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 LC status at follow-up, at a false discovery rate (FDR) q <0.05 level. random forest model to identify important SOMAmers at each timepoint Functional enrichment analysis was applied at each timepoint to identify pathways enriched among those with LC compared to regular COVID participants, by FDR q-value p-<0.05. 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.
