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. 2022 Jul 28:13:951552.
doi: 10.3389/fendo.2022.951552. eCollection 2022.

Integrated analysis of proteomics and metabolomics in girls with central precocious puberty

Mei Li  1 , Dan Lan  1 , Yanfei Chen  1
Affiliations

Integrated analysis of proteomics and metabolomics in girls with central precocious puberty

Mei Li et al. Front Endocrinol (Lausanne). .

Abstract

Background: Central precocious puberty (CPP) is a multifactorial and complex condition. Traditional studies focusing on a single indicator cannot always elucidate this panoramic condition but these may be revealed by using omics techniques.

Objective: Proteomics and metabolomics analysis of girls with CPP were compared to normal controls and the potential biomarkers and pathways involved were explored.

Methods: Serum proteins and metabolites from normal girls and those with CPP were compared by LC-MS/MS. Multivariate and univariate statistical analysis were used to identify the differentially expressed proteins (DEPs) and differentially expressed metabolites (DEMs). Functional annotation and pathway enrichment analysis were performed by using GO and KEGG databases, and candidate markers were screened. Finally, bioinformatic analysis was used to integrate the results of proteomics and metabolomics to find the key differential proteins, metabolites and potential biomarkers of CPP.

Results: 134 DEPs were identified in girls with CPP with 71 up- and 63 down-regulated, respectively. Up-regulated proteins were enriched mainly in the extracellular matrix, cell adhesion and cellular protein metabolic processes, platelet degranulation and skeletal system development. The down-regulated proteins were mainly enriched in the immune response. Candidate proteins including MMP9, TIMP1, SPP1, CDC42, POSTN, COL1A1, COL6A1, COL2A1 and BMP1, were found that may be related to pubertal development. 103 DEMs were identified, including 42 up-regulated and 61 down-regulated metabolites which were mainly enriched in lipid and taurine metabolic pathways. KGML network analysis showed that phosphocholine (16:1(9Z)/16:1(9Z)) was involved in arachidonic acid, glycerophospholipid, linoleic acid and α-linolenic acid metabolism and it may be used as a biomarker of CPP.

Conclusions: Our study is the first to integrate proteomics and metabolomics to analyze the serum of girls with CPP and we found some key differential proteins and metabolites as well as a potential biomarker for this condition. Lipid metabolism pathways are involved and these may provide a key direction to further explore the molecular mechanisms and pathogenesis of CPP.

Keywords: biomarker; central precocious puberty (CPP); lipid pathway; metabolomics; proteomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
A volcano plot of the differentially expressed proteins. The red and blue dots represent significantly up-regulated and down-regulated DEPs, respectively. The horizontal dotted line represents p value <0.05, two vertical dashed lines indicate Fold change=1.2 and Fold change=0.83.
Figure 2
Figure 2
Subcellular localization and functional enrichment analysis of differentially expressed proteins. (A) Subcellular localization of the differentially expressed proteins. (C) GO terms of the differentially expressed proteins in the up-regulated group. (D) GO terms of the differentially expressed proteins in the down-regulated group. (B) The top 20 bubbles of KEGG enrichment in the differentially expressed proteins.
Figure 3
Figure 3
PPI network of the differentially expressed proteins in CPP. The circles in the figure indicate differentially expressed proteins, red circles represent up-regulated proteins and green circles represents down-regulated proteins. The size of the circle represents the degree of connection, and the larger the circle, the more connected it is.
Figure 4
Figure 4
A PCA diagram of the samples used for analysis (including QC). QC samples were closely clustered in the middle of all samples, and no outlier samples were found, indicating good stability of instrumental analysis system and stable and reliable experimental data.
Figure 5
Figure 5
Results of replacement test and OPLS-DA score of girls with CPP vs the control group. (A) The closer R2Y was to 1, the more stable and effective the model was. Q2<0 indicated that the model was reliable and effective without over-fitting. (B) There was significant difference in OPLS-DA score between the two groups.
Figure 6
Figure 6
A volcano plot of the differential metabolites. The volcano map can be used to visualize p value, VIP value and FC value, which is beneficial to screen differential metabolites. The red and blue dots represent significantly up-regulated and down-regulated DEMs, respectively.
Figure 7
Figure 7
A bubble diagram of top-20 metabolic pathways. The color from green to red indicates that p-values decrease successively. The larger the point is, the more metabolites are enriched into the metabolic pathway.
Figure 8
Figure 8
Correlation analysis of the differential proteins and metabolites. In the figure, red is positively correlated and blue is negatively correlated. *** represents correlation p value less than 0.001, ** represents correlation p value less than 0.01, and * represents correlation p value less than 0.05.
Figure 9
Figure 9
KGML network of the differential genes and metabolites. The squares represent genes and the triangles represent metabolites. Red represents up-regulated genes or metabolites, and bright green represents down-regulated genes or metabolites.
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