ABSTRACT Mastitis in dairy cows is a complex disease regulated by a series of gene networks. However, the expression patterns and molecular roles of long non‐coding RNAs (lncRNAs) in the mammary glands of mastitic dairy cows remain unclear. In this study, an Escherichia coli ( E. coli )‐induced mastitis model was established, and RNA‐seq was employed to profile lncRNA expression and identify differentially expressed genes in the mammary glands of healthy and E. coli ‐induced mastitis dairy cows. A total of 44 up‐regulated and 111 down‐regulated differentially expressed lncRNAs (DE‐lncRNAs) were identified in the mammary tissues of E. coli ‐induced mastitis dairy cows compared to healthy controls. Functional annotation and interaction network analysis revealed that eight DE‐lncRNAs (TCONS_00077660, TCONS_00020252, TCONS_00042123, TCONS_00104816, TCONS_00111594, TCONS_00057669, TCONS_00084031, and TCONS_00076361) were predicted to be associated with 61 miRNAs and nine mRNAs ( BOLA , PPP3R1 , MAPKAPK2 , NECTIN2 , C3AR1 , JSP.1 , SERPINF2 , ITGAM , and PIM1 ), potentially providing insights into the molecular mechanisms associated with mastitis. These findings enhance our understanding of the molecular regulatory network underlying dairy cow mastitis and provide a theoretical foundation for future molecular breeding and biotherapeutic strategies for mastitis prevention and treatment.
ABSTRACT Studying canine behavioral genetics could provide important insights for dog breeding and for understanding both canine and human behavior. To identify genetic determinants of behavior in German Shepherd Dogs, we used a cohort of 373 dogs to search for genome‐wide associations with behavioral variables and personality traits measured by the Dog Mentality Assessment (DMA), a standardized dog behavior evaluation widely used in Sweden. Multidimensional scaling using genetic data revealed two clusters identified by merits as working and show sub‐populations differing in most of their DMA scores. These behavioral differences were strongly correlated with BOLDNESS—a higher‐order trait considered a measure of the shyness‐boldness personality continuum in dogs. A region with highest allele frequency differences between sub‐populations was found on CFA24: 23.5–25.5 Mb containing genes for coat color specification and previously reported candidate regions of selection. The strongest association signals were found for traits/behavioral variables correlating with BOLDNESS as, for example, on CFA1 within a region encompassing the TCF4 gene for which, in humans, multiple associations with neuroticism (a reverse equivalent in humans of BOLDNESS in dogs) were reported. Signals of association were found in regions containing genes with important roles in the nervous system and associations with comparable personality traits, such as neuroticism, in humans. Our results indicate that within‐breed selection in GSDs has had an effect on the shyness‐boldness personality continuum, with working dogs scoring higher in all aspects of BOLDNESS—being more playful, prone to chase after toys, curious, fearless and socially interested in strangers compared with show dogs.
ABSTRACT The gut microecological differences associated with high‐altitude habitats remain poorly understood. The Snowcock ( Tetraogallus tibetanus ), a high‐altitude bird endemic to the Qinghai‐Xizang Plateau, and the low‐altitude Pheasant ( Phasianus colchicus ) differ strikingly in habitat elevation and diet, providing an ideal comparative model. Fecal samples from six Snowcocks and six Pheasants were analyzed using 16S rRNA gene sequencing and UPLC‐MS/MS untargeted metabolomics. Multi‐omics integration was performed via Procrustes analysis, correlation networks, and random forest classification. Snowcocks displayed significantly lower alpha diversity and distinct beta diversity clustering compared with Pheasants. The Snowcock microbiota was dominated by Actinobacteriota and enriched in cold‐tolerant genera such as Psychrobacillus . In contrast, the Pheasant microbiota showed higher diversity and was dominated by Firmicutes and Bacteroidota. Metabolomics revealed that differential metabolites enriched in Pheasants were predominantly mapped to amino acid metabolism and terpenoid biosynthesis pathways, while those enriched in Snowcocks were linked to glycerophospholipid and sphingolipid metabolism. Multi‐omics integration demonstrated a significantly correlated microbiome–metabolome association, and the random forest model discriminated the two species in this dataset. The Snowcock microbiota was simplified and enriched with psychrophilic genera, and its fecal metabolome showed specific glycerophospholipid signatures. These features may reflect microecological divergence associated with the harsh plateau environment. Our findings provide multi‐omics insights into high‐altitude divergence in wild birds.
ABSTRACT Gastrointestinal nematodes (GIN) are one of the main constraints affecting sheep production worldwide. For decades, control methods based on anthelmintics have been extensively used, which ended up in multiple drug‐resistant helminth strains. Selective breeding for disease resistance is a long‐term and sustainable strategy to improve animal health, welfare and productivity. Therefore, we performed a genome‐wide association study using a customized SNP panel to identify genomic regions and molecular mechanisms associated with GIN resistance/susceptibility among Portuguese Merino and Merino‐related sheep. Two SNPs located within centromere protein K (chr 16; 14 439 999 bp) and NADH: ubiquinone oxidoreductase complex assembly factor 2 (chr 16; 19 366 442 bp) genic regions reached genome‐wide Bonferroni significance ( p = 2.08 × 10 −6 ). Additionally, five genome‐wide suggestive SNPs were detected within genic regions of adenomatous polyposis coli regulator of WNT signaling pathway (chr 7; 1 050 994 bp), regulator of G protein signaling 8 (chr 12; 68 278 650 bp) and hypoxia inducible factor 3 subunit alpha (chr 14; 57 059 428 bp), as well as in intergenic regions near calcium binding and coiled‐coil domain 1 and ATP synthase membrane subunit C locus 2 (chr 3 142 391 546 bp) and semaphorin‐6D (chr 7; 65 321 727 bp). Candidate genes located in these regions are involved in intestinal homeostasis, epithelial barrier reinforcement, and regulatory mechanisms of immune response. These results provide new and useful information about the genetic architecture underlying GIN infection in Portuguese Merino sheep. Further analyses are needed to validate these SNPs and assess their contribution to selection strategies focused on breeding animals with enhanced GIN resistance.
ABSTRACT Generative adversarial networks (GANs) are artificial intelligence models designed to generate novel data that is indistinguishable from real data. Originally developed to generate images to complement existing datasets and create more diverse samples, GAN‐based data can enhance downstream analysis for machine learning and deep learning applications. GAN‐based approaches are also versatile and suitable for augmenting omics data, improving downstream analyses such as genotype prediction, biomarker detection, and protein de novo design. In addition, GANs can preserve and reconstruct genomic regions under selective pressure, increase sample sizes from underrepresented groups, and mitigate class imbalance effects in genomic analyses, transcriptomics, and single‐cell omics. Recent work also demonstrates that GAN‐generated data can support privacy‐preserving data sharing and the design of novel biological sequences and proteins. This literature review presents the foundational concepts of GANs, explores diverse applications across omics fields, and highlights novel opportunities for applying GAN approaches in livestock genomics.
