The milk flavor can be attributed to the presence of numerous flavor molecules and precursors. In this study, we employed widely targeted metabolomic analysis techniques to analyze the metabolic profiles of various milk samples obtained from goats, sheep, dairy cows, and buffaloes. A total of 631 metabolites were identified in the milk samples, which were further categorized into 16 distinct classes. Principal component analysis (PCA) suggested that the metabolite profiles of samples from the same species exhibit clustering, while separated patterns of metabolite profiles are observed across goat, sheep, cow, and buffalo species. The differential metabolites between the groups of each species were screened based on fold change and variable importance in projection (VIP) values. Five core differential metabolites were subsequently identified, including 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid, inosine 5′-triphosphate, methylcysteine, N-cinnamylglycine, and small peptide (L-tyrosine–L-aspartate). Through multiple comparisons, we also screened biomarkers of each type of milk. Our metabolomic data showed significant inter-species differences in the composition and concentration of some compounds, such as organic acids, amino acids, sugars, nucleotides, and their derivatives, which may affect the overall flavor properties of the milk sample. These findings provided insights into the molecular basis underlying inter-species variations in milk flavor.

As a crucial economic trait, fat deposition is directly related to carcass quality and feed efficiency in sheep. The purpose of this study was to investigate the polymorphisms of the FGB gene related to fat deposition and detect the expression features of the FGB gene in different adipose tissues of sheep by using Sanger sequencing, MassARRAY® SNP technique, and quantitative real-time PCR (qRT-PCR). Results showed that in the intron region of the FGB gene, a SNP g. 3378953 A > T has been identified, and significant association was found between perirenal fat weight, perirenal fat relative weight, mesenteric fat weight, and mesenteric fat relative weight (P < 0.05). Moreover, qRT-PCR analysis showed that FGB was expressed in all three adipose tissues, and FGB gene expression level in the AA genotype was significantly lower than that in the AT or TT genotypes (P < 0.05). Therefore, the FGB gene can be used as a candidate gene to reduce fat deposition in Hu sheep breeding, and the selection of the AA genotype in Hu sheep in production practice is more conducive to improving production efficiency.

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.

Single-cell protein feed (SCPF) is an important supplement to protein feed materials, but its authenticity is often affected by antibiotic mycelial dregs (AMD). Headspace-gas chromatography–ion mobility spectrometry (HS-GC-IMS), integrated with chemometrics, was utilized to differentiate nucleotide residue (NR), three AMDs, and adulterated samples with concentrations ranging from 0.1% to 20% (w/w). Orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component analysis (PCA) were applied to classify the adulterated samples. In addition, the feasibility of quantitative analysis of the AMDs content in adulterated SCPF based on partial least squares regression (PLSR) algorithm. In total, 88 volatile organic compounds (VOCs) were detected. The differences in VOCs between NR and AMD mainly came from aldehydes, alcohols, and esters. The OPLS-DA models effectively identified AMD in adulterated NR samples (Accuracy = 100%), demonstrating the HS-GC-IMS data’s good application potential for the SCPF adulteration. Nine VOCs, i.e., 2-ethyl-3-methylpyrazine, dihydro-5-methyl-2(3H)-furanone, 2-methylpropanol, (E,E)-2,4-heptadienal, linalool, 2,3,5-trimethylpyrazine, citronellol, acetoin, and 3-methylbutan-1-ol, were proposed as key markers for detecting NR adulterated with AMDs. The PLSR algorithm was further used to determine the AMD content in NR (R²_cal = 0.96, R²_cv = 0.94). This study validated HS-GC-IMS’s ability to analyze volatile organic compounds in feed and showcased its utility as a convenient, quick, and affordable tool for SCPF authenticity screening.