0.53ij	(19.36, 21.46)	22.01 ± 0.52cde	(20.98, 23.04)	23.54 ± 0.51i	(22.53, 24.55)
Kim-Irenge-Ricchi	20.31 ± 0.52j	(19.27, 21.34)	21.87 ± 0.52ef	(20.84, 22.90)	23.35 ± 0.50j	(22.36, 24.35)
Kim-Sevilla-Herthnek	20.43 ± 0.52hij	(19.41, 21.46)	22.06 ± 0.51abcde	(21.05, 23.06)	23.80 ± 0.49cdefg	(22.82, 24.77)
Kim-Sevilla-Irenge	20.48 ± 0.53ghijk	(19.44, 21.53)	22.06 ± 0.52bcde	(21.04, 23.08)	23.72 ± 0.50efgh	(22.74, 24.69)
Kim-Sevilla-Ricchi	20.32 ± 0.51j	(19.32, 21.32)	22.06 ± 0.52bcde	(21.02, 23.10)	23.50 ± 0.51i	(22.49, 24.51)
Slana-Irenge-Herthnek	20.74 ± 0.56cdefgh	(19.63, 21.85)	21.85 ± 0.61def	(20.66, 23.05)	23.87 ± 0.53bcde	(22.83, 24.91)
Slana-Irenge-Irenge	20.50 ± 0.61fhijk	(19.30, 21.70)	21.83 ± 0.59ef	(20.66, 23.00)	23.75 ± 0.50defg	(22.77, 24.73)
Slana-Irenge-Ricchi	20.58 ± 0.59defghij	(19.41, 21.75)	22.04 ± 0.52bcde	(21.01, 23.07)	23.58 ± 0.54ghi	(22.51, 24.65)
Slana-Sevilla-Herthnek	20.74 ± 0.53cdefg	(19.69, 21.78)	22.19 ± 0.54abc	(21.13, 23.26)	24.07 ± 0.53a	(23.03, 25.12)
Slana-Sevilla-Irenge	20.86 ± 0.53abcdef	(19.81, 21.91)	22.27 ± 0.54a	(21.22, 23.33)	23.97 ± 0.52ab	(22.95, 25.00)
Slana-Sevilla-Ricchi	20.90 ± 0.54abcd	(19.83, 21.97)	22.24 ± 0.55ab	(21.16, 23.32)	23.95 ± 0.51ab	(22.94, 24.96)

^a qPCR cycle of quantification values obtained for each target and multiplex assay is presented as adjusted means ± SE, and 95% CI are indicated in parentheses; mean differences between designs were evaluated using multiple comparisons with a Tukey adjustment; ^a–kvalues in the same column with different superscripts differ (P < 0.05).

^b The mean Cq value (± SD) using the VetMAX-Gold MAP Detection Kit was 22.22 (± 1.58).

For samples from animals excreting moderate levels of MAP, all multiplex PCR combinations successfully detected the three MAP targets (samples F11–F16, Table S5). However, in samples with low MAP levels (Cq VetMAX > 33; F05–F10, Fig. S1A), significant limitations were observed with certain IS900 TaqMan designs (Table 5). Specifically, the IS900-Kim and IS900-Slana designs failed to detect IS900 in low-level samples (see examples in Fig. S2, F06–F10), whereas the IS900-Herthnek design successfully detected IS900 in all low MAP content cases. Therefore, only IS900-Herthnek-based assays were retained for statistical analysis of these low-MAP samples (Table 5), which revealed that diagnostic sensitivity was not compromised by multiplexing with ISMAP02 and F57 (P < 0.05). The detection rates for IS900 in low MAP content samples (F06–F10) were 0% for IS900-Kim, 3% for IS900-Slana, and 81% for IS900-Herthnek (Chi-square test, P < 0.0001), indicating that the IS900-Kim and IS900-Slana designs are not suitable for multiplexing for detecting low levels of MAP shedding.

TABLE 5.

Results of the 18 qPCR multiplex assays for the detection of MAP in fecal samples from cows excreting low levels of MAP (F05–F10)^a

Design (IS900-ISMAP02-F57)	IS900 target		ISMAP02 targetb		F57 target
Herthnek-Irenge-Herthnek	36.10 ± 2.22a	(31.52, 40.67)	27.36 ± 2.30bc	(22.75, 31.98)	37.03 ± 0.74cde	(35.55, 38.52)
Herthnek-Irenge-Irenge	37.04 ± 2.66a	(31.57, 42.52)	29.76 ± 5.16abc	(19.41, 40.11)	36.84 ± 0.49cde	(35.85, 37.83)
Herthnek-Irenge-Ricchi	36.01 ± 2.05a	(31.78, 40.23)	27.10 ± 2.18c	(22.73, 31.47)	39.62 ± 1.07abc	(37.47, 41.76)
Herthnek-Sevilla-Herthnek	36.12 ± 2.10a	(31.80, 40.44)	37.18 ± 1.54a	(34.10, 40.27)	36.34 ± 1.27cde	(33.80, 38.89)
Herthnek-Sevilla-Irenge	35.47 ± 2.09a	(31.17, 39.77)	36.84 ± 1.37a	(34.09, 39.60)	36.81 ± 1.40bcde	(34.00, 39.61)
Herthnek-Sevilla-Ricchi	35.42 ± 2.09a	(31.12, 39.73)	37.41 ± 2.40ab	(32.59, 42.23)	42.30 ± 0.86ab	(40.56, 44.03)
Kim-Irenge-Herthnek	n.d.		29.57 ± 4.77abc	(20.00, 39.15)	38.56 ± 0.65bc	(37.24, 39.87)
Kim-Irenge-Irenge	n.d.		29.23 ± 3.95abc	(21.31, 37.15)	36.56 ± 0.26cde	(36.05, 37.08)
Kim-Irenge-Ricchi	n.d.		28.46 ± 5.80abc	(16.81, 40.10)	42.64 ± 0.36a	(41.91, 43.36)
Kim-Sevilla-Herthnek	n.d.		38.21 ± 1.21a	(35.78, 40.64)	35.32 ± 0.26e	(34.79, 35.85)
Kim-Sevilla-Irenge	n.d.		38.66 ± 1.51a	(35.63, 41.68)	35.96 ± 0.39cde	(35.19, 36.74)
Kim-Sevilla-Ricchi	n.d.		37.04 ± 1.16a	(34.72, 39.36)	42.53 ± 1.13ab	(40.27, 44.80)
Slana-Irenge-Herthnek	n.d.		27.41 ± 2.27bc	(22.87, 31.96)	37.14 ± 0.51cde	(36.11, 38.16)
Slana-Irenge-Irenge	n.d.		27.16 ± 2.12c	(22.90, 31.42)	37.40 ± 0.45cd	(36.50, 38.31)
Slana-Irenge-Ricchi	n.d.		27.16 ± 2.18c	(22.79, 31.54)	41.51 ± 1.60abc	(38.29, 44.74)
Slana-Sevilla-Herthnek	n.d.		37.02 ± 1.48a	(34.05, 39.99)	35.43 ± 0.47de	(34.48, 36.38)
Slana-Sevilla-Irenge	n.d.		36.88 ± 1.57a	(33.73, 40.02)	35.52 ± 0.86cde	(33.79, 37.24)
Slana-Sevilla-Ricchi	n.d.		37.16 ± 1.62a	(33.90, 40.41)	39.02 ± 1.60abcde	(35.80, 42.24)

^a qPCR cycle of quantification values obtained for each target and multiplex assay are presented as adjusted means ± SE, and 95% CI are indicated in parentheses; mean differences between designs were evaluated using multiple comparisons with a Tukey adjustment; ^a–evalues in the same column with different superscripts differ (P < 0.05); n.d., not detected.

^b The mean Cq value (± SD) using the VetMAX-Gold MAP Detection Kit was 36.22 (± 1.77).

Another inconsistency was observed when quantifying low-abundance MAP samples (F05–F10, Fig. S2). Although VetMAX Cq values (Cq = 36.2 ± 1.69) fell within CIs for ISMAP02-Sevilla assays, they were outside the CI range for ISMAP02-Irenge (Table 5). ISMAP02-Irenge yielded significantly lower Cq values (ΔCq = 6–7 cycles relative to IS900), independent of the F57 or IS900 multiplex combination used along with it. In multiplexed qPCR combinations, ISMAP02-Irenge showed lower concordance with IS900 detection in low-MAP samples, with only 50% of samples positive compared to 89% with ISMAP02-Sevilla (Chi-square, P < 0.0001). Detection of F57, a single-copy gene, proved challenging in samples with low MAP content, with no significant differences between TaqMan designs (Chi-square test, P = 0.32).

While conducting environmental analysis using the different multiplex qPCR combinations, compatibility issues, especially in the case of low MAP content samples, were also observed for the IS900-Kim, IS900-Slana, and ISMAP02-Irenge designs (see Fig. S2 V-AC: samples E01–E08). In low-MAP content samples (e.g., Fig. S2 V, E01; VetMAX Cq = 35), IS900-Kim and IS900-Slana failed to detect MAP. In environmental MAP samples (E02–E08), no significant differences were attributed to the F57 design (Table 6). However, ISMAP02-Irenge produced Cq values approximately two cycles lower than ISMAP02-Sevilla (a fourfold difference), with VetMAX Cq values (28.4 ± 1.9) falling outside the CI range for ISMAP02-Irenge (Table 6). These observations were corroborated using 14 additional environmental samples (E09–E22, data not shown), where IS900-Kim and IS900-Slana failed to detect IS900 in seven and five low-MAP samples, respectively. ISMAP02-Irenge also overestimated ISMAP02 abundance relative to IS900 in 6 of 14 samples. In contrast, the ISMAP02-Sevilla multiplex assays consistently produced results that aligned closely with those obtained using the USDA-licensed commercial VetMAX qPCR assay.

TABLE 6.

Results of the 18 qPCR multiplex assays for the detection of MAP in environmental samples (E02–E08)^a

Design (IS900-ISMAP02-F57)	IS900 targetb		ISMAP02 targetc		F57 target
Herthnek-Irenge-Herthnek	26.71 ± 0.66bcf	(25.39, 28.02)	26.28 ± 0.46ab	(25.38, 27.19)	29.88 ± 0.84a	(28.22, 31.54)
Herthnek-Irenge-Irenge	26.82 ± 0.68abcde	(25.47, 28.17)	26.52 ± 0.51ab	(25.50, 27.54)	29.84 ± 0.83a	(28.21, 31.48)
Herthnek-Irenge-Ricchi	26.69 ± 0.62cf	(25.46, 27.92)	26.37 ± 0.45ab	(25.47, 27.28)	30.56 ± 1.45a	(27.69, 33.44)
Herthnek-Sevilla-Herthnek	27.05 ± 0.64abde	(25.79, 28.31)	28.07 ± 0.64ab	(26.79, 29.34)	29.84 ± 0.69a	(28.46, 31.21)
Herthnek-Sevilla-Irenge	27.08 ± 0.66ade	(25.77, 28.40)	28.07 ± 0.67ab	(26.74, 29.40)	29.84 ± 0.80a	(28.26, 31.42)
Herthnek-Sevilla-Ricchi	27.13 ± 0.65ade	(25.85, 28.42)	28.05 ± 0.66ab	(26.74, 29.37)	30.25 ± 1.07a	(28.14, 32.37)
Kim-Irenge-Herthnek	27.09 ± 1.24abcde	(24.64, 29.55)	26.43 ± 0.59ab	(25.25, 27.60)	30.50 ± 1.11a	(28.31, 32.70)
Kim-Irenge-Irenge	27.36 ± 1.30abcde	(24.78, 29.94)	26.60 ± 0.55ab	(25.50, 27.70)	30.04 ± 0.87a	(28.31, 31.76)
Kim-Irenge-Ricchi	26.87 ± 1.06ef	(24.77, 28.96)	26.45 ± 0.55ab	(25.37, 27.54)	31.08 ± 1.29a	(28.52, 33.64)
Kim-Sevilla-Herthnek	26.68 ± 0.90abcde	(24.89, 28.47)	28.01 ± 0.74ab	(26.55, 29.48)	29.77 ± 0.83a	(28.12, 31.42)
Kim-Sevilla-Irenge	26.88 ± 1.01abcde	(24.87, 28.89)	28.05 ± 0.75ab	(26.57, 29.53)	29.88 ± 0.88a	(28.14, 31.62)
Kim-Sevilla-Ricchi	26.39 ± 0.83df	(24.75, 28.03)	28.05 ± 0.75ab	(26.57, 29.53)	30.25 ± 1.20a	(27.86, 32.64)
Slana-Irenge-Herthnek	27.40 ± 1.09abcde	(25.23, 29.57)	26.57 ± 0.49a	(25.60, 27.54)	29.93 ± 0.75a	(28.45, 31.42)
Slana-Irenge-Irenge	26.97 ± 0.97abcde	(25.05, 28.89)	26.40 ± 0.51b	(25.39, 27.42)	29.79 ± 0.86a	(28.08, 31.50)
Slana-Irenge-Ricchi	27.40 ± 1.15abcde	(25.13, 29.68)	26.53 ± 0.45ab	(25.64, 27.43)	29.93 ± 0.95a	(28.04, 31.82)
Slana-Sevilla-Herthnek	26.88 ± 0.78abce	(25.34, 28.42)	28.10 ± 0.67ab	(26.77, 29.44)	29.80 ± 0.66a	(28.49, 31.10)
Slana-Sevilla-Irenge	27.11 ± 0.90abce	(25.32, 28.89)	28.22 ± 0.68ab	(26.87, 29.56)	29.82 ± 0.72a	(28.39, 31.24)
Slana-Sevilla-Ricchi	27.52 ± 1.12abcd	(25.30, 29.75)	28.24 ± 0.70ab	(26.86, 29.62)	30.21 ± 0.91a	(28.40, 32.01)

^a qPCR cycle of quantification values obtained for each target and multiplex assay are presented as adjusted means ± SE, and 95% CI are indicated in parentheses; mean differences between designs were evaluated using multiple comparisons with a Tukey adjustment; ^a–fvalues in the same column with different superscripts differ (P < 0.05).

^b The sample E02 was excluded from the multiple comparisons for IS900, as most of the “Kim”-multiplexed assays failed.

^c The mean Cq value (± SD) using the VetMAX-Gold MAP Detection Kit was 28.45 (± 1.89).

Validation of the qPCR multiplex

For validating the diagnostic sensitivity of our final version of the multiplex qPCR, results obtained with the IS900-Herthnek, ISMAP02-Sevilla, and F57-Herthnek qPCR assays were compared to those obtained from individual simplex qPCRs. The Cq values of each target in the multiplex qPCR were strongly correlated with those of the corresponding simplex qPCR, for IS900 (r_c = 0.9956), ISMAP02 (r_c = 0.9960), and F57 (r_c = 0.9858). We developed a decision-making algorithm (Fig. 3), which was validated using an independent data set comprising 2,110 dairy cows and 463 environmental samples (see Table S6A and C). Our finalized multiplex qPCR assay demonstrated high analytical specificity and sensitivity across MAP genotypes, detecting all C-type (Type II) strains collected from herds (GenBank BioProject PRJNA925907). In silico analyses confirmed that IS900-Herthnek detected all 17 IS900 copies in bison-type strains (MAPK J) and all 22 in Telford S-type (subtype I) sequences with 100% identity. ISMAP02-Sevilla also detected all six MAPK J and seven Telford S-type ISMAP02 copies with perfect match, while F57-Herthnek detected the single F57 locus with 100% identity. Therefore, our multiplex TaqMan assay is suitable for detecting cases of Bison-type MAP infection, a subcategory of C strain/Type II lineage strains, and for the detection of infection in sheep or goats (Telford S-type, subtype I). The detection limit of the multiplex qPCR assay was 0.5 genome equivalent of MAP bacteria (Table 3), which is equivalent to one IS900 DNA copy/μL in 95% of reactions. With the detection of ISMAP02 (LOD of 2 Ge of MAP; 1.5 copies/μL in 95% of the reactions) and F57 (LOD of 10 MAP; one copy/μL in 95% of the reactions), the multiplex qPCR assay provides a highly sensitive and specific tool for detecting MAP in diverse sample types and infection contexts.

Fig 3.

Decisional algorithm for analyzing fecal or environmental samples by multiplex qPCR (IS900-Herthnek, ISMAP02-Sevilla, and F57-Herthnek). The protocol follows a two-step approach. First, the integrity of the 96-well plate setup is verified through the inclusion of both negative and positive controls. Second, the validity of the qPCR results from each sample is confirmed using an IAC (ACTB), which serves to monitor the efficiency of both DNA extraction and qPCR amplification. This algorithm does not allow for the detection of non-MAP pathogens. However, it could potentially indicate the presence of MAH pathogens if a clear detection of ISMAP02—outside the low-abundance MAP range of the shedding spectrum—is observed in the absence of F57 and IS900, which would warrant further investigation of the animal. This decisional algorithm ensures robust and reliable interpretation of multiplex-qPCR data across diverse sample types. The algorithm offers a streamlined and practical framework for interpreting multiplex-qPCR results, balancing analytical rigor with operational efficiency. UND, undetermined result (no quantitative signal detected).

ACTB as a DNA extraction control

The SensiFAST qPCR mix offers the advantage of accommodating a fourth detection channel (ROX), which can be utilized to monitor failures in either gDNA extraction or qPCR amplification. Given the pervasive presence of bovine tissue residues on dairy farms, we sought to evaluate whether the bovine ACTB gene could serve as an IAC for gDNA extraction and amplification. The 18 multiplex qPCR combinations included ACTB as both a DNA extraction and an internal qPCR amplification control. ACTB amplification was consistent and reliable across all sample types, with a mean Cq of 27.43 ± 1.19 for fecal samples and 25.14 ± 1.33 for environmental samples. The inclusion of ACTB confirmed the functionality of all PCRs, regardless of the multiplex combination used. No significant differences were observed between the multiplex qPCR combinations (data not shown), supporting the comparability of results across all combinations. Additionally, ACTB levels in samples from JD-negative cows, defined as MAP negative by fecal qPCR and consistently negative by serum ELISA throughout the longitudinal study, were comparable to those in MAP-positive samples. The inclusion of ACTB as IAC confirmed the validity of PCR assays even in samples where MAP was not detected.

DISCUSSION

Using pure MAP DNA, all 18 multiplex qPCR assays demonstrated high and comparable analytical sensitivity. The 95% LODs (LOD₉₅) were determined to be 0.5 Ge for IS900 and 2.0 for ISMAP02, indicating robust detection in 95% of replicates. For F57, all designs had a LOD₉₅ of 10 Ge. The relative sensitivity (Cq values) of the targets generally followed the expected trend: F57 > ISMAP02 > IS900. Using the optimal multiplex-qPCR assay (IS900-Herthnek/ISMAP02-Sevilla/F57-Herthnek), this pattern was confirmed for the standard curve and most MAP positive samples with moderate and high levels of bacterial loads (Fig. 4). This is clearly illustrated by the amplification curves, which show the relative increase in fluorescence for IS900 (solid line), ISMAP02 (dot-dash line), and F57 (dashed line).

Fig 4.

Amplification curves of IS900-Herthnek, ISMAP02-Sevilla, and F57-Herthnek. (A) Multiplex-qPCR results were obtained using a standard curve generated with MAP K10 Ge at concentrations of 0.1 (purple), 1 (yellow), 10 (green), 100 (pink), 1,000 (blue), and 10,000 (red). MAH 101 (black) and MAH 700898 (gray) were also analyzed at 10, 100, and 1,000 Ge. All samples were processed under identical qPCR conditions, except for the MAH samples, which were run with 40 cycles. (B) Field samples representing different levels of MAP shedding—MAP negative (F04, purple), low (F08, green), moderate (F11, pink), and high (F19, blue)—were analyzed using multiplex-qPCR. Targets included IS900 (solid line), ISMAP02 (dot-dash line), and F57 (dashed line), with the threshold indicated by a dotted horizontal line. The x-axis represents the number of cycles, and the y-axis shows ΔRn (normalized fluorescence value, log scale).

We developed a decision-making algorithm to ensure robust and reliable interpretation of multiplex qPCR data across diverse sample types. This algorithm was validated on 2,110 cows confirmed positive or negative for MAP infection during the longitudinal study and also validated on environmental samples collected from dairy herds with paratuberculosis (Table S6). Given that F57 exists as a single copy, a higher LOD threshold is expected based on Poisson distribution principles. In low-MAP content samples such as those detected by ISMAP02 with VetMAX Cq values >35, the absence of F57 detection is not surprising. A ΔCq of 2.58 between ISMAP02 and F57 corresponds to a substantial reduction in F57 template availability, leading to signal loss due to stochastic effects in low-copy number scenarios. Examples of such results are shown in Table S6B, where ΔCq values in red do not quite correspond to the expected ratio of gene copy number in the genome. This discrepancy is seen to occur only with samples containing few MAP. An example of such loss of the F57 signal can be visualized by the amplification plots of qPCR results from a low-MAP sample shown in Fig. 4B (sample F08), where IS900 and ISMAP02 were detected but F57 never crossed the detection threshold. At low MAP abundance, where concentrations of MAP DNA templates are low, PCR amplification becomes a stochastic process. In this detection range, the probability of amplifying each target molecule follows a Poisson distribution, with each target representing an independent probabilistic event. This randomness can lead to variability in detection, where the presence or absence of amplification for each target depends on whether at least one copy was included in the reaction mixture (in the 8 μL picked from the fecal DNA extract). Therefore, for low MAP samples, it is not unusual for the Cq values to deviate from the expected F57 > ISMAP02 > IS900 rule.

In probe-based assays, the elevated concentrations of primers and probes can promote the formation of primer dimers (homodimers or heterodimers), which may significantly compromise PCR efficiency (16). To assess this potential interference, we evaluated the propensity for secondary structure formation, including hairpins, homodimers, and heterodimers, using Gibbs free energy (ΔG) as a predictive metric. A ΔG value below a defined threshold indicates that such structures are thermodynamically favorable and therefore likely to impair qPCR performance (42). However, our analysis revealed no correlation between ΔG values (either minimum or average) or the number of predicted secondary structures and the empirical performance of the qPCR designs. Notably, all TaqMan designs performed well in simplex (data not shown) and across all 18 multiplex qPCR combinations tested, exhibiting near-perfect PCR efficiencies, approaching 100%. Despite this, certain qPCR designs using field samples negatively impacted the diagnostic sensitivity of the multiplex assays, indicating that factors beyond PCR efficiency, such as cross-reactivity or non-specific amplification, may influence assay performance. The causes of the false-negative results with IS900-Kim and IS900-Slana have not been elucidated, but it is possible that the lack of sensitivity is due to nonspecific interactions, which we have not demonstrated.

When evaluating diagnostic sensitivity, it is essential to validate that PCR inhibitors do not bias MAP detection. It is also important to include animals exhibiting varying levels of MAP shedding to ensure that the assay performs reliably across the full spectrum of sample types it is intended to assess (44). The use of fecal or environmental samples for diagnostics presents two main challenges. First, efficient DNA extraction from mycobacteria can be difficult, and the presence of PCR inhibitors in these complex matrices may compromise amplification. Second, the inherent complexity of these samples, rich in diverse biological materials and microorganisms, can lead to non-specific interactions. These factors may ultimately reduce diagnostic sensitivity (45). For fecal PCR, we previously demonstrated that the Quick-DNA Fecal/Soil Microbe Miniprep Kit protocol offers superior sensitivity. We previously used two approaches to validate that DNA extracts were free from PCR inhibitors. The first and most straightforward strategy involved performing 10-fold serial dilutions of the DNA extract to estimate the presence of PCR inhibitors. We compared the Quick-DNA Fecal/Soil Microbe Miniprep Kit to two other commercially available DNA extraction methods. A series of 1:10 dilutions of fecal samples reported that using this approach, the Quick-DNA Fecal/Soil Microbe Miniprep Kit showed the expected 3.3-Ct difference between the 1:10 dilutions (9). In contrast, two other commercially available DNA extraction methods did not yield the expected ΔCt values, as the ΔCt was reduced to ~1 (i.e., improved detection), suggesting that dilution attenuated the inhibitory effects on qPCR. A second validation was performed by evaluating PCR efficiency (%Eff) using standard curves prepared from pure MAP genomic DNA (serial dilutions equivalent to 0.1, 0.5, 1, 10, 20, 100, 200, 1,000, 2,000, and 10,000 Ge). The same standard curve was spiked into the fecal DNA extract (8 μL in a final 25 μL PCR). The presence of fecal DNA extract did not affect %Eff (Fig. 4, in reference [21]), confirming that the extraction protocol effectively removed PCR inhibitors. The enhanced performance of the Quick-DNA Fecal/Soil Microbe Miniprep Kit is largely due to the mechanical bead-beating step, which effectively disrupts the thick, waxy mycobacterial cell wall. Combined with spin column-based DNA purification, this method enables the elution of MAP and fecal DNA, free of PCR inhibitors. In the present study, we applied the same rigorous DNA extraction and purification protocol to evaluate 18 multiplex qPCR assay combinations.

We previously validated the TaqMan Environmental Master Mix (EnviMM) for its robust performance using feces collected directly from animals or from the dairy farm environment (21). EnviMM includes a ROX fluorophore (emission at 617 nm), a passive internal reference dye commonly used to correct for inter-well variation and pipetting inconsistencies. The SensiFAST master mix demonstrated performance comparable to EnviMM across all targets (F57, ISMAP02, and IS900; data not shown), while also allowing the inclusion of an IAC. By selecting a master mix without an internal reference dye (e.g., ROX), the fourth detection channel could be repurposed to monitor extraction and amplification efficiency. The choice of ACTB as the IAC was particularly appropriate, given its ubiquitous presence in both fecal and environmental matrices. The ACTB gDNA was consistently detected in all 1,737 environmental samples (data not shown) and in all 2,110 dairy cows (independent population, data not shown) tested by us to date, without exception. ACTB was found to be relatively stable. In addition, ACTB as the IAC is especially valuable in the context of MAP detection, where target abundance is often low. In the absence of the MAP signal, successful amplification of ACTB confirms the integrity of both the DNA extraction and qPCR detection protocols.

In the past, the diagnostic specificity of IS900-based assays for MAP detection was questioned due to the presence of IS900-like sequences in non-MAP mycobacteria (46–49). However, with the availability of the complete MAP genome and those from many other mycobacteria, it is now possible to confirm that IS900 detection assays are specific for MAP. In silico BLAST analyses confirmed the analytical specificity of these IS900 assays, namely IS900-Herthnek, IS900-Kim, and IS900-Slana. They do not share sequence similarity with IS900-like elements found in MAC, notably IS1613 (GenBank acc. No. AJ011837.1), IS1626 (GenBank acc. No. AF071067.1), IS901/902 (GenBank acc. No. X58030.1), IS1110 (GenBank acc. No. Z23003.1), and IS110 (GenBank acc. No. Z23003.1) (data not shown). However, some of these MAP-specific IS900 assays lacked diagnostic sensitivity, especially when applied to samples with low MAP content. For instance, the IS900-Kim and IS900-Slana assays failed to detect MAP in low-MAP samples. In a previous study using singleplex qPCR, we observed that IS900-Kim and IS900-Slana failed to detect MAP in some low-shedding cows (21). In the present study, false negatives with IS900-Kim and IS900-Slana were also observed when these assays were multiplexed. That failure is not attributable to interactions with other TaqMan probe designs, as no such interactions were observed with pure MAP DNA. Rather, the reduced diagnostic sensitivity of the IS900-Kim and IS900-Slana assays appears to result from non-specific interactions with fecal or environmental sequences. This limitation was not observed with the IS900-Herthnek assay or the USDA-licensed VetMAX commercial assay, both of which consistently demonstrated high diagnostic sensitivity, regardless of the MAP load present in the sample. The MAP-specific target of the commercial USDA-licensed VetMAX assay is not publicly disclosed. Interestingly, testing of MAP-positive field samples (n = 181) in parallel with both multiplex-qPCR and VetMAX assays suggests that the VetMAX target gene is ISMAP02, based on Cq values. The correlation between Cq values from two assays is 0.96, which increased to 0.99 when considering Cq values <34 (n = 102). Our in-house multiplex-qPCR assays are an alternative to the USDA-licensed VetMAX commercial assay that requires an import permit for use in Canada.

Multiplex qPCR provides a major advantage by enabling the simultaneous detection of multiple MAP-specific genetic targets, without compromising sensitivity compared to single-target qPCR (Table S6D). By measuring the cross-sensitivity of the three targets, the multiplex qPCR approach enables a more robust evaluation of the design. For example, the ISMAP02-Irenge assay initially appeared more sensitive than the ISMAP02-Sevilla assay, as indicated by consistently lower Cq values in both high-MAP (Table 5) and low-MAP (Table 6) content samples. Given the well-characterized MAP genome, the relative abundance of target genes is expected to reflect their genomic copy numbers. Theoretically, the ΔCq between IS900 (17 copies) and ISMAP02 (6 copies) should be approximately +1.5 cycles, based on the log₂ ratio of their copy numbers. However, in the ISMAP02-Irenge multiplexed assays, low-MAP samples showed ISMAP02 Cq values that were 6–7 cycles lower than those of IS900. This would imply that ISMAP02 is 2⁶–2⁷ times more abundant than IS900, an outcome that contradicts the known genomic structure of MAP. For moderate to high-abundant MAP samples, such an incongruity strongly suggests the presence of assay-related technical problems, possibly related to unspecific primer/probe design, and underscores the need for cautious interpretation of diagnosis results. The discrepancy was also confirmed by comparing results to the F57 target. While the expected ΔCq between F57 and ISMAP02 is approximately 2.6, the ISMAP02-Irenge multiplex yielded a ΔCq that often exceeded 7 Cq. In contrast, the ISMAP02-Sevilla design produced results consistent with expectations based on IS900, F57, and the USDA-licensed VetMAX assay. These findings underscore the value of multiplexing targets for detecting inconsistencies that may indicate a potential false-negative result in certain assay designs, thereby enhancing the interpretability and reliability of results.

Another valuable feature of the multiplex qPCR assay is its ability to differentiate MAP from other members of MAC, due to their distinct genomic architectures. For instance, an analysis of 190 MAH genomes from the NCBI database revealed 14 records with 100% query coverage and 100% identity to ISMAP02 (E-value = 0). These 14 MAH strains contained only one or two copies of ISMAP02 (data not shown). For the other MAH strains, restricted regions showed similarity with ISMAP02 (Fig. 1, black boxes), and the absence of the F57 gene was confirmed for strains 101 and 104. These findings highlight the importance of using a multiplex qPCR assay, especially in environments where MAH strains carrying ISMAP02 may be present. In animals such as cattle and pigs, MAH is considered an opportunistic pathogen. Therefore, clear detection of ISMAP02 (outside the low-MAP abundance range of the excretion spectrum) in the absence of F57 (GenBank acc. no. PP971135) and IS900 (GenBank acc. no. PP417235) may indicate the presence of MAH strains, warranting further investigation. The NCBI database is continuously expanding, and improvements in sequencing technologies, genome assembly quality, and curation by NCBI staff enhance the reliability of in silico BLAST analyses for assessing the analytical specificity of primers and probes. Nonetheless, it remains essential to validate specificity using field samples, which may contain a wide diversity of microorganisms not represented in reference databases.

The ISMAP02-Sevilla qPCR design consistently outperformed the TaqMan ISMAP02-Irenge design, particularly in fecal and environmental samples with low MAP content. The ISMAP02-Irenge design either reported false-negative results or inconsistently overestimated ISMAP02 abundance in both cow fecal and environmental low-MAP samples. Inconsistent results with IS900-Kim and IS900-Slana were also observed with low-MAP samples (Fig. S2, F06–F10). Notably, these discrepancies cannot be attributed to herd-specific signatures, as the challenging samples originated from multiple herds (Fig. S1A through C), ruling out geographical or source-related biases. Such inaccuracies were not observed in low-MAP samples when purified MAP DNA was used in single-qPCR (21) or multiplex-qPCR (present study) format. However, when fecal or environmental DNA was present, a lack of sensitivity was observed with low-MAP samples. Fecal or environmental DNA, which contains various biological matrices and microorganisms, may induce non-specific competitive reactions masking MAP amplification because primers are diverted toward non-specific amplification. Therefore, it is important to evaluate assay performance prior to optimization, and not solely with pure MAP DNA. Further optimization—such as adjusting the concentration of primers or probes—may help attenuate non-specific competitive reactions. In the present study, validation was carried out using the recommended probe concentration (400 nM), which is relatively high. Non-specific probes may undergo hydrolysis while annealing to non-MAP sequences, potentially leading to an overestimation of MAP abundance. Nevertheless, the multiplex qPCR demonstrated high stability across a broad range of annealing temperatures (56°C–66°C), and the sensitivity of all three assays remained unaffected (Table S6E). These results suggest that the selected multiplex qPCR designs are highly robust with complex fecal samples. The selected designs, namely IS900-Herthnek, ISMAP02-Sevilla, and F57-Herthnek, consistently produced reliable and specific amplification in both pure and field-derived DNA samples, and results were congruent with expected MAP genomic structure, including the relative abundance of IS900 and F57. Interestingly, the overall results suggest that the target gene of the commercial USDA-licensed VetMAX-Gold MAP Detection kit is ISMAP02, based on relative gene intensity detected for all samples (Fig. S2).

Conclusions

We developed a multiplex qPCR assay incorporating TaqMan designs that include the F57 (F57-Herthnek), ISMAP02 (ISMAP02-Sevilla), and IS900 (IS900-Herthnek) genes for the specific detection of MAP. This assay was evaluated for both analytical sensitivity using pure DNA and diagnostic sensitivity using fecal and environmental samples. While all 18 multiplex combinations demonstrated excellent sensitivity and PCR efficiency with pure DNA, the IS900-Herthnek and ISMAP02-Sevilla designs showed superior diagnostic sensitivity in detecting low MAP abundance in complex field samples. The inclusion of both markers of repetitive sequence (IS900 and ISMAP02) and the single-copy F57 gene enhances the sensitivity and specificity of the assay. In addition, the strength of our approach lies in the use of ΔCq values between target genes as an indicator of diagnostic specificity issues.

Our multiplex qPCR assay demonstrated high specificity for MAP, reliably detecting all C-strain (Type II) strains collected from Canadian dairy herds. It also effectively identified Bison-type MAP, a subgroup within the C-strain/Type II lineage, as well as the S-type (subtype I), typically associated with infections in sheep and goats. Overall, our multiplex qPCR assay provides a robust, highly sensitive, and specific tool for the detection of MAP, supporting accurate diagnosis of JD for both animal and environmental samples.

DATA AVAILABILITY

All data supporting the findings of this study are available as supplementary files.

ETHICS APPROVAL

This study was conducted using samples collected from cows, and informed consent was obtained from all owners. The protocol of study was confirmed by the Animal Ethics Committee (protocols 466 and 545) from the Agriculture and Agri-Food Canada (AAFC) in accordance with the Canadian Council on Animal Care guidelines for institutional animal use.

SUPPLEMENTAL MATERIAL

The following material is available online at https://doi.org/10.1128/jcm.00872-25.

Figure S1. jcm.00872-25-s0001.pdf.

Quantification of MAP excreted in feces (Cq value) and anti-MAP antibody levels (serum ELISA, %S/P) according to the age of animals of low (A), moderate (B) and high (C) shedders and MAP level (Cq) from the environnemental samples (D).

Figure S2. jcm.00872-25-s0002.pdf.

Graphs of the qPCR results. All samples (cows and feces) were analyzed using the 18 multiplex-qPDR assays.

Table S1. jcm.00872-25-s0003.docx.

Profiles (Fecal qPCR [Cq] and serum ELISA) of cows F05-F25 and Environmental qPCR results (Cq values) for E01-E08 samples.

Table S2. jcm.00872-25-s0004.docx.

Results of literature search: TaqMan designs for ISMA02 and F57.

Table S3. jcm.00872-25-s0005.xlsx.

Blast results of the primer and probe sequences of the TaqMan assays for ISMAP02 (A), F57 (B), IS900 (C) and unique F57 TaqMan designs (D).

Table S4. jcm.00872-25-s0006.xlsx.

Secondary structures calculated in silico within each of the 18 multiplex-qPCR assays.

Table S5. jcm.00872-25-s0007.docx.

Statistical analysis of the qPCR (Cq) results obtained with the moderate MAP shedders using the 18 multiplex-qPCR assays.

Table S6. jcm.00872-25-s0008.xlsx.

Validation of the final multiplex-qPCR assay. A. individual cows; B. ΔCq analysis; C. environmental samples; D. Simplex vs Multiplex; E. different annealing temperature of the PCR program.

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