Gastrointestinal (GI) microbiota play an important role in promoting growth in piglets. However, studies on microbiota composition at various growth stages are lacking. We measured body weights of Jinfen White and Mashen piglets every 7 days and collected their fecal samples by rectal swabbing at nine time points during suckling (1–28 days) and nursery (35–70 days) stages to gain insight into microbiota variability during piglet growth. The fecal microbiota were characterized via 16S rRNA gene sequencing to analyze the effects of microbial diversity on piglet growth and development preliminarily. The results showed that although the two breeds of piglets have similar body weights at birth, weaned Jinfen White piglets demonstrated a significantly greater body weight and daily weight gain than weaned Mashen piglets (P < 0.01). A total of 1,976 operational taxonomic units (OTUs) belonging to 27 phyla and 489 genera were uncovered, in which the highest numbers of OTUs belong to the phyla Firmicutes and Bacteroidetes. Lactobacillus, Bacteroides, and Prevotellaceae NK3B31 groups accounting for 12.4, 8.8, and 5.8% of OTUs, respectively, showed relatively high abundance at the genus level. Nine sampling time points were divided into three growth stages, namely, immediate postfarrowing (1 day old), suckling (7, 14, and 21 days old), and nursery (28, 35, 49, 63, and 70 days old), on the basis of the results of microbial diversity, principal coordinate, and co-occurrence network analyses. In addition, it identified 54 discriminative features in the microbiota between two breeds of piglets by LEfSe analysis, in which 17 genera enriched the microbiota community of Jinfen White piglets. Finally, abundances of 29 genera showed significant positive correlations with body weights and daily weight gain of piglets. Conversely, abundances of 12 genera demonstrated significant negative correlations with body weights of piglets. The results of our study will provide a theoretical basis for succession patterns in fecal microbiota of piglets and suggest the need for meticulous management of piglets in pig production.

Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO–TCN–GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R²) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO–TCN–GRU model, OTDBO–TCN–GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R² value is improved by 2.5%. Compared with the OOA model, the OTDBO–TCN–GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R² value improved by 3.6%. In addition, the improved OTDBO–TCN–GRU model has a prediction error of less than 0.3 mg/m³ for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO–TCN–GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses.

The aim of this study was to select the candidate genes affecting meat quality and preliminarily explore the related molecular mechanisms in the Mashen pig. The present study explored genetic factors affecting meat quality in the Mashen pig using RNA sequencing (RNA-Seq). We sequenced the transcriptomes of 180-day-old Mashen and Large White pigs using longissimus dorsi to select differentially expressed genes (DEGs). The results indicated that a total of 425 genes were differentially expressed between Mashen and Large White pigs. A gene ontology enrichment analysis revealed that DEGs were mainly enriched for biological processes associated with metabolism and muscle development, while a Kyoto encyclopedia of genes and genomes analysis showed that DEGs mainly participated in signaling pathways associated with amino acid metabolism, fatty acid metabolism, and skeletal muscle differentiation. A MCODE analysis of the protein-protein interaction network indicated that the four identified subsets of genes were mainly associated with translational initiation, skeletal muscle differentiation, amino acid metabolism, and oxidative phosphorylation pathways. Based on the analysis results, we selected glutamic-oxaloacetic transaminase 1, malate dehydrogenase 1, pyruvate dehydrogenase 1, pyruvate dehydrogenase kinase 4, and activator protein-1 as candidate genes affecting meat quality in pigs. A discussion of the related molecular mechanisms is provided to offer a theoretical basis for future studies on the improvement of meat quality in pigs.

This study aims to elucidate the regulatory mechanisms of host genetics on the porcine gut microbiota and their subsequent impact on the feed conversion ra