The Application of Mass Spectrometry to Detect Peptides Deriving from Mycobacterium tuberculosis for Future Tuberculosis Diagnostics
Abstract
This investigation included the construction of a spectral library of Mtb H37Rv peptides followed by a comparison of the results of data-dependent acquisition (DDA) and data-independent acquisition (DIA) workflows for detecting Mycobacterium tuberculosis (Mtb) H37Rv proteins in human urine, with potential applications in tuberculosis diagnostics. The spectral library was constructed from DDA runs on tryptic peptides from Mtb H37Rv cell lysate. Library features were mapped to 30,341 unique peptides, 13,714 of which could be mapped to 2,332 proteins, 59.70% of the Mtb H37Rv proteome accounted for in the Sanger Institute database (NC_000962.3). Serial dilutions of Mtb H37Rv cell lysate in urine from PPD-negative volunteers were analyzed using both DDA and DIA. The resulting DDA spectra were analyzed in Proteome Discoverer™
2.4.0 and DIA spectra in Skyline 20.2 using the above-mentioned spectral library with mProphet for peak-scoring. Data analysis parameters in the final DIA workflow were qix value (< 0.0076), library intensity dot-product (> 0.6), product mass error (< 0.85 ppm), retention time difference (< 3 min), and default dotp (> 0.6). The minimum Mtb total protein concentration that yielded a count of Mtb peptides higher than the negative controls was 6.25 ng/mL, with the final DIA workflow. In comparison, the DDA workflow only distinguished the 800 and 200 ng/mL samples from negative controls by peptide count. Therefore, the final DIA workflow may have an advantage over the DDA workflow employed for peptide detection within a lower analyte concentration range, suggesting it is more clinically relevant for Mtb diagnostics because lower concentrations of Mtb bioanalytes would be associated with less advanced TB infections.
Description
This thesis has been embargoed for 5 years. It will not be available until July 2026 at the earliest.
Keywords
Tuberculosis diagnostics, Urinary biomarker, Proteomics, Tandem mass spectrometry, Data-independent acquisition