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.

Heat stress (HS) severely significantly reduces milk yield and causes substantial economic losses of dairy cows. TMT-based proteomes and an untargeted metabolomics approach were used to conduct the proteomics and metabolomics in heat-stressed (HS, n = 6) and heat-resistant (HR, n = 6) Chinese Holstein. The proteomics showed that 29 differentially expressed proteins (DEPs), with SERPINA3-7, ACTN4, and PLOD1 up-regulated, and GSN down-regulated in HR cows. The metabolomics showed that 168 differential positive metabolites and 170 differential negative metabolites were identified, with HR cows exhibiting lower levels of anti-inflammatory compounds, such as N6-Acetyl-L-lysine. In addition, 29 DEPs and 338 metabolites revealed four key pathways, including the lysine degradation (ko00310) and metabolic pathway (ko01100) with underlying protein–metabolite interactions, where up-regulated PLOD1 and ACTN4 and down-regulated EXT1 and GSN were observed to be interacting with the down-regulated N6-Acetyl-L-lysine, citric acid, 4-Pyridoxic acid, uracil, and uric acid, and the up-regulated arachidonic acid was enriched, which could be used for rapid and noninvasive screening of heat-tolerant cows. Functional validation through cell experiments, qPCR, and Western blot analyses showed that the interference of the ACTN4 gene could induce dairy cow mammary epithelial cell apoptosis, which could be regarded as a potential biomarker for HS in Chinese Holstein. Our results facilitate a better understanding of the molecular mechanism underlying the HS issue in dairy cows and provide a crucial insight into the alternative strategies to enhance animal welfare and productivity under high-temperature conditions.

This integrated multi-omics resource delineates the molecular and phenotypic trajectories underlying male morphotype differentiation (Blue Claw [BC], Orange Claw [OC], Small Male [SM]) in Macrobrachium rosenbergii during determinative developmental stages (100, 110, and 120 days post-stocking). The dataset comprises hemolymph metabolomes, gut microbiota 16S rRNA sequencing data and quantitative morphological trait data, thereby establishing a holistic framework capturing metabolic flux alterations, microbial community structure and phenotypic manifestations throughout morphotype specification. The synergistic integration of these layers provides support for the identification of heritable biomarkers linked to commercially advantageous morphotypes and reveals the important role of host-microbiota interactions in phenotypic divergence. All raw and processed omics data are deposited in NCBI SRA and Metabolights.

The giant freshwater prawn (GFP; Macrobrachium rosenbergii), a crustacean of high nutritional and economic value, is crucial for aquaculture. During the same growth cycle, male GFPs develop into three distinct forms: small males, orange claw males, and blue claw males. These morphotypes display varying social behaviors, which severely constrain their industrial development. To address this, this study collected male GFP samples at critical developmental time points (100, 110, and 120 days post-hatching) for phenotypic trait measurement and analysis to obtain external morphological data. Through gut microbiota diversity analysis, we identified key gut bacteria (Lactococcus garvieae and Lactobacillus taiwanensis) influencing male morphotype differentiation. Transcriptomic analysis revealed host Kyoto Encyclopedia of Gene and Genome pathways and key genes (Wnt-6, CTSB, CTSL, PPAE, and TP53) associated with morphotype differentiation. The interactions among phenotypic traits, gut microbiota, and key genes were systematically studied through association analysis. Weighted gene co-expression network analysis was employed to construct co-expression modules, from which critical gene modules influencing phenotypic variation were identified. Through association network analysis, we established an "Achromobacter-CD-TRINITY_DN93139_c0_g2 (calpain clp-1)" interaction model. Our findings provide novel insights into the genetic enhancement of GFPs and offer guidelines for future research regarding gut symbiotic bacteria and breeding initiatives. IMPORTANCE:Male Macrobrachium rosenbergii (giant freshwater prawn [GFP]) in the same growth cycle will develop into small males, orange claw males, and blue claw males. This individual heterogeneity in growth significantly impacts the benefits of aquaculture. However, the factors influencing the differentiation of male GFP morphotype remain unclear. This study analyzed the phenotypic data of various GFP levels, the structure of the intestinal microbiota, and the differential genes within the gonadal transcriptome at critical time points of male GFP-level type differentiation. The aim was to explore the potential role of intestinal microbiota and differential genes in this phenomenon. This study offers new insights into the research on the phenomenon of male GFP-level type differentiation.

A2 β-CN milk has gained widespread acceptance due to its nutritional benefits. To verify the authenticity and detect adulteration and contamination in A2 milk, we developed an HPLC-MS/MS method for determining the characteristic peptides of A1 and A2 β-CN in cow milk. The method demonstrated good specificity, sensitivity, and linearity for both A1 and A2 characteristic peptides, with limit of detection of 0.01 and 0.03 mg/L, limit of quantitation of 0.03 and 0.1 mg/L, and determination coefficients of 0.9994 and 0.9992, respectively. Whereas accuracy and precision were reasonable, the recoveries varied (69.4%-151%) across concentration levels (0.04, 0.2, and 1.0 g/kg), with higher recoveries for both peptides at low concentrations and lower recoveries for A2 peptide at medium and high concentrations, influenced by factors such as adsorption and ionization efficiency. We optimized the tryptic hydrolysis conditions, selecting a trypsin-to-casein ratio of 1:25 and a hydrolysis time of 6 h at 37°C. However, the hydrolysis of A1 and A2 β-CN was incomplete and asynchronous, exhibiting parabolic relationships with their respective concentrations, with hydrolysis degrees of 12.3% for A1 β-CN and 9.6% for A2 β-CN in pure powders. We finally established a regression model to calculate the actual proportion of A1 and A2 β-CN, with the detection limits of 5% for both β-CN. In the quantitation range of this model, A1 β-CN accounting for 10% to 80% or A2 β-CN accounting for 20% to 90%, the measured value of A1/A2 or A2/A1 was a power function relationship with the theoretical value. This method effectively verifies the authenticity of A1 and A2 milk, providing a reliable tool for detecting adulteration and contamination.

The genomic diversity of Chinese Holstein cattle remains insufficiently characterized, and the identification of genes associated with body conformation traits is still limited. In this study, we aimed to explore the genome-wide diversity and population structure, and to identify candidate genes associated with 20 body conformation traits in Chinese Holstein cattle from six farms using the GGP Bovine 50 K single nucleotide polymorphism (SNP) chip. We analyzed runs of homozygosity and population admixture across farms and observed genetic diversity differences among herds. A genome-wide association study identified 21 significant SNPs linked to five body conformation traits. Notably, a missense mutation in TJP1 (BovineHD2100008355) was associated with foot heel depth. Ten SNPs within intronic regions of genes such as GABRG3, NEGR1, and CDH2 were associated with various udder and leg traits. The remaining ten SNPs were located in intergenic regions and influenced transcription factor binding sites. Among them, BovineHD2100013266, BovineHD0500004109, and BTB-00042676 were shown to regulate transcriptional activity through dual-luciferase reporter assays. Our findings offer valuable insights for managing genetic inbreeding in cattle farms, enhancing the understanding of the genetic architecture underlying body conformation traits in Holstein cattle, and accelerating the genomic selection process in Chinese Holsteins.

Macrobrachium rosenbergii (giant freshwater prawn; GFP) holds considerable importance in aquaculture due to its high market demand and economic significance. Female GFP growth varies significantly, however, the processes responsible for these growth disparities remain unknown. In this study, intestinal and hemolymph samples of large (FL), medium (FM), and small (FS) female GFPs were collected to investigate the molecular mechanism of female GFP growth. Through the utilization of 16S rRNA sequencing and liquid chromatography-mass spectrometry metabolomics, significant intestinal flora and metabolites linked to the growth performance of female GFPs were identified. The dominant phyla of the three groups were the same, namely Firmicutes and Proteobacteria. Among groups, small females exhibited the lowest abundance of Proteobacteria (27.26 %) and the highest abundance of Firmicutes (70.10 %). The most abundant genus in each group was Lactococcus. Liquid chromatography-mass spectrometry identified 115 annotated differential metabolites, and essential metabolites related to female GFP growth performance were screened. The concentration of serum metabolites in the larger females exhibited a statistically significant variance compared to that of the smaller females. Through association analysis, we identified key genes, metabolites, and gut microbiota that influence the growth of female GFPs. Likewise, we used multi-omics techniques to establish two relationship models ("gut microbiota-GFP phenotype-metabolite", "gut microbiota-GFP phenotype-transcript"), and three important network association models ("DN5520_c0_g1-CW1-Bacteroides", "DN537746_c0_g1-BW-Roseburia" and "Picolinic acid-phenotype-Roseburia") were further developed. The present study provides novel insights into the mechanisms underlying the variability in individual growth among female GFPs. Our findings offer valuable information for future investigations exploring the correlation between gut flora and host organisms in aquatic environments.

Giant freshwater prawn (GFP; Macrobrachium rosenbergii) is an important aquaculture species with high market demand and economic value. Female GFP growth varies significantly; however, the mechanisms underlying these growth differences are unclear. In this study, intestinal and hemolymph samples of large (FL), medium (FM), and small (FS) female GFPs were collected to investigate the molecular mechanism of female GFP growth. Many key intestinal flora and metabolites related to the growth performance of female GFPs were identified using 16S rRNA sequencing and liquid chromatography-mass spectrometry metabolomics. The dominant phyla of the three groups were the same, namely Firmicutes and Proteobacteria. The abundance of Firmicutes in small females (70.10%) was significantly higher than that in other groups, and the abundance of Proteobacteria (27.26%) was the lowest. The most abundant genus in each group was Lactococcus. Liquid chromatography-mass spectrometry identified 115 annotated differential metabolites, and 63 key metabolites related to female GFP growth performance were screened. The abundance of serum metabolites in the large females was significantly different from that of the small females. Furthermore, we used multi-omics techniques to establish two relationship models ("gut microbiota-GFP phenotype-metabolite" and "gut microbiota-GFP phenotype-transcript"), and two important network association models ("DN5520_c0_g1-CW1-Bacteroides" and "DN537746_c0_ g1-BW-Roseburia") were further developed. This investigation provides new insights into the mechanism of the individual growth differences among female GFPs. Our findings will aid future studies of the interaction between the host and the intestinal flora in aquatic animals.

Macrobrachium rosenbergii is one of the most economically important crustacean species worldwide because of its nutritional value and good meat quality. There are obvious growth differences between individuals of M. rosenbergii; however, the exact mechanism underlying these growth differences remains unclear. Herein, we first measured the phenotypic traits of M. rosenbergii and analyzed the correlations among them. Furthermore, next-generation sequencing was employed to determine the transcriptome differences between different sized individuals of M. rosenbergii. Many differentially expressed genes (DEGs) related to growth were identified and subsequently verified using quantitative real-time reverse transcription PCR. A relationship model between phenotypic traits and DEGs of M. rosenbergii was established, which will provide useful information for molecular assisted selection in genetic improvement. In addition, weighted gene co-expression network analyses was conducted, and the purple module was identified to have the highest correlation coefficient with the palm length. Gene ontology functional enrichment analysis showed that the DEGs from different sized M. rosenbergii participated in different biological functions. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that DEGs of males were mainly enriched in actin cytoskeleton regulation, starch and sucrose metabolism, apoptosis, and unsaturated fatty acid biosynthesis; whereas the DEGs of females were mainly enriched in glutathione metabolism, starch and sucrose metabolism, galactose metabolism, lysosome, animal mitosis, and other pathways. The results revealed the molecular mechanism of the growth differences of M. rosenbergii and established a relationship model between phenotypic traits and key genes, which will provide important support for the genomic selection and genetic improvement of traits in M. rosenbergii during modern breeding.

Abstract Milk mid‐infrared (MIR) spectra have been shown to provide valuable information on a wide range of traits to be used in dairy cattle breeding programs. Selecting the most informative variables from complex data can improve the prediction accuracy and model robustness and, consequently, the interpretability of MIR spectra. Thus, we aimed to investigate the prediction performance of feature selection methods based on MIR spectra data, using the milk fatty acid (FA) profile as an example to illustrate the evaluated procedure. Data of MIR spectra, milk test‐day records, and reference FA concentrations of 155 first‐parity Holstein cows were used in the analyses. Four models comprising different explanatory variables and three feature selection methods were evaluated. The results indicated that competitive adaptive reweighted sampling (CARS) method can effectively select the most informative variables from the MIR spectra, resulting in higher prediction accuracies than other variable selection approaches. The model including selected MIR spectra and cow information variables yielded the best FA profile predictions based on partial least square regression. C8:0, C10:0, C14:1, C17:0 isomers, C18:1, C18:1 isomer, medium‐chain FA, unsaturation FA, monounsaturated FA, and polyunsaturated FA presented accuracies based on the determination coefficient ranging from 0.66 to 0.85 in internal validation and from 0.65 to 0.84 in external validation. The most related wavenumbers to 35 FAs were found within 1003 to 1145 cm−1. Generally, using CARS and cow information improved predictions of FAs based on MIR spectra in Chinese Holstein dairy cows. Additional validation studies should be conducted as larger datasets become available.

The giant freshwater prawn (GFP; Macrobrachium rosenbergii), a tropical species cultured worldwide, has high market demand and economic value. Male GFP growth varies considerably; however, the mechanisms underlying these growth differences remain unclear. In this study, we collected gut and hemolymphatic samples of large (ML), medium (MM), and small (MS) male GFPs and used the 16S rRNA sequencing and liquid chromatography–mass spectrometry-based metabolomic methods to explore gut microbiota and metabolites associated with GFP growth. The dominant bacteria were Firmicutes and Proteobacteria; higher growth rates correlated with a higher Firmicutes/Bacteroides ratio. Serum metabolite levels significantly differed between the ML and MS groups. We also combined transcriptomics with integrative multiomic techniques to further elucidate systematic molecular mechanisms in the GFPs. The results revealed that Faecalibacterium and Roseburia may improve gut health in GFP through butyrate release, affecting physiological homeostasis and leading to metabolic variations related to GFP growth differences. Notably, our results provide novel, fundamental insights into the molecular networks connecting various genes, metabolites, microbes, and phenotypes in GFPs, facilitating the elucidation of differential growth mechanisms in GFPs.

The elite bull plays an extremely important role in the genetic progression of the dairy cow population. The previous results indicated the potential positive relationship of large scrotal circumference (SC) with improved semen volume, concentration, and motility. In order to improve bull’s semen quantity and quality by selection, it is necessary to estimate the genetic parameters of semen traits and their correlations with other conformation traits such as SC that could be used for an indirect selection. In this study, the genetic parameters of seven semen traits (n = 66,260) and nine conformation traits (n = 3,642) of Holstein bulls (n = 453) were estimated by using the bivariate repeatability animal model with the average information-restricted maximum likelihood (AI-REML) approach. The results showed that the estimated heritabilities of semen traits ranged from 0.06 (total number of motile sperm, TNMS) to 0.37 (percentage of abnormal sperm, PAS) and conformation traits ranged from 0.23 (pin width, PW) to 0.69 (hip height, HH). The highest genetic correlations were found between semen volume per ejaculation (SVPE), semen concentration per ejaculation (SCPE), total number of sperm (TNS), and TNMS traits that were 0.97, 0.98, 1.00, and 0.99, respectively. Phenotypic correlations between SC and SVPE, SCPE, TNS, and TNMS were 0.35, 0.35, 0.48, and 0.42, respectively. In summary, the moderate or high heritability of semen traits indicates that genetic improvement of semen quality by selection is feasible, where SC could be a useful trait for indirect selection or as correlated information to improve semen quantity and production in the practical bull breeding programs.

Cell-mediated immune responses (CMIRs) are critical to building a robust immune system and reducing disease susceptibility in cattle. Long non-coding RNAs (lncRNAs) regulate various biological processes. However, to the best of our knowledge, the characterization and functions of lncRNAs and their regulations on the bovine CMIR have not been investigated comprehensively. In this study, experimental bulls were immunized with heat-killed preparation of Candida albicans (HKCA) to induce delayed-type hypersensitivity (DTH). Three bulls were classified as high- CMIR responders and three were low-CMIR responders, based on their classical DTH skin reactions. LncRNAs were identified in the submandibular lymph nodes, peripheral blood, and spleen of high- and low-CMIR animals using strand-specific RNA sequencing. A total of 21,003 putative lncRNAs were identified across tissues, and 420, 468, and 599 lncRNAs were differentially expressed between the two groups in the submandibular lymph node, peripheral blood, and spleen tissues, respectively. Functional analysis of the differentially expressed lncRNA (DElncRNA) target genes showed that a number of immune-related Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched, including immune response, cell adhesion, nucleosome, DNA packaging, antigen processing and presentation, and complement and coagulation cascades. Tissue specificity analysis indicated that lncRNA transcripts have stronger tissue specificity than mRNA. Furthermore, an interaction network was constructed based on DElncRNAs and DEGs, and 11, 14, and 11 promising lncRNAs were identified as potential candidate genes influencing immune response regulation in submandibular lymph nodes, peripheral blood, and spleen tissues, respectively. These results provide a foundation for further research into the biological functions of lncRNAs associated with bovine CMIR and identify candidate lncRNA markers for cell-mediated immune responses.

Probiotics are live microbial food supplements that have been shown to have beneficial effects on animal health. Endogenous probiotic bacteria have long been used for their proposed health-promoting properties and have become a hot research topic in growth improvement in aquaculture. The endogenous probiotic bacteria from intestines of Macrobrachium rosenbergii (giant river prawn) was explored for their probiotic potential, from which 367 bacterial strains were isolated from the intestine of M. rosenbergii. After 16S rDNA sequence analysis, 234 isolates were identified as Lactococcus garvieae, which accounted for 63.76% of the total number of culturable intestinal bacteria, suggesting that this bacterium was the main component of the microbiota. Furthermore, to reveal the probiotic properties of L. garvieae, this isolated bacterial strain was characterized morphologically, physiologically, and biochemically. Its enzyme production capacity, bacteriostatic activity, and resistance to acid, high temperature, and pH, were assessed. In vitro experiments showed that the L. garvieae (No. C6a2) had a fast growth rate and entered the logarithmic phase rapidly. Besides, it had characteristics of acid-production and resistance, enzyme-producing capacity, and strong antibacterial activity against pathogenic Staphylococcus aureus, Aeromonas hydrophila, and Aeromonas veronii. However, it lacked the ability to tolerate high temperature. Our results provide novel data to deepen our understanding of the intestinal bacteria structure of M. rosenbergii and valuable information for probiotic screening and the application for M. rosenbergii.

我国已开展奶牛生产性能测定(DHI)工作有10余年的时间,目前已形成了一套完整的工作流程.乳成分-体细胞联体仪凭借着检测速度快、重复性好、样品用量少等优势,成为了DHI实验室检测乳成分和体细胞的核心仪器.但目前缺少有资质的专业计量部门对乳成分-体细胞联体仪进行校准.基于现状,本文以本特利乳成分-体细胞联体仪(Bentley FTS-Somacount FCM)为例,从空白样测试、重复性测试、残留效应测试、准确性测试、均质效率测试及稳定性测试等方面,进行了年度自校实验,为行业内部仪器的年度自校提供参考.

Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky–Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm−1 and 3017~2823 cm−1/1805~1734 cm−1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.

Heat stress has been a big challenge for animal survival and health due to global warming. However, the molecular processes driving heat stress response were unclear. In this study, we exposed the control group rats (n = 5) at 22 °C and the other three heat stress groups (five rats in each group) at 42 °C lasting 30, 60, and 120 min, separately. We performed RNA sequencing in the adrenal glands and liver and detected the levels of hormones related to heat stress in the adrenal gland, liver, and blood tissues. Weighted gene co-expression network analysis (WGCNA) was also performed. Results showed that rectal temperature and adrenal corticosterone levels were significantly negatively related to genes in the black module, which was significantly enriched in thermogenesis and RNA metabolism. The genes in the green-yellow module were strongly positively associated with rectal temperature and dopamine, norepinephrine, epinephrine, and corticosterone levels in the adrenal glands and were enriched in transcriptional regulatory activities under stress. Finally, 17 and 13 key genes in the black and green-yellow modules were identified, respectively, and shared common patterns of changes. Methyltransferase 3 (Mettl3), poly(ADP-ribose) polymerase 2 (Parp2), and zinc finger protein 36-like 1 (Zfp36l1) occupied pivotal positions in the protein-protein interaction network and were involved in a number of heat stress-related processes. Therefore, Parp2, Mettl3, and Zfp36l1 could be considered candidate genes for heat stress regulation. Our findings shed new light on the molecular processes underpinning heat stress.

Tail venous blood samples were collected from four dairy farms in Shandong province, China. Milk MIR spectra data was detected at Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences.