An N-glycopeptide MS/MS data analysis workflow leveraging two complementary glycoproteomic software tools for more confident identification and assignments.

Complete coverage of all N-glycosylation sites on the SARS-CoV2 spike protein would require the use of multiple proteases in addition to trypsin. Subsequent identification of the resulting glycopeptides by searching against database often introduces assignment errors due to similar mass differences between different permutations of amino acids and glycosyl residues. By manually interpreting the individual MS2 spectra, we report here the common sources of errors in assignment, especially those introduced by the use of chymotrypsin. We show that by applying a stringent threshold of acceptance, erroneous assignment by the commonly used Byonic software can be controlled within 15%, which can be reduced further if only those also confidently identified by a different search engine, pGlyco3, were considered. A representative site-specific N-glycosylation pattern could be constructed based on quantifying only the overlapping subset of N-glycopeptides identified at higher confidence. Applying the two complimentary glycoproteomic software in a concerted data analysis workflow, we found and confirmed that glycosylation at several sites of an unstable Omicron spike protein differed significantly from those of the stable trimeric product of the parental D614G variant.

Elevated ubiquitination contributes to protective immunity against severe SARS-CoV-2 infection.

BACKGROUND: The crosstalk between the ubiquitin-proteasome and the immune system plays an important role in the health and pathogenesis of viral infection. However, there have been few studies of ubiquitin activation in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS: We investigated the effect of ubiquitination on SARS-CoV-2 infection and patient prognosis by integrating published coronavirus disease 2019 (COVID-19) multi-transcriptome data and bioinformatics methods. RESULTS: The differential expression of COVID-19 samples revealed changed ubiquitination in most solid and hollow organs, and it was activated in lymphatic and other immune tissues. In addition, in the respiratory system of COVID-19 patients, the immune response was mainly focused on the alveoli, and the expression of ubiquitination reflected increasing immune infiltration. Ubiquitination stratification could significantly differentiate patients' prognosis and inflammation levels through the general transcriptional analysis of the peripheral blood of patients with COVID-19. Moreover, high ubiquitination levels were associated with a favourable prognosis, low inflammatory response, and reduced mechanical ventilation and intensive care unit. Moreover, high ubiquitination promoted a beneficial immune response while inhibiting immune damage. Finally, prognostic stratification and biomarker screening based on ubiquitination traits played an important role in clinical management and drug development. CONCLUSION: Ubiquitination characteristics provides new ideas for clinical intervention and prognostic guidance for COVID-19 patients.

Assessment of Financial Toxicity Among Patients With Advanced Lung Cancer in Western China.

Background: Lung cancer is the leading source of cancer-caused disability-adjusted life years. Medical cost burden impacts the well-being of patients through reducing income, cutting daily expenses, curtailing leisure activities, and depleting exhausting savings. The COmprehensive Score for Financial Toxicity (COST) was created and validated by De Souza and colleagues. Our study intends to measure the financial burdens of cancer therapy and investigate the link between financial toxicity and health-related quality of life (HRQoL) in an advanced lung cancer population. Methods: Patients aged ≥ 18 years with confirmed stage III to IV lung cancer were eligible. The COST questionnaire verified by de Souza et al. was used to identify financial toxicity. Multivariable linear regression analysis with log transformation univariate analysis and Pearson correlations were used to perform the analysis. Results: The majority of the patients (90.8%, n = 138/152) had an annual income of $50,000 ($7,775). The cohort's insurance situation was as follows: 64.5% of the cohort had social insurance, 20.4% had commercial insurance, and 22.0% had both. Patients who were younger age (50-59, P < 0.001), employed but on sick leave, and had lower income reported increased levels of financial toxicity (P < 0.05). The risk factors for high financial toxicity: (i) younger age (50-59), (ii) <1 month of savings, and (iii) being employed but on sick leave. Increased financial toxicity is moderately correlated with a decrease in QoL. Conclusion: Poorer psychological status and specific demographics are linked to increased financial toxicity (lower COST). Financial toxicity has a modest relationship with HRQoL and may have a clear link with HRQoL measurements.