Prevotella stercorea increases fat deposition in Jinhua pigs fed alfalfa grass-based diets

Abstract

Background: Fat is a key component of body composition in both humans and animals, with intramuscular fat (IMF) being a critical determinant of pork quality. Higher IMF level enhances meat qualities such as flavor, tenderness, and juiciness, directly influencing consumer preference and market demand. Therefore, identifying microbial biomarkers associated with fat deposition is essential for improving meat quality in livestock and understanding how gut microbiota regulates host metabolism.

Results: In this study, we examined changes in meat quality, fat metabolism, and gut microbiota during the pig life cycle, from weaning to marketing. We found that Jinhua pig exhibited higher IMF content and marbling score, and higher α diversity of colonic microbial communities. Microbiome Multivariate Association with Linear Models was used to identify the core genera associated with age, breed, and feed, and Prevotella was found to respond to both age and breed factors. The correlation analysis of fat deposition indicators with microbial genera revealed that Prevotella was a potential biomarker in response to IMF. In addition, the P. stercorea DSM 18206 (P. stercorea) was identified in porcine sample and administered to pseudo sterile mouse to examine the effect on IMF deposition. We found that the gavage of P. stercorea with alfalfa-enriched diet led to a significant increase in triglyceride (TG) and IMF contents in muscle. Metabolomic analysis further confirmed P. stercorea may potentially regulate fat deposition through the sphingolipid signaling pathway.

Conclusions: We identified P. stercorea as a potential biomarker linked to higher IMF deposition and validated their role in shaping the gut microbiota and promoting fat accumulation in a mouse model, which correlated with the sphingolipid signaling pathway. These findings provide valuable insights into the role of P. stercorea in regulating fat deposition and metabolic health, offering implications for improving both livestock meat quality and lipid metabolism in humans.

Keywords: Prevotella; Intramuscular fat; Jinhua pig; Microbiome.

Fig. 1

Meat quality index and carcass index related to fat deposition. A IMF of LDM. B Marbling score of LDM. C Backfat thickness. D Loin muscle area. *, ** and *** indicate P < 0.05, P < 0.01 and P < 0.001, respectively

Fig. 2

Microbial community diversity differs between age, feed type and pig breed groups. A The violin plots showed Shannon index of different groups among 60 d, 90 d and 180 d. B–D The PCoA for the 60 d (B), 90 d (C) and 180 d (D) comparison shows distinctive microbial community structure. E Contribution of feed type, pig breed and age to the colonic microbiota. *, ** and *** indicate P < 0.05, P < 0.01 and P < 0.001, respectively

Fig. 3

Multivariable statistical analysis and mixed-effects linear regression identified genera associated with age. A The coefficient values of MaAsLin2 model of top 60 genera with minimum abundance of 0.1%, used normalized relative abundance combined 60 d, 90 d and 180 d. B The abundance of top 24 genera associated with age were visualized by stacked plot. C Boxplot showed increase or decrease in abundance with age for the top ten most strongly age-associated genera

Fig. 4

Multivariable statistical analysis and LEfSe analysis identified genera associated with feed type and pig breed. A The coefficient values of MaAsLin2 model responding to breed factor with normalized relative abundance in combined all time points dataset. B The coefficient values of MaAsLin2 model responding to feed factor in combined all time points dataset. C, D LEfSe analysis revealed the most differentially abundant taxa with LDA threshold > 2.0 between pig breeds (C) and between feed types (D). E, F Lineplots showed abundance with age of the strongly breed-associated genera (E) and feed- associated genera (F) identity by MaAsLin2 and LEfSe analysis

Fig. 5

Correlation analysis and random forest model identified biomarker indicate high IMF. A Heatmap of the co-correlation of top 50 genera, the TSS abundance was calculated for the Pearson’s rank correlation coefficient, 10 cluster were generated (marked by different colored boxes). B The 10 most clusters were related to meat qualities and carcass indicators by Mantel tests using Spearman's correlation analysis. C Correlation network generated by the top 50 genera. D Correlation network between top 50 genera and indicators related to IMF deposition. E Random forest model was constructed by all samples at 180 d, the number of decision tree was set to 500. F The receiver operating characteristic curve of the random forest model

Fig. 6

The influence of P. stercorea on growth factors, serum components, and adipocyte development in mice. A–D The body weight and feed intake of mice. E–H The tissue weight and body composition. I–P The serum parameters. Q Histological images depict mouse subcutaneous adipose tissue using haematoxylin and eosin staining. R–S The average diameter and average area of mouse subcutaneous adipose. *, ** and *** indicate P < 0.05, P < 0.01 and P < 0.001, respectively

Fig. 7

P. stercorea increases fat deposition in mouse. A In vivo micro-CT scanning of adipose tissue in mice. B–E Adipose tissue volumes and fat ratio of mouse. F: Representative images of immunofluorescence analysis for Bodipy (green) and 4ʹ,6-diamidino-2-phenylindole (DAPI; blue) as staining on the tibialis anterior muscle sections. G–I The TG, TCHO and IMF content of mouse tibialis anterior muscle. J–L The TG, TCHO and IMF content of mouse gastrocnemius muscle. M–N The TG and TCHO content of mouse liver. *, ** and *** indicate P < 0.05, P < 0.01 and P < 0.001, respectively

Fig. 8

Altered microbial communities in the mouse colon. A Heatmap of top 40 genera by Pearson’s rank, the most eight dominant clusters identified are highlighted by different colored boxes. * and ** indicate P < 0.05 and P < 0.01, respectively. B Mantel test of the eight dominant clusters and fat deposition traits, using Spearman's rank correlation coefficient. C, D Bar charts show differences in relative abundance of the top 40 genera cross groups, Kruskal–Wallis H test used to evaluate significant differences. E Histograms of LDA scores reveal the most differentially abundant taxa among different feed types. F–G Microbial correlation network diagram with Spearman’s rank correlation coefficients. Positive associations are denoted by red lines and negative associations by blue lines

Fig. 9

KEGG pathway enrichment and association analysis with gut microbes. A–B KEGG pathway enrichment of differential metabolites on colon content of pig (A) and mice samples (B). C Spearman association analysis between pig metabolites within Sphingolipid signaling pathway and genera of Cluster2. D Spearman association analysis between mice metabolites within Sphingolipid signaling pathway and genera of Cluster5. *, ** and *** indicate P < 0.05, P < 0.01 and P < 0.001, respectively