Novel predictive biomarkers for atonic postpartum hemorrhage as explored by proteomics and metabolomics

Abstract

Background: Postpartum hemorrhage (PPH) is the leading cause of maternal mortality worldwide, with uterine atony accounting for approximately 70% of PPH cases. However, there is currently no effective prediction method to promote early management of PPH. In this study, we aimed to screen for potential predictive biomarkers for atonic PPH using combined omics approaches.

Methods: Collection of cervicovaginal fluid (CVF) samples from 27 women with atonic PPH and 32 women with normal delivery was performed for metabolomic (LC-MS/MS) and proteomic (LC-MS/MS) detection and subsequent confirmation experiments in this nested case-control study. Mass spectrum and enzyme-linked immunosorbent assays (ELISA) were used to validate significantly different metabolites and proteins for screening potential biomarkers of atonic PPH. Furthermore, multivariate logistic regressions were performed for the prediction of PPH using the identified biomarkers mentioned above, and the area under the curve (AUC) was computed.

Results: We identified 216 and 311 metabolites under positive and negative ion modes, respectively, as well as 1974 proteins. The PPH group had significant differences in metabolites and proteins belonging to the β-alanine metabolic pathway. Specifically, the PPH group had downregulation of critical metabolites, including histidine and protein dihydropyrimidine dehydrogenase (DPYD). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional enrichment analysis of significantly differentially expressed proteins revealed that atonic PPH was associated with T cell- and macrophage-related immune inflammatory responses. Furthermore, we verified that concentrations of histidine (350.85 ± 207.87 vs. 648.33 ± 400.87) and DPYD (4.01 ± 2.56 vs. 10.96 ± 10.71), and immune cell-related proteins such as CD163 (0.29 ± 0.19 vs. 1.51 ± 0.83) and FGL2 (5.98 ± 4.23 vs. 11.37 ± 9.42) were significantly lower in the PPH group. Finally, the AUC for independent prediction of PPH using CD163, histidine, DPYD, and FGL2 are 0.969 (0.897-1), 0.722 (0.536-0.874), 0.719 (0.528-0.864), and 0.697 (0.492-0.844), respectively. A relatively high predictive efficiency was obtained when using joint histidine, DPYD, CD163, and FGL2, with AUC = 0. 964 (0.822-1).

Conclusions: This study suggested that immune inflammation may play a role in the occurrence of PPH. The metabolite histidine and proteins of DPYD, CD163, and FGL2 in CVF were associated with uterine atony and could be used as predictive biomarkers for atonic PPH.

Keywords: Atonic postpartum hemorrhage; Maternal mortality; Metabonomic; Prediction; Proteomic.

Fig. 1

Flowchart of the study design, population, and experiment. A) Sample selection; B) Overview of proteomics and metabolomics data process, confirmation experiment and the construction of predictive models

Fig. 2

Significant differences in metabolites and proteins and analysis results. Analysis of significant differences in metabolite expression in positive ion mode (A) and negative ion mode (B) by multiple differences: The horizontal axis represents the log2 FC value of differential metabolites, which is the logarithmic value of the difference multiple of differential metabolites based on 2. The vertical axis represents significant differential metabolites. Red indicates upregulation of differential metabolites, while green indicates downregulation of differential metabolites; OrthoPLSDA map showing metabolomics results (C): T score [1] represents principal component 1, Orthogonal T score [1] represents principal component 2, and the dots of the same color represent various biological replicates within a group, and the distribution status of the dots reflects the degree of difference between and within groups; Enrichment pathways for all differential metabolites (D); Histogram of significantly differentially expressed proteins (E), where blue represents the number of downregulated proteins and red represents the number of upregulated proteins; Cluster analysis of differentially expressed proteins (F), represented by a tree heat map; Biological process related GO enrichment pathway map (G) and KEGG enrichment pathway map (H) of significantly different proteins: (G) Each row represents a differential protein and each column represents a group of samples. Red represents significant upregulation, blue represents significant downregulation, and color depth indicates the degree of upregulation and downregulation. Proteins with similar expression patterns are clustered in the same cluster on the left. (H) The x-axis represents the p value of Fisher’s exact test (logarithmic to 10), and the y-axis represents the path name. The pathways involved in upregulation and downregulation of proteins are represented by the right and left bars respectively

Fig. 3

Joint analysis results of significant differences in metabolites and proteins. The correlation coefficients of differential proteins and metabolites showed by layered heat maps (A); The Venn diagram (B) of differentially expressed proteins and metabolites involved in the pathway, where blue represents the proteome, yellow represents the metabolome, and the cross region represents metabolic pathways involved in both omics; A metabolite-protein interaction network centered on histidine (C), showing the correlation analysis network between significantly different proteins and significantly different metabolites; Boxplot showing significant different proteins related to histidine (D): Data were presented as the median and interquartile range (25-75%); * indicate statistical differences between two groups

Fig. 4

Differentially expressed proteins validation, and their correlation with blood loss, gestational age, and maternal age. Boxplot showing concentration of histidine (A), DPYD (B), CD163 (C) and FGL2 (D) between the PPH and non-PPH group: Data were presented as the median and interquartile range (25-75%); Scatter plot of correlation coefficients showing the correlation between histidine (A), CD163 (C), FGL2 (D) with blood loss, gestational age, and maternal age between the PPH and non-PPH group, respectively. DPYD, dihydropyrimidine dehydrogenase; CD163, scavenger receptor cysteine-rich type 1 protein M130; FGL2, fibroleukin; PPH, postpartum hemorrhage; * indicate statistical differences between two groups

Fig. 5

Correlation analysis between significantly different proteins. Scatter plot of correlation coefficients showing the correlation between CD163 (A), FGL2 (B), ADA (C), GPX3 (D) and FIS1 (E) with histidine and DPYD between the PPH and non-PPH group, respectively. CD163, scavenger receptor cysteine-rich type 1 protein M130; FGL2, fibroleukin; ADA, adenosine deaminase; GPX3, glutathione peroxidase 3; FIS1, mitochondrial fission 1 protein; DPYD, dihydropyrimidine dehydrogenase; PPH, postpartum hemorrhage

Fig. 6

Prediction models for atonic PPH using histidine, DPYD, CD163 and FGL2. ROC curves of histidine, DPYD, CD163 and FGL2 in the prediction of atonic PPH (A); ROC curves of histidine combined with DPYD (B) in the prediction of atonic PPH; ROC curves of histidine combined with CD163, histidine combined with DPYD and CD163, histidine combined with DPYD, CD163 and FGL2 (C) in the prediction of atonic PPH. AUC, area under the curve; DPYD, dihydropyrimidine dehydrogenase; PPH, postpartum hemorrhage; ROC, receiver operating characteristic