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Article

Using Digital PCR to Unravel the Occurrence of Piroplasmids, Bartonella spp., and Borrelia spp. in Wild Animals from Brazil

by
Ana Cláudia Calchi
1,
Anna Claudia Baumel Mongruel
1,
Fernanda Beatriz Pereira Cavalcanti
2,
Lilliane Bartone
3,
José Maurício Barbanti Duarte
4,
Emília Patrícia Medici
5,6,
Danilo Kluyber
7,8,9,10,
Mayara G. Caiaffa
7,
Mario Henrique Alves
7,11,
Arnaud Leonard Jean Desbiez
7,12,
Taciana Fernandes Souza Barbosa Coelho
13,
Rosangela Zacarias Machado
1,
Edward B. Breitschwerdt
3,*,
Ricardo G. Maggi
3,* and
Marcos Rogério André
1,*
1
Vector-Borne Bioagents Laboratory (VBBL), Department of Pathology, Reproduction and One Health, School of Agricultural and Veterinarian Sciences (FCAV), São Paulo State University (UNESP), Jaboticabal 14884-900, SP, Brazil
2
Programa de Pós-Graduação em Ciências Veterinárias, FCAV-UNESP, São Paulo State University (UNESP), Jaboticabal 14884-900, SP, Brazil
3
Intracellular Pathogens Research Laboratory, Comparative Medicine Institute, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607, USA
4
Deer Research and Conservation Center (NUPECCE), São Paulo State University (UNESP), Jaboticabal 14884-900, SP, Brazil
5
Lowland Tapir Conservation Initiative (LTCI), Instituto de Pesquisas Ecológicas (IPÊ), Nazaré Paulista 12960-000, SP, Brazil
6
IUCN SSC Tapir Specialist Group (TSG), Campo Grande 79046-150, MS, Brazil
7
ICAS—Instituto de Conservacao de Animais Silvestres, Campo Grande, Mato Grosso do Sul 79040-290, MS, Brazil
8
Naples Zoo at the Caribbean Gardens, Naples, FL 34102, USA
9
São Paulo Institute of Tropical Medicine, Faculdade de Medicina, University of São Paulo, São Paulo 05403-000, SP, Brazil
10
Research and Development Institute, Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Evolution et Controlê, Montpellier University, F-34394 Montpellier, France
11
Department of Veterinary Medicine, University of Bari, 70121 Bari, Italy
12
RZSS—The Royal Zoological Society of Scotland, Murrayfield, Edinburgh EH12, UK
13
Section of Arbovirology and Hemorrhagic Fevers, Coordinator of the Rabies Diagnosis Laboratory, Evandro Chagas Institute MS-SVS, São Brás, Belém 66093-020, PA, Brazil
 
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*
Authors to whom correspondence should be addressed.
Pathogens 2025, 14(6), 567; https://doi.org/10.3390/pathogens14060567
Submission received: 24 April 2025 / Revised: 28 May 2025 / Accepted: 4 June 2025 / Published: 6 June 2025
(This article belongs to the Section Parasitic Pathogens)
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Abstract

Piroplasmids (Babesia spp., Rangelia spp., Theileria spp., Cytauxzoon spp.) are tick-borne apicomplexan protozoa that infect, depending on the species, erythrocytes and leucocytes in a wide variety of mammals and birds. The genera Bartonella and Borrelia include vector-borne bacteria that can infect and cause disease in both animals and humans. Detection of hemotropic bacteria and piroplasmids in wild animals is often challenging due to low bacteremia or parasitemia. Digital (d)PCR has proven to be an effective modality for the detection and quantification of DNA of hemotropic pathogens with low parasitemia. This study compared dPCR results from 366 biological samples from seven different Brazilian wild animal groups (5 Xenarthra species, 5 deer species, 3 felid species, 1 canid species, 3 rodent species, 1 bat species, 1 tapir species, and 12 bird species) to two other molecular diagnostic techniques: quantitative real-time (qPCR) and nested (nPCR). For this study, DNA extracted from wild animal blood and spleen samples were subjected to a multiplex dPCR assay for piroplasmids, Bartonella spp., and Borrelia spp. For comparison, the same primers and probes for each agent were used in qPCR assays. Additionally, an nPCR based on the 18S rRNA gene for piroplasmids was performed. The proportions of positive results obtained using dPCR were 85.5% for piroplasmids, 33.6% for Bartonella spp., and 16.7% for Borrelia spp. For all tested agents, dPCR proved to be the technique with the highest sensitivity, making it a useful tool for screening vector-borne agents in biological samples from wild animals with low parasitemia.

1. Introduction

Arthropod-borne diseases currently account for 22.8% of emerging diseases [1]. Climate, environment, and ecology changes can disrupt the natural cycles of microorganisms, leading to the emergence of new diseases [2,3]. These changes also broaden the distribution of vectors and increase pathogen proximity among wildlife, domestic animals, and humans. As a result, closer interactions among reservoirs, vectors, and susceptible hosts are expected, facilitating the spread of diseases and presenting significant challenges to public and animal health [2,3].
Bartonella spp. (Hyphomicrobiales: Bartonellaceae) are Gram-negative fastidious facultative intracellular alphaproteobacteria that are primarily transmitted by arthropods (e.g., fleas, lice, ticks, and sand flies), as well as through scratches and bites [4,5,6]. Borrelia spp. (Spirochaetales: Spirochaetaceae) are spirochete bacteria transmitted by ixodid and argasid ticks and lice. Three distinct groups of Borrelia spp. are currently recognized: the Lyme group, the relapsing fever group, and the Echidna-Reptile group [7,8,9]. Piroplasmids (Order Piroplasmida) are apicomplexan protozoa transmitted by ixodid ticks. This group consists of two families: Babesiidae (genera Babesia and Rangelia) and Theileriidae (genera Theileria and Cytauxzoon) [10,11]. These three pathogen groups contain zoonotic species that can cause disease in domestic and wild animals and human patients [7,8,12,13].
Brazil is a country with a highly diverse fauna [14]. Research into these agents among wild animals has increased substantially in recent years, resulting in the discovery of putative new species [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]. Despite numerous published studies, molecular detection of pathogen DNA in wild animals remains a diagnostic challenge due to the low parasitemia associated with these agents, which underestimates organism prevalence in epidemiological and clinical studies [3,11,19].
Currently, techniques known as third-generation PCR are being developed to increase the sensitivity of molecular assays. The most notable of these, digital PCR (dPCR), shares the same amplification principle as real-time PCR (qPCR) by using hydrolysis probes, but it allows samples to be fractionated into micro-well plates, capillaries (dPCR), or emulsions (digital droplet (dd)PCR) [37,38]. Fractionation enables separate amplification reactions in each partition, enhancing the detection of low concentrations of DNA, such as in samples from animals with low parasitemia or in the detection of rare genetic mutations [39,40,41]. Additionally, absolute quantification is achieved without the need for a standard curve using Poisson statistics to compare the number of positive and negative partitions [42,43,44,45]. Another advantage of this technique is its effectiveness in diluting out potential PCR inhibitors compared to qPCR [38,45,46,47,48]. The main disadvantages are its high cost and the inability to sequence dPCR-positive samples [39,40,41,42].
To date, dPCR has been used to enhance the molecular detection of different hemoparasites such as Borrelia spp. [49,50,51], Bartonella spp. [51,52], Anaplasma phagocytophilum [53], Coxiella burnetii [54], Ehrlichia canis [40], Trypanosoma cruzi [55], Leishmania spp. [56,57], Plasmodium spp. [58,59,60], and piroplasmids [51,61,62,63]. The use of this technique is still not widespread worldwide, including in Brazil. Due to the enhanced sensitivity, this study aimed to compare the positivity rates obtained using different PCR techniques (nested PCR–nPCR, qPCR, and dPCR) for piroplasmids, Bartonella spp., and Borrelia spp. in order to determine which technique is most suitable for detecting the selected vector-borne agents in wild animal blood and spleen samples.

2. Materials and Methods

2.1. Samples Analyzed

In total, 366 blood (n = 116), buffy coat (n = 174), and spleen (n = 76) DNA samples from wild animals in Brazil were analyzed. The animal species included: 174 deer (139 Blastocerus dichotomus, 6 Ozotocerus bezoarticus, 22 Subulo gouazoubira, 3 Mazama americana, and 4 Mazama bororo), 51 vampire bats (Desmodus rotundus), 11 Cerdocyon thous, 35 felids (12 Leopardus pardalis, 21 Panthera onca, and 2 Puma concolor), 44 Xenarthrans (17 Myrmecophaga tridactyla, 9 Tamandua tetradactyla, 7 Dasypus novemcinctus, 5 Euphractus sexcinctus, and 6 Priodontes maximus), 24 tapirs (Tapirus terrestris), 9 rodents (4 Oecomys mamorae, 4 Thrichomys fosteri, and 1 Clyomis laticeps), and 18 birds (1 Pseudoseisura unirufa, 3 Saltator coerulescens, 2 Turdus leucomelas, 1 Cercomacra melanaria, 2 Ramphocelus carbo, 1 Basileuterus flaveolus, 3 Leptotila verreauxi, 1 Icterus cayanensis, 1 Lepidocolaptes angustirostris, 1 Fumarius rufus, 1 Fumarius leucopus, and 1 Cyamocorax chrysops). All of the samples had been used in previously published studies [23,25,27,28,31,32,33,35].

2.2. Molecular Assays

2.2.1. dPCR Assay (Multiplex for Piroplasmids, Bartonella spp., Borrelia spp., and Housekeeping Gene)

A multiplex dPCR assay for piroplasmids, Bartonella spp., Borrelia spp., and the housekeeping gene was performed as previously described, with modifications [51]. All primer and probe sequences are described in Table 1.
The assays were performed using a QIAcuity Nanoplate 8.5k 96-well format (QIAGEN, Germantown, MD, USA). Each reaction (12 μL final volume) included 4.5 μL of molecular-grade water, 3 μL of 4X QIAcuity Probe PCR Master Mix (QIAGEN), 0.15 μL of each species-specific primer at 100 μM (IDT-DNA Technologies, Coralville, IA, USA), 0.05 μL of each species-specific probe at 100 μM, and 5 μL of DNA sample. Molecular-grade water and DNA from a negative dog blood sample were used as negative controls, while Babesia microti, Bartonella henselae SA1, and Borrelia burgdorferi B31 DNA were used as positive controls.
Amplification was performed on a QIAcuity One 5plex Platform System using 8.5k 96-well nanoplates under the following conditions: 95 °C for 2 min, followed by 40 cycles of denaturation at 95 °C for 20 s, and annealing/extension at 64 °C for 20 s. The detection of each species was assessed by reading the fluorescent signals (dots) using the following color codes: FAM (green) for Bartonella spp., HEX (yellow) for Borrelia spp., TAMRA (orange) for the housekeeping gene, and Cy5 (crimson) for piroplasmids. The fluorescent signal threshold was assessed as positive or negative for DNA amplification. The results were analyzed using QIAcuity Software Suite version 2.2.0.26. Partitions with fluorescence signals below the defined threshold were classified as negative, while those above the threshold were considered positive. Samples were deemed positive when they contained one or more positive partitions, provided the signal was clearly above the threshold, in order to minimize the risk of false positive results. The number of copies per microliter (copies/µL) was calculated by the software based on the number of positive and negative partitions using Poisson statistics.

2.2.2. qPCR Assay (Multiplex for Piroplasmids, Bartonella spp., and Borrelia spp.)

Real-time qPCR assays were performed using the same primers and probes described above for the detection of piroplasmids, Bartonella spp., and Borrelia spp. in order to compare the results obtained using both PCR techniques.
Multiplex real-time qPCR was performed using a reaction mixture with a final volume of 25 μL per reaction containing: 0.25 μL of each primer for Bartonella spp. and Borrelia spp., 0.375 μL of each primer for piroplasmids at 100 μM (IDT-DNA Technologies, Coralville, IA, USA), 0.125 μL of 100 μM of each species-specific probe, 12.5 μL of 2X SsoAdvanced Probe Master Mix (Bio-Rad, Hercules, CA, USA), 5 μL of template DNA, and 5.875 μL of molecular-grade water (QIAGEN, Germantown, MD, USA). Molecular-grade water and DNA from a negative dog blood sample were used as PCR negative controls. Babesia microti, Bartonella henselae SA1, and Borrelia burgdorferi B31 DNA were used as positive controls.
Amplification was performed using a Bio-Rad CFX 96-well Opus PCR system under the following conditions: 95 °C for 3 min, followed by 45 cycles of denaturation at 94 °C for 10 s, annealing at 68 °C for 10 s, and extension at 72 °C for 10 s. Fluorescent signals were used to assess the detection of each species, using FAM (green) for Bartonella spp., HEX (yellow) for Borrelia spp., and Cy5 (crimson) for piroplasmids. The fluorescent signal threshold value (Cq) was recorded to determine positive and negative DNA amplification.
Additionally, positive samples were purified and sequenced by Sanger’s method (Genewiz, Azenta, Research Triangle Park, NC, USA) and analyzed using SnapGene Version 7.0.2.

2.2.3. Nested (n)PCR Assays for Piroplasmid Detection

nPCR was performed to amplify approximately an 800 bp fragment of the 18S rRNA gene of piroplasmids using the primers BTF1 (5′-GGCTCATTACAACAGTTATAG-3′) and BTR1 (5′-CCCAAAGACTTTGATTTCTCTC-3′) in the first reaction, and BTF2 (5′-CCGTGCTAATTGTAGGGCTAATAC-3′) and BTR2 (5′-GGACTACGACGGTATCTGATCG-3′) in the second reaction, as described by Jefferies et al. (2007) [64].
The amplification reaction was carried out with a total final volume of 25 μL containing the following components: 5 μL of sample DNA, 0.2 mM of each deoxynucleotide, 0.5 μM of each primer oligonucleotide, 1.5 mM of magnesium chloride, 0.75 U of Taq DNA polymerase, PCR buffer, and sterile ultrapure water to complete the volume. For the second amplification, 1 μL of the product from the first reaction was used.
The thermal cycling conditions for the first reaction were the following: initial denaturation at 94 °C for 3 min, followed by 45 cycles of 94 °C for 30 s, 58 °C for 20 s, and 72 °C for 30 s, with a final extension at 72 °C for 7 min. For the second reaction, the same thermal conditions were used, with the exception that the annealing temperature was set to 62 °C. Ultrapure sterile water was used as a negative control in all PCR assays, while Babesia vogeli DNA (Jaboticabal sample) was used as a positive control.

2.3. Statistical Analysis

To compare the performance of the assays for detecting piroplasmids, Fleiss’ Kappa test was initially used to assess the level of agreement between all tests (dPCR, qPCR, and nPCR), beyond what would be expected by chance [65]. As a post hoc analysis, agreement beyond chance between test pairs was evaluated using Cohen’s Kappa test [66]. For the other agents (Bartonella spp. and Borrelia spp.), Cohen’s Kappa test was applied.
The interpretation of Kappa (κ) values was based on the criteria outlined by McHugh (2012) [67], which are as follows: values below zero indicate agreement below that expected by chance (disagreement); values between 0.00 and 0.20 suggest no agreement; values between 0.21 and 0.39 represent minimal agreement; values between 0.40 and 0.59 indicate weak agreement; values between 0.60 and 0.79 reflect moderate agreement; values between 0.80 and 0.90 indicate strong agreement; and values above 0.90 reflect almost perfect agreement.
To assess the difference between the proportions obtained by the tests, McNemar’s test [68] was performed for each pair of tests. All statistical analyses were carried out using RStudio 4.2.3 software with the psych [69] and irr [70] packages.
The sensitivity and specificity of the assays evaluated (qPCR and nPCR for piroplasmids; qPCR for Bartonella spp. and Borrelia spp.) were determined using dPCR as the gold standard due to its superior sensitivity compared to the other assays [51]. Finally, the Matthews correlation coefficient (MCC) [71] was calculated for each pair, using dPCR as the gold standard, to assess the performance of the assays. The MCC values range from −1 to 1, with extreme values indicating maximum performance (1) or total failure in classification (−1). A value of zero indicates performance equivalent to random choice [71].
The formulas shown in Figure 1 were used to calculate sensitivity (Se), specificity (Sp), and MCC.

3. Results

3.1. Positivity for Each Technique

Table 2 depicts the number of positive animals for each group and species based on the agent analyzed (piroplasmids, Bartonella spp., and Borrelia spp.). For all agents, dPCR had a higher number of positive samples compared to the other techniques, with the exception of piroplasmid detection in Xenarthra species, where nPCR detected one more positive sample (n = 22) than dPCR (n = 21).
To better understand the positivity rates and compare the number of positive samples across techniques, Venn diagrams were generated (Figure 2). These diagrams showed that the number of positive samples was higher when using the dPCR technique for all target agents. For Bartonella spp. (Figure 2A) and Borrelia spp. (Figure 2B), only 27 and 1 samples, respectively, were positive when both techniques were used (dPCR and qPCR). Additionally, 12 and 1 samples were exclusively positive when qPCR was used for Bartonella spp. and Borrelia spp., respectively.
In the case of piroplasmids (Figure 2C), 73 samples tested positive across all three techniques performed (dPCR, nPCR, and qPCR). A larger number of samples (n = 161) was positive when both dPCR and nPCR were used, while 43 samples were positive when both dPCR and qPCR were used. Only 25 samples were positive using techniques other than dPCR—18 in nPCR alone, 2 in qPCR alone, and 5 in both nPCR and qPCR.
Given that the objective of this study was to compare the positivity between techniques, and considering the absence of standard calibration curves for qPCR, absolute quantification of the samples using this method was not performed. Nonetheless, the number of positive partitions, the concentration expressed as copies per microliter (copies/µL) of the target fragment obtained via dPCR, and the quantification cycle (Cq) values derived from qPCR were duly documented.
For Bartonella spp. and Borrelia spp., most samples exhibited only a single positive partition in the dPCR assays (n = 50 and n = 47, respectively) (Figure 3A,B). For Bartonella spp., the lowest concentration of the amplified 16S–23S rRNA gene fragment was 0.356 copies/µL, while the highest concentration reached 4150.8 copies/µL in the dPCR assays (Table S1). In the qPCR assays, the mean Cq value was 30.86, with a minimum of 14.62 and a maximum of 44.7 (Table S1). For Borrelia spp., dPCR detected a minimum of 0.356 copies/µL and a maximum of 2.636 copies/µL of the target fragment (Table S2). In the qPCR analyses, Cq values of 43.26 and 44.93 were obtained for the two amplified bat DNA samples (Table S2).
Regarding piroplasmids, the dPCR analyses revealed that the majority of positive samples (n = 207) exhibited between 2 and 100 positive partitions, while 20 samples presented with more than 1000 positive partitions (Figure 3C). This technique was capable of detecting as few as 0.356 copies/µL of the 18S rRNA gene fragment, with the highest concentration recorded at 1784.1 copies/µL (Table S3). In the qPCR assays, the mean Cq value was 33.22, ranging from a minimum of 25.12 to a maximum of 43.57 (Table S3).
With regard to the samples that tested positive using qPCR but negative using dPCR, the majority presented Cq values greater than 39. Among the seven piroplasmid samples that were positive only using qPCR, five exhibited Cq values above 39. Similarly, of the twelve Bartonella spp. samples that were qPCR-positive only, eight had Cq values exceeding 39. For the two Borrelia spp. samples derived from bat DNA that tested positive exclusively using qPCR, both exhibited Cq values above 43.
Only samples with Cq values lower than 38 using qPCR were sent for sequencing to confirm that there was targeted amplification of the group of agents surveyed. In total, 94 and 29 samples were submitted for DNA sequencing for piroplasmids and Bartonella spp., respectively. Of these samples, 49 piroplasmid and 12 Bartonella spp. were successfully sequenced. The qPCR fragment obtained for piroplasmids, ranging from 50 to 70 bp, had high identity (97 to 100% similarity) with Theileria spp. or Babesia spp. For Bartonella spp., the sequences (approximately 100 bp) were mostly obtained from vampire bats (Desmodus rotundus) and were identical to a Bartonella spp. sequence deposited from the same host species in Guatemala (MN504749). A sequence obtained from B. dichotomus was 98.29% identical to a Bartonella henselae sequence found in a flea from Brazil (MT095054). A sequence obtained from T. fosteri was identical to a Bartonella machadoae sequence (CP087114).
Additional Venn diagrams were generated to compare the results obtained using each technique for the different target agents, considering dPCR-positive samples as those with one or more, two or more, and three or more positive partitions (Supplementary Material Figure S1). For all agents evaluated, the highest concordance between the techniques was observed when samples with one or more positive partitions were considered positive in the dPCR analyses.

3.2. Statistical Analyses

3.2.1. Level of Agreement Between Tests

Piroplasmids
The Fleiss’ Kappa test indicated that the agreement among the three tests (dPCR vs. qPCR vs. nPCR) was lower than expected by chance; however, this difference was not statistically significant (p > 0.05). When analyzing the agreement between pairs of tests, the Cohen’s Kappa test revealed that the levels of agreement were lower than expected by chance, or absent, but all were statistically significant (p < 0.05). These results suggested that the agreement levels among assays varied depending upon which pair of tests was being compared. Among all comparisons, the lowest Kappa index was observed between qPCR and nPCR results (k = −0.09), indicating a significant disagreement. The agreement between nPCR and dPCR results was considered minimal (k = 0.20), while the agreement between dPCR and qPCR results was considered absent (k = 0.089). Table 3 summarizes the results from both the Fleiss’ Kappa and Cohen’s Kappa tests.
Bartonella spp. and Borrelia spp.
For the detection of Bartonella spp., the Cohen’s Kappa test indicated that the agreement between qPCR and dPCR was absent (k = 0.1887) but statistically significant (p = <0.0001; p < 0.05). These results indicated a non-random disagreement between these molecular assays, with dPCR presenting superior performance for detecting Bartonella spp. compared to qPCR. For Borrelia spp., the Cohen’s Kappa test showed that the agreement between qPCR and dPCR was absent (k = 0.0214) and not statistically significant (p = 0.4261; p > 0.05).

3.2.2. Analysis of Differences Between Proportions

Piroplasmids
The difference in proportions between the tests was analyzed in pairs using McNemar’s test. All comparisons yielded statistically significant results (p < 0.05), indicating that the PCR assays significantly differed in their ability to detect piroplasmid-positive samples. The most notable difference was observed between dPCR and qPCR (χ² = 117.09), reinforcing a discrepancy in positive results obtained using the two methods. Table 4 summarizes the results of McNemar’s test.
Bartonella spp. and Borrelia spp.
The difference in proportions between qPCR and dPCR, for both Bartonella spp. and Borrelia spp. detection, was statistically significant (p < 0.05), indicating that qPCR and dPCR significantly differed in their ability to amplify the DNA of the targeted organisms in wild animal samples. Table 5 summarizes the results of McNemar’s test for these assays.

3.2.3. Sensitivity and Specificity

Piroplasmids
The sensitivity and specificity values for qPCR, compared to the gold standard (dPCR), were 36.82% and 86.27%, respectively. These values also indicated a low capacity to identify positive samples compared to dPCR. For nPCR, the sensitivity and specificity values were 74.60% and 54.90%, respectively, demonstrating an unsatisfactory ability to correctly identify negative results compared to dPCR.
Bartonella spp. and Borrelia spp.
For the Bartonella spp. qPCR assay, the sensitivity and specificity values, compared to the gold standard (dPCR), were 20.93% and 94.94%, respectively. These results indicated a poor ability to detect positive samples compared to dPCR, but a high specificity in distinguishing Bartonella spp.-negative samples.
For the Borrelia spp. detection assay, the sensitivity and specificity values were 1.64% and 99.67%, respectively, indicating an extremely low capacity to detect positive samples when compared to the gold standard, but a high ability to identify negative samples.

3.2.4. Matthews Correlation Coefficient (MCC)

Piroplasmids
The MCC was calculated to assess the degree of agreement between the qPCR and nPCR assays compared to the gold standard (dPCR). The value obtained for the comparison between qPCR and dPCR was 0.1693538, while the value for the comparison between nPCR and dPCR was 0.2240352. These values indicated that both qPCR and nPCR had a low correlation with dPCR, although nPCR had a slightly higher correlation.
Bartonella spp. and Borrelia spp.
For the Bartonella spp. detection assay, the MCC value for the comparison between qPCR and dPCR was 0.24566722, indicating a low correlation with dPCR. For the Borrelia spp. detection assay, the MCC value was −0.0256786, indicating no correlation between the tests.

4. Discussion

In this study, we evaluated the performance of dPCR, qPCR, and nPCR in detecting three vector-borne agents in biological samples from wild animals from Brazil, in which low parasitemia was expected. In general, there was no agreement between the molecular tests performed for piroplasmids, Bartonella spp., and Borrelia spp. nPCR and dPCR had slightly better agreement for the detection of piroplasmids, albeit the level of agreement was still low. The qPCR protocol used demonstrated low sensitivity in detecting the targeted agents investigated in the selected wild animal samples, whereas dPCR proved to be much more sensitive in detecting these selected agents compared to the other two techniques.
Some previous studies contradict our findings, reporting a high level of agreement between results obtained using ddPCR and qPCR [46,57,62]. These studies investigated the detection of Cryptosporidium oocysts in fecal samples from animals and humans previously confirmed positive by microscopy [46], Cytauxzoon spp. in blood samples from cats with acute cytauxzoonosis [62], and Leishmania infantum in spleen samples from dogs exhibiting clinical signs consistent with leishmaniasis [57]. Notably, none of these studies included samples from wild animals, and all analyzed specimens originated from individuals either displaying clinical signs or previously confirmed positive by other diagnostic methods. By contrast, a study published by Lashnits et al. (2021) [72], which compared the sensitivity of Bartonella diagnostic assays in dogs with hemangiosarcoma, found that ddPCR was significantly more sensitive than qPCR for the detection of Bartonella DNA in blood samples (36% vs. 0%, p < 0.0001). Additionally, a study by Belmonte et al. (2016) [73] compared the effectiveness of ddPCR and qPCR in detecting low-abundance mitochondrial DNA (mtDNA) deletions in human samples. The results demonstrated that ddPCR was highly effective and preferable for samples with moderate deletion loads, whereas qPCR was only effective in samples with high deletion levels.
Furthermore, in previous studies targeting helminth DNA in liver samples from small mammals [74] and in sheep fecal samples [75], ddPCR demonstrated superior sensitivity compared to qPCR. For example, the positivity rate for Echinococcus multilocularis in small mammal liver samples was 31.13% using ddPCR, versus only 4.72% with qPCR [74]. In another study, the detection rates for Trichuris spp. in sheep fecal samples were 80.6% using ddPCR and 72.4% using qPCR [75]. According to the authors, one possible explanation for these differences was the presence of PCR inhibitors in fecal samples, which may interfere with the amplification and detection of parasite DNA using qPCR. However, the relative abundance of the target DNA may also represent an equally important factor influencing detection sensitivity.
In the present study, the discrepancy between the results obtained using dPCR and qPCR, especially in the detection of Bartonella spp. and Borrelia spp., might be attributed to extremely low parasitemia in the samples, precluding detection of the target DNA using qPCR. Indeed, the low number of positive partitions in dPCR across most samples indicated low parasitemia. It has been demonstrated that ddPCR can quantify smaller amounts of DNA than the standard curves used in qPCR [61]. Additionally, ddPCR has been shown to identify a higher number of positive samples for Bartonella spp. and Candida spp. compared to qPCR [39,52]. This can be explained by the fact that DNA samples are fractionated and amplification reactions occur individually in each fraction in dPCR, which favors the detection of low copy numbers of the target DNA fragment. By contrast, qPCR measures fluorescent signals, which can sometimes be less sensitive for low-abundance targets [39,40,41,44].
Herein, some samples were positive using qPCR but negative using dPCR for the agents investigated. When analyzed individually, all of these samples had a Cq value above 39, suggesting that the positive qPCR results were likely due to nonspecific amplification.
Regarding the detection of piroplasmids, only 19.9% samples were positive using all three molecular techniques in this study, while 44% samples were only positive using both nPCR and dPCR, and 11.7% were positive using both dPCR and qPCR. The higher number of positive samples observed using nPCR compared to qPCR is noteworthy; however, when comparing the specificities for each test, nPCR yielded a specificity of 54.9%, while qPCR had a specificity of 86.3%. These findings likely indicate that nPCR might have generated false positive results through nonspecific amplification or due to a higher risk of amplicon contamination, as nPCR requires extensive sample processing between reactions.
An enrichment blood culture protocol for the detection of piroplasmids targeting the 18S rRNA gene, applied to DNA extracted from human blood samples and from 7-, 14-, and 21-day enrichment blood cultures, also demonstrated greater sensitivity using dPCR compared to qPCR. However, when qPCR protocols specifically designed for the amplification of species-specific ITS-1 and ITS-2 intergenic regions for Babesia odocoilei, B. microti, and B. divergens were employed, qPCR proved to be highly effective and sensitive for species detection, with the results confirmed by sequencing [76,77].
Most published studies have employed ddPCR rather than dPCR, with the primary distinction being that ddPCR is performed in an oil emulsion, while dPCR relies on micro-well plates or capillary systems [38]. In the present study, dPCR was conducted using the QIAcuity Digital PCR System (QIAGEN), utilizing 8.5k 96-well nanoplates in an effort to reduce costs while evaluating whether the method would offer sufficient sensitivity for detecting hemoparasite DNA in biological samples from wild animals. Although the use of 26k 24-well plates could potentially yield improved results—given that the fourfold increase in partition number may enhance assay sensitivity—the results obtained demonstrated that the chosen approach was both effective and suitable for the study’s objectives.
There remains some disagreement among manufacturers of dPCR and ddPCR platforms regarding the minimum number of positive partitions or droplets required to consider a sample positive. In the present study, samples that had one or more positive partitions were utilized for the three agents studied. Although a low number of positive partitions could reflect false positive testing results, it was likely attributable to the low levels of parasitemia or bacteremia in the tested samples. This conclusion was supported by the low copy numbers of the target fragments obtained for all the agents analyzed using dPCR, with values ranging from as low as 0.356 copies/µL to a maximum of only 4150.8 copies/µL.
The detection of Bartonella spp. in deer blood samples was particularly noteworthy, marking it as the first report of this agent in deer from Brazil. Previously, Bartonella ribC genotypes closely related to Bartonella schoenbuchensis were detected in Lipoptena mazamae flies collected from deer in Rio Grande do Sul, Brazil [78]. Another interesting result was the detection of Borrelia spp. in the tested animals. Since all of the positive samples showed only one positive partition, this may help to explain why Borrelia spp. are rarely reported in wild animals and ectoparasites from Brazil [20,26,79,80]. A new genospecies of Borrelia burgdorferi, called ‘Candidatus Borrelia paulista’, was found in two cricetid rodents sampled in the state of São Paulo through qPCR and PCR characterization assays using different gene markers (16S rRNA, flaB, ospC, clpA, nifS, pepX, pyrG, recG, rlpB, and uvrA) [26]. Additionally, genotypes of Borrelia spp. phylogenetically related to Borrelia spp. detected in bats in Colombia were found circulating in five vampire bats sampled in the state of Ceará, Brazil, based on the 16S rRNA and flaB genes [80]. Our research group recently reported the occurrence of Bartonella spp. and Borrelia theileri in the same tapir samples reported in this study [34,81]. In these two previous tapir studies, positive rates of 8% and 2% were reported for Bartonella spp. and Borrelia spp., respectively, using qPCR assays. The present study yielded positive rates of >16% for both genera, reinforcing the greater sensitivity of dPCR in detecting these bacterial agents in wildlife.
High diversity of piroplasmids has been reported in wild animals from Brazil, with five new clades added to the phylogeny of Piroplasmida based on the detection of putative novel species in non-hematophagous bats [22], opossums [21,29], tapirs [25], capybaras [30], and wild rodents [36]. Additionally, previous studies using some of the samples analyzed in the present study have reported the detection of a new Babesia species, namely Babesia pantanalensis, in C. thous blood samples [32], as well as the identification of a new Theileria species (Theileria terrestris) in tapirs [25]. Moreover, at least two species of Cytauxzoon—Cytauxzoon brasiliensis and genovariants of Cytauxzoon felis—have been reported in ocelots and jaguars [35]. The dPCR protocol employed in this study proved to be both useful and sensitive for detecting the diverse range of piroplasmids circulating in the country, as it yielded positive results across all analyzed animal orders. Furthermore, this protocol detected the presence of piroplasmids in all rodent samples and in some bird samples that had tested negative using nPCR, highlighting its potential as a reliable method for detecting these parasites in wildlife hosts.

5. Conclusions

This study demonstrated that dPCR has high sensitivity and is a very useful tool for detecting vector-borne agents in biological samples from wild animals with parasitemia levels that range from very low to very high. Additionally, qPCR was not found to be a sensitive technique for the types of samples examined in this study. nPCR exhibited high sensitivity but moderate specificity for detecting piroplasmid DNA when compared to dPCR.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pathogens14060567/s1, Table S1: Results obtained for each test carried out for Bartonella spp.; Table S2: Results obtained for each test carried out for Borrelia spp.; Table S3: Results obtained for each test carried out for piroplasmids; Figure S1: Venn diagrams comparing the positivity between different techniques for the detection of Bartonella spp. (A), Borrelia spp. (B), and piroplasmids (C), considering samples as positive using dPCR when presenting one or more, two or more, or three or more positive partitions in the assays performed.

Author Contributions

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

Funding

This study was supported by grants from the Steven & Alexandra Cohen Foundation; funding provided to the Intracellular Pathogens Research Laboratory via private donors and the State of North Carolina; the São Paulo Research Foundation (FAPESP) of Brazil (Process Numbers #2020/07826-5, #2022/08543-2, #2020/12037-0, and #2022/16555-0); the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001; and a CNPq (National Council for Scientific and Technological Development) Productivity Grant to M.R.A. [CNPq Process #303701/2021-8]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Institutional Review Board Statement

All animal procedures and management protocols were approved by the Ethics Committee on Animal Use of the School of Agricultural and Veterinarian Sciences (FCAV/UNESP) (protocol number 9412/2022).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Acknowledgments

The authors would like to thank the INSANAHUNA Research Group, Instituto Reprocon, Instituto Onça Pintada, ICAS (Instituto de Conservacao de Animais Silvestres, Projeto Bandeiras e Rodovias e Projeto de Conservacao do Tatu- Canastra), Wildlife Pathology Service of the School of Agricultural and Veterinarian Sciences, Iniciativa Nacional para a Conservação da Anta Brasileira (INCAB), Instituto de Pesquisas Ecológicas (IPÊ), Murillo Dapare Kirnew, Matheus Folgearini Silveira, Roberto Andres Navarrete Ampuero, Laiza de Queiroz Viana Braga, Fabiana Lopes Rocha, Heitor Miraglia Herrera, Alan Fechio, Débora Regina Yougui, Karin Werther, Renan Bressianini do Amaral, Victória Valente Califre de Mello, and Amir Salvador Alabí Cordova for access to blood/tissue or DNA samples and/or for contributing to animal capture and sample collection.

Conflicts of Interest

E. B. Breitschwerdt is a co-founder, shareholder, and the Chief Scientific Officer for Galaxy Diagnostics. Ricardo Maggi is a co-founder and the Chief Technical Officer for Galaxy Diagnostics Inc. All other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
dPCRdigital PCR
qPCRquantitative PCR
nPCRnested PCR
ddPCRdroplet digital PCR
MCCMatthews correlation coefficient

References

  1. Zanella, J.R.C. Zoonoses emergentes e reemergentes e sua importância para saúde e produção animal. Pesqui. Agropecu. Bras. 2016, 51, 510–519. [Google Scholar] [CrossRef]
  2. Shaw, S.E.; Birtles, R.J.; Day, M.J. Review: Arthropod-transmitted infectious diseases of cats. J. Feline Med. Surg. 2001, 3, 193–209. [Google Scholar] [CrossRef] [PubMed]
  3. André, M.R. Diversity of Anaplasma and Ehrlichia/Neoehrlichia agents in terrestrial wild carnivores worldwide: Implications for human and domestic animal health and wildlife conservation. Front. Vet. Sci. 2018, 5, 293–297. [Google Scholar] [CrossRef] [PubMed]
  4. Birtles, R.J.; Raoult, D. Comparison of partial citrate synthase gene (gltA) sequences for phylogenetic analysis of Bartonella species. Int. J. Syst. Bacteriol. 1996, 46, 891–897. [Google Scholar] [CrossRef]
  5. Breitschwerdt, E.B. Bartonellosis: One health perspectives for an emerging infectious disease. ILAR J. 2014, 55, 46–58. [Google Scholar] [CrossRef]
  6. Kosoy, M.; McKee, C.; Albayrak, L.; Fofanov, Y. Genotyping of Bartonella bacteria and their animal hosts: Current status and perspectives. Parasitology 2018, 145, 543–562. [Google Scholar] [CrossRef]
  7. Margos, G.; Gofton, A.; Wibberg, D.; Dangel, A.; Marosevic, D.; Loh, S.M.; Oskam, C.; Fingerle, V. The genus Borrelia reloaded. PLoS ONE 2018, 13, e0208432. [Google Scholar] [CrossRef]
  8. Trevisan, G.; Cinco, M.; Trevisini, S.; di Meo, N.; Chersi, K.; Ruscio, M.; Forgione, P.; Bonin, S. Borreliae Part 1: Borrelia Lyme Group and Echidna-reptile Group. Biology 2021, 10, 1036. [Google Scholar] [CrossRef]
  9. Trevisan, G.; Cinco, M.; Trevisini, S.; di Meo, N.; Ruscio, M.; Forgione, P.; Bonin, S. Borreliae Part 2: Borrelia Relapsing Fever Group and unclassified Borrelia. Biology 2021, 10, 1117. [Google Scholar] [CrossRef]
  10. França, R.T.; Da Silva, A.S.; Loretti, A.P.; Mazzanti, C.M.; Lopes, S.T. Canine rangeliosis due to Rangelia vitalii: From first report in Brazil in 1910 to current day—A review. Ticks Tick-Borne Dis. 2014, 5, 466–474. [Google Scholar] [CrossRef]
  11. Alvarado-Rybak, M.; Solano-Gallego, L.; Millán, J. A review of piroplasmid infections in wild carnivores worldwide: Importance for domestic animal health and wildlife conservation. Parasites Vectors 2016, 9, 538. [Google Scholar] [CrossRef] [PubMed]
  12. Maggi, R.C.; Harms, C.A.; Breitschwerdt, E.B. Bartonelosis. An emerging disease of humans, domestic animals, and wildlife. In New Directions in Conservation Medicine: Applied Cases of Ecological Health, 1st ed.; Aguirre, A.A., Ostfeld, R.S., Daszak, P., Eds.; Oxford University Press: New York, NY, USA, 2012; p. 239. [Google Scholar]
  13. Kumar, A.; O’Bryan, J.; Krause, P.J. The global emergence of human babesiosis. Pathogens 2021, 10, 1447. [Google Scholar] [CrossRef] [PubMed]
  14. Dias, B.F.S. First National Report for the Convention on Biological Diversity, Brazil. 1999. Available online: https://antigo.mma.gov.br/estruturas/chm/_arquivos/chapter2a.pdf (accessed on 15 May 2025).
  15. Soares, J.F.; Dall’Agnol, B.; Costa, F.B.; Krawczak, F.S.; Comerlato, A.T.; Rossato, B.C.D.; Linck, C.M.; Sigahi, E.K.O.; Teixeira, R.H.F.; Sonne, L.; et al. Natural infection of the wild canid, Cerdocyon thous, with the piroplasmid Rangelia vitalii in Brazil. Vet. Parasitol. 2014, 202, 156–163. [Google Scholar] [CrossRef] [PubMed]
  16. de Quadros, R.M.; Soares, J.F.; Xavier, J.S.; Pilati, C.; da Costa, J.L.; Miotto, B.A.; Milettti, L.C.; Labruna, M.B. Natural infection of the wild canid Lycalopex gymnocercus by the protozoan Rangelia vitalii, the agent of canine rangeliosis. J. Wildl. Dis. 2015, 51, 787–789. [Google Scholar] [CrossRef] [PubMed]
  17. Silveira, J.A.G.; Rabelo, M.L.; Ribeiro, M.F.B. Detection of Theileria and Babesia in brown brocket deer (Mazama gouazoubira) and marsh deer (Blastocerus dichotomus) in the State of Minas Gerais, Brazil. Vet. Parasitol. 2011, 177, 61–66. [Google Scholar] [CrossRef]
  18. Soares, H.S.; Arlei, M.; Barbieri, R.M.; Minervino, A.H.H.; Moreira, T.R.; Gennari, S.M.; Labruna, M.B. Parasites and wildlife novel piroplasmid and Hepatozoon organisms infecting the wildlife of two regions of the Brazilian Amazon. Int. J. Parasitol. Parasites Wildl. 2017, 6, 115–121. [Google Scholar] [CrossRef]
  19. Calchi, A.C.; Vultão, J.G.; Alves, M.H.; Yogui, D.R.; Desbiez, A.L.J.; do Amaral, R.B.; Santi, M.; Teixeira, M.M.G.; Werther, K.; Machado, R.Z.; et al. Multi-locus sequencing reveals a novel Bartonella in mammals from the Superorder Xenarthra. Transbound. Emerg. Dis. 2020, 67, 2020–2033. [Google Scholar] [CrossRef]
  20. Muñoz-Leal, S.; Ramirez, D.G.; Luz, H.R.; Faccini, J.L.; Labruna, M.B. “Candidatus Borrelia ibitipoquensis,” a Borrelia valaisiana–related genospecies characterized from Ixodes paranaensis in Brazil. Microb. Ecol. 2020, 80, 682–689. [Google Scholar] [CrossRef]
  21. Gonçalves, L.R.; Paludo, G.; Bisol, T.B.; Perles, L.; de Oliveira, L.B.; de Oliveira, C.M.; da Silva, T.M.V.; Nantes, W.A.G.; Duarte, M.A.; Santos, F.M.; et al. Molecular detection of piroplasmids in synanthropic rodents, marsupials, and associated ticks from Brazil, with phylogenetic inference of a putative novel Babesia sp. from white-eared opossum (Didelphis albiventris). Parasitol. Res. 2021, 120, 3537–3546. [Google Scholar] [CrossRef]
  22. Ikeda, P.; Menezes, T.R.; Torres, J.M.; de Oliveira, C.E.; Lourenco, E.C.; Herrera, H.M.; Machado, R.Z.; André, M.R. First molecular detection of piroplasmids in non-hematophagous bats from Brazil, with evidence of putative novel species. Parasitol. Res. 2021, 120, 301–310. [Google Scholar] [CrossRef]
  23. Do Amaral, R.B.; Cardozo, M.V.; Varani, A.M.; Gonçalves, L.R.; Furquim, M.E.C.; Dias, C.M.; Santana, M.S.; de Assis, W.O.; da Silva, A.R.; Herrera, H.M.; et al. Bartonella machadoae sp. nov. isolated from wild rodents in the Pantanal wetland. Acta Trop. 2022, 229, 106368. [Google Scholar] [CrossRef] [PubMed]
  24. Do Amaral, R.B.; Cardozo, M.V.; Varani, A.M.; Furquim, M.E.C.; Dias, C.M.; Assis, W.O.; da Silva, A.R.; Herrera, H.M.; Machado, R.Z.; André, M.R. First report of Bartonella spp. in marsupials from Brazil, with a description of Bartonella harrusi sp. nov. and a new proposal for the taxonomic reclassification of species of the genus Bartonella. Microorganisms 2022, 10, 1609. [Google Scholar] [CrossRef] [PubMed]
  25. Mongruel, A.C.B.; Medici, E.P.; Canena, A.C.; Calchi, A.C.; Perles, L.; Rodrigues, B.C.B.; Soares, J.F.; Machado, R.Z.; André, M.R. Theileria terrestris nov. sp.: A novel Theileria in lowland tapirs (Tapirus terrestris) from two different biomes in Brazil. Microorganisms 2022, 10, 2319. [Google Scholar] [CrossRef] [PubMed]
  26. Weck, B.C.; Serpa, M.C.A.; Labruna, M.B.; Muñoz-Leal, S. A novel genospecies of Borrelia burgdorferi sensu lato associated with cricetid rodents in Brazil. Microorganisms 2022, 10, 204. [Google Scholar] [CrossRef]
  27. Calchi, A.C.; Yogui, D.R.; Alves, M.H.; Desbiez, A.L.J.; Kluyber, D.; Vultão, J.G.; Arantes, P.V.C.; de Santi, M.; Werther, K.; Teixeira, M.M.G.; et al. Molecular detection of piroplasmids in mammals from the Superorder Xenarthra in Brazil. Parasitol. Res. 2023, 122, 3169–3180. [Google Scholar] [CrossRef]
  28. De Mello, V.V.C.; Calchi, A.C.; de Oliveira, L.B.; Coelho, T.F.S.B.; Lee, D.A.B.; Franco, E.O.; Machado, R.Z.; André, M.R. Molecular survey of piroplasmids and hemosporidians in vampire bats, with evidence of distinct Piroplasmida lineages parasitizing Desmodus rotundus from the Brazilian Amazon. Parasitologia 2023, 3, 248–259. [Google Scholar] [CrossRef]
  29. De Oliveira, A.F.X.; Calchi, A.C.; Stocco, A.V.; Stocco, N.V.; Costa, A.C.; Mureb, E.N.; Pires, J.R.; Guimarães, A.; Raimundo, J.M.; Balthazar, D.A.; et al. Expanding the universe of Piroplasmids: Morphological detection and phylogenetic positioning of putative novel piroplasmids in black-eared opossums (Didelphis aurita) from southeastern Brazil, with description of “South American Marsupialia Group” of Piroplasmida. Parasitol. Res. 2023, 122, 1519–1530. [Google Scholar] [CrossRef]
  30. Krawczak, F.D.S.; Calchi, A.C.; Neves, L.C.; Dias, S.A.; da Silva, B.B.F.; Paula, W.V.F.; de Paula, L.G.F.; Tavares, M.A.; Pádua, G.T.; de Lima, N.J.; et al. Phylogenetic inferences based on distinct molecular markers confirm a novel Babesia species (Babesia goianiaensis nov. sp.) in capybaras (Hydrochoerus hydrochaeris) and associated ticks. Microorganisms 2023, 11, 2022. [Google Scholar] [CrossRef]
  31. Alabí Córdova, A.S.; Fecchio, A.; Calchi, A.C.; Dias, C.M.; Machado, R.Z.; André, M.R. Molecular evidence of Bartonella spp. in tropical wild birds from the Brazilian Pantanal, the largest wetland in South America. Vet. Res. Commun. 2024, 48, 1631–1640. [Google Scholar] [CrossRef]
  32. Calchi, A.C.; Braga, L.D.Q.V.; Bassini-Silva, R.; Castro-Santiago, A.C.; Herrera, H.M.; Soares, J.F.; Barros-Battesti, D.M.; Machado, R.Z.; Rocha, F.L.; André, M.R. Phylogenetic inferences based on distinct molecular markers reveals a novel Babesia (Babesia pantanalensis nov. sp.) and a Hepatozoon americanum-related genotype in crab-eating foxes (Cerdocyon thous). Exp. Parasitol. 2024, 262, 108786. [Google Scholar] [CrossRef]
  33. Calchi, A.C.; Duarte, J.M.B.; Castro-Santiago, A.C.; Bassini-Silva, R.; Barros-Battesti, D.M.; Machado, R.Z.; André, M.R. Genetic diversity of Theileria spp. in deer (Artiodactyla: Cervidae) from Brazil. Parasitol. Res. 2024, 123, 384. [Google Scholar] [CrossRef] [PubMed]
  34. Mongruel, A.C.B.; Medici, E.P.; Canena, A.C.; Machado, R.Z.; Clay, K.; Labruna, M.B.; André, M.R. First molecular detection of Borrelia sp. in tapirs (Tapirus terrestris). Vet. Res. Commun. 2024, 48, 2767–2774. [Google Scholar] [CrossRef] [PubMed]
  35. Calchi, A.C.; May-Júnior, J.A.; Baggio-Souza, V.; Berger, L.; Fagundes-Moreira, R.; Mallmann-Bohn, R.; Braga, L.Q.V.; Kirnew, M.D.; Silveria, M.F.; Ampuero, R.A.N.; et al. Diversity of Cytauxzoon spp. (Piroplasmida: Theileriidae) in Wild Felids from Brazil and Argentina. Pathogens 2025, 14, 148. [Google Scholar] [CrossRef] [PubMed]
  36. Pacheco, T.A.; Lee, D.A.B.; Maia, M.O.; Semedo, T.B.F.; de Mendonça, R.F.B.; Pedroni, F.; Dutra, V.; Nakazato, L.; Rossi, R.V.; André, M.R.; et al. Molecular survey of Hepatozoon spp. and piroplasmids in rodents and marsupials from midwestern Brazil, with evidence of a novel Piroplasmida clade (“South American Rodentia”) in the echimyid rodent Thrichomys pachyurus. Parasitol. Res. 2025, 124, 19. [Google Scholar] [CrossRef]
  37. Feng, Z.; Shu, Y. An Overview of Digital PCR. Bing Du Xue Bao 2017, 33, 103–107. [Google Scholar]
  38. Mirabile, A.; Sangiorgio, G.; Bonacci, P.G.; Bivona, D.; Nicitra, E.; Bonomo, C.; Musso, N. Advancing pathogen identification: The role of digital PCR in enhancing diagnostic power in different settings. Diagnostics 2024, 14, 1598. [Google Scholar] [CrossRef]
  39. Chen, B.; Jiang, Y.; Cao, X.; Liu, C.; Zhang, N.; Shi, D. Droplet digital PCR as an emerging tool in detecting pathogens nucleic acids in infectious diseases. Clin. Chim. Acta 2021, 517, 156–161. [Google Scholar] [CrossRef]
  40. Wichianchot, S.; Hongsrichan, N.; Maneeruttanarungroj, C.; Pinlaor, S.; Iamrod, K.; Purisarn, A.; Donthaisong, P.; Karanis, P.; Nimsuphan, B.; Rucksaken, R. A newly developed droplet digital PCR for Ehrlichia canis detection: Comparisons to conventional PCR and blood smear techniques. J. Vet. Med. Sci. 2022, 84, 831–840. [Google Scholar] [CrossRef]
  41. Tsokana, C.N.; Symeonidou, I.; Sioutas, G.; Gelasakis, A.I.; Papadopoulos, E. Current applications of digital PCR in veterinary parasitology: An overview. Parasitologia 2023, 3, 269–283. [Google Scholar] [CrossRef]
  42. Hoshino, T.; Inagaki, F. Molecular quantification of environmental DNA using microfluidics and digital PCR. Syst. Appl. Microbiol. 2012, 35, 390–395. [Google Scholar] [CrossRef]
  43. Gutiérrez-Aguirre, I.; Rački, N.; Dreo, T.; Ravnikar, M. Droplet digital PCR for absolute quantification of pathogens. Methods Mol. Biol. 2015, 1302, 331–347. [Google Scholar] [CrossRef] [PubMed]
  44. Li, H.; Bai, R.; Zhao, Z.; Tao, L.; Ma, M.; Ji, Z.; Jian, M.; Ding, Z.; Dai, X.; Bao, F.; et al. Application of droplet digital PCR to detect the pathogens of infectious diseases. Biosci. Rep. 2018, 38, BSR20181170. [Google Scholar] [CrossRef] [PubMed]
  45. Capobianco, J.A.; Clark, M.; Cariou, A.; Leveau, A.; Pierre, S.; Fratamico, P.; Strobaugh, T.P., Jr.; Armstrong, C.M. Detection of Shiga toxin-producing Escherichia coli (STEC) in beef products using droplet digital PCR. Int. J. Food Microbiol. 2020, 319, 108499. [Google Scholar] [CrossRef] [PubMed]
  46. Yang, R.; Paparini, A.; Monis, P.; Ryan, U. Comparison of next-generation droplet digital PCR (ddPCR) with quantitative PCR (qPCR) for enumeration of Cryptosporidium oocysts in faecal samples. Int. J. Parasitol. 2014, 44, 1105–1113. [Google Scholar] [CrossRef]
  47. Dewantoro, A.; Anggundari, W.C.; Prasetya, B. Review of digital PCR potential for surveillance of emerging disease from wastewater. IOP Conf. Ser. 2021, 926, 012065. [Google Scholar] [CrossRef]
  48. Wu, J.; Tang, B.; Qiu, Y.; Tan, R.; Liu, J.; Xia, J.; Zhang, J.; Huang, J.; Qu, J.; Sun, J.; et al. Clinical validation of a multiplex droplet digital PCR for diagnosing suspected bloodstream infections in ICU practice: A promising diagnostic tool. Crit. Care 2022, 26, 243. [Google Scholar] [CrossRef]
  49. King, J.L.; Smith, A.D.; Mitchelli, E.A.; Allen, M.S. Validation of droplet digital PCR for the detection and absolute quantification of Borrelia DNA in Ixodes scapularis ticks. Parasitology 2017, 144, 359–367. [Google Scholar] [CrossRef]
  50. Das, S.; Hammond-Mckibben, D.; Guralski, D.; Lob, S.; Fiedler, R.P.N. Development of a sensitive molecular diagnostic assay for detecting Borrelia burgdorferi DNA from the blood of Lyme disease patients by digital PCR. PLoS ONE 2020, 15, e0235372. [Google Scholar] [CrossRef]
  51. Maggi, R.G.; Breitschwerdt, E.B.; Quorollo, B.; Miller, J.C. Development of a multiplex droplet digital PCR assay for the detection of Babesia, Bartonella, and Borrelia species. Pathogens 2021, 10, 1462. [Google Scholar] [CrossRef]
  52. Maggi, R.G.; Richardson, T.; Breitschwerdt, E.B.; Miller, J.C. Development and validation of a droplet digital PCR assay for the detection and quantification of Bartonella species within human clinical samples. J. Microbiol. Methods 2020, 176, 106022. [Google Scholar] [CrossRef]
  53. Lesiczka, P.M.; Modry, D.; Sprong, H.; Fonville, M.; Pikula, J.; Piacek, V.; Hrazdilova, K. Detection of Anaplasma phagocytophilum in European brown hares (Lepus europaeus) using three different methods. Zoonoses Public Health 2021, 68, 917–925. [Google Scholar] [CrossRef] [PubMed]
  54. Basanisi, M.G.; La Bella, G.; Nobili, G.; Raele, D.A.; Cafiero, M.A.; Coppola, R.; Damato, A.M.; Fraccalvieri, R.; Sottili, R.; La Salandra, G. Detection of Coxiella burnetii DNA in sheep and goat milk and dairy products by droplet digital PCR in south Italy. Int. J. Food Microbiol. 2022, 366, 109583. [Google Scholar] [CrossRef] [PubMed]
  55. Ramírez, J.D.; Herrera, G.; Hernández, C.; Cruz-Saavedra, L.; Muñoz, M.; Flórez, C.; Butcher, R. Evaluation of the analytical and diagnostic performance of a digital droplet polymerase chain reaction (ddPCR) assay to detect Trypanosoma cruzi DNA in blood samples. PLoS Negl. Trop. Dis. 2018, 12, e0007063. [Google Scholar] [CrossRef] [PubMed]
  56. Ramírez, J.D.; Herrera, G.; Muskus, C.; Mendez, C.; Duque, M.C.; Butcher, R. Development of a digital droplet Polymerase Chain Reaction (ddPCR) assay to detect Leishmania DNA in samples from Cutaneous Leishmaniasis patients. Int. J. Infect. Dis. 2019, 79, 1–3. [Google Scholar] [CrossRef]
  57. Pereira, D.C.A.; Teixeira-Neto, R.G.; Lopes, V.V.; Pena, H.P.; Paz, G.F.; Custodio, C.H.X.; Belo, V.S.; Fonseca-Júnior, A.A.; da Silva, E.S. Development of quantitative PCR and digital PCR for the quantification of Leishmania infantum in dogs. Mol. Cell. Biochem. 2023, 478, 2445–2450. [Google Scholar] [CrossRef]
  58. Koepfli, C.; Nguitragool, W.; Hofmann, N.E.; Robinson, L.J.; Ome-Kaius, M.; Sattabongkot, J.; Felger, I.; Mueller, I. Sensitive and accurate quantification of human malaria parasites using droplet digital PCR (ddPCR). Sci. Rep. 2016, 6, 39183. [Google Scholar] [CrossRef]
  59. Srisutham, S.; Saralamba, N.; Malleret, B.; Rénia, L.; Dondorp, A.M.; Imwong, M. Four human Plasmodium species quantification using droplet digital PCR. PLoS ONE 2017, 12, e0175771. [Google Scholar] [CrossRef]
  60. Mahendran, P.; Liew, J.W.K.; Amir, A.; Ching, X.T.; Lau, Y.L. Droplet digital polymerase chain reaction (ddPCR) for the detection of Plasmodium knowlesi and Plasmodium vivax. Malar. J. 2023, 19, 241. [Google Scholar] [CrossRef]
  61. Wilson, M.; Glaser, K.C.; Adams-Fish, D.; Boley, M.; Mayda, M.; Molestina, R.E. Development of droplet digital PCR for the detection of Babesia microti and Babesia duncani. Exp. Parasitol. 2015, 149, 24–31. [Google Scholar] [CrossRef]
  62. Kao, Y.F.; Peake, B.; Madden, R.; Cowan, S.R.; Scimeca, R.C.; Thomas, J.E.; Miller, C.A. A probe-based droplet digital polymerase chain reaction assay for early detection of feline acute cytauxzoonosis. Vet. Parasitol. 2021, 29, 109413. [Google Scholar] [CrossRef]
  63. Murthy, S.; Suresh, A.; Dandasena, D.; Singh, S.; Subudhi, M.; Bhandari, V.; Bhanot, V.; Arora, J.S.; Sharma, P. Multiplex ddPCR: A promising diagnostic assay for early detection and drug monitoring in bovine theileriosis. Pathogens 2023, 12, 296. [Google Scholar] [CrossRef] [PubMed]
  64. Jefferies, R.; Ryan, U.M.; Jardine, J.; Broughton, D.K.; Robertson, I.D.; Irwin, P.J. Blood, bull terriers and babesiosis: Further evidence for direct transmission of Babesia gibsoni in dogs. Aust. Vet. J. 2007, 85, 459–463. [Google Scholar] [CrossRef] [PubMed]
  65. Fleiss, J.L. Measuring nominal scale agreement among many raters. Psychol. Bull. 1971, 76, 378–382. [Google Scholar] [CrossRef]
  66. Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960, 20, 37–46. [Google Scholar] [CrossRef]
  67. McHugh, M.L. Interrater reliability: The kappa statistic. Biochem. Med. 2012, 22, 276–282. [Google Scholar] [CrossRef]
  68. McNemar, Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 1947, 12, 153–157. [Google Scholar] [CrossRef]
  69. Revelle, W. psych: Procedures for Psychological, Psychometric, and Personality Research, R Package Version 2.3.12; Northwestern University: Evanston, IL, USA, 2024. Available online: https://CRAN.R-project.org/package=psych (accessed on 28 February 2025).
  70. Gamer, M.; Lemon, J.; Fellows, I.; Singh, P. irr: Various Coefficients of Interrater Reliability and Agreement, R Package Version 0.84. Available online: https://cran.r-project.org/package=irr (accessed on 28 February 2025).
  71. Chicco, D.; Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 2020, 21, 6. [Google Scholar] [CrossRef]
  72. Lashnits, E.; Neupane, P.; Bradley, J.M.; Richardson, T.; Maggi, R.G.; Breitschwerdt, E.B. Comparison of serological and molecular assays for Bartonella species in dogs with hemangiosarcoma. Pathogens 2021, 10, 794. [Google Scholar] [CrossRef]
  73. Belmonte, F.R.; Martin, J.L.; Frescura, K.; Damas, J.; Pereira, F.; Tarnopolsky, M.A.; Kaufman, B.A. Digital PCR methods improve detection sensitivity and measurement precision of low abundance mtDNA deletions. Sci. Rep. 2016, 6, 25186. [Google Scholar] [CrossRef]
  74. Massolo, A.; Gerber, A.; Umhang, G.; Nicholas, C.; Liccioli, S.; Mori, K.; Klein, C. Droplet digital PCR as a sensitive tool to assess exposure pressure from Echinococcus multilocularis in intermediate hosts. Acta Trop. 2021, 223, 106078. [Google Scholar] [CrossRef]
  75. Yu, Z.; Zhao, Z.; Chen, L.; Li, J.; Ju, X. Development of a droplet digital PCR for detection of Trichuriasis in sheep. J. Parasitol. 2020, 106, 603–610. [Google Scholar] [CrossRef] [PubMed]
  76. Calchi, A.C.; Moore, C.O.; Bartone, L.; Kingston, E.; André, M.R.; Breitschwerdt, E.B.; Maggi, R.G. Development of multiplex assays for the identification of zoonotic Babesia species. Pathogens 2024, 13, 1094. [Google Scholar] [CrossRef] [PubMed]
  77. Maggi, R.G.; Calchi, A.C.; Moore, C.O.; Kingston, E.; Breitschwerdt, E.B. Human Babesia odocoilei and Bartonella spp. co-infections in the Americas. Parasites Vectors 2024, 17, 302. [Google Scholar] [CrossRef] [PubMed]
  78. Souza, U.; Dall’Agnol, B.; Michel, T.; Webster, A.; Klafke, G.; Martins, J.R.; Reck, J. Detection of Bartonella sp. in deer louse flies (Lipoptena mazamae) on gray brocket deer (Mazama gouazoubira) in the neotropics. J. Zoo Wildl. Med. 2017, 48, 532–535. [Google Scholar] [CrossRef]
  79. Farias, I.F.; Moura, L.M.; Sá, J.C.B.; Souza, D.S.; Torres-Santos, P.T.; Oliveira, J.B.; Muñoz-Leal, S.; Horta, M.C. Evaluation of infection by Borrelia sp. in domestic and wild mammals and ticks from the Catimbau National Park, Pernambuco. Pesq. Vet. Bras. 2023, 43, e07203. [Google Scholar] [CrossRef]
  80. Jorge, F.R.; Muñoz-Leal, S.; De Oliveira, G.M.; Serpa, M.C.A.; Magalhães, M.M.; De Oliveira, L.M.; Moura, F.R.P.; Teixeira, B.M.; Labruna, M.H. Novel Borrelia genotypes from Brazil indicate a new group of Borrelia spp. associated with South American bats. J. Med. Entomol. 2023, 60, 213–217. [Google Scholar] [CrossRef]
  81. Mongruel, A.C.B.; Medici, E.P.; Canena, A.C.; Dias, C.M.; Machado, R.Z.; André, M.R. Molecular evidence of Bartonella spp. in wild lowland tapirs (Tapirus terrestris), the largest land mammals in Brazil. Comp. Immunol. Microbiol. Infect. Dis. 2023, 101, 102042. [Google Scholar] [CrossRef]
Pathogens 14 00567 g001
Figure 1. Formulas used to calculate sensitivity (A), specificity (B), and Matthews correlation coefficient (C). In these formulas, TP represents the number of true positive samples (identified as positive by the gold standard), FN represents the number of false negative samples (identified as negative by the assay evaluated but as positive by the gold standard), TN represents the number of true negative samples (identified as negative by the gold standard), and FP represents the number of false positive samples (identified as positive by the assay evaluated but as negative by the gold standard).
Figure 1. Formulas used to calculate sensitivity (A), specificity (B), and Matthews correlation coefficient (C). In these formulas, TP represents the number of true positive samples (identified as positive by the gold standard), FN represents the number of false negative samples (identified as negative by the assay evaluated but as positive by the gold standard), TN represents the number of true negative samples (identified as negative by the gold standard), and FP represents the number of false positive samples (identified as positive by the assay evaluated but as negative by the gold standard).
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Figure 2. Venn diagram illustrating the samples positive for Bartonella spp. (A), Borrelia spp. (B), and piroplasmids (C), as identified by the tests analyzed in this study.
Figure 2. Venn diagram illustrating the samples positive for Bartonella spp. (A), Borrelia spp. (B), and piroplasmids (C), as identified by the tests analyzed in this study.
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Figure 3. Number of samples positive for Bartonella spp. (A), Borrelia spp. (B), and piroplasmids (C) across different ranges of positive partitions. The x-axis represents the range of the number of positive partitions, while the y-axis shows the number of samples corresponding to each range of positive partitions.
Figure 3. Number of samples positive for Bartonella spp. (A), Borrelia spp. (B), and piroplasmids (C) across different ranges of positive partitions. The x-axis represents the range of the number of positive partitions, while the y-axis shows the number of samples corresponding to each range of positive partitions.
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Table 1. Description of primers, probes, and amplicon sizes of multiplex dPCR assays.
Table 1. Description of primers, probes, and amplicon sizes of multiplex dPCR assays.
AgentGeneSizePrimer SequencesProbe Sequences
Bartonella spp.16S-23S rRNA (ITS)90–120 bpBsppITS325s
5′-CCTCAGATGATGATCCCAAGCCTTCTGGCG-3′
BsppITS543as
5′-AATTGGTGGGCCTGGGAGGACTTG-3′		BsppITS500probe
5′-FAM-GTTAGAGCGCGCGCTTGATTAAG-BHQ1-3′
Borrelia spp.16S-23S rRNA (ITS)104 bpBobulITS120s
5′-AGGTCATTTTGGGGGTTTAGCTCAGTTGGCT-3′
BobulITS200as
5′-AATGGAGGTTAA GGGACTCGAACCCT-3′		BobulITS160probe
5′-HEX-CGGCTTTGCAAGCCGAGGGTCAAG-BHQ2-3′
Piroplasmids18S rRNA125–138 bpPiro18S-238s
5′-TCGGTGATTCATAATAAACTRGCGAATCGC-3′
Piro18S-380as
5′-AATCGAACCCCAATTCCCCGTTACCCG-3′		Piro18S-340probe
5′ CY5-GACGGTAGGGTATTGGCCTACCG-BHQ2-3′
Housekeeping geneB-Raf Proto-Oncogene (BRAF) gene120 bpCaFeBRAF-15s
5′-TCAYGA AGACCTCACAGTAAAAATAGG T-3′
CaFeBRAF-110as
5′-GATCCAGACAAC TGTTCA AACTGATG-3′		CaFeBRAF-50
5′-TAMRA-GTCTAGCCACAGTGAAATCTCGATG-BHQ2-3′
Table 2. Number of animals positive for each agent and test performed.
Table 2. Number of animals positive for each agent and test performed.
Number of Positive Samples
Bartonella spp.Borrelia spp.Piroplasmids
Wild Animal GroupdPCRqPCRdPCRqPCRnPCRdPCRqPCR
Deer (Total)56 (32.18%)12 (6.9%)25 (14.37%)0137 (78.73%)154 (88.51%)51 (29.31%)
Subulo gouazoubira626022192
Mazama rufa0010320
Mazama jucunda2030440
Blastocerus dichotomus451015010212447
Ozotocerus bezoarticus3000652
Desmodus rotundus (Total)46 (90.2%)17 (33.33%)14 (27.45%)2 (3.92%)42 (82.35%)47 (92.15%)16 (31.37%)
Cerdocyon thous (Total)3 (27.27%)1 (9.09%)1 (9.09%)03 (27.27%)11 (100%)9 (81.81%)
Felids (Total)6 (17.14%)1 (2.86%)7 (20%)029 (82.86%)34 (97.14%)19 (54.29%)
Leopardus pardalis101091210
Panthera onca414018219
Puma concolor1020220
Xenarthra (Total)6 (13.64%)6 (13.64%)6 (13.67%)022 (50%)21 (47.73%)14 (31.82%)
Myrmecophaga tridactyla021012106
Tamandua tetradactyla1010431
Euphractus sexcinctus3110132
Dasypus novemcinctus2220423
Priodontes maximus0110132
Birds (Total)4 (22.22%)1 (5.56%)2 (11.11%)08 (44.44%)13 (72.22%)6 (33.33%)
Pseudoseisura unirufa0000111
Saltator coerulescens2010222
Turdus leucomelas1000110
Cercomacra melanaria0000110
Ramphoceluls carbo0000100
Basileuterus flaveolus0000110
Leptotila verreauxi0100121
Icterus cayanensis0000011
Lepidocolaptes angustirostris0010010
Fumarius rufus0000010
Fumarius leucopus0000010
Cyamocorax chrysops1000011
Tapirus terrestris (Total)4 (16.67%)04 (16.67%)016 (66.67%)24 (100%)4 (16.67%)
Rodents (Total)4 (44.44%)1 (11.11%)2 (22.22%)009 (100%)4 (44.44%)
Oecomys mamorae3000043
Thrichomys fosteri1110041
Clyomis laticeps0010010
Total129 (35.26%)39 (10.66%)61 (16.67%)2 (0.55%)257 (70.22%)313 (85.52%)123 (33.61%)
Table 3. Results obtained in the Fleiss’ Kappa and Cohen’s Kappa analyses of the agreement between test results for piroplasmids.
Table 3. Results obtained in the Fleiss’ Kappa and Cohen’s Kappa analyses of the agreement between test results for piroplasmids.
TestComparisonKappa (k)p-Value
Fleiss’ KappadPCR vs. qPCR vs. nPCR−0.05170.0865
Cohen’s KappaqPCR vs. nPCR−0.09350.0185
nPCR vs. dPCR0.2010<0.0001
dPCR vs. qPCR0.08960.0012
p-value is considered significant when <0.05.
Table 4. Analysis of the difference between the proportions obtained in the tests for detecting piroplasmids evaluated in this study.
Table 4. Analysis of the difference between the proportions obtained in the tests for detecting piroplasmids evaluated in this study.
Comparationχ2p-Value
qPCR vs. nPCR79.101<0.0001
nPCR vs. qPCR30.447<0.0001
dPCR vs. qPCR177.09<0.0001
The p-value is considered significant when <0.05; χ² = chi-squared value.
Table 5. Analysis of the difference between the proportions obtained in the tests for detecting Bartonella spp. and Borrelia spp. evaluated in this study.
Table 5. Analysis of the difference between the proportions obtained in the tests for detecting Bartonella spp. and Borrelia spp. evaluated in this study.
AgentComparationχ2p-Value
Bartonella spp.qPCR vs. dPCR69.482<0.0001
Borrelia spp.qPCR vs. dPCR55.148<0.0001
The p-value is considered significant when <0.05; χ² = chi-squared value.
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MDPI and ACS Style

Calchi, A.C.; Mongruel, A.C.B.; Cavalcanti, F.B.P.; Bartone, L.; Duarte, J.M.B.; Medici, E.P.; Kluyber, D.; Caiaffa, M.G.; Alves, M.H.; Desbiez, A.L.J.; et al. Using Digital PCR to Unravel the Occurrence of Piroplasmids, Bartonella spp., and Borrelia spp. in Wild Animals from Brazil. Pathogens 2025, 14, 567. https://doi.org/10.3390/pathogens14060567

AMA Style

Calchi AC, Mongruel ACB, Cavalcanti FBP, Bartone L, Duarte JMB, Medici EP, Kluyber D, Caiaffa MG, Alves MH, Desbiez ALJ, et al. Using Digital PCR to Unravel the Occurrence of Piroplasmids, Bartonella spp., and Borrelia spp. in Wild Animals from Brazil. Pathogens. 2025; 14(6):567. https://doi.org/10.3390/pathogens14060567

Chicago/Turabian Style

Calchi, Ana Cláudia, Anna Claudia Baumel Mongruel, Fernanda Beatriz Pereira Cavalcanti, Lilliane Bartone, José Maurício Barbanti Duarte, Emília Patrícia Medici, Danilo Kluyber, Mayara G. Caiaffa, Mario Henrique Alves, Arnaud Leonard Jean Desbiez, and et al. 2025. "Using Digital PCR to Unravel the Occurrence of Piroplasmids, Bartonella spp., and Borrelia spp. in Wild Animals from Brazil" Pathogens 14, no. 6: 567. https://doi.org/10.3390/pathogens14060567

APA Style

Calchi, A. C., Mongruel, A. C. B., Cavalcanti, F. B. P., Bartone, L., Duarte, J. M. B., Medici, E. P., Kluyber, D., Caiaffa, M. G., Alves, M. H., Desbiez, A. L. J., Coelho, T. F. S. B., Machado, R. Z., Breitschwerdt, E. B., Maggi, R. G., & André, M. R. (2025). Using Digital PCR to Unravel the Occurrence of Piroplasmids, Bartonella spp., and Borrelia spp. in Wild Animals from Brazil. Pathogens, 14(6), 567. https://doi.org/10.3390/pathogens14060567

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