Multiple enteropathogens such as bovine rotavirus (BRV), bovine coronavirus (BCoV), Escherichia coli K99 (ETEC) and Cryptosporidium spp. (Crypto) are the most common causes of calf diarrhoea during the first 30 days of animal age. Three weighted-stratified random-effects meta-analyses were performed to calculate the worldwide prevalence of mixed infections of the causative agents (i.e., BRV-BCoV, BRV-ETEC, BRV-Crypto) and their potential influencing factors. The meta-analysis covered 41 studies (94 sub-studies) in 21 countries that determined the presence or absence of mixed infections in global calf populations. The highest worldwide estimated pooled prevalence was identified for BRV-Crypto (6.69%), followed by BRV-BCoV (2.84%), and BRV-ETEC (1.64%). The chance of detecting BCoV in calves with diarrhoea was 1.83 higher in the presence of BRV compared to calves without BRV, whereby an inhibition effect (odds ratio: 0.77) was determined between BRV and Crypto infections. The diagnostic methods were identified as a significant influencing factor in the detection of all considered mixed infections, while the other analysed factors differed in relation to their effect on prevalence. In contrast to BRV-BCoV, the prevalence of BRV-ETEC and BRV-Crypto mixed infections followed the course of individual ETEC and Crypto prevalence related to the age class of the sampled animals.

The most common worldwide diarrhoea-causing agents in neonatal calves are Cryptosporidium spp. (Crypto), bovine rotavirus (BRV), bovine coronavirus (BCoV), and enterotoxigenic Escherichia coli F5 (K99) (ETEC). Crypto is a zoonotic pathogen of diarrhoea in humans, particularly for children and immunocompromised adults. Four weighted-stratified random-effect meta-analyses including meta-regression analyses were performed to calculate the worldwide mean prevalence of Crypto and associated concurrent infections with BRV, BCoV and ETEC, as well as their potential influencing factors. The meta-analysis incorporated 28 studies (56 substudies) in 17 countries that determined the presence or absence of concurrent infections with Crypto in the global calf population. Approximately half of all considered studies presented here were conducted in Europe independently of the type of infections with Crypto. Within Europe, the highest estimated mean Crypto-BRV prevalence was identified in Ireland (16.7%), the highest estimated mean Crypto-BCoV prevalence was detected in the United Kingdom (4.3%), and the highest estimated mean Crypto-ETEC prevalence across the literature was determined in Turkey (4.7%). The chance of detecting BRV, BCoV, and ETEC in calves with diarrhoea was 0.8 (confidence interval (CI): 0.6–1.0), 0.7 (CI: 0.5–1.0) and 0.6 (CI: 0.4–0.9) lower in the presence of Crypto compared to calves without Crypto. This may indicate an inhibitory effect between BRV, BCoV, ETEC, and Crypto in calves. The variance in the published prevalence across the literature can mainly be explained by the “diagnostic” factor (R² min–max: 0.0–40.3%), followed by the “health status of the sampled animals” (R² min–max: 1.4–27.3%) and “geographical region” (R² min–max: 5.9–23.6%).

The microbial dynamics of vacuum-packed (VP) beef are shaped by interactions between bacterial and fungal communities, influencing spoilage and meat quality during storage. While bacterial succession is well studied, fungal roles remain underexplored. We examined microbial communities in VP beef over 85 days using spike-in, qPCR, 16S/18S rRNA gene amplicon sequencing, culture-based methods, whole genome sequencing, and co-culture experiments. Initially dominated by Pseudomonas and Brochothrix, the bacterial community shifted toward lactic acid bacteria (LAB) by day 15. Fungal communities remained diverse, with Kurtzmaniella, Barnettozyma, Debaryomyces, and Yarrowia as key genera. Co-culture experiments revealed a triangular interaction: yeasts enhanced LAB, LAB inhibited Enterobacterales, and Enterobacterales suppressed yeasts. Genomic analyses suggest yeast metabolites support LAB, LAB inhibit via acids and bacteriocins, and Enterobacterales produce fungal cell wall-degrading enzymes. These findings highlight fungi’s overlooked role and the importance of inter-kingdom interactions in meat microbiomes, offering a foundation for strategies to improve meat safety and shelf life.

Knowledge of pathogenic potential, frequency and antimicrobial resistance patterns of porcine Streptococcus (S.) spp. other than S. suis is scarce. Between 2016 and 2020, altogether 553 S. spp. isolates were recovered from clinical specimens taken from Austrian swine stocks and submitted for routine microbiological examination. Antimicrobial susceptibility testing towards eight antimicrobial substances was performed using disk diffusion test. All isolates from skin lesions belonged to the species S. dysgalactiae subspecies equisimilis (SDSE). S. hyovaginalis was mainly isolated from the upper respiratory tract (15/19) and S. thoraltensis from the genitourinary tract (11/15). The majority of S. suis isolates were resistant to tetracycline (66%), clindamycin (62%) and erythromycin (58%). S. suis isolates from the joints had the highest resistance rates. S. suis and SDSE isolates resistant to tetracycline were more likely to be resistant to erythromycin and clindamycin (p < 0.01). Results show that different species of Streptococcus tend to occur in specific body sites. Nevertheless, a statement whether these species are colonizers or potential pathogens cannot be given so far. High resistance rates of S. suis towards tetracyclines and erythromycin and high recovery rates of S. suis from lung tissue should be considered when treating pigs with respiratory diseases.

Antimicrobial use in livestock production is a controversial subject. While antimicrobials should be used as little as possible, it is still necessary, from both an animal health and welfare point of view, to treat infected animals. The study presented here aimed to analyse antimicrobial use on Austrian dairy farms by calculating the number of Defined Course Doses (DCD_vet) administered per cow and year for dry cow therapy. Antimicrobial use was analysed by production system and whether farmers stated that they used blanket dry cow therapy (i.e., all cows in the herd were treated) or selective dry cow therapy (i.e., only cows with a positive bacteriological culture or current/recent history of udder disease were treated). A statistically significant difference (p < 0.001) was determined between antimicrobial use for blanket (median DCD_vet/cow/year: 0.88) and selective dry cow therapy (median DCD_vet/cow/year: 0.41). The difference between antimicrobial use on conventional and organic farms for dry cow therapy as a whole, however, was not statistically significant (p = 0.22) (median DCD_vet/cow/year: 0.68 for conventional; 0.53 for organic farms). This analysis demonstrates that selective dry cow therapy leads to a lower overall use of antimicrobials and can assist in a more prudent use of antimicrobials on dairy farms.

Translocations of Rhinocerotidae are commonly performed for conservation purposes but expose the animals to a variety of stressors (e.g. prolonged fasting, confinement, novel environment, etc.). Stress may change the composition of gut microbiota, which can impact animal health and welfare. White rhinoceroses in particular can develop anorexia, diarrhea and enterocolitis after translocation. The aim of this study was to investigate the associations of age, sex and translocation on the rhinoceros’ fecal bacterial microbiota composition. fecal samples were collected from rhinoceroses at capture (n = 16) and after a >30-hour road transport (n = 7). DNA was isolated from these samples and submitted for 16S rRNA V3-V4 phylotyping. Alpha diversity indices of the rhinoceros’ fecal microbiota composition of different age, sex and before and after transport were compared using non-parametric statistical tests and beta diversity indices using Permutational Multivariate Analysis Of Variance (PERMANOVA). Resulting P-values were alpha-corrected (Padj.). Alpha and beta diversity did not differ between rhinoceroses of different age and sex. However, there was a significant difference in beta diversity between fecal samples collected from adult animals at capture and after transport. The most abundant bacterial phyla in samples collected at capture were Firmicutes and Bacteroidetes (85.76%), represented by Lachnospiraceae, Ruminococcaceae and Prevotellaceae families. The phyla Proteobacteria (Padj. = 0.009) and Actinobacteria (Padj. = 0.012), amongst others, increased in relative abundance from capture to after transport encompassing potentially pathogenic bacterial families such as Enterobacteriaceae (Padj. = 0.018) and Pseudomonadaceae (Padj. = 0.022). Important commensals such as Spirochaetes (Padj. = 0.009), Fibrobacteres (Padj. = 0.018) and Lachnospiraceae (Padj. = 0.021) decreased in relative abundance. These results indicate that the stressors associated with capture and transport cause an imbalanced fecal microbiota composition in white rhinoceroses that may lead to potentially infectious intestinal disorders. This imbalance may result from recrudescence of normally innocuous pathogens, increased shedding of pathogens or increased vulnerability to new pathogens.