Background: The Xinjiang Black pig is an excellent breed developed by the Xinjiang Production and Construction Corps in the 1990s; however, it has been endangered by the impact of commercial breeds. Methods: Whole genomes of 224 individuals from the Xinjiang Black pig conservation population were resequenced. Results: Genetic structure and diversity analyses revealed that Xinjiang Black pigs underwent severe inbreeding and were genetically closely linked to Landrace pigs. The genetic diversity of the F₂ generation was well preserved in the existing breeding scheme. A total of 686 significant selection regions and 406 candidate genes were identified using F_ST and θπ complementary methods, with Xinjiang Black pigs, Min pigs, and Laiwu pigs as ancestral populations, and F₂. Based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and quantitative trait loci annotations, potential germplasm candidate genes were identified. Among these, SOX5, HMG20A, and NEDD4 are associated with fat deposition; SPRY1, MNS1, DMXL2, and ALB are closely associated with male reproductive ability; ARPP19 and TLN2 are strongly associated with oestrous cycle regulation and oocyte maturation; and SLC4A4 and SLC12A1 are extremely important for osmotic regulation and foetal survival. Conclusions: These findings deepen our understanding of the genetic mechanisms of artificial selection in Xinjiang Black pigs and provide a theoretical basis for subsequent breeding and genetic research on this breed.

Ovarian follicular development is essential for reproductive performance in sows, with granulosa cells playing a central role in follicular growth and function. This study investigated the function and regulatory mechanism of lncRNA-ALDBSSCT0000005583 (lncRNA-5583), previously identified as differentially expressed in M2-stage follicles (5.0–6.9 mm in diameter) between Meishan and Duroc sows. Overexpression and loss-of-function experiments were performed in porcine granulosa cells, and candidate microRNA interactions and downstream targets were examined using bioinformatic prediction, dual-luciferase reporter assays, and molecular and functional analyses. Porcine granulosa cell experiments were performed using three independent pooled granulosa-cell isolations (n = 3). LncRNA-5583 was identified as a 588-nucleotide non-coding RNA. Its overexpression promoted granulosa cell proliferation and reduced apoptosis, accompanied by corresponding changes in PCNA, BAX, Bcl-2, and the BAX/Bcl-2 ratio; miR-193a-5p overexpression attenuated these effects. LncRNA-5583 interacted with miR-193a-5p in a sequence-dependent manner, and miR-193a-5p directly targeted ACVR1. Inhibition of miR-193a-5p increased ACVR1 expression and was accompanied by increased Smad3 phosphorylation, whereas lncRNA-5583 knockdown decreased both ACVR1 expression and Smad3 phosphorylation. These findings support the involvement of the lncRNA-5583/miR-193a-5p/ACVR1 regulatory axis in porcine granulosa cell proliferation.

The conventional window for ultrasonic pregnancy diagnosis in sows is 22–25 days post-insemination, which often results in missed opportunities for the optimal re-insemination of non-pregnant sows and elevated production costs. This present study aimed to establish an early pregnancy detection method for sows at 12–18 days post-insemination, thereby providing a reference for efficient reproductive management. Saliva, urine and vaginal secretions were collected from sows during this period, and seven metabolites were quantified. Seven machine learning models were employed for data analysis, after which the optimal combination was determined, and the detection protocol was refined using recursive feature elimination. The results revealed that the majority of metabolites in saliva and urine differed significantly between pregnant and non-pregnant groups (p < 0.05). Among the models evaluated, the random forest algorithm exhibited the best predictive performance, with accuracy ranging from 0.59 to 1.00. Saliva sampled at 17 days post-insemination was identified as the optimal diagnostic medium, and 100% prediction accuracy was achieved by measuring only three metabolites: Glc, Ste, and Xan. The diagnostic approach established in this study allows pregnancy detection 5–8 days earlier than conventional methods, with the additional benefits of non-invasive sampling and minimal stress to sows. Accordingly, it provides a novel reference for enhancing the efficiency of swine production.