Bin Hu

The mechanistic underlying the favorable meat quality of Tibetan pigs has not been fully elucidated. This study integrated flavor chemistry, histomorphology, and proteomics to explore the structural and molecular features of their meat. Longissimus dorsi samples from Tibetan and Duroc pigs (n = 6 each biological replicates) were quantitatively analyzed for amino acid profiling, histological assessment, and proteomic characteristic. Statistical approaches included weighted correlation network analysis, t-tests, and functional enrichment. Tibetan pork contained 34 mg/100g more total free amino acids, notably sweet-tasting Ala (+49.2%) and Thr (+32.2%). Muscle fiber density was >250% higher and diameter > 30% smaller, indicating finer texture. Proteomics revealed 149 upregulated proteins, including 57 mitochondrial differentially expressed proteins (DEPs)—11 of which belonged to electron transport chain complexes (e.g., NDUFAB1, COX2). The significant enrichment of oxidative phosphorylation pathways may be associated with mitochondrial efficient energy metabolism under hypoxic in Tibetan pigs, potentially linking to the breed’s unique meat characteristics. Ala levels showed strong correlations with metabolic and structural protein modules. The finer fibers and mitochondrial protein profile of Tibetan pigs contribute to higher amino acid content and meat quality. This structural–metabolic–flavor axis supports both hypoxia adaptation and high meat quality. Given the central role of mitochondrial electron transport chain (ETC) proteins in energy metabolism and Ala in flavor presentation, their synergistic action provides a molecular bridge between hypoxia adaptation and meat quality. Therefore, this study suggests that ETC and Ala may serve as key biomarkers for meat quality differences, offering new perspectives for meat quality research.

Background/Objectives: Fat deposition traits in pigs directly influence pork flavor, tenderness, and juiciness and are closely linked to overall pork quality. The Tibetan pig, an indigenous breed in China, not only possesses a high intramuscular fat content but also exhibits a unique fat metabolism pattern due to long-term adaptation to harsh environments. This makes it an excellent genetic and physiological model for investigating fat deposition characteristics. Adipose tissue from different body regions displays varying morphologies, cytokines, and adipokines. This study aimed to examine adipose tissue deposition characteristics in different parts of Tibetan pigs and provide additional data to explore the underlying mechanisms of differential fat deposition. Methods: Our research identified significant differences in the morphology and gene expression patterns between subcutaneous fat (abdominal fat [AF] and back fat [BF]) and intramuscular fat (IMF) in Tibetan pigs. Results: Histological observations revealed that subcutaneous fat cells were significantly larger in area and diameter compared to IMF cells. The transcriptomic analysis further identified differentially expressed genes (DEGs) between subcutaneous fat and IMF, with a total of 65 DEGs in BF vs. IMF and 347 DEGs in AF vs. IMF, including 25 DEGs common to both comparisons. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these genes were significantly associated with lipid metabolism-related signaling pathways, such as the Wnt, mTOR, and PI3K-Akt signaling pathways. Several DEGs, including DDAH1, ADRA1B, SLCO3A1, and THBS3, may be linked to the differences in fat deposition in different parts of Tibetan pigs, thereby affecting meat quality and nutritional value. Conclusions: These findings provide new insights into the unique fat distribution and deposition characteristics of Tibetan pigs and establish a foundation for breeding strategies aimed at improving pork quality.

Introduction: This study aims to explore the important factors affecting the characteristics of different parts of pork.Methods: Lipidomics and proteomics methods were used to analyze DAL (differential lipids) and DAPs (differential proteins) in five different parts (longissimus dorsi, belly meat, loin, forelegs and buttocks) of Duhua pig (Duroc × Guangdong small spotted pig), to identify potential pathways affecting meat quality, investigating fat deposition in pork and its lipid-protein interactions.Results: The results show that TG (triglyceride) is the lipid subclass with the highest proportion in muscle, and the pathway with the most significantly enriched lipids is GP. DAP clustered on several GO terms closely related to lipid metabolism and lipogenesis (lipid binding, lipid metabolism, lipid transport, and lipid regulation). In KEGG analysis, there are two main DAP aggregation pathways related to lipid metabolism, namely Fatty acid degradation and oxidative phosphorylation. In PPI analysis, we screened out 31 core proteins, among which NDUFA6, NDUFA9 and ACO2 are the most critical.Discussion: PC (phosphatidylcholine) is regulated by SNX5, THBS1, ANXA7, TPP1, CAVIN2, and VDAC2 in the phospholipid binding pathway. TG is regulated by AUH/HADH/ACADM/ACADL/HADHA in the lipid oxidation and lipid modification pathways. Potential biomarkers are rich in SFA, MUFA and PUFA respectively, the amounts of SFA, MUFA and PUFA in the lipid measurement results are consistent with the up- and down-regulation of potential biomarker lipids. This study clarified the differences in protein and lipid compositions in different parts of Duhua pigs and provided data support for revealing the interactions between pork lipids and proteins. These findings provide contributions to the study of intramuscular fat deposition in pork from a genetic and nutritional perspective.

This research was conducted to study the effects of dietary inclusion of mulberry leaf powder (MLP) on growth performance, meat quality, antioxidant activity, and carcass traits of Tibetan pigs. Eighteen Tibetan pigs (33.8 ± 1.1 kg) were assigned to two treatment groups randomly and received either the control diet (CON) or a basal diet containing 8% MLP (MLP) for two months. After the two-month feeding trial, the MLP group showed lower backfat thickness while a higher lean percentage. Compared with CON pigs, MLP pigs had higher serum CAT activity. In addition, dietary MLP supplementation significantly decreased the muscle shear force. Muscle fiber morphology analysis showed that MLP pigs had larger muscle fiber density while smaller muscle fiber cross-sectional area. Up-regulated gene expression of myosin heavy chain (MyHC)IIa was also observed in MLP pigs. These results indicate that the enhanced antioxidant activity and altered muscle fiber type and morphology appeared to contribute to the improvement of meat quality in Tibetan pigs fed diets containing MLP.

Background: Identification of protein phenotypic annotations is an essential and challenging problem in modern genetics. Such problem is related to some serious diseases, including cancers, HIV and so on. The factors of genotype and environment increase the difficulties in determining the phenotype of proteins. The experiment methods to achieve such a goal are always timeconsuming and expensive. Objective: The aim of this study was to design a quick and cheap method for determining the phenotypes of proteins. Methods: In this study, we proposed a network computational method to identify novel phenotypic annotations of proteins. To execute such method, a heterogeneous network was constructed, which contained three sub-networks: protein network, phenotypic type network, and protein-phenotypic type network. The method tried to find out all paths with limited length, which connected one protein and one phenotypic type. A scoring scheme was adopted to count obtained paths and induced a score to indicate the associations between them. Results and Conclusion: The ROC and PR curve analyses were done to evaluate the performance of the method, indicating the utility of the method. Our method was superior to other network methods, which incorporated popular network algorithms.

Due to the high rate of spontaneous abortion (SAB) in porcine pregnancy, there is a major interest and concern on commercial pig farming worldwide. Whereas the perturbed immune response at the maternal–fetal interface is an important mechanism associated with the spontaneous embryo loss in the early stages of implantation in porcine, data on the specific regulatory mechanism of the SAB at the end stage of the implantation remains scant. Therefore, we used high-throughput sequencing and bioinformatics tools to analyze the healthy and arresting endometrium on day 28 of pregnancy. We identified 639 differentially expressed lncRNAs (DELs) and 2357 differentially expressed genes (DEGs) at the end stage of implantation, and qRT-PCR was used to verify the sequencing data. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and immunohistochemistry analysis demonstrated weaker immune response activities in the arresting endometrium compared to the healthy one. Using the lasso regression analysis, we screened the DELs and constructed an immunological competitive endogenous RNA (ceRNA) network related to SAB, including 4 lncRNAs, 11 miRNAs, and 13 genes. In addition, Blast analysis showed the applicability of the constructed ceRNA network in different species, and subsequently determined HOXA-AS2 in pigs. Our study, for the first time, demonstrated that the SAB events at the end stages of implantation is associated with the regulation of immunobiological processes, and a specific molecular regulatory network was obtained. These novel findings may provide new insight into the possibility of increasing the litter size of sows, making pig breeding better and thus improving the efficiency of animal husbandry production.

Metabolic pathway is an important type of biological pathways. It produces essential molecules and energies to maintain the life of living organisms. Each metabolic pathway consists of a chain of chemical reactions, which always need enzymes to participate in. Thus, chemicals and enzymes are two major components for each metabolic pathway. Although several metabolic pathways have been uncovered, the metabolic pathway system is still far from complete. Some hidden chemicals or enzymes are not discovered in a certain metabolic pathway. Besides the traditional experiments to detect hidden chemicals or enzymes, an alternative pipeline is to design efficient computational methods. In this study, we proposed a powerful multilabel classifier, called iMPTCE-Hnetwork, to uniformly assign chemicals and enzymes to metabolic pathway types reported in KEGG. Such classifier adopted the embedding features derived from a heterogeneous network, which defined chemicals and enzymes as nodes and the interactions between chemicals and enzymes as edges, through a powerful network embedding algorithm, Mashup. The popular RAndom k-labELsets (RAKEL) algorithm was employed to construct the classifier, which incorporated the support vector machine (polynomial kernel) as the basic classifier. The ten-fold cross-validation results indicated that such a classifier had good performance with accuracy higher than 0.800 and exact match higher than 0.750. Several comparisons were done to indicate the superiority of the iMPTCE-Hnetwork.