Longitudinal proteomic investigation of COVID-19 vaccination

Proteome analysis develops novel plasma proteins classifier in predicting the mortality of COVID-19

COVID-19 has been a global concern for 3 years, however, consecutive plasma protein changes in the disease course are currently unclear. Setting the mortality within 28 days of admission as the main clinical outcome, plasma samples were collected from patients in discovery and independent validation groups at different time points during the disease course. The whole patients were divided into death and survival groups according to their clinical outcomes. Proteomics and pathway/network analyses were used to find the differentially expressed proteins and pathways. Then, we used machine learning to develop a protein classifier which can predict the clinical outcomes of the patients with COVID-19 and help identify the high-risk patients. Finally, a classifier including C-reactive protein, extracellular matrix protein 1, insulin-like growth factor-binding protein complex acid labile subunit, E3 ubiquitin-protein ligase HECW1 and phosphatidylcholine-sterol acyltransferase was determined. The prediction value of the model was verified with an independent patient cohort. This novel model can realize early prediction of 28-day mortality of patients with COVID-19, with the area under curve 0.88 in discovery group and 0.80 in validation group, superior to 4C mortality and E-CURB65 scores. In total, this work revealed a potential protein classifier which can assist in predicting the outcomes of COVID-19 patients and providing new diagnostic directions.

Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer

BACKGROUND

Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear.

METHODS

Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy.

RESULTS

A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues.

CONCLUSIONS

We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

Proteomic and Cellular Characterization of Omicron Breakthrough Infections and a Third Homologous or Heterologous Boosting Vaccination in a Longitudinal Cohort

The understanding of dynamic plasma proteome features in hybrid immunity and breakthrough infection is limited. A deeper understanding of the immune differences between heterologous and homologous immunization could assist in the future establishment of vaccination strategies. In this study, 40 participants who received a third dose of either a homologous BBIBP-CorV or a heterologous ZF2001 protein subunit vaccine following two doses of inactivated coronavirus disease 2019 vaccines and 12 patients with BA2.2 breakthrough infections were enrolled. Serum samples were collected at days 0, 28, and 180 following the boosting vaccination and breakthrough and then analyzed using neutralizing antibody tests and mass spectrometer-based proteomics. Mass cytometry of peripheral blood mononuclear cell samples was also performed in this cohort. The chemokine signaling pathway and humoral response markers (IgG2 and IgG3) associated with infection were found to be upregulated in breakthrough infections compared to vaccination-induced immunity. Elevated expression of IGKV, IGHV, IL-17 signaling, and the phagocytosis pathway, along with lower expression of FGL2, were correlated with higher antibody levels in the boosting vaccination groups. The MAPK signaling pathway and Fc gamma R-mediated phagocytosis were more enriched in the heterologous immunization groups than in the homologous immunization groups. Breakthrough infections can trigger more intensive inflammatory chemokine responses than vaccination. T-cell and innate immune activation have been shown to be closely related to enhanced antibody levels after vaccination and therefore might be potential targets for vaccine adjuvant design.

Baseline immune states (BIS) associated with vaccine responsiveness and factors that shape the BIS

Vaccines are among the greatest inventions in medicine, leading to the elimination or control of numerous diseases, including smallpox, polio, measles, rubella, and, most recently, COVID-19. Yet, the effectiveness of vaccines varies among individuals. In fact, while some recipients mount a robust response to vaccination that protects them from the disease, others fail to respond. Multiple clinical and epidemiological factors contribute to this heterogeneity in responsiveness. Systems immunology studies fueled by advances in single-cell biology have been instrumental in uncovering pre-vaccination immune cell types and genomic features (i.e., the baseline immune state, BIS) that have been associated with vaccine responsiveness. Here, we review clinical factors that shape the BIS, and the characteristics of the BIS associated with responsiveness to frequently studied vaccines (i.e., influenza, COVID-19, bacterial pneumonia, malaria). Finally, we discuss potential strategies to enhance vaccine responsiveness in high-risk groups, focusing specifically on older adults.