The Platelet Activation Signaling Pathway Regulated by Fibrinogen and Homo-Gamma-Linolenic Acid (C20:3)-Associated Lipid Metabolism Is Involved in the Maintenance of Early Pregnancy in Chinese Native Yellow Cattle

Abstract

Identifying the specific factors secreted during early pregnancy is an effective method for pregnancy detection in cattle, helping to reduce empty pregnancies in the industry. To systematically investigate metabolic variations between early pregnancy and the estrous cycle and their relationship with pregnancy progression, this study utilized four-dimensional data-independent acquisition (4D-DIA) proteomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics to analyze serum samples collected from Chinese native yellow cattle at day 0 and day 21 post-mating, combining bioinformatics analysis with experimental validation. The platelet activation signaling pathway and angiogenesis-related proteins were significantly upregulated. Among them, fibrinogen alpha/beta/gamma chains (FG) exhibited notable differences, with their branched-chain protein FGB showing highly significant upregulation (p = 0.003, Log₂FC = 2.167) and tending to increase gradually during early pregnancy, suggesting that FGB could be one of the important indicators of early pregnancy in Chinese native yellow cattle. Among the differential metabolites, 11-Deoxy prostaglandin F1α (p < 0.001, Log₂FC = 1.563), Thromboxane B1 (p = 0.002, Log₂FC = 3.335), and Homo-Gamma-Linolenic Acid (C20:3) (p = 0.018, Log₂FC = 1.781) were also increased, indicating their involvement in the regulation of the platelet activation signaling pathway. The platelet activation signaling pathway plays a crucial role in maternal immune tolerance and placental vascularization, which are essential for embryo implantation and placental development. These findings indicate that FGB has the potential to be a valuable biomarker for early cattle pregnancy detection, thereby improving pregnancy diagnosis accuracy, reducing economic losses caused by undetected empty pregnancies and enhancing reproductive efficiency in the cattle industry. Undoubtedly, our research outcomes must be validated with future studies, and a larger sample size as well as the evaluation of the potential endocrine effects induced by the synchronized estrus treatment must be considered.

Keywords: Chinese native yellow cattle; angiogenesis; early pregnancy; immune response; metabolism.

Figure 1

Differential protein analysis of early pregnancy serum from cattle. (A) Volcano plot of differentially expressed proteins (DEPs) in early pregnancy. The x-axis represents fold changes (log₂FC), while the y-axis indicates statistical significance (-log₁₀ p-value). The red and green dots denote significantly upregulated and downregulated proteins, respectively, while black dots represent non-significant proteins. (B) Hierarchical clustering heatmap of DEPs. The x-axis represents sample clustering, and the y-axis represents protein clustering, with shorter branches indicating a higher similarity. (C) GO enrichment analysis of DEPs, categorizing proteins based on biological processes, molecular functions, and cellular components. The x-axis represents GO terms, while the y-axis indicates the proportion of enriched DEPs relative to the total DEPs. (D) KEGG pathway enrichment analysis of DEPs, highlighting the most significantly affected pathways in early pregnancy. The x-axis represents the proportion of DEPs within each pathway, while color intensity and bubble size indicate the p-values and the number of proteins involved. (E) Protein–protein interaction (PPI) network analysis. Each node represents a protein, with inner circles indicating DEPs and outer circles denoting functionally related proteins. Node size and color intensity reflect the degree of interaction. (F) IPR (InterPro) structural domain enrichment analysis, identifying the key protein domains associated with pregnancy. The x-axis represents the proportion of enriched DEPs, with color and size indicating statistical significance.

Figure 2

Differential metabolite analysis in the early pregnancy serum of cattle. (A) Volcano plot of differentially abundant metabolites (DAMs). The x-axis represents fold changes (log₂FC), while the y-axis denotes statistical significance (-log₁₀ p-value). The red and green dots indicate significantly upregulated and downregulated metabolites, respectively, while the black dots represent non-significant metabolites. The size of each dot corresponds to the VIP (variable importance in projection) score. (B) Hierarchical clustering heatmap of DAMs, illustrating the metabolic differences between pregnant and non-pregnant cattle. The x-axis represents sample clustering, and the y-axis represents metabolite clustering, with shorter branches indicating higher similarity. (C) KEGG pathway enrichment analysis of DAMs, identifying the key metabolic pathways involved in early pregnancy. The x-axis represents the proportion of DAMs within each pathway, while bubble color and size indicate statistical significance and the number of metabolites involved. (D) Correlation heatmap of differential proteins and metabolites, showing the relationships between DAMs and DEPs. The color scale represents the correlation coefficients, with red indicating positive correlations and blue denoting negative correlations. p-values < 0.05 were considered significant.

Figure 3

Multi-omics integration of differential proteins and metabolites. (A) Heatmap of the correlation analysis between DEPs and DAMs. The x-axis lists DEPs, and the y-axis lists DAMs. Red represents positive correlations, while blue indicates negative correlations. The asterisk (*) indicates statistical significance, defined as p < 0.05. (B) KEGG co-enrichment analysis, identifying the pathways jointly enriched in proteomic and metabolomic datasets. The x-axis represents the proportion of enriched DEPs and DAMs, with bubble color and size indicating statistical significance and the number of molecules involved. (C) KEGG pathway visualization illustrating metabolite and protein interactions within the enriched pathways. Nodes are colored based on log₂FC values, with proteins represented in a green-to-red gradient and metabolites in a blue-to-yellow gradient.

Figure 4

Western blot validation of differentially expressed proteins in early pregnancy. (A) Western blot validation of the 4D-DIA proteomic findings. Serum samples from pregnant and non-pregnant cattle (collected at days 0 and 21 post-mating) were analyzed to verify the differential protein expression. Representative immunoblot bands and corresponding quantifications are shown (n = 3 per group). The numbers before the hyphen represent the sample ID, while the numbers after the hyphen indicate the days of pregnancy. (B) Western blot validation of FGB expression in an independent cohort. Serum samples from three pregnant and three non-pregnant cattle were assessed to confirm the diagnostic consistency of FGB at early pregnancy time points (days 0 and 21). (C) Longitudinal profiling of FGB expression during early pregnancy. Western blot analysis of serum samples from pregnant and non-pregnant cattle across multiple time points (days 0, 7, 14, 21, 28, and 35 post-mating) revealed a progressive increase in FGB levels in the pregnant group. In contrast, FGB expression remained relatively stable in non-pregnant cattle. Data are presented as mean ± SEM, with *** p < 0.001 indicating statistical significance; ns = not significant.

Figure 5

Metabolomic validation and temporal dynamics of key differential metabolites. (A) Hierarchical clustering heatmap of DAMs. Pregnant and non-pregnant cattle exhibit distinct metabolic profiles, with strong intra-group consistency and clear inter-group differences. (B) Temporal trends of key platelet-associated metabolites. Line graphs depict the relative abundance of 11-Deoxy prostaglandin F1α, Thromboxane B1, and Homo-γ-Linolenic acid across the early pregnancy time points (days 0, 21, and 28 post-mating). Thromboxane B1 shows a continuous increase from day 0 to day 28, whereas 11-Deoxy prostaglandin F1α and Homo-γ-Linolenic acid exhibit a sharp rise at day 28 followed by a slight decline. Data were derived from LC-MS/MS analysis and are presented as mean ± SEM.