Horizontal transfer of mitochondrial DNA into the nuclear genome generates nuclear mitochondrial sequences (NUMTs), which serve as molecular fossils reflecting long-term mitochondrial-nuclear interactions and genome evolution. However, the biological mechanisms governing NUMT integration, retention, and evolutionary fate remain incompletely understood in domesticated animals. Here, using the latest pig reference genome assembly (Sscrofa11.1), we present a comprehensive genome-wide characterization of NUMTs in pigs and provide new insights into their genomic distribution and evolutionary constraints. We identified 513 high-confidence NUMTs, of which 460 were chromosomally mapped, accounting for 0.0106% of the nuclear genome. Beyond increased detection, our analyses reveal that pig NUMTs exhibit non-random origins, preferentially integrate into genomic regions under weak selective constraint, and are frequently associated with repetitive elements, consistent with a DNA repair-mediated insertion mechanism. NUMTs predominantly occur as short, fragmented sequences and show signatures of long-term neutral evolution, while insertions disrupting coding sequences are strongly selected against. Synteny-based analyses further identified clustered NUMT regions and duplicated NUMTs, suggesting secondary genomic duplication events following initial integration. Comparative analysis with the earlier Sscrofa10.2 assembly demonstrates that improved genome quality substantially enhances NUMT detection, particularly in repetitive and GC-rich regions, clarifying previously ambiguous sequence-context associations. Together, this high-quality pig NUMT map provides a robust foundation for future functional, evolutionary, and population-level investigations and contributes to the conservation and utilization of pig genetic resources.

Abstract Loin muscle weight is an important indicator of carcass yield in meat rabbit production, but the host genetic and intestinal microbial factors associated with its variation remain poorly understood. Because the cecum is the primary site of hindgut fermentation in rabbits, we integrated whole-genome resequencing, cecal transcriptome profiling, and cecal and rectal 16S rRNA sequencing data from 321 Kangda meat rabbits, with rectal microbiome data used as a downstream comparative reference. Compared with the rectum, the cecum contained a richer microbial community, with more ASVs and genera and significantly higher microbial diversity, whereas predicted metabolic functions were largely conserved between the two segments. Loin muscle weight showed moderate SNP-based heritability (h² = 0.39), and the cecal microbiome explained a smaller but detectable proportion of phenotypic variation (m² = 0.13). Multi-strategy microbial screening identified 19 candidate cecal genera associated with loin muscle weight, with Methanosphaera showing the strongest negative association. Host GWAS prioritized candidate loci near MEX3C and TCF4 on chromosome 10, and integration of cecal cis-eQTL and GWAS summary statistics further prioritized GJB3 as a candidate gene associated with loin muscle weight. Consistent with the cecum-centered model, host genetic relatedness was weakly but significantly correlated with cecal microbial similarity, whereas no such global association was observed for the rectal microbiome. Microbial GWAS and SMR analyses further prioritized a cecal MRAP2–Methanobrevibacter association as the main host-regulated microbial signal, while rectal analyses identified distinct segment-specific signals, including SULF1–Roseburia. These findings suggest that host genetic variation may be linked to loin muscle deposition partly through cecal gene expression and fermentation-related cecal microbial taxa, with the rectal microbiome providing comparative evidence for hindgut segment specificity. This study provides candidate host and microbial targets for future functional validation and microbiome-informed nutritional strategies to improve carcass traits in meat rabbits.

Genome-wide association studies (GWAS) has identified many genetic variants associated with milk-related traits in dairy cattle. However, the causal variants or genes remain largely unknown. In this study, using a large population (> 10,000 individuals) of Chinese Holstein cattle, we performed GWAS for six milk-related traits (milk yield, fat percentage, protein percentage, fat yield, protein yield, and somatic cell score) and subsequently prioritized putative causal variants by multi-trait Bayesian fine-mapping and examined the causal genes by Mendelian randomization (MR) analysis incorporating GWAS and cis-eQTL summary data from CattleGTEx. We also conducted a colocalization analysis to identify the share putative causal variants behind the milk-related traits and gene expressions. A total of 9,688 genome-wide significant SNPs (P < 1.2 × 10−7) were identified across the GWAS results for six milk-related traits, and these SNPs were distributed in 25 unique QTL regions. Subsequently, the multi-trait Bayesian fine-mapping identified 211 independent credible sets (CS) containing putative causal variants within these QTL regions. Among these CSs, 189 CSs were significantly associated with at least one trait (average lfsr < 0.01). Notably, the lead SNPs within these significant CSs included 3 missense variants and 62 non-coding transcript variants. The MR analysis detected 268 causal associations between gene expression and milk-related traits. The colocalization analysis identified two regions containing common putative causal variants for one or multiple milk-related traits and the expressions of some genes. Our integrative analysis of GWAS, Bayesian fine-mapping, MR, and colocalization further confirmed the well-known causal associations of DGAT1 and GHR and the milk-related traits. In addition, we revealed some novel potential causal genes, including AHNAK, ARHGEF2, SOX13, FDPS, SCGB2A2, and MROH2B. These results enhance our understanding of genetic mechanisms underlying the milk-related traits in dairy cattle.

Background: Machine learning (ML) holds great promise for genomic breeding value prediction in livestock and poultry, yet its application in layer breeding remains limited. Methods: In this study, we used whole-genome resequencing data from 834 Wenshui Luhua Green-Shelled (WLGS) laying hens to predict genomic breeding values for eight egg production and egg quality traits using multilayer perceptron (MLP), random forest (RF), and genomic best linear unbiased prediction (GBLUP). Model performance was evaluated via 10-fold cross-validation, and the effects of data type and single nucleotide polymorphism (SNP) density were examined. Results: Heritability analysis indicated moderate heritability for egg number (EN) at 0.327. Egg weight-related traits (EW-30W, EW-40W, and EHD-40W) exhibited high heritability (0.570-0.631), while eggshell strength (ESS-40W) and thickness (EST-40W) showed moderate heritability at 0.228 and 0.220, respectively. Model comparisons revealed that RF performed best for egg shape index (ESI-30W, 0.395) and most egg quality traits, whereas GBLUP yielded optimal results for egg weight traits, achieving prediction accuracies of 0.392 for EW-30W and 0.432 for EW-40W. Whole-genome resequencing data consistently outperformed 50K chip data across all models, with GBLUP improving EW-40W prediction accuracy by 24.9%. SNP density analysis further showed that GBLUP remained stable under low-density conditions, while MLP and RF progressively improved with increasing density, with RF demonstrating the most pronounced advantage at high densities. Conclusions: In summary, the GBLUP model is suitable for traits with high heritability and low-density marker scenarios, while the RF model demonstrates significant predictive advantages for egg production and specific egg quality traits under high-density conditions. This study provides scientific basis for model selection in the genomic selection program for laying hens.

Hyperuricemia, driven by disrupted purine metabolism, predisposes individuals to hepatic and renal injury. To explore potential microbial interventions, 50 lactic acid bacterial (LAB) strains were isolated from Tibetan fermented foods, and 3 nucleoside-degrading candidates were identified, including Lactiplantibacillus plantarum 15-5, Lactiplantibacillus plantarum YL-2, and Lacticaseibacillus paracasei XS23. Strain 15-5 eliminated 99% of inosine and guanosine within 1 h, surpassing YL-2 and XS23. In hyperuricemic mice, L. plantarum 15-5 reduced serum uric acid by 42.91%, normalized hepatic xanthine oxidase activity by 22.57%, restored BUN and creatinine levels toward baseline, and markedly alleviated hepatic and renal tissue damage, while also suppressing proinflammatory cytokines IL-1β and TNF-α. The L. plantarum 15-5 increased fecal short-chain fatty acids, particularly propionate and butyrate (2- to 3-fold), and partially recovered gut microbial diversity and composition under hyperuricemia. Comparative genomics indicated that 15-5 possesses broader metabolic and ecological capacities than YL-2 or XS23, consistent with its superior functional performance. These results reveal a strain-specific framework linking nucleoside catabolism, microbiota-mediated fermentation, and host metabolic and inflammatory regulation, identifying L. plantarum 15-5 as a metabolically versatile candidate for intervention in hyperuricemia and associated hepatic and renal injury.

Abstract Background The significant temperature variations across northern and southern China have driven the adaptive evolution of Chinese native cattle breeds, allowing them to thrive in diverse and extreme bioclimate environments. Understanding how these breeds have adapted to varying temperatures is essential for identifying genetic factors that contribute to their survival in such conditions. Results In this study, using whole-genome sequence data of 336 individuals (with an average sequencing depth of 30.12 ×) from 21 cattle breeds, including 8 breeds from cold regions, 3 from warm regions, and 10 from hot regions, clear genetic differentiation among the three groups of breeds was revealed. Using whole-genome SNP, InDel, and SV data, a series of selective genomic regions, genes, and variants/SVs associated with cold or hot temperature adaptability were identified. Key genes, including KLB, HSPA4, ECSCR, DNAJC18 and SLC9A1 are speculated to be responsible for cold/hot adaptability based on the extreme difference in allele frequency of the selective variants/SVs harbored by these genes, their known biological functions, protein–protein interaction network, findings from previous studies on their relation to environmental adaptation, and their tissue specificities. Conclusions By integrating SNP, InDel, and SV data, this study provides a comprehensive genetic framework for understanding selective environmental adaptation. These findings enhance our understanding of the mechanisms underlying temperature adaptation in cattle and offer a molecular foundation for the development of new breeds.

Heat stress limits dairy production. The temperature-humidity index (THI), combining temperature and relative humidity, is widely used to assess heat stress. However, in Chinese Holstein cattle, the phenotypic responses of milk traits and genotype-environment interaction mechanisms under different THI conditions are understudied. Based on 63,334 records from 7240 cows (milk yield, fat percentage, protein percentage), matched with meteorological data and 113,297 SNPs, we employed a random-effects GWAS to examine SNP effects across a continuous THI gradient, comparing results with conventional, temperature-, and humidity-interaction GWAS. As THI increased, all traits declined with distinct patterns. Random regression GWAS identified 149 significant SNP × THI interactions (5 for MY, 86 for FP, 58 for PP), distributed across BTA5, BTA6, BTA14, and BTA20. Candidate gene annotation identified 52 candidate genes near significant SNPs, of which 50 core candidate genes were supported in both temperature and humidity GWAS. The most robustly supported core candidate genes include DGAT1, CPSF1, ABCG2, MGST1, VPS28, PPP1R16A, ZNF250, GRID2, KCNC2, and LOC787350—of which DGAT1, ABCG2, and MGST1 have been functionally validated in milk production traits, whereas others represent novel candidates requiring further investigation. Temperature and THI-GWAS showed high consistency, while humidity-GWAS detected both overlapping and specific signals. Incorporating THI as a continuous environmental gradient identifies environment-dependent regulatory signals not captured by conventional GWAS, providing candidate genes that may contribute to future breeding strategies after further validation.

Antimicrobial peptides (AMPs) are increasingly used as feed additives to enhance the growth, health, and immunity of farmed animals. However, their effectiveness varies across species, dosages, and feeding durations, and an integrated assessment is lacking. This study performed a global meta-analysis to quantify the effects of dietary AMPs supplementation on growth performance, blood metabolites, immune response, intestinal morphology, and gut microbiota diversity. A total of 58 peer-reviewed studies comprising 926 effect sizes were included from five animal taxa, encompassing 29 species. Effect sizes were calculated using Hedges' g, and meta-regression was applied to explore dose-response relationships. Results showed that AMPs supplementation significantly improved growth performance and blood metabolite profiles, particularly in omnivorous livestock, such as chickens and pigs. Conversely, the impact of AMPs on the gut microbiota diversity of aquatic animals was less pronounced than in terrestrial livestock, likely attributable to differences in habitat and physiological characteristics. Regarding supplementation strategies, a 60-day regimen of 1000 mg/kg of AMP produced the most favorable outcomes in aquatic species. For livestock, a period of at least 30 days with doses above 250 mg/kg yielded substantial benefits. This study offers empirically substantiated confidence for the utilization of AMPs as efficacious substitutes for antibiotics. These findings contribute to the development of targeted, species-specific feeding strategies for sustainable and efficient animal production.

Cattle are integral to global food security, yet the molecular architecture of their complex traits remains poorly understood. Here, we present the Cattle Genotype–Tissue Expression (CattleG-TEx) Phase 1 resource (https://cattlegtex.farmgtex.org/), a substantial expansion of the pilot study. By leveraging 12,422 RNA-seq profiles across 43 tissues and 82 breeds, we characterized 433,972 primary and 161,428 non-primary regulatory effects spanning seven molecular phenotypes. This high-resolution atlas resolves 75% of GWAS signals for 44 complex traits, significantly addressing the "missing regulation" in livestock. We propose a genetic regulatory model demonstrating how variants across multiple biological layers interact with specific biological contexts to shape pheno-typic variation. Furthermore, CattleGTEx elucidates mechanisms underlying adaptive evolution between Bos taurus and Bos indicus, as well as artificial selection in dairy and beef breeds. Finally, by mapping evolutionary constraints on these regulatory effects, we demonstrate the translational value of this resource for prioritizing causal variants in human complex diseases. Together, Phase 1 of CattleGTEx provides a transformative framework for functional genomics, precision breeding, and comparative genetics.

Sexual dimorphism is a defining vertebrate feature, yet its sex-specific molecular architecture remains poorly understood. Here we established a sex-balanced, uniformly reared chicken cohort to map this landscape, integrating individual whole-genome sequencing with 7,969 bulk and 779,380 single nucleus transcriptomes across 32 tissues from 280 birds. We identified 495,098 independent expression quantitative trait loci for 20,194 genes, including 10,937 loci modulated by cell-type composition. Notably, 340 genes were regulated by 449 loci in a sex-dependent manner, significantly enrichment in endocrine tissues like adipose and the adrenal gland. Furthermore, we fine-mapped 1,219 structural variants, demonstrating their unique roles to tissue- and sex-specific expression beyond SNPs. Ultimately, we showed the utility of these regulatory effects in elucidating the molecular basis of metabolism and complex traits in both chickens and humans. This comprehensive atlas of regulatory effects provides profound insights into the genomic and molecular basis of sexual dimorphism in vertebrates.

Metabolites are important intermediate molecular phenotypes that reflect physiological and biochemical processes within an organism. However, the molecular mechanisms linking genetic variation to metabolite abundance remain poorly understood in dairy cattle. Here, we integrated whole-genome sequencing, whole-blood transcriptomic, and plasma metabolomic data from 80 Jersey cattle to investigate the genetic regulation of circulating metabolites. After quality control, 10,696,212 high-quality SNPs, 15,559 expressed genes, and 841 stable plasma metabolites were retained for downstream analyses. Cis-eQTL mapping identified 1863 eGenes regulated by 1,340,105 significant cis-eQTLs. Transcriptome–metabolome association analysis further detected 258 significant gene–metabolite associations involving 148 genes and 177 metabolites. By integrating cis-eQTLs with gene–metabolite associations, mediation analysis identified 218 significant SNP–gene–metabolite trios involving 124 genes and 157 metabolites. Network analysis further identified several highly connected mediator genes, including MEGF9, S1PR5, and CD27 and revealed two distinct mediation patterns, complete and partial mediation. Together, these findings indicate that gene expression serves as an important intermediate layer connecting genetic variation with circulating metabolites. This study provides a comprehensive multi-omics resource for investigating the genetic regulation of the blood metabolome in Jersey cattle and offers new insights into the molecular basis of metabolic variation.

The advent of third-generation sequencing, particularly Oxford Nanopore Technologies (ONT), has revolutionized epigenetic studies by enabling direct detection of DNA methylation modifications and single-base resolution profiling of methylation patterns. While this technology has been predominantly utilized in human and bacterial research, its applications in livestock and poultry remain limited. In this study, we employed ONT sequencing to construct comprehensive 5-methylcytosine modification maps for ten representative pig breeds, explored the mechanism of high altitude adaptive methylation and allele-specific methylation events in these pigs. Through genome-wide integration of sequencing data, we identified 27,857,021 CpG sites, with 71.5

The implementation of embryo genomic selection (EGS) is constrained by the lack of a standardized workflow that balances high genotyping accuracy with the preservation of embryo viability, particularly given embryonic heterogeneity. To address this, we systematically optimized the critical steps of trophectoderm biopsy and whole-genome amplification, establishing a diameter-stratified strategy that links embryo diameter to a biopsy area window designed to protect embryo recovery while ensuring sufficient DNA input for reliable genotyping. Our results demonstrated high concordance (> 98%) between embryo and calf genomic estimated breeding values (GEBVs), supporting the reliability of predictions. Crucially, we defined a diameter-based biopsy framework: for embryos < 150 μm, biopsy areas < 1000 μm2 supported viability, whereas larger embryos (≥ 150 μm) tolerated up to 1500 μm2, with a minimum threshold of 840 μm2 guaranteeing > 90% genotype call rates. By preventing oversampling in small embryos and undersampling across embryo sizes, this diameter-guided control improves both embryo survival and GEBV prediction accuracy. This study provides a validated technical pathway for EGS that addresses the major practical bottleneck in livestock breeding. By ensuring reliable selection without compromising developmental potential, our work facilitates the rapid introgression of superior genetics and marks a significant step toward making EGS scalable, accelerating genetic progress in cattle and other species.

In this study, we applied random regression test-day model for genomic prediction in the Holstein population in Shandong Province of China with respect to different reference populations, using either 150 k chip genotypes or imputed sequence genotypes. Three different reference populations were considered, i.e., the Shandong (SD) reference population consisting of 1 688 Holstein cows from Shandong Province, the Non-SD reference population consisting of 5 299 Holstein cows from other parts of China, and the combined population of the two. The SD reference resulted in higher prediction accuracy than the Non-SD reference, although the former was much smaller than the latter. The combined reference further increased the accuracy. These results indicate that the accuracy of genomic prediction cross-population within breed is low, even though the reference population is large. Using imputed sequence data may not significantly improve the cross-population prediction ability. However, the inclusion of data from other populations into the reference population can improve the accuracy of genomic selection.

Genomic prediction holds significant potential for advancing precision medicine in humans, as well as accelerating genetic improvement in animals and plants. For multi-trait prediction, the conventional multi-trait models are primarily based on global genetic correlations between traits. With the development of local genetic correlation (LGC) estimation methods, it is now possible to analyze LGCs confined to specific genomic regions and it is expected that incorporating LGCs into multi-trait prediction model would enhance the prediction ability. Here, we proposed three models to address this issue and evaluated their performances using simulated data and three real datasets from human, cow, and pig populations. Our results demonstrate that LGCs are heterogeneous across the genome and incorporating LGCs in multi-trait prediction would increase the prediction accuracy by an average of 12.76% ± 2.07% compared to conventional multi-trait genomic prediction method (MTGBLUP) in the real datasets. Our findings highlight the importance of considering LGCs in improving multi-trait genomic prediction. Three local genetic correlation (LGC) genomic prediction methods are proposed to incorporate LGCs into multi-trait genomic prediction and could universally improve prediction accuracy compared to conventional multi-trait prediction methods.

Genetic mutation and drift, coupled with natural and human-mediated selection and migration, have produced a wide variety of genotypes and phenotypes in farmed animals. We here introduce the Farm Animal Genotype-Tissue Expression (FarmGTEx) Project, which aims to elucidate the genetic determinants of gene expression across 16 terrestrial and aquatic domestic species under diverse biological and environmental contexts. For each species, we aim to collect multiomics data, particularly genomics and transcriptomics, from 50 tissues of 1,000 healthy adults and 200 additional animals representing a specific context. This Perspective provides an overview of the priorities of FarmGTEx and advocates for coordinated strategies of data analysis and resource-sharing initiatives. FarmGTEx aims to serve as a platform for investigating context-specific regulatory effects, which will deepen our understanding of molecular mechanisms underlying complex phenotypes. The knowledge and insights provided by FarmGTEx will contribute to improving sustainable agriculture-based food systems, comparative biology and eventual human biomedicine.

Eggshells not only protect the contents of the egg from external damage but are also a key factor influencing consumer choice, second only to price. In the later stages of egg production, the incidence of pimpled eggs significantly increases, severely affecting the hatchability and food safety of the eggs. This study compares the differences in the uterine proteomes and metabolomes of hens producing pimpled eggs and those producing normal eggs, aiming to identify the proteins and metabolites that may play a crucial role in the formation of pimpled eggs. A total of 242 differentially expressed proteins (DEPs) were identified in uterine tissue, of which 116 were upregulated and 126 were downregulated. Enrichment analysis revealed that the DEPs were enriched in pathways related to ion transport, energy metabolism, and immune responses. The study found that in the normal eggs (NE) group, HCO₃⁻ was predominantly transported via SLC4A1, although other transport pathways may also play a role. In contrast, in the pimpled eggs (PE) group, bicarbonate ions (HCO₃⁻) was primarily transported through SLC4A4. Additionally, a total of 44 differentially metabolites (DMs) were identified in the uterus, with 5′-Adenylic acid (ATP) being significantly downregulated in the PE group. The ions and matrix proteins required for eggshell formation are transported from uterine cells to the uterine fluid against a concentration gradient, a process that consumes a substantial amount of energy. The decrease in ATP concentration in the PE group may be a significant factor influencing the formation of pimpled eggs. Subsequently, we found that the DEPs and DMs were jointly enriched in several signaling pathways, including the FoxO signaling pathway related to energy metabolism, nicotinate and nicotinamide metabolism, and tryptophan metabolism associated with immune response. Notably, the DMs involved in these signaling pathways were all downregulated in the PE group. Our research findings indicate that SLC4A1, SLC4A2, and ATP2B4 (DEPs), along with 5′-adenylic acid and trigonelline (DMs), influence the formation of eggshells through mechanisms related to energy metabolism, ion transport, and immune response. These DEPs and DMs may serve as potential biomarkers for the genetic improvement of eggshell quality.

Fatty liver disease is prevalent during parturition in dairy cattle. Therefore, there is an urgent need to develop novel, sensitive biomarkers for the early diagnosis of the metabolic disorders. Macroproteomics revealed that the faecal microbial community changes significantly when animal develops fatty liver disease. The microbial changes in cows with severe fatty liver (SFL) were greater than cows with moderate fatty liver (MFL) and normal condition (Norm). This suggests that microorganisms play an important role in the pathogenesis of metabolic disorders. In this study, faeces-sourced microorganisms and microbial proteins were identified and testified as novel biomarkers for the early diagnosis of fatty liver disease in cattle. For example, the AUC (area under curve) values, based on Receiver Operating Characteristics analysis, of using the combination of Lachnoanaerobaculum and Bifidobacterium (at the genus level) to discriminate MFL and SFL animals reached 0.944 and 0.867, respectively, and 0.922 and 0.985, respectively, for the combination of Bifidobacterium pseudolongum and Lachnospiraceae bacterium (at the species level). Interestingly, the differentially expressed microbial proteins are closely related to the identified microorganisms. For example, the majority of the top 20 microbial proteins with significant expression differences were derived from Bifidobacterium pseudolongum. Bifidobacterium pseudolongum was considered a prominent potential biomarker for the diagnosis of metabolic disorders, especially in fatty liver cattle. The results of this study confirm that faecal microbial dysbiosis signatures can serve as a diagnosis biomarker for non-alcoholic fatty liver disease (NAFLD), but also shed light on faecal microbiota transfer (FMT) experiments in treating NAFLD.

INTRODUCTION:The impact of non-antibiotic feed additives on livestock performance and health is contingent upon a multitude of variables, including the animal species, dosage and type of feed additives, and duration of oral administration. However, there is a paucity of knowledge regarding the relationship between these factors and the performance of livestock animals. OBJECTIVES:The objective of this study was to conduct a global meta-analysis based on a pool of empirical studies to investigate the effects of dietary additives on growth, production, blood metabolites, immunity, intestinal morphology, and the abundance of gut microbiota in livestock. METHODS:A meta-regression coupled with dose-effect analysis was performed to ascertain the optimal dosage and feeding duration for the optimal body function. A total of 71 papers, estimating 1, 035 effect size across 9 species and 7 types of non-antibiotic feed additives were recruited in our meta-dataset. RESULTS:Overall assessment confirmed that these additives in diet can significantly improve livestock production and immune function across species. Our findings indicated that the effects of additives on animal performance were more pronounced in herbivores than in omnivores. The dose-response results indicated that the overall optimal doses for antimicrobial peptides, enzymes, oligosaccharides, organic acids, phytogenic, probiotics and prebiotics were 100 mg/kg, 30 mg/kg, 200 mg/kg, 50 mg/kg, 200 mg/kg, 10⁶ CFU/kg, and 10 mg/kg, respectively. Oral administration of these additives for a 2-month period effectively improves livestock performance and health. CONCLUSION:This evidence-based approach provides a foundation for implementing customized feeding strategies designed to optimize livestock performance, enhance immunity and reduce feed costs. Our assessment shows that these feed additives are promising alternatives to antibiotics in reducing the use of antibiotics. Furthermore, these findings suggest that the use of these feed additives can lead to evidence-based recommendations for practical feeding strategies, providing livestock producers with a sustainable and cost-effective approach to animal health management.

Eggshells not only protect the contents of the egg from external damage but are also a key factor influencing consumer choice, second only to price. In the later stages of egg production, the incidence of pimpled eggs significantly increases, severely affecting the hatchability and food safety of the eggs. This study compares the differences in the uterine proteomes and metabolomes of hens producing pimpled eggs and those producing normal eggs, aiming to identify the proteins and metabolites that may play a crucial role in the formation of pimpled eggs. A total of 242 differentially expressed proteins (DEPs) were identified in uterine tissue, of which 116 were upregulated and 126 were downregulated. Enrichment analysis revealed that the DEPs were enriched in pathways related to ion transport, energy metabolism, and immune responses. The study found that in the normal eggs (NE) group, HCO3- was predominantly transported via SLC4A1, although other transport pathways may also play a role. In contrast, in the pimpled eggs (PE) group, bicarbonate ions (HCO3-) was primarily transported through SLC4A4. Additionally, a total of 44 differentially metabolites (DMs) were identified in the uterus, with 5 '-Adenylic acid (ATP) being significantly downregulated in the PE group. The ions and matrix proteins required for eggshell formation are transported from uterine cells to the uterine fluid against a concentration gradient, a process that consumes a substantial amount of energy. The decrease in ATP concentration in the PE group may be a significant factor influencing the formation of pimpled eggs. Subsequently, we found that the DEPs and DMs were jointly enriched in several signaling pathways, including the FoxO signaling pathway related to energy metabolism, nicotinate and nicotinamide metabolism, and tryptophan metabolism associated with immune response. Notably, the DMs involved in these signaling pathways were all down- regulated in the PE group. Our research findings indicate that SLC4A1, SLC4A2, and ATP2B4 (DEPs), along with 5 '-adenylic acid and trigonelline (DMs), influence the formation of eggshells through mechanisms related to energy metabolism, ion transport, and immune response. These DEPs and DMs may serve as potential biomarkers for the genetic improvement of eggshell quality.