SPP1 is a plasma biomarker associated with the dia gnosis and prediction of prognosis in sepsis

Abstract

In this study, peripheral whole blood samples from 22 hospitalized patients and 10 healthy individuals were analyzed using a combination of Data Independent Acquisition (DIA) and Enzyme-Linked Immunosorbent Assay (ELISA) techniques to identify differentially expressed proteins (DEPs) in sepsis patients' plasma. The aim was to provide accurate and detailed biomarkers, such as SPP1, for determining the pathological stages of sepsis. SPP1, known as osteopontin1 is a pleiotropic protein with a wide distribution and multifunctional effects. Its protein expression is associated with inflammatory changes, including variations in expression levels in infectious diseases, allergic diseases, and situations involving tissue damage. The registration number was ChiCTR1900021261.The full date of first registration year is 2018. In the affiliated hospital of southwest medical university, 22 sepsis patients were hospitalized from January 2019 to September 2020 and 10 normal healthy individuals were selected for DIA-based quantitative proteomics analysis. In addition to gene ontology analysis and Kyoto Encyclopedia of genes and genomes analysis, enrichment analysis of data was performed and target protein network was screened through joint protein-protein interaction and visualization techniques. The selected protein targets were then validated by Elisa kit. The software was used to analyze the differences comparing the control group to the sepsis group and the sepsis group, as well as between sepsis survivals and non-survivals, and a ROC curve was drawn to evaluate the diagnostic value and prognostic effect of the method of the corresponding target proteins. A total of 174 DEPs were screened by bioinformatics analysis. An analysis of go pathway enrichment revealed the following: These proteins were mainly involved in biological processes among them are the inflammation response, the metabolism of extracellular matrix, the secretion of cell secretions, the activation of cells, and the immune response. According to the Kegg pathway analysis, they were mainly involved in complement cascade polymerization, extracellular protease and glycosylase activation, protein synthesis process, biotin metabolism, leukocyte transmembrane migration, bacterial infection and phagosome formation. SPP1 was identified as a possible plasma biomarker and was therefore further validated using Elisa. As a result of experiments, it has been demonstrated that level in sepsis patients is significantly compared to the normal control group and the level is also higher in non-survivals of sepsis. The ROC curve can be used to see that it can diagnose sepsis more accurately and improve prognostic ability prediction. Cell experiments confirm that SPP1 is highly expressed in sepsis. There is a significant difference in the levels of SPP1 protein between the normal group and the sepsis group; it not only has good diagnostic significance for sepsis, but also provides corresponding reference value for patient prognosis; Therefore, it is more likely to become a biological marker of sepsis over time.

Keywords: DIA; ELISA; SPP1; Sepsis.

Fig. 1

RT-qPCR. RT-qPCR detection of the expression of core genes in the sepsis cell model. Blue represents normal, and red represents sepsis. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Fig. 2

Main sources of recruited sepsis patients: Sepsis patients (A), survivors (B), non-survivors (C).

Fig. 3

Box plot (A), principal component analysis (B), volcano map (C), heat map (D) of sepsis samples. The criteria for selection of differentially expressed proteins was set as fold-change ≥ 1, a false discovery rate of < 0.05 is considered to be a false discovery. (Note: 1. Different colors signify distinct meanings. On the left side of the heatmap, the blue bar represents the number of downregulated proteins, while the yellow band indicates the number of upregulated proteins. Within the heatmap, the green band range signifies low expression levels within the group, whereas the red band range indicates high expression levels within the group. 2. The detailed information on the up-regulated and down-regulated proteins has been uploaded to Supplementary material 4.)

Fig. 4

(A): Network of protein–protein interactions between differentially expressed proteins; (B): Heat map of proteins indicted that were at the central of this network. (Note: 1. Group1 represents the control group; Group2 represents the sepsis group. 2. Different color scale bands represent different meanings; a larger red scale indicates higher expression levels, while a larger blue band signifies lower expression levels.)

Fig. 5

The results of Gene Ontology analysis: including BP, CC, MF (A–C) and KEGG pathway analysis (D). (Note: BP: Biological Process; CC: Cell Component; MF: Molecular Function).

Fig. 6

The Elisa results of SPP1 expression between healthy normal and sepsis groups (A), survivals and non-survivals (B); Normal control was 315.3 ± 540.74ng/mL, sepsis group 761.2 ± 191.13ng/mL, survivals692.5 ± 72.19 ng/mL and non-survivals 845.1 ± 160.58ng/Ml.

Fig. 7

ROC curve of SPP1 for the comparison between normal people vs sepsis patients (A); ROC curve analysis of SPP1 for the comparison of the survivals vs non-survivals (B).