Mariette F. Ducatez

Bovine respiratory disease complex (BRDC) is a multifactorial syndrome resulting from interactions among pathogens, host responses, and environmental stressors. However, substantial variation in disease severity persists among animals exposed to similar infectious challenges. To assess the contribution of the respiratory and gut microbiota to BRDC severity, we experimentally inoculated 28 calves with Mycoplasma bovis , bovine coronavirus (BCoV), and influenza D virus (IDV), with half receiving metaphylactic tilmicosin. Clinical trajectories were classified into mild, moderate, and severe phenotypes using unsupervised clustering of longitudinal health scores. Severe BRDC was associated with higher and more persistent nasal IDV loads, yet pathogen burden alone failed to explain the marked inter-individual variability in clinical outcomes. Tilmicosin-treated calves developed substantially milder disease despite comparable pathogen loads, coinciding with lower systemic inflammation, suggesting that host-pathogen interactions, rather than pathogen replication alone, influenced disease severity. Neither nasal nor faecal microbiota diversity consistently explained BRDC severity. However, calves that later developed severe disease displayed a more homogeneous nasal microbiota before infection, indicating that early microbial community structure may influence susceptibility. In contrast, the strongest disease-associated microbial signatures were observed in the faecal microbiota. Gut antibiotic-induced dysbiosis revealed associations between BRDC severity, specific intestinal bacterial taxa, systemic inflammation, and faecal short-chain fatty acid (SCFA) profiles. Notably, Chlamydia was strongly associated with disease severity, while several other taxa were linked to inflammatory biomarkers and SCFA concentrations. Collectively, these findings support a holobiont-driven model of BRDC in which disease severity emerges from interactions among pathogens, host inflammatory responses, and microbiota-associated functions. The stronger associations observed in the intestinal microbiota, along with links between gut bacteria, SCFAs, and disease outcomes, provide evidence for a functional gut-lung axis in calves and highlight microbial metabolism as a potential target for strategies to improve respiratory health and reduce antibiotic use. Implications: Bovine respiratory disease is one of the most costly health and welfare problems in cattle production. This study shows that disease severity is not determined only by pathogen infection but is also linked to changes in the gut microbiome and its metabolic activity. Calves with different microbiome profiles developed markedly different clinical outcomes despite similar pathogen exposure. The findings provide new evidence for a gut-lung connection in cattle and identify microbial functions associated with disease resilience. These results may support the development of microbiome-based strategies to improve animal health, reduce production losses, and decrease reliance on antibiotics in livestock systems.

Early detection of avian influenza virus circulation in wild birds is critical for anticipating infection pressure on poultry and regional virus dissemination. Current European legislation primarily targets dead, sick or hunted wild birds, whereas environmental sampling is not formally integrated into surveillance programmes. In this study, we compare the sensitivity of wild bird and environmental sampling strategies for avian influenza surveillance and evaluate the added value of environmental sampling. During the 2024–2025 high pathogenicity avian influenza season in north‐eastern Italy, we sampled wild birds and their environment in high‐risk wetlands. Captured and hunted wild birds were tested using oropharyngeal, cloacal, feather swabs and brain samples; environmental samples included fresh faecal droppings and surface water collected using passive samplers. Bayesian latent class models were used to estimate the sensitivity of individual sample types (bird level) and surveillance strategies (field‐visit level), and virus prevalence. At the bird level, the estimated sensitivity for H5 high pathogenicity avian influenza detection was highest for brain samples (0.82, 95% credible interval = 0.58–0.99), followed by oropharyngeal swabs (0.63, 95% credible interval = 0.25–0.98), whereas cloacal (0.05, 95% credible interval = 0.00–0.22) and feather swabs (0.09, 95% credible interval = 0.00–0.32) had substantially lower sensitivity. At the field‐visit level, environmental‐based strategies had the highest estimated sensitivities for M‐gene detection: 0.77 (95% credible interval = 0.63–0.89) for surface water and 0.71 (95% credible interval = 0.57–0.84) for faecal samples. Estimated prevalence among hunted and captured wild birds was 5.2% (95% credible interval = 2.4%–8.6%) for M gene, 3.1% (95% credible interval = 1.3%–6.3%) for H5 subtype and 2.7% (95% credible interval = 1.1%–6.0%) for H5 high pathogenicity avian influenza. Synthesis and applications . Environmental sampling strategies showed higher sensitivity than bird‐based samples and, although relying mostly on M‐gene detection, can complement wild bird sampling by increasing opportunities to detect avian influenza virus circulation at high‐risk wetlands without depending on access to infected birds. Integrating rapid environmental screening with targeted follow‐up sampling in birds could strengthen early‐warning systems, improve spatial targeting of surveillance and support more timely measures to reduce the risk of virus introduction into poultry populations.

Influenza D virus (IDV) is a respiratory pathogen affecting the health of cattle, thereby causing economic losses. This study was conducted to detect and isolate IDV, investigate its molecular characteristics, and examine the antibody response to IDV in naturally infected cattle. Real-time RT-PCR was used to analyse 279 nasal swabs and blood sera collected from cattle on farms in the Thrace district of Türkiye, bordering the European Union, for IDV-RNA. One sample from 2020 from a small, family-owned farm (D/OK lineage), two samples from 2021 from an integrated farm (D/OK lineage), and two samples from 2024 (D/Yama2019) from an integrated farm were found to be positive for IDV-RNA. All five samples were collected from farms located in Kırklareli. Positive samples were subjected to virus isolation and phylogenetic analyses. Serum samples were analysed using ELISA and HI tests. Of the 279 samples collected, 1.79% tested positive for IDV-RNA in real-time RT-PCR. Seroprevalence, measured for the first time in Türkiye, was found to be 45.5% and 55.19% by ELISA and HI tests, respectively. Key factors statistically associated with IDV seropositivity included the study year, geographical location, and farm type. Clinically, nasal discharge and cough were the primary symptoms in seropositive animals. Genetic analysis revealed that the 2020–2021 samples clustered within the D/OK lineage, while the 2024 samples belonged to the D/Yama2019 lineage. The findings suggest the co-circulation of multiple IDV strains, although this should be interpreted cautiously given the limited number of sequences in the Thrace region. Considering the lack of an influenza D virus (IDV) vaccine, these findings emphasise the importance of continued surveillance and diagnostic testing of cattle to limit viral spread, protect cattle health, and reduce economic losses associated with IDV.

The ongoing global spread of highly pathogenic avian influenza (HPAI) continues causing major impacts on poultry, wildlife, and public health. Among the most affected countries in Europe, Italy experienced three major epidemic waves in 2016–23, caused by 2.3.4.4b H5N8 and H5N1 subtypes. Yet, the underlying spatio-temporal dispersal dynamics and drivers of these epidemics remain to be elucidated. Here, we combined viral genome sequencing with continuous phylogeographic inference to reconstruct the evolutionary and spatio-temporal history of these epidemics. By combining genetic data with landscape, poultry-related variables, and wild bird abundances, we explored how the environment was associated with viral diffusion in space and time. We identified distinct diffusion patterns and environmental associations across the three epidemics. The first two waves (2016–17 and 2021–22) were dominated by local poultry-to-poultry transmission in high-density farming areas, with agricultural areas being significantly associated with a relatively higher viral lineage dispersal velocity in 2021–22. In contrast, the most recent wave (2022–23) showed evidence of mid- to long-distance dispersal events positively associated with wetlands, waterbodies, and gull abundances, pointing to a larger role of wild birds in HPAI spatial dynamics. The estimated weighted diffusion coefficients and spatial wavefront distances revealed a shift from localised poultry-driven to long-distance spread, likely related to wild birds. Our findings highlight a progressive shift from poultry-driven epidemics to more complex environmental transmission dynamics involving wild birds and natural habitats. These changes underscore the need to adapt surveillance and control strategies to an evolving and ecologically diverse viral landscape.

Background and Aim: Highly pathogenic avian influenza (HPAI) remains a global threat to poultry production, trade, and public health. While Morocco has not yet reported confirmed HPAI outbreaks, the endemic circulation of low-pathogenic avian influenza (LPAI) H9N2 since 2016, proximity to affected neighboring countries, and Morocco’s position along migratory bird flyways highlight the country’s vulnerability. This study aimed to identify high-risk areas for HPAI introduction and spread to inform risk-based surveillance and control policies. Materials and Methods: We applied a spatial multi-criteria decision analysis integrated with geographic information systems at the provincial scale. Relevant risk factors were identified through a literature review and expert consultation, and categorized into the introduction (wetlands, live poultry imports, recreational bird imports, and poultry products) and spread (poultry density and type, live bird markets, transport networks, and human population density) domains. Weights were assigned to factors using the analytic hierarchy process based on responses from 73 poultry-sector experts. Data were normalized, integrated into composite risk maps, and validated against historical LPAI H9N2 outbreak data (2016). Sensitivity and uncertainty analyses were used to assess model robustness. Results: The final maps revealed that 25 provinces (33.3% of the national territory) exhibited high-to-very high risk of HPAI introduction, particularly along northern coastal provinces, border regions, and areas linked to recreational bird imports. For spread risk, 41 provinces (41.3%) were classified as high to very high, concentrated in the Casablanca–Settat, Rabat–Salé–Kenitra, Fès–Meknès, and Marrakech–Safi regions, which are characterized by dense poultry production, major trade hubs, and extensive transport networks. Sensitivity analyses confirmed the model's stability, with variations in weight producing a minimal impact on risk classifications. Conclusion: This study provides the first comprehensive spatial risk maps of HPAI introduction and spread in Morocco, highlighting priority provinces for early detection, targeted surveillance, and preventive biosecurity measures. Despite limitations arising from reliance on LPAI data and expert judgment, the approach offers a robust decision-support tool for veterinary authorities. The methodology is adaptable to regional applications and can be refined with real-time surveillance data, enhancing Morocco’s preparedness and resilience against future avian influenza incursions. Keywords: avian influenza, biosecurity, geographic information system, Morocco, multi-criteria decision analysis, risk mapping, surveillance.

The ongoing global spread of highly pathogenic avian influenza (HPAI) continues causing major impacts on poultry, wildlife, and public health. Among the most affected countries in Europe, Italy experienced three major epidemic waves in 2016–2023, caused by H5N8 and H5N1 subtypes. Yet, the underlying spatio-temporal dispersal dynamics and drivers of these epidemics remain to be elucidated. Here, we combined viral genome sequencing with continuous phylogeographic inference to reconstruct the evolutionary and spatio-temporal history of these epidemics. By combining genetic data with landscape, poultry-related variables, and wild bird abundances, we explored how the environment was associated with viral diffusion in space and time.We identified distinct diffusion patterns and environmental associations across the three epidemics. The first two waves (2016–2017 and 2021–2022) were dominated by local poultry-to-poultry transmission in high-density farming areas, with agricultural areas being significantly associated with a relatively higher viral lineage dispersal velocity in 2021–2022. In contrast, the most recent wave (2022–2023) showed evidence of mid- to long-distance dispersal events positively associated with wetlands, waterbodies, and gull abundances, pointing to a larger role of wild birds in HPAI spatial dynamics. The estimated weighted diffusion coefficients and spatial wavefront distances revealed a shift from localised poultry-driven to long-distance spread, likely related to wild birds.Our findings highlight a progressive shift from poultry-driven epidemics to more complex environmental transmission dynamics involving wild birds and natural habitats. These changes underscore the need to adapt surveillance and control strategies to an evolving and ecologically diverse viral landscape.

Parmi tous les agents pathogènes respiratoires identifiés chez les jeunes bovins, les virus Influenza D (IDV) et Coronavirus bovin (BCoV) sont deux virus pour lesquels de nombreuses questions scientifiques demeurent quant à leur pouvoir pathogène et leur imputabilité dans les bronchopneumonies infectieuses bovines. Cet article fait le point sur les connaissances récentes sur ces deux virus. L’IDV est un virus émergent avec un potentiel zoonotique à envisager et le BCoV un virus déjà bien connu dont le pouvoir pathogène respiratoire est maintenant clairement admis. Ces deux virus agissent le plus souvent en co-infection avec d’autres agents pathogènes, notamment Mycoplasma bovis, e t l’association de ces pathogènes est positivement corrélée avec la gravité des signes cliniques respiratoires observés. L’IDV et le BCoV sont maintenant inclus dans les trousses de diagnostic qPCR des agents pathogènes respiratoires. Ils font aussi l’objet de recherches quant à leur détection dans des prélèvements environnementaux ou au chevet de l’animal via de nouvelles méthodes de diagnostic. Enfin, ils sont au centre de nouveaux projets visant à mieux comprendre leurs interactions avec l’holobionte du veau.

Increased evidence suggests that cattle are the primary host of Influenza D virus (IDV) and may contribute to respiratory disease in this species. The aim of this study was to detect and characterise IDV in the Swedish cattle population using archived respiratory samples. This retrospective study comprised a collection of a total 1763 samples collected between 1 January 2021 and 30 June 2024. The samples were screened for IDV and other respiratory pathogens using real-time reverse transcription quantitative PCR (rRT-qPCR). Fifty-one IDV-positive samples were identified, with a mean cycle threshold (Ct) value of 27 (range: 15–37). Individual samples with a Ct value of <30 for IDV RNA were further analysed by deep sequencing. Phylogenetic analysis was performed by the maximum likelihood estimation method on the whole IDV genome sequence from 16 samples. The IDV strains collected in 2021 (n = 7) belonged to the D/OK clade, whereas samples from 2023 (n = 4) and 2024 (n = 5) consisted of reassortants between the D/OK and D/660 clades, for the PB2 gene. This study reports the first detection of IDV in Swedish cattle and the circulation of D/OK and reassortant D/OK-D/660 in this population.

Mali is a country where little information is known about the circulation of avian influenza viruses (AIVs) in poultry. Implementing risk-based surveillance strategies would allow early detection and rapid control of AIVs outbreaks in the country. In this study, we implemented a multi-criteria decision analysis (MCDA) method coupled with geographic information systems (GIS) to identify risk areas for AIVs occurrence in domestic poultry in Mali. Five risk factors associated with AIVs occurrence were identified from the literature, and their relative weights were determined using the analytic hierarchy process (AHP). Spatial data were collected for each risk factor and processed to produce risk maps for AIVs outbreaks using a weighted linear combination (WLC). We identified the southeast regions (Bamako and Sikasso) and the central region (Mopti) as areas with the highest risk of AIVs occurrence. Conversely, northern regions were considered low-risk areas. The risk areas agree with the location of HPAI outbreaks in Mali. This study provides the first risk map using the GIS-MCDA approach to identify risk areas for AIVs occurrence in Mali. It should provide a basis for designing risk-based and more cost-effective surveillance strategies for the early detection of avian influenza outbreaks in Mali.