| Literature DB >> 30863278 |
Li-Li Liu1,2, Yong Lin1, Wei Chen3, Man-Li Tong1,2, Xi Luo1, Li-Rong Lin1,2, Hui-Lin Zhang1, Jiang-Hua Yan4, Jian-Jun Niu1,2, Tian-Ci Yang1,2.
Abstract
The mechanism underlying the stealth property of neurosyphilis is still unclear. Global metabolomics analysis can provide substantial information on energy metabolism, physiology and possible diagnostic biomarkers and intervention strategies for pathogens. To gain better understanding of the metabolic mechanism of neurosyphilis, we conducted an untargeted metabolomics analysis of cerebrospinal fluid (CSF) from 18 neurosyphilis patients and an identical number of syphilis/non-neurosyphilis patients and syphilis-free patients using the Agilent, 1290 Infinity LC system. The raw data were normalized and subjected to subsequent statistical analysis by MetaboAnalyst 4.0. Metabolites with a variable importance in projection (VIP) greater than one were validated by Student's T-test. A total of 1,808 molecular features were extracted from each sample using XCMS software, and the peak intensity of each feature was obtained. Partial-least squares discrimination analysis provided satisfactory separation by comparing neurosyphilis, syphilis/non-neurosyphilis and syphilis-free patients. A similar trend was obtained in the hierarchical clustering analysis. Furthermore, several metabolites were identified as significantly different by Student's T-test, including L-gulono-gamma-lactone, D-mannose, N-acetyl-L-tyrosine, hypoxanthine, and S-methyl-5'-thioadenosine. Notably, 87.369-fold and 7.492-fold changes of N-acetyl-L-tyrosine were observed in neurosyphilis patients compared with syphilis/non-neurosyphilis patients and syphilis-free patients. These differential metabolites are involved in overlapping pathways, including fructose and mannose metabolism, lysosomes, ABC transporters, and galactose metabolism. Several significantly expressed metabolites were identified in CSF from neurosyphilis patients, including L-gulono-gamma-lactone, D-mannose, N-acetyl-L-tyrosine, and hypoxanthine. These differential metabolites could potentially improve neurosyphilis diagnostics in the future. The role of these differential metabolites in the development of neurosyphilis deserves further exploration.Entities:
Keywords: Treponema pallidum; cerebrospinal fluid; metabolites; neurosyphilis; untargeted metabolomics
Year: 2019 PMID: 30863278 PMCID: PMC6399405 DOI: 10.3389/fnins.2019.00150
Source DB: PubMed Journal: Front Neurosci ISSN: 1662-453X Impact factor: 4.677
Clinical information of study participants.
| Syphilis/Neurosyphilis | Syphilis-non-neurosyphilis | free | ||
|---|---|---|---|---|
| patients | patients | patients | ||
| Variable | ( | ( | ( | |
| Age (Years) | 58.5(21) | 58.5(11.8) | 53.0(27) | 0.747 |
| Gender | ||||
| Male n(%) | 10 (55.6) | 9 (50.0) | 9 (50.0) | 0.929 |
| Female n(%) | 8 (44.5) | 9 (50.0) | 9 (50.0) | |
| CSF RPR | ||||
| Negative n(%) | 2 (11.1) | 18 (100.0) | 18 (100.0) | |
| Positive n(%) | 16 (88.9) | 0 (0.0) | 0 (0.0) | |
| CSF TPPA | ||||
| Negative n(%) | 0 (0.0) | 18 (100.0) | 18 (100.0) | |
| Positive n(%) | 18 (100.0) | 0 (0.0) | 0 (0.0) | |
FIGURE 1The molecular features of 1,808 metabolite components based on the normalized peak intensity. Purple dots indicate metabolites with a fold change greater than 2, while the gray dots indicate the remaining metabolites. (A) Neurosyphilis patients vs. syphilis/non-neurosyphilis patients; (B) neurosyphilis patients vs. syphilis-free patients; (C) syphilis/non-neurosyphilis patients vs. syphilis-free patients.
FIGURE 2The distribution of input data values before (left) and after (right) normalization.
FIGURE 3PCA score plots based on the UHPLC-Q-TOF/MS data of CSF samples. Light red oval represents the 95% CI of score calculated from each neurosyphilis patient. Light green oval represents the 95% CI of score calculated from each syphilis/non-neurosyphilis patient. Light blue oval represents the 95% CI of score calculated from syphilis-free patient.
FIGURE 4PLS-DA plots based on the UHPLC-Q-TOF/MS data of CSF samples. (A) Neurosyphilis patients vs. syphilis/non-neurosyphilis patients (R2 = 0.985, Q2 = 0.5); (B) neurosyphilis patients vs. syphilis-free patients (R2 = 0.993, Q2 = 0.5); (C) syphilis/non-neurosyphilis patients vs. syphilis-free patients (R2 = 0.986, Q2 = 0.339).
FIGURE 5Heatmap of clustering analysis of three groups. (A) Neurosyphilis patients vs. syphilis/non-neurosyphilis patients; (B) neurosyphilis patients vs. syphilis-free patients; (C) syphilis/non-neurosyphilis patients vs. syphilis-free patients.
List of differential CSF metabolites in three groups of study participants.
| Comparison | Metabolite | Rt (sec) | m/z | VIP | Fold change | |
|---|---|---|---|---|---|---|
| NS vs. Non-NS | 206.4 | 196.0808 | 4.371 | 0.517 | 0.024* | |
| 559.8 | 198.0968 | 3.142 | 0.872 | 0.020* | ||
| N-acetyl-L-tyrosine | 69.34 | 268.0621 | 5.156 | 87.369 | 0.019* | |
| Hypoxanthine | 303.4 | 137.0447 | 2.042 | 0.765 | 0.002* | |
| NS vs. syphilis-free | 163.6 | 196.0808 | 2.342 | 0.646 | <0.001* | |
| 559.8 | 198.0968 | 3.621 | 0.850 | 0.061 | ||
| N-acetyl -L-tyrosine | 69.34 | 268.0621 | 4.791 | 7.492 | 0.069 | |
| NN cases VS NS cases | Hypoxanthine | 303.366 | 137.0447 | 2.0645 | 0.765066 | 0.000692 |
| Non-NS vs. syphilis free | 206.4 | 196.0808 | 4.223 | 1.591 | 0.026* | |
| 498.7 | 198.0966 | 1.078 | 0.797 | 0.071 | ||
| N-acetyl -L-tyrosine | 69.34 | 268.0621 | 2.424 | 0.086 | 0.061 | |
| 144.7 | 298.0964 | 1.421 | 0.855 | 0.054 | ||
| L-Leucine | 485.931 | 132.1009 | 1.32648 | 1.16155 | 0.071789 | |
FIGURE 6KEGG pathway analysis of the differential metabolite components in the three groups. The P-value of each pathway was demonstrated by the color of bar, and the rich factor of each pathway generated by using KEGG analysis was presented in the number after the bar. (A) Neurosyphilis patients vs. syphilis/non-neurosyphilis patients; (B) neurosyphilis patients vs. syphilis-free patients; (C) syphilis/non-neurosyphilis patients vs. syphilis-free patients. The numbers presented in the bar were rich factors generated by using KEGG analysis.