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[Establishment of mouse model and preliminary screening of serum biomarkers for Candidemia]
1School of Laboratory and Life Sciences, Wenzhou Medical University, Wenzhou 325035, China.
Abstract:
Objective: To establish mouse models of Candidemia, and investigates statistically significant polypeptide peaks to provide auxiliary diagnosis of this disease. Methods: A total of 170 specific pathogen free adult male ICR mice with body mass of 27-30 g were completely randomly divided into Candida albicans infection group (n=80), Candida parapsilosis infection group (n=80) and the normal control group (n=10), and the two kinds of Candidemia mouse models were established via tail vein injection. The serum samples were analyzed by Matrix-assisted laser desorption-ionization time of flight mass spectrometry and relevant software, and the polypeptide peaks with significant differences were screened to establish diagnostic models. Results: A total of 65 differential polypeptide peaks were obtained compared with the Candida albicans infection group and the normal control group. Combined with m/z 1 100.4, 1 581.0, 3 808.0 as differential polypeptide peaks to established the diagnostic model, the sensitivity was 95.24%(40/42), the specificity was 90.63%(29/32), the accuracy rate was 93.24%(69/74), and the AUC value of the ROC curve was 0.972(95%CI: 0.941-1.000). A total of 73 differential polypeptide peaks were obtained compared with Candida parapsilosis infection group and the normal control group. Combined with m/z 1 433.2, 1 148.5, 4 093.5, 4 522.2, 8 140.9, 8 234.6 as differential polypeptide peaks to established the diagnostic model, the sensitivity was 95%(38/40), the specificity was 81.25%(26/32), the accuracy rate was 88.89%(64/72), and the AUC value of the ROC curve was 0.953(95%CI: 0.903-1.000). A total of 78 differential polypeptide peaks were obtained compared with Candida albicans infection group and Candida parapsilosis infection group. Combined with m/z 2 736.9, 8 091.5, 8 153.7 as differential polypeptide peaks to established the diagnostic model, the accuracy of distinguishing C. albicans infection from C. parapsilosis infection was 98.78%(81/82). Conclusions: Successfully screened the differential polypeptides and established the related diagnostic models. Which is helpful to find serum biomarkers for the auxiliary diagnosis of Candidemia, and provides a basis for the early diagnosis and the rational use of drugs.
Insights
This study developed mouse models for Candidemia, identifying specific polypeptide peaks in serum for auxiliary diagnosis. These findings aid in early detection and treatment of fungal infections.
Area of Science:
- Medical diagnostics
- Infectious diseases
- Proteomics
Background:
- Candidemia is a serious bloodstream fungal infection.
- Accurate and early diagnosis is crucial for effective treatment.
- Current diagnostic methods can be time-consuming.
Purpose of the Study:
- To establish mouse models of Candidemia caused by Candida albicans and Candida parapsilosis.
- To identify statistically significant polypeptide peaks in serum as potential biomarkers for auxiliary diagnosis.
- To develop diagnostic models based on these polypeptide peaks.
Main Methods:
- Established mouse models of Candidemia via tail vein injection of Candida species.
- Analyzed serum samples using Matrix-assisted laser desorption-ionization time of flight mass spectrometry (MALDI-TOF MS).
- Screened for differential polypeptide peaks and developed diagnostic models using ROC curve analysis.
Main Results:
- Identified 65 differential peaks for Candida albicans infection vs. control, achieving 97.2% AUC.
- Identified 73 differential peaks for Candida parapsilosis infection vs. control, achieving 95.3% AUC.
- Achieved 98.78% accuracy in distinguishing Candida albicans from Candida parapsilosis infections using 3 specific peaks.
Conclusions:
- Successfully identified differential polypeptides and established diagnostic models for Candidemia.
- These findings support the use of serum biomarkers for auxiliary Candidemia diagnosis.
- Provides a basis for earlier diagnosis and optimized antifungal drug selection.
