Past & current proteomics analyses with the AZHEROES are listed below:
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- Long COVID: Understanding Long COVID (LC) in Frontline and Essential Workers (FEWs) through Proteomic Profiling: A Longitudinal Cohort Study
- Led analytical team and developed statistical pipeline for proteomics analysis in AZHEROES
- Processed and analyzed proteomics data from 130+ frontline and essential workers with COVID-19 infection to identify biomarkers associated with Long COVID
- Applied bioinformatics techniques to map 20+ significant biological pathways
- Lead author for a manuscript currently under internal review (Liu et al., 2026)
- COVID Breakthrough: Serum Proteomics in Omicron SARS-CoV-2 breakthrough: a nested case-control study within a prospective cohort of frontline workers from eight locations in the United States (US)
- Assisted in proteomics analysis for 100+ breakthrough cases to identify risk factors for Omicron breakthrough
- Contributed to a manuscript submitted to PLoS ONE (Liu et al., 2026)
References
2026
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Understanding Long COVID (LC) in Frontline and Essential Workers (FEWs) through Proteomic Profiling: A Longitudinal Cohort Study
Tuo Liu, Danielle Stea, Shawn C Beitel, and 3 more authors
in prep, 2026
Background: Long COVID (LC) can debilitate due to lingering symptoms. However, there is incomplete information on the mechanistic understanding of LC, and if proteomic profiles of individuals can predict long-COVID status. Objective: To examine differences in proteomic profiles and cellular activities between frontline and essential workers (FEWs) who developed LC and who recovered at baseline, during COVID-19 convalescence, and at follow-up, to understand LC development in FEWs. Method: Frontline and essential workers (FEWs) in the HEROES-RECOVER cohorts were tested weekly for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from 2020-2022. Participants with long-COVID (N = 57) (symptoms persisting for at one month) and participants with COVID-19 without LC (N = 80) were selected for untargeted proteomic profiling using the SOMAscan Assay at baseline (before COVID-19 infection), convalescence (within 60 days of COVID-19 infection), and follow-up (60 days after infection onset). We fitted a logistic regression model for each SOMAmer to extract crude risk of developing at each visit, at a false discovery rate (FDR) q <0.05 level. Results: We profiled a total of 7,659 serum SOMAmers with SOMAScan in 137 FEWs at 3 study timepoints, separately. A total of 116 SOMAmers were differentially expressed between FEWs who ultimately developed LC (N=57) and regular COVID (N=80) at adjusted p-value 0.05 level. Of these, 20 differential SOMAmers were unique to baseline, 14 were unique to convalescence and 17 were unique to follow-up. Functional enrichment analysis revealed sustained immune activities and inflammatory profiles across the entire study (FDR q<0.05), and potential contribution from occupational hazards to the sustained enrichments. Conclusion: Our findings in FEWs suggest that potential interplay of pre-existing immune variability, acute immune activation during SARS-CoV-2 infection, and persistent inflammatory signaling throughout the recovery phase may collectively contribute to the LC development in this population.
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Unveiling Post-Vaccination Proteomic Signatures in Infection-Naïve Individuals Associated with Omicron Breakthrough Infections
Yiwen Liu, Eric Lu, Katherine D. Ellingson, and 6 more authors
PLoS ONE, submitted, 2026
Background: Given the persistence of the SARS-CoV-2 virus, it is important to understand the proteome associated with breakthrough infections among fully COVID-19 vaccinated individuals. Methods: We utilized a nested case-control study within the frontline worker HEROES-RECOVER cohorts, comparing serum proteomic profiles from SARS-CoV-2 infection-naïve participants following a third dose of COVID-19 origin strain WA-1 monovalent mRNA vaccine and subsequently experiencing Omicron breakthrough infections with the proteomic profiles of matched controls without infections. Our study leveraged proteomics data generated from the SomaScan Platform and adopted a robust feature selection method, elastic net regularized conditional logistic regression together with bootstrapping technique, to identify key proteins. Enrichment analyses were performed to investigate biological pathways.Results: We identified 28 significantly different proteins out of over 7,000 candidate proteins. Key findings included downregulated chemokines (CXCL2, CXCL3, CCL19, CCL23) and elevated cytokine IL-7 levels in breakthrough cases, with pathway analysis revealing enrichment in chemokine signaling and cytokine-cytokine interaction pathways. Other key proteins, such as LGALS1, HAVCR2, and SELE were upregulated in breakthrough cases. Discussion: These results reveal potential immune response mechanisms in breakthrough infections, characterized by viral immune evasion and compensatory T-cell regeneration. The identified biomarkers may provide valuable insights for future predictive profiles and therapeutic strategies.
