N6-methyladenosine (m6A), a predominant and reversible modification of mammalian RNA, plays a critical role in regulating growth, development, and metabolism. While methyltransferase-like 14 (METTL14) is an essential component of the m6A methyltransferase complex, its specific function in regulating milk fat metabolism in dairy goats remains unexplored. This study therefore aimed to elucidate the role of METTL14 in lipid metabolism within dairy goat mammary epithelial cells (GMECs). METTL14 overexpression significantly promoted the synthesis of TAG (Triacylglycerol) and TC (Total cholesterol), as well as lipid droplet accumulation in GMECs. Furthermore, METTL14 upregulated CCAAT enhancer binding protein beta (CEBPB) expression at both the mRNA and protein levels by directly inducing m6A modification on its transcripts. Finally, we confirmed that m6A modification occurs specifically at site 1662 of CEBPB mRNA, and the “Readers” YTH N6-methyladenosine RNA binding protein F1 and F3 (YTHDF1/3) were found responsible for the m6A site recognition and interpretation. This study demonstrated that METTL14 facilitates lipid synthesis and deposition in GMECs. Mechanistically, METTL14 installs the m6A modification at site 1662 of CEBPB transcripts. This m6A mark is specifically recognized by the readers YTHDF1 and YTHDF3, which promote the translation of CEBPB mRNA, thereby upregulating its expression.

This study investigated the effects of amylose/amylopectin (AM/AP) ratios in low-protein (LP) diets on the growth performance, fat deposition, and nutrient utilization in goslings. A total of 288 healthy, 35-day-old male Jiangnan White Geese were randomly divided into four treatment groups: one group fed a normal protein diet (16%) with an AM/AP ratio of 0.34 (NPR0.34), and three groups fed low protein diets (14%) with different AM/AP ratios (LPR0.26, LPR0.34, LPR0.44). Each group consisted of six replicates, with 12 geese per replicate, and they were fed for 28 days. The results showed that the body weight at 63 days and average daily gain (ADG) of the LPR0.44 group geese were significantly higher than those of the other groups (p < 0.01), while the feed-to-gain ratio (F/G) was lower (p < 0.05). The abdominal and mesenteric fat contents were lower in the LPR0.44 group than in the LPR0.26 group (p < 0.05), whereas the breast and leg muscle yields were higher (p < 0.05). The breast muscle redness (a*) of the LPR0.34 and LPR0.44 groups was higher than in the NPR0.34 group at 45 min (p < 0.05). The levels of C6:0, C8:0, C11:0, C12:0, and C13:0 in breast muscle saturated fatty acids (SFAs) of the LPR0.44 group were higher, while that of C18:0 was lower compared with the LPR0.26 group (p < 0.05). The serum total cholesterol (TC) and triglycerides (TGs) in the LPR0.44 group were lower than in the LPR0.26 group (p < 0.05). Hepatic lipase (HL) activity was significantly lower in the LPR0.44 group (p < 0.01). Regarding hepatic fatty acids, the levels of butyric acid (C4:0), lauric acid (C12:0), and nervonic acid (C24:1) were lower in the LPR0.44 group than in the LPR0.26 group (p < 0.05). No significant differences were observed in intestinal morphology, digestive enzyme activities, or nutrient utilization among the groups. (p > 0.05). In conclusion, adjusting the AM/AP ratio to 0.44 in a low-protein diet improved growth performance, regulated lipid metabolism, and maintained intestinal function in goslings.

Ionotropic receptors (IRs) are a divergent subfamily of ionotropic glutamate receptors (iGluRs) that detect olfactory and environmental cues, influencing behaviors such as foraging and adaptation. To explore the evolution of IRs in relation to feeding ecology, we identified IRs and iGluRs from the genomes of four gastropods with distinct diets: Pomacea canaliculata (9 IRs/18 iGluRs), Bellamya purificata (10/22), Cipangopaludina chinensis (11/23), and Babylonia areolata (22/41). IRs were markedly expanded in B. areolata, suggesting lineage-specific diversification. Phylogenetic analysis grouped IRs and iGluRs into three clades, with IRs clustered with GluD, supporting early functional divergence following gene duplication. In all species, IR25b showed tandem duplication and played a central role in protein–protein interaction (PPI) networks. Most IRs were acidic, whereas IR-A and IR-C subgroups were basic, suggesting functional specialization among subfamilies. Structural analysis showed that IRs share conserved domains and motifs across species. Most IRs experienced purifying selection, while P. canaliculata showed relaxed constraints, suggesting weaker functional limitation. Collinearity analysis identified conserved genes, such as BarIR-A.6 and BarIR-D.1, across species. qPCR confirmed tissue-specific expression of IRs in multiple organs. Together, these results reveal the molecular features and evolutionary patterns of IRs in gastropods, highlighting their potential roles in olfaction and dietary adaptation.

To help assess the relationship between N-terminal pro-B-type natriuretic peptide (NT-proBNP) and feline cardiogenic arterial thromboembolism (ATE), the objective of this retrospective study was to compare plasma NT-proBNP concentrations between cats with cardiomyopathy that developed ATE (ATE group) and cats with occult cardiomyopathy that did not develop ATE within 1 year of testing (occult cardiomyopathy [OCM] group). Cats with cardiomyopathy and congestive heart failure (CHF) but no ATE (CHF group) were included for comparison. Cats with cardiomyopathy that had NT-proBNP testing were classified into ATE, OCM, or CHF groups. Clinical, echocardiographic, treatment, and NT-proBNP data from medical records were reviewed and compared among groups. A receiver operating characteristic curve was generated to develop a cutoff point for NT-proBNP. Cats were then compared based on this cutoff point. The ATE group (n = 25) had significantly higher NT-proBNP concentrations than the OCM group (n = 31; p < 0.001); there was no significant difference in NT-proBNP concentrations between the ATE and CHF groups (p = 0.92). The estimated optimal NT-proBNP cutoff point to separate OCM and ATE groups was 491 pmol/L (sensitivity = 96.0%, specificity = 93.5%). Cats with NT-proBNP > 491 pmol/L had a larger left atrium, thicker left ventricle, lower fractional shortening, and higher prevalence of spontaneous echogenic contrast and left atrial thrombi on echocardiography. These preliminary, hypothesis-generating findings suggest that NT-proBNP concentrations > 491 pmol/L may help detect cats with OCM at risk for ATE, but given the limitations of this retrospective study, prospective studies are needed to evaluate the potential utility of this measurement.

Objective: The effects of physical exercise on protein expression and metabolites in the plasma exosomes of Yili horses are not yet clear. This study aims to elucidate the effects of exercise training on the expression of plasma extracellular vesicle proteins and metabolites in Yili horses through multi-omics analysis, providing reference indicators for the training and performance evaluation of Yili horses. Methods: Eight healthy 3-year-old Yili horses were selected for the study, with four untrained horses as the control group and four trained horses as the training group. After conducting proteomic and metabolomic analyses of the plasma exosomes of each group of horses, the differences in protein expression and metabolites were analyzed. Results: Compared with the control group, training significantly increased the levels of proteins in the plasma exosomes of Yili horses that promote muscle function and repair and regulate metabolism and immune function. In addition, carbohydrates and their metabolites were significantly reduced. Conclusion: Training significantly altered the protein composition and metabolites of Yili horse exosomes. These changes mainly provide new insights for the training and evaluation of Yili horse exercise ability, but further research is still needed.

Obesity and metabolic disorders are an increasing concern in companion animals, creating demand for herb-derived nutraceuticals and functional feeds. This study evaluated whether a water extract of Aster pekinensis (AP) ameliorates high-fat-diet (HFD)-induced obesity and metabolic dysfunction in mice. The phytochemical profile of AP was characterized by mass spectrometry, revealing oleanane-type triterpenoid saponins and dicaffeoylquinic acids. Male C57BL/6 mice were fed an HFD and orally given AP (10–200 mg/kg/day) for 12 weeks, with normal diet and untreated HFD groups as controls. AP at 50–200 mg/kg/day reduced body-weight gain, adipose tissue mass and food efficiency without lowering food intake, and improved fasting glucose and atherogenic lipid indices. AP also enhanced glucose tolerance and insulin sensitivity, attenuated hepatic steatosis, hepatocellular ballooning, lobular inflammation and non-alcoholic fatty liver disease (NAFLD) Activity Score, and decreased serum liver enzyme activities. These effects were accompanied by modulation of hepatic genes involved in lipogenesis and inflammation. Together, these findings indicate that AP extract mitigates diet-induced obesity and NAFLD-like liver injury and supports further development as a herb-derived nutraceutical or functional feed ingredient for managing obesity-related metabolic disorders in companion animals.

Background: Identifying causal genes underlying quantitative trait loci (QTLs) remains challenging due to small effect sizes and the prevalence of non-coding variants. Although multi-omics integration frameworks such as eQTL- and epigenomic-based approaches and TWAS have advanced gene prioritization, their application in poultry and livestock is often constrained by limited reference panels and tissue resources. This review introduces a cost-effective F2-based integrative framework and compares it with existing multi-omics strategies. Methods: The proposed framework combines QTL remapping, transcriptome analysis, haplotype frequency comparison, association analysis, and conditional correlation analysis within a single workflow. Causal analysis and quantitative complementation tests using knockout birds are incorporated to identify causal genes. Results: By reusing the original F2 population employed for QTL mapping, this approach enables hypothesis-independent gene prioritization without requiring additional fine-mapping crosses. Its effectiveness is demonstrated through comparison with conventional multi-omics methods, and the integration of causal analysis and quantitative complementation testing provides robust genetic evidence for pinpointing causal genes. Conclusions: This F2-based framework efficiently prioritizes and verifies causal gene candidates directly within the mapping population, offering a cost-effective alternative to multi-omics approaches that require large-scale resources. It is broadly applicable to diverse chicken crosses and readily transferable to other small livestock species and model organisms.

Non-invasive and intelligent technologies have been utilized to monitor agricultural systems in real time, facilitating expedient decision-making and the reduction in animal stress in diverse climatic conditions. The objective of this study was to develop a deep learning supervised model to classify Nelore cattle (Bos indicus) into two groups: those in comfort and those under thermal stress. Thirty cattle, aged between 18 and 20 months, were evaluated between June and December 2023, resulting in 676 samples collected across four daily periods (6:00, 12:00, 18:00, and 24:00). Biotic variables included rectal temperature (RT) and respiratory rate (RR), while abiotic variables included air temperature (AT) and relative humidity (RH). The neural network model exhibited an accuracy and recall of 72% but a low specificity of 42%. These metrics indicate that while the model is effective in detecting stress situations, it faces challenges in correctly identifying animals in thermal comfort, likely due to class imbalance and the need for additional input features to capture environmental adaptability. Consequently, it can be posited that supervised learning models are valuable tools for precision livestock farming, provided that discriminatory limitations are mitigated by refining input characteristics and data balancing.

Accurate monitoring of fish movement is essential for understanding behavioral patterns and group dynamics in aquaculture systems. Underwater scenes—characterized by dense populations, frequent occlusions, non-rigid body motion, and visually similar appearances—present substantial challenges for conventional multi-object tracking methods. We propose an improved CenterTrack-based framework tailored for multi-fish tracking in such environments. The framework integrates three complementary components: a multi-branch feature extractor that enhances discrimination among visually similar individuals, occlusion-aware output heads that estimate visibility states, and a three-stage cascade association module that improves trajectory continuity under abrupt motion and occlusions. To support systematic evaluation, we introduce a self-built dataset named Multi-Fish 25 (MF25), continuous video sequences of 75 individually annotated fish recorded in aquaculture tanks. The experimental results on MF25 show that the proposed method achieves an IDF1 of 82.5%, MOTA of 85.8%, and IDP of 84.7%. Although this study focuses on tracking performance rather than biological analysis, the produced high-quality trajectories form a solid basis for subsequent behavioral studies. The framework’s modular design and computational efficiency make it suitable for practical, online tracking in aquaculture scenarios.

Mastitis remains a prevalent and economically detrimental disease within the dairy industry, profoundly affecting animal welfare, milk quality, and overall production output. Nowadays, Somatic Cell Count (SCC) is widely recognized as the gold-standard indicator for the detection of mastitis; however, its limitations in pathogens discrimination and the lack of early-stage characterization of mastitis highlight the need for complementary diagnostic approaches. This review synthesizes recent research into the development and validation of novel biomarkers for the early and accurate identification of mastitis in dairy cows. The investigation encompasses a range of biological molecules for improving mastitis diagnosis. Biomarkers such as lactoferrin (LTF), β-defensin 4 (DEFB4), vitronectin, paraoxonase 1 (PON1), and N-acetyl-β-D-glucosaminidase (NAGase) show promise in distinguishing between cows not susceptible and cows susceptible to mastitis. Concurrently, nucleic acid-based biomarkers are emerging as a particularly promising frontier. While mitochondrial DNA (mtDNA) has demonstrated insufficient specificity, microRNAs (miRNAs) are gaining attention as highly stable and sensitive indicators of intramammary inflammation, potentially enabling the detection of subclinical infections before they become clinically apparent. Despite these advances, significant challenges related to specificity, reliability, and cost-effectiveness currently hinder the widespread practical application of any single biomarker. Therefore, future research should be directed towards the validation of a synergistic panel of multiple biomarkers to improve mastitis management in dairy cow farms.