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PubMed2026

Effect of Saccharomyces cerevisiae fermentation postbiotic supplementation on metagenomics of digital dermatitis lesions in lactating Holstein cows.

Digital dermatitis (DD) is the leading cause of lameness in cattle, posing major animal welfare and economic concerns. Effective prevention strategies are increasingly important given emerging antimicrobial resistance associated with common DD treatments. Supplementation with Saccharomyces cerevisiae fermentation postbiotics (SCFP) has been shown to enhance innate immunity and reduce DD lesion development. This study evaluated the effect of a commercial SCFP supplement on the microbial composition of DD lesions using shotgun metagenomic sequencing to characterize microbial communities and associated antimicrobial resistance genes. Beta diversity analysis revealed that stage M4 DD lesions from SCFP-supplemented cows had a trend for different microbial compositions compared with controls (P = 0.051). At the genus level, M2 lesions were found to have statistically significant lower abundance of the genera Desulfovibrio, Pseudomonas, Staphylococcus, Anaerotignum, Caproicibacterium, and Bacteroides in the SCFP treatment group compared with the control (P < 0.05). M2 lesions from the SCFP treatment group were also found to have statistically significant higher abundance of the genera Fusobacterium, Citricoccus, Listeria, and Fundicoccus as compared with the control (P < 0.05). M4 lesions were found to have statistically significant lower abundance of the genera Blautia and Petrimonas in the SCFP treatment group compared with the control (P < 0.05). At the species level, M2 lesions were found to have statistically significant lower abundance of the species Desulfovibrio sp. G11, Anaerotignum sp. MB30-C6, Caproicibacterium argilliputei, and Prevotella intermedia in the SCFP treatment group compared with the control (P < 0.05). M2 lesions from the SCFP treatment group were also found to have statistically significant higher abundance of the species Fundicoccus culcitae and Helcococcus ovis as compared with the control (P < 0.05). Metagenomic analysis identified antimicrobial resistance genes associated with multiple antibiotics commonly used for DD treatment, including tetracyclines, lincosamides, and pleuromutilins. These findings demonstrate the potential for SCFP supplementation to alter the microbial composition of DD lesions while highlighting the ongoing concerns regarding antimicrobial resistance in DD management.IMPORTANCEDigital dermatitis (DD) causes substantial economic loss and welfare concerns in cattle production systems worldwide. Our findings show that dietary supplementation with Saccharomyces cerevisiae fermentation postbiotics (SCFP) has the potential to alter the microbial ecology of DD lesions. Importantly, this work identifies antimicrobial resistance genes within DD lesions, underscoring the limitations of antibiotic-based control strategies. By linking nutritional supplementation to changes in microbial communities and resistance gene profiles, this study advances understanding of non-antibiotic approaches to disease mitigation and supports the development of sustainable, microbiome-informed management practices in food animal production.

Animals↗
PubMed2026

Spectral Transforms as a Tool to Optimize Digital Phenotyping in Biological Images.

Modern livestock breeding has mastered genotyping. Genome-wide association studies, genomic selection, and SNP arrays enable genetic merit prediction at lower cost. However, phenotyping remains the bottleneck, as manual measurement is slow, expensive, subjective, and unable to capture spatial or temporal trait organization. Digital phenotyping via artificial intelligence could resolve this, but deep learning requires thousands of labelled examples, impractical when phenotyping cost itself limits datasets to hundreds of individuals. This creates a paradox: AI could accelerate phenotyping but requires large numbers of samples to train the models. Here, we demonstrate that integrating computer vision with machine learning offers sample-efficient digital phenotyping using eggshell colour as a model system. Rather than learning features from scratch (deep learning), we engineer physically motivated features via Wavelet transforms that decompose images into multi-scale spatial components. Wavelet features captured 14.2 percentage points more variance (R2&#x2009;=&#x2009;0.976 vs. 0.834, p&#x2009;<&#x2009;0.001) than standard colorimetry, with 50% better sample efficiency (achieving at n&#x2009;=&#x2009;60 what colorimetry required n&#x2009;=&#x2009;120). Variance decomposition revealed 77% of discriminative capacity derives from spatial patterns (bands, spots, gradients) invisible to scalar averages. Additionally, we identified "cryptic phenotypes" (3.3%) where spatial patterns contradicted average colour, cases where colorimeters failed but Wavelets succeeded. The underlying principle-that spatial decomposition can recover organizational information lost by scalar averaging-may be applicable to other traits with spatial or temporal structure, such as marbling, dermatitis, or pigmentation rhythms, although whether comparable performance gains would be observed remains to be tested empirically. Hence, for breeding programs implementing genomic selection, computer vision-based digital phenotyping captures complex trait variation without massive training datasets, addressing the bottleneck that increasingly limits genetic progress as genotyping becomes trivial.

Wavelet transform↗