Markerview

GRAVY scores were determined using http://www.gravy-calculator.de/, and pI and molecular weight using http://web.expasy.org/compute_pi/. The SWATH Acquisition Replicates Template was used to evaluate the reproducibility of the transitions based on the biological replicates one to five of diff hESC. Differential protein expression analysis between undiff and diff hESC was performed in MarkerView (Sciex) using a two‐sample t‐test of the normalized protein peak areas based on total area sums. Default MarkerView parameters were used, including 0.0 for missing values and arithmetic sums of transitions/peptides for peptide/protein measurements. Venn diagrams were constructed using http://bioinfogp.cnb.csic.es/tools/venny/index.html. Pathway overrepresentation enrichment analysis was performed using Webgestalt (http://www.webgestalt.org/option.php). For each library, its set of differentially expressed proteins (p‐value < 0.05) with a 1.5 fold in‐ or decrease was uploaded and searched against the Wikipathways database. Enrichment analysis was performed after comparison with the list of quantified proteins with the respective library. Benjamini & Hochberg correction was used for multiple testing adjustment, and the significance level was set to 0.05.

Peak extraction of the SWATH data was performed using either the Spectronaut software (ver 8.0, Biognosys, Switzerland) or SWATH micro App embedded in PeakView (ver2.0, Sciex). SWATH data were processed with default settings in Spectronaut. Reference peptides from the iRT-kit (Biognosys) spiked into each sample were used to calibrate the retention time of extracted peptide peaks using Spectronaut. Peptide identification results were filtered with a q-value of < 1%, and excluding shared peptides. RT calibration was also performed based on iRT peptide elution profiles in PeakView using the SWATH App module (v2.0). After peak extraction with either Spectronaut or PeakView, the sum of MS2 ion peak areas of SWATH quantified peptides for individual proteins were exported to calculate the protein peak areas. For statistical analysis of the SWATH data set, peak extraction output data matrix from PeakView was imported into MarkerView (v2, Sciex) and MSstats (R package, Bioconductor) for data normalization and relative protein quantification. Proteins with a fold change > 1.5 and statistical p-value < 0.05 estimated by MarkerView and MSstats were regarded differentially expressed under different conditions.

The quantitative data obtained by PeakView were analysed using MarkerView (v1.2; Sciex). First, signal peak areas were normalised by the total area sum. MultiExperiment Viewer (http://www.tm4.org/mev.html) was used to identify the proteins differentially expressed in saliva between female and male ticks using Welch’s t-test with Bonferroni correction. Salivary proteins for which the adjusted P-value was ≤ 0.05 were considered significantly differentially expressed between female and male ticks. For every protein, its quantity in each sex was expressed as the mean signal peak area in the three replicated saliva samples, and the fold change in expression between female and male saliva was calculated as the ratio between the mean protein areas for females versus males. The results of the hierarchical clustering analysis of the differentially expressed proteome profile of female and male samples were shown using a heat map after z-score normalisation using Euclidean distances.

Data generated from the mass spectrometry measurements was transformed from Thermo Fisher Scientific’s raw proprietary format to.txt files containing centroided m/z peaks using an in-house script. The converted data was then imported to R statistical software to assess the data and to perform quality control checks. Peak alignment was done using Sciex MarkerView software and the aligned data was further processed in R. A mass defect filter [35 (link)] was applied to remove salt clusters and artefacts from the dataset. Next, peaks with signal to noise ratio (S/N) < 3 were removed. Subsequently, a blank and medium subtraction filter was applied to remove background peaks. Finally, data was normalized using log2 transformation and scaled using pareto-scale. The dataset was then divided into three groups: ECs treated and untreated with Ebola VLP, M1 macrophages treated and untreated with Ebola VLP, and M2 macrophages treated and untreated with Ebola VLP.

Data extraction from SWATH runs was carried out by PeakView using MS/MSALL with SWATH Acquisition MicroApp, resulting in a library containing 2915 proteins. Peptide retention times for each protein were realigned in each run according to indexed retention time (iRT) peptides (Biognosys AG, Schlieren/Zürich, Switzerland). Chromatograms of the extracted ions were created for each selected ionic fragment. PeakView calculated a score and FDR for each assigned peptide using chromatographic and spectral components. MarkerView (version 1.2.1; Sciex) allowed signal normalization, and a t test was applied for testing differential abundance.

To assess population clustering and the parameters responsible for the distinction among groups, multivariate analyses were conducted both in a non-supervised method (PCA) and in a supervised method (PCA-DA), using the MarkerView (version 1.2.1, SCIEX) program with data normalized by the Pareto method. The mean values of the variables among patients with active TB and the other groups were then compared by Student’s t-test, and the false discovery rate adjusted p-values (q-values) were calculated to generate a volcano plot. After the selection of the key metabolites from those analyses, the median values of the metabolites among the patient groups were compared using Kruskal-Wallis or Mann-Whitney U tests using GraphPad Prism version 6.00 for Windows (GraphPad Software, La Jolla, CA, USA). ROC analysis was used to test the diagnostic utility of the metabolites in differentiating patients with active TB from those in the other groups using a R package ‘fbroc’ version 0.4.1 (Erik Peter, 2019). Finally, for the comparison of median values of the metabolites among the subgroups of patients with active TB, the patients were divided into two sub-groups according to the degree of risk burden (low or mid/high) and pulmonary extent (<1/3 or ≥1/3 of the total lung area), and Mann-Whitney U tests were conducted. We considered p-values <0.05 to be statistically significant.