Quench me if you can: Alpha-2-macroglobulin trypsin complexes enable serum biomarker analysis by MALDI mass spectrometry.

One of the main functions of alpha-2-macroglobulin (A2M) in human blood serum is the binding of all classes of protease. It is known that trypsin, after such interaction, possesses modified proteolytic activity. Trypsin first hydrolyzes two bonds in A2M's 'bait region', and the peptide 705VGFYESDVMGR715 is released from A2M. In this work, specifics of the A2M-trypsin interaction were used to determine A2M concentration directly in human blood serum using MALDI mass-spectrometry. Following exogenous addition of trypsin to human blood serum in vitro, the concentration of the VGFYESDVMGR peptide was measured, using its isotopically-labeled analogue (18O), and A2M concentration was calculated. The optimized mass spectrometric approach was verified using a standard method for A2M concentration determination (ELISA) and the relevant statistical analysis methods. It was also shown that trypsin's modified proteolytic activity in the presence of serum A2M can be used to analyze other serum proteins, including potential biomarkers of pathological processes. Thus, this work describes a promising approach to serum biomarker analysis that can be technically extended in several useful directions.

Rapid Detection of COVID-19 Using MALDI-TOF-Based Serum Peptidome Profiling.

The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.