Analysis of Crosstalk Between Pathogens and Immune System in Human Airway Mucus via Machine Learning-Enhanced DIA...

Peptide-centric machine learning enhanced (PCML) data-independent acquisition tandem mass spectrometry (LC-MS/MS-DIA) matches low-abundance MS fragmentation spectra to in silico predicted peptide spectra deduced from libraries of customized protein sequences. The study's goal was to determine proteomic depth of coverage in microbial pathogen-containing clinical samples using that method.

We employed a published machine learning method based on neural networks (Dia-NN) to the LC-MS/MS analysis of sputum protein digests derived from patients with lung infections.

Nearly 6800 proteins in total and 1530 proteins of microbial origin were identified from single experiments, with CVs of protein quantities among technical replicates as low as 0.12. Conventional spectral library searches of data from these experiments yielded less than 1600 and 60 protein identifications, respectively. Samples of two patients revealed colonization by pathogens difficult to clear from chronically infected lungs, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. Abundant virulence factors in the datasets were the insulin-cleaving metalloproteinase IcmP (P. aeruginosa) and an inducer of human interleukin-10 expression (S. maltophilia). Each bacterium showed signs of adaptation to a hostile milieu, such as the expression of systems to generate energy anaerobically and the acquisition of host-sequestered metals.

This work constitutes a step forward for protein-centered translational medicine on infectious diseases.

We demonstrate excellent depth of proteome coverage and experimental repeatability for low-abundance pathogen proteomes in human airway secretions via data-independent acquisition liquid chromatography tandem mass spectrometry leveraging machine learning for spectral analysis. The host's sputum proteome was also profiled, allowing inferences of immune defense mechanisms against pathogens. This proof-of-principle study shows the opportunity to gain insights into respiratory disease burdens and bacterial virulence by directly analyzing clinical specimens and the potential for biomarker discovery and pharmacodynamic response monitoring in interventional studies related to respiratory tract infections.