Mycobacterium bovis in Wildlife: Surveillance Methods and One Health Implications
Abstract
Mycobacterium bovis represents the primary etiological agent of bovine tuberculosis (bTB) and maintains a complex multi-host ecology involving domestic livestock and wild mammalian reservoirs. This review synthesizes current surveillance methodologies with emphasis on molecular epidemiological tools including spoligotyping and whole-genome sequencing (WGS), diagnostic performance in key wildlife hosts such as European badgers (Meles meles) and cervids, and the quantitative assessment of transmission risk at the wildlife-livestock interface. Integration of genomic surveillance data with host ecological parameters enables high-resolution reconstruction of transmission networks and informs evidence-based One Health interventions.
Introduction
Mycobacterium bovis, a member of the Mycobacterium tuberculosis complex (MTBC), exhibits a broad host range encompassing domestic cattle, numerous wildlife species, and humans. The pathogen's ability to establish persistent infection in maintenance hosts creates reservoirs that sustain transmission cycles independent of domestic livestock populations. In temperate regions, the European badger serves as a principal maintenance host, while in subtropical and tropical ecosystems, diverse ungulate species including white-tailed deer (Odocoileus virginianus), red deer (Cervus elaphus), and wild boar (Sus scrofa) fulfill analogous epidemiological roles. The persistence of M. bovis in wildlife populations constitutes a major obstacle to bTB eradication programs globally, necessitating surveillance frameworks that integrate molecular typing, serological monitoring, and ecological modeling.
Molecular Epidemiology: Spoligotyping and Whole-Genome Sequencing
Spoligotyping Principles and Applications
Spoligotyping exploits polymorphism within the direct repeat (DR) locus of the M. bovis genome. The DR region comprises multiple 36-base-pair direct repeats interspersed with unique spacer sequences of 35 to 41 base pairs. The presence or absence of 43 defined spacers generates a binary pattern convertible to an octal code, enabling strain classification into spoligotype families such as SB0140, SB0134, and SB0121. This method provides rapid, cost-effective strain discrimination suitable for large-scale surveillance; however, its discriminatory power is limited by convergent evolution and homoplasy, particularly in clonal populations.
Recent applications demonstrate the utility of spoligotyping for initial outbreak characterization. In South African wildlife, spoligotyping of M. bovis isolates from multiple host species identified predominant spoligotypes shared between buffalo (Syncerus caffer), greater kudu (Tragelaphus strepsiceros), and domestic cattle, suggesting interspecies transmission at the wildlife-livestock interface [10]. Similarly, the first whole-genome sequence of an M. bovis strain (3/86Rv) isolated from a cow in India revealed spoligotype SB0121, providing a reference for subsequent molecular epidemiological investigations in the region [14].
Whole-Genome Sequencing for High-Resolution Transmission Inference
Whole-genome sequencing (WGS) has superseded spoligotyping as the gold standard for molecular epidemiology. Single-nucleotide polymorphism (SNP) analysis of core genome alignments enables resolution of transmission events at the farm and individual-animal level. The mutation rate of M. bovis is estimated at 0.3 to 0.5 SNPs per genome per year, providing a molecular clock for temporal inference of transmission chains.
WGS reveals genetic diversity and transmission dynamics unattainable with lower-resolution methods. A study of M. bovis in South African wildlife employed WGS to demonstrate that isolates from sympatric wildlife species formed distinct phylogenetic clusters corresponding to host species and geographic location, with limited evidence of recent cross-species transmission [10]. This approach identified microevolution within host populations and quantified the contribution of wildlife reservoirs to livestock reinfection.
Comparative genomics of M. bovis strains from diverse geographic origins illuminates phylogeographic structure. The Indian isolate 3/86Rv clustered within the European 1 clonal complex, consistent with historical cattle importation events [14]. Such analyses inform the design of targeted surveillance by identifying high-risk lineages and predicting host adaptation markers.
Virulence Factor Genomics: ESAT-6 and CFP-10 Polymorphism
The region of difference 1 (RD1) encodes the ESX-1 secretion system and its substrates ESAT-6 (esxA) and CFP-10 (esxB), which are critical for virulence and host immune modulation. Strain-dependent variation in ESAT-6 and CFP-10 sequences modulates inflammasome activation in bovine macrophages, influencing disease progression and diagnostic antigen performance [4]. Specific polymorphisms in the ESAT-6 C-terminal domain alter binding to host pattern recognition receptors, with implications for both pathogenesis and the sensitivity of ESAT-6-based diagnostic assays. Surveillance programs incorporating virulence gene sequencing can detect emergent variants with altered host tropism or immune evasion properties.
Diagnostic Approaches in Wildlife Reservoirs
European Badger (Meles meles) Diagnostics
The European badger represents the most extensively studied wildlife reservoir of M. bovis in Europe. Diagnostic strategies for badgers must balance sensitivity, specificity, and practicality for field deployment.
Serological Assays
Antibody detection offers advantages for wildlife surveillance including non-invasive sample collection (blood, serum, or oral fluids) and detection of infection prior to bacteriological confirmation. Proteome microarray-guided antigen discovery has identified novel immunodominant antigens for badger serology. A polyprotein construct incorporating multiple M. bovis antigens demonstrated superior sensitivity compared to single-antigen formats in European badgers [12]. ELISA-based peptide mapping further refined epitope selection, enabling differentiation of M. bovis infection from exposure to environmental mycobacteria or Bacillus Calmette-Guérin (BCG) vaccination.
Specificity evaluation of polyprotein-based ELISAs across multiple species confirmed minimal cross-reactivity with Mycobacterium avium subspecies paratuberculosis (MAP) and other non-tuberculous mycobacteria [13]. This specificity is critical in badger populations where environmental mycobacterial exposure is ubiquitous.
Microbiome Interactions and Diagnostic Confounders
The fecal microbiome of European badgers varies with social group, age, and M. bovis infection status [3]. Infected individuals exhibit reduced microbial diversity and altered community composition, potentially influencing both disease susceptibility and diagnostic test performance. Microbiome-mediated modulation of host immunity may affect antibody kinetics and cell-mediated immune responses, introducing variability in test interpretation. Longitudinal microbiome monitoring in conjunction with serological surveillance may improve detection of early infection and prediction of disease progression.
Vaccination and Diagnostic Interference
BCG vaccination of badgers is a cornerstone of bTB control in the United Kingdom. Co-administration of BCG with contraceptive vaccines does not significantly impair the immune response to BCG, supporting integrated population management strategies [15]. However, BCG vaccination induces antibody responses that compromise the specificity of serological assays targeting shared antigens. Differential diagnostic strategies employing antigens absent from BCG (e.g., ESAT-6, CFP-10, or RD1-encoded proteins) are essential for surveillance in vaccinated populations.
Cervid Diagnostics: Deer Surveillance
Cervids, particularly white-tailed deer in North America and red deer/fallow deer (Dama dama) in Europe, function as maintenance or spillover hosts depending on population density and ecological context.
Seroprevalence Estimation
Serological surveillance in wild deer populations employs ELISA platforms detecting antibodies to MPB83, MPB70, or multi-antigen fusion proteins. A study estimating seroprevalence in a wild deer population in southwest England utilized a dual-antigen ELISA (MPB83/MPB70) with Bayesian latent class analysis to account for imperfect test sensitivity and specificity [6]. The apparent seroprevalence was 8.2 percent (95 percent credible interval: 4.1 to 14.7 percent), with higher prevalence in adults and in areas of high badger density, suggesting bidirectional transmission.
Antigen Detection and Molecular Assays
Antigen detection assays targeting lipoarabinomannan (LAM) in urine or feces offer non-invasive alternatives but suffer from lower sensitivity in subclinical infection. Polymerase chain reaction (PCR) targeting the IS6110 insertion element or the mpb70 gene enables direct pathogen detection in tissue, lymph node aspirates, or environmental samples. Quantitative PCR (qPCR) provides bacterial load estimation, correlating with disease severity and transmission potential.
Comparative Diagnostics in Suids
Wild boar and feral pigs serve as significant reservoirs in Mediterranean and subtropical ecosystems. Nationwide seroprevalence surveys in Korea employing a multi-antigen ELISA detected M. bovis antibodies in 3.8 percent of wild boars and 1.2 percent of domestic sows, with spatial clustering at the wildlife-livestock interface [9]. The higher prevalence in wild boars supports their role as maintenance hosts in this ecosystem. Cross-species validation of serological assays is essential given differences in immunoglobulin structure and epitope recognition between suids, cervids, and mustelids.
Transmission Dynamics at the Wildlife-Livestock Interface
Quantitative Risk Assessment
Transmission risk is governed by the product of contact rate, pathogen shedding intensity, and environmental persistence. M. bovis survives in the environment for weeks to months depending on temperature, humidity, and substrate. Indirect transmission via contaminated feed, water, or fomites is documented in high-density wildlife-livestock contact zones.
Genomic epidemiology quantifies the directionality and frequency of cross-species transmission. WGS of M. bovis isolates from sympatric cattle and wildlife populations in South Africa revealed asymmetric transmission, with wildlife-to-cattle events predominating in buffer zones adjacent to protected areas [10]. In contrast, in regions with intensive cattle management, cattle-to-wildlife transmission was more frequent, reflecting higher cattle density and management practices such as shared water points.
Raw Milk and Foodborne Transmission
The detection of M. bovis in raw milk represents a direct zoonotic and livestock transmission pathway. A herd-level prevalence study in the Sylhet region of Bangladesh employing PCR and ELISA on bulk tank milk samples identified M. bovis DNA in 12.5 percent of herds, with concurrent antibody detection in 18.3 percent [7]. The persistence of viable M. bovis in raw milk and traditional dairy products poses risks for both human consumption and calf infection via milk feeding. Pasteurization effectively eliminates this risk; however, raw milk consumption persists in many pastoralist communities.
Host-Pathogen Immunological Interactions
The outcome of M. bovis exposure ranges from clearance to latent infection to progressive disease, determined by host genetic factors and immune competence. The neonatal Fc receptor (FcRn) alleviates mycobacterium-induced lung injury by triggering YBX1-mediated autophagy, representing a host-protective mechanism that may vary between species and individuals [11]. Polymorphisms in FcRn or autophagy-related genes could influence reservoir competence and diagnostic test performance. Strain-dependent effects of ESAT-6 and CFP-10 on inflammasome activation further modulate the host-pathogen equilibrium [4]. Understanding these interactions informs the selection of diagnostic antigens and the prediction of transmission potential.
One Health Implications and Integrated Surveillance
Zoonotic Risk and Public Health
M. bovis is a confirmed zoonotic pathogen causing tuberculosis in humans clinically indistinguishable from M. tuberculosis infection. The proportion of human TB attributable to M. bovis varies geographically, exceeding 10 percent in some pastoralist communities. In Mexico and Latin America, bovine tuberculosis is recognized as a neglected zoonotic disease with significant underreporting due to limited diagnostic capacity and surveillance infrastructure [8]. Molecular typing of human and animal isolates in these regions demonstrates shared genotypes, confirming zoonotic transmission.
Integrated Surveillance Frameworks
Effective One Health surveillance requires harmonized data standards, shared laboratory networks, and joint risk assessment across veterinary and public health sectors. Key components include:
- Genomic Data Sharing: Standardized WGS pipelines with open-access databases (e.g., NCBI Pathogen Detection, BV-BRC) enable real-time phylogenetic monitoring across sectors.
- Serological Harmonization: Cross-validated ELISA protocols using defined antigen panels allow comparison of seroprevalence across host species and geographic regions.
- Ecological Modeling: Integration of host density, movement ecology, and environmental persistence data into mechanistic transmission models predicts high-risk interfaces for targeted intervention.
- Diagnostic Algorithm Standardization: Tiered testing strategies combining screening (serology) with confirmation (PCR, culture, WGS) optimize resource allocation.
Control Strategies Informed by Surveillance
Surveillance data directly inform control policy. In badger populations, vaccination deployment is guided by seroprevalence mapping and social group structure. In cervids, population density reduction via managed hunting or fertility control is implemented when seroprevalence exceeds threshold levels. Livestock biosecurity measures, including badger-proof fencing, feed storage protection, and pasture management, are prioritized in high-risk zones identified through molecular epidemiological clustering.
Surveillance Workflow and Decision Framework
The following diagram illustrates the integrated surveillance workflow from sample collection through molecular characterization to policy action.
flowchart TD
A[Sample Collection], > B{Host Species}
B, >|Badger| C[Serum/Oral Fluid]
B, >|Deer| D[Blood/Lymph Node]
B, >|Wild Boar| E[Serum/Tissue]
B, >|Cattle| F[Milk/Blood/Tissue]
B, >|Environmental| G[Feces/Water/Feed]
C, > H[Screening ELISA]
D, > H
E, > H
F, > H
G, > I[qPCR IS6110/mpb70]
H, > J{Result}
J, >|Positive| K[Confirmatory Testing]
J, >|Negative| L[Archive/Repeat per Protocol]
K, > M[Culture on Solid/Liquid Media]
K, > N[PCR Confirmation]
K, > O[WGS Library Prep]
M, > P[Isolate Biobanking]
N, > P
O, > Q[Bioinformatics Pipeline]
Q, > R[SNP Phylogeny]
Q, > S[Spoligotype In Silico]
Q, > T[Virulence Gene Typing]
Q, > U[AMR Gene Screening]
R, > V[Transmission Cluster ID]
S, > V
T, > V
U, > V
V, > W[Epidemiological Integration]
W, > X[Host Movement Data]
W, > Y[Environmental Persistence]
W, > Z[Livestock Movement Records]
X, > AA[Risk Mapping]
Y, > AA
Z, > AA
AA, > AB{Policy Action}
AB, >|High Risk| AC[Targeted Vaccination]
AB, >|High Risk| AD[Movement Restrictions]
AB, >|High Risk| AE[Population Management]
AB, >|Moderate Risk| AF[Enhanced Surveillance]
AB, >|Low Risk| AG[Routine Monitoring]
AC, > AH[Outcome Evaluation]
AD, > AH
AE, > AH
AF, > AH
AG, > AH
AH, > A
Emerging Technologies and Future Directions
Metagenomic Surveillance
Shotgun metagenomic sequencing of environmental samples (soil, water, feces) enables pathogen detection without prior cultivation. This approach captures the full microbial community context, including co-infections and antimicrobial resistance determinants. Computational subtraction of host reads followed by assembly and binning of M. bovis genomes from complex samples is technically feasible but requires high sequencing depth due to low pathogen biomass.
CRISPR-Based Diagnostics
CRISPR-Cas systems (Cas12, Cas13) coupled with isothermal amplification (RPA, LAMP) offer field-deployable molecular detection with single-copy sensitivity. Species-specific crRNAs targeting M. bovis RD regions or mpb70 enable discrimination from other MTBC members. These platforms are amenable to multiplexing for simultaneous detection of M. bovis, M. caprae, and M. microti in wildlife samples.
Host Transcriptomics and Biomarker Discovery
Blood RNA sequencing of infected wildlife identifies host transcriptional signatures correlating with infection stage and disease progression. Conserved gene modules across species (e.g., interferon-gamma response, inflammasome activation, autophagy) provide targets for next-generation diagnostics that differentiate latent infection from active disease, a critical gap in current surveillance.
Computational Modeling of Transmission Networks
Integration of WGS-derived transmission trees with animal movement databases and landscape genetics enables mechanistic modeling of M. bovis spread. Agent-based models incorporating host demography, contact networks, and environmental contamination predict the impact of control interventions (vaccination, culling, biosecurity) under varying ecological scenarios. These models require parameterization from longitudinal field studies and are validated against independent genomic surveillance data.
Conclusion
Mycobacterium bovis surveillance in wildlife has transitioned from reactive outbreak investigation to proactive, genomics-informed systems surveillance. The convergence of high-resolution molecular typing (WGS), species-validated serological assays, ecological modeling, and cross-sectoral data integration provides the evidentiary foundation for One Health interventions. Sustained investment in laboratory capacity, bioinformatics infrastructure, and transdisciplinary collaboration is essential to translate surveillance data into effective bTB control and zoonotic risk mitigation. The persistence of M. bovis in multi-host systems demands adaptive surveillance frameworks that evolve with changing host communities, land use patterns, and pathogen genetics.
References
[1] Kader NA, Barman NN, Changmai K et al. Molecular detection of Mycobacterium tuberculosis complex (MTBC) in a wild Asian elephant (Elephas maximus) from Assam, India. Vet Res Commun. 2026. https://pubmed.ncbi.nlm.nih.gov/42183932/
[2] Wang Q, Wang XN, Liu XQ et al. Prevalence and risk factors of bovine tuberculosis in dairy cattle, 2020-2025: A systematic review and meta-analysis of available literature. Prev Vet Med. 2026. https://pubmed.ncbi.nlm.nih.gov/42176585/
[3] Meadows NML, Delahay RJ, McDonald RA et al. Fecal Microbiome Varies With Social Group, Age and Bovine Tuberculosis Infection in the European Badger (Meles meles). Mol Ecol. 2026. https://pubmed.ncbi.nlm.nih.gov/42175702/
[4] Blanco FC, Vazquez CL, Bigi MM et al. Mycobacterium bovis Strain-Dependent Effects of ESAT-6 and CFP-10 on Inflammasome Activation in Bovine Macrophages. Int J Mol Sci. 2026. https://pubmed.ncbi.nlm.nih.gov/42123677/
[5] N G K, S J N, T R. Continental Prevalence of Tuberculosis Infection and Associated Risk Factors From African Cattle: Systematic Review and Meta-Analysis. Vet Med Sci. 2026. https://pubmed.ncbi.nlm.nih.gov/42030422/
[6] Jinks R, Hollingdale A, Avigad R et al. Estimating the seroprevalence of Mycobacterium bovis infection in a wild deer population in southwest England. Vet Rec. 2026. https://pubmed.ncbi.nlm.nih.gov/41995129/
[7] Basak LR, Islam MT, Mazumder SK et al. Bovine Tuberculosis in Raw Milk: A Herd-Level Prevalence Study by PCR and ELISA Testing in Sylhet Region of Bangladesh. Vet Med Sci. 2026. https://pubmed.ncbi.nlm.nih.gov/41910171/
[8] Rodríguez-Martínez LM, Chavelas-Reyes JL, Medina-Ramírez CF et al. Bovine Tuberculosis as a Neglected Zoonotic Disease in Mexico and Latin America: Epidemiological Challenges, Diagnostic Insights, and Public Health Implications in Emerging Economies. Vet Sci. 2026. https://pubmed.ncbi.nlm.nih.gov/41893676/
[9] Moon SJ, Bae DY, Cho YC et al. Nationwide seroprevalence of Mycobacterium bovis and Mycobacterium avium in domestic sows and wild boars in Korea under a one health framework. Front Microbiol. 2026. https://pubmed.ncbi.nlm.nih.gov/41834877/
[10] Okunola A, Loubser J, Ghielmetti G et al. Whole-genome sequencing reveals genetic diversity and transmission dynamics of Mycobacterium bovis in South African wildlife. Microb Genom. 2026. https://pubmed.ncbi.nlm.nih.gov/41665621/
[11] Xu Y, Gong Z, Yu J et al. FcRn alleviates mycobacterium-induced lung injury by triggering YBX1-mediated autophagy. Int Immunopharmacol. 2026. https://pubmed.ncbi.nlm.nih.gov/41633016/
[12] Williams GA, Rahou S, Bateman O et al. Proteome microarray-guided identification of mycobacterial antigens and ELISA-based peptide mapping for improved serological detection of Mycobacterium bovis infection in European badgers. J Clin Microbiol. 2026. https://pubmed.ncbi.nlm.nih.gov/41589841/
[13] Moyano RD, Colombatti Olivieri MA, Basconez González M et al. Specificity Evaluation of a Polyprotein-Based ELISA Designed for the Detection of Paratuberculosis in Multiple Species. Vet Med Int. 2026. https://pubmed.ncbi.nlm.nih.gov/41567710/
[14] Verma R, Gandham RK, Mathesh K et al. First report of whole genome sequence of a Mycobacterium bovis (3/86Rv) strain isolated from a cow in India. Braz J Microbiol. 2025. https://pubmed.ncbi.nlm.nih.gov/41456234/
[15] Palphramand KL, Anderson P, Bellamy F et al. Immune response to co-administration of Bacillus Calmette-Guérin (BCG) and contraceptive vaccines in badgers (Meles meles). Vaccine. 2026. https://pubmed.ncbi.nlm.nih.gov/41371097/