2.1 Participants
Informed consent was obtained from all subjects in this study. All experiments were performed following the approved guidelines. The patients included those with latent syphilis (Y, n=39), those with asymptomatic neurosyphilis (W, n=9), and those with symptomatic neurosyphilis (Z, n=33) as well as non-syphilis patients (F, n=7). Briefly, neurosyphilis was defined based on positive treponemal test results and the toluidine red unheated serum test (TRUSRT) [12,13]. Patients were considered to have symptomatic neurosyphilis if they have obvious clinical symptoms of neurosyphilis, such as meningitis, stroke, acute changes in mental status, abnormal hearing or vision; neurosyphilis patients with no neurological symptoms have asymptomatic neurosyphilis. Latent syphilis was defined based on a positive serological test for syphilis but a negative CSF test and no neurological symptoms or classic syphilis symptoms. Non-syphilis patients were negative based on serological and CSF tests.
2.2 Sample preparation
Approximately 1 mL of CSF was collected from each participant via lumbar puncture. The samples were transported on ice immediately after collection, and a syphilis serological test was carried out. CSF proteins and white blood cells were counted within six hours after CSF collection and stored at -80℃ until use. CSF samples were thawed, and 100 μL of sample and 300 μL of methanol were transferred to a 1.5-mL Eppendorf tube and vortexed for 30 s. All samples were kept at -40°C for 1 h, vortexed for 30 s and centrifuged at 12000 rpm and 4°C for 15 mins. Next, 200 μL supernatant and 5 μL DL-o-chlorophenylalanine (internal reference, 140 μg/mL) were transferred to vials for HPLC-MS analysis.
2.3 UPLC-Q Exactive MS
The HPLC-MS analysis was performed on an Ultimate 3000 UPLC system combined with Q-Exactive Orbitrap-MS (Thermo, Waltham, MA, USA). The LC system is comprised of an ACQUITY UPLC HSS T3 (100×2.1mm 1.8 μm) with Ultimate 3000LC. The mobile phase was composed of solvent A (0.05% formic acid-water) and solvent B (acetonitrile) with gradient elution (1-1.5 min, 95-70% A; 1.5-9.5 min, 70-5% A; 9.5-13.5 min, 5% A; 13.5-13.6 min, 5-95% A, 13.6-16 min, 95% A). The flow rate of the mobile phase was 0.3 mL/min. The column temperature was maintained at 40°C, and the sample manager temperature was set at 4°C. Mass spectrometry parameters in ESI+ and ESI- mode are listed as follows: Heater Temp 300 °C; Sheath Gas Flow rate, 45 arb; Aux Gas Flow Rate, 15 arb; Sweep Gas Flow Rate, 1 arb; Capillary Temp, 350 °C; S-Lens RF Level, 30%.; spray voltage, 3.0 kV in ESI+ mode and 3.2 kV in ESI- mode. The settings for full scan data acquisition were as follows: resolution, 70,000 fwhm; automatic gain control (AGC) target, 3×106; maximum injection time, 100 ms; scan range, 70–1050 m/z; polarity, negative or positive; spectrum data type, centroid. Ten quality control (QC) samples were run to avoid small changes in both chromatographic retention time and signal intensity at the beginning of the sequence. QC samples were also injected at regular intervals (every ten samples) throughout the analytical run [14].
2.4 MS data processing and identification
Raw data were acquired and aligned using Compound Discover (version 3.0, ThermoFisher Scientific, Waltham, MA, USA) according to the m/z value and the retention time of the ion signals. In the case of some large or small variables, normalization was often performed after alignment, and a line plot was used to evaluate the methodology. The confidence interval of 95% of the sample value was considered to be stable and feasible.
Normalized data were imported into the SIMCA-P program (version 14.1, Umetric, Umea, Sweden) for principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and the calculation of variable importance in the projection (VIP). As an unsupervised pattern recognition method for processing metabonomic data, PCA can classify metabolic phenotypes according to all input samples. All data from differentially expressed compounds were used to build PCA models. Different colours and icons represent different groups. PLS-DA was employed to supervise regression modelling of the data set to identify potential biomarkers. The quality of the models was described by relevant R2 and Q2 values. R2 displays the variance explained in the model and indicated the quality of the fit. Q2, as calculated by a cross-validation procedure, indicates the predictability of the model.
Fold change (FC) analysis and independent-sample t-test statistics were first applied for comparison of metabolite levels to determine statistically significant differences among the four groups. The cut-off criteria for screening differentially expressed metabolites were FC>2, VIP>1.5, and P<0.05. According to databases, the chemical structures of important metabolites were then identified, such as the Human Metabolome Database (http://www.hmdb.ca), using accurate mass and MS/MS fragment data.
HCA was performed and visualized by using the embedded module of MetaboAnalyst 4.0 [15]. By applying the Euclidean distance measure and Ward clustering algorithm, dynamic changes in significantly different metabolites were compared, and the ratio obtained was used to draw an HCA heat map. Colours in the heat maps correlate with the degree of increase (red) and decrease (blue) relative to the mean metabolite ratio.
Based on the differentially expressed metabolites, metabolic pathway and metabolite biofunction analyses were performed using the network database (KEGG pathway http://www.genome.jp/kegg/) to investigate the bioprocesses affected by T. pallidum infection. In brief, the enrichment level was calculated by the t-test, and metabolic pathways with P values less than 0.05 were considered statistically significant.