CROI 2025 Abstract eBook

Abstract eBook

Poster Abstracts

(4.61 [95% CI: 4.17-5.10] vs 3.85 [95% CI: 3.10-4.77]), with a positive correlation observed between NAb titers and Tregs in C-NACC (r= 0.709). Conclusions: Higher frequencies of Tregs contributed to reduced T cell activation, resulting in a more specific response to SARS-CoV-2 in C-NACC. Conversely, prior immune activation led to significant cell replication, as evidenced by greater telomere shortening, which may have contributed to cardiac contractility alterations in C-ACC. The figure, table, or graphic for this abstract has been removed. Autoantibody Prevalence After SARS-CoV-2 Infection: Comparing Baltimore, USA, and Rakai, Uganda Adam Epstein-Shuman 1 , Joanne H. Hunt 1 , Xianming Zhu 2 , Reinaldo Fernandez 3 , Grace Rozek 3 , Patrizio Caturegli 3 , Ronald Galiwango 4 , Godfrey Kigozi 4 , M. Kate Grabowski 5 , Larry W. Chang 3 , Andrew D. Redd 1 , Yu-Hsiang Hsieh 3 , Steven J. Reynolds 1 , Oliver Laeyendecker 1 1 National Institute of Allergy and Infectious Diseases, Baltimore, MD, USA, 2 The Johns Hopkins University, Baltimore, MD, USA, 3 The Johns Hopkins University School of Medicine, Baltimore, MD, USA, 4 Rakai Health Sciences Program, Kalisizo, Uganda, 5 The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Background: Autoantibodies (AAbs), particularly those targeting type 1 interferon subtype alpha (aIFNα), nuclear antigens (ANAs), cardiolipin (aCL), and beta 2 glycoprotein 1 (aβ2GP1), have been associated with the severity of acute COVID-19. Whether SARS-CoV-2 induces these AAbs, however, and whether there are differences across settings, remains underexplored. We investigated AAbs prevalence and induction in individuals from the Johns Hopkins Hospital Emergency Department (JHHED) in Baltimore Maryland and the Rakai Community Cohort Study (RCCS) in Uganda. Methods: Serum samples were derived from 224 JHHED and from 150 RCCS unique individuals before and after SARS-CoV-2 infection. Pre-infection samples were collected between July 2020 - February 2022, for JHHED and between June 2018 - October 2020 for the RCCS. Post-infection samples were collected between September 2020 - December 2022 for JHHED, and February 2021 - March 2023 for RCCS. aIFNα, aCL, and aβ2GP1 AAbs were assessed using commercially available ELISAs. ANAs were measured using a line immunoassay. McNemar’s tests assessed within-cohort changes, and Fisher’s exact tests compared prevalence across cohorts, with significance set at p<0.05 after Benjamini-Hochberg correction. Results: For both cohorts, no differences in AAb prevalence were observed before and after SARS-CoV-2 infection. Additionally, individuals were as likely to see the appearance of an AAb as have one resolve. The prevalence of AAbs for the JHHED were: aIFNα (2% vs 2%); ANAs (25% vs 25%); aCL (3% vs 4%,); aβ2GP1 (6% vs 4%). For the RCCS: aIFNα (1% vs 2%); ANAs (45% vs 45%); aCL (1% vs 3%); aβ2GP1 (5% vs 10%). Pre-SARS-CoV-2 infection, there was a significantly higher prevalence of six ANAs in Ugandan individuals compared to their Baltimore counterparts: aPCNA (10% vs 0%, p<0.001); aSS-B/LA (13% vs 2%, p<0.001); aCENPB (4% vs 0%, p=0.01); aU1snRNP (10% vs 1%, p=0.001); aPM-Scl (12% vs 4%, p=0.01); and aKu (22% vs 3%, p<0.001). There was no effect of age or sex on AAb prevalence. Conclusions: We found no evidence that SARS-CoV-2 induced the examined AAbs in either the US or Ugandan population. Compared to the JHHED, we observed a significantly higher prevalence of several ANAs in the RCCS. Given ANA's association with various autoimmune disorders, this disparity warrants further investigation. Novel Plasma Biomarkers for the Detection of Long COVID Defined by Multiapproach Analysis Mohamed R. Joma 1 , Gildas Bertho 2 , Cédric Caradeuc 2 , Samuel Samuel Lebourgeois 3 , Julien Pansiot 4 , Lara Tabet 4 , Rachelle Saleh 4 , Wiem Bouchneb 5 , Isabelle Pellegrin 6 , Pierre Gressens 1 , Esaïe Marshall 1 , Agathe Goumbard 7 , Dominique Salmon-Ceron 8 , Luc De Chaisemartin 8 , Mireille Laforge 4 , for the NeuroDiderot Research Group 1 French National Institute of Health and Medical Research (Inserm), Paris, France, 2 Paris Descartes University, Paris, France, 3 Institut Pasteur, Paris, France, 4 Institut national de la santé et de la recherche médicale (Inserm), Paris, France, 5 Assistance Publique Hôpitaux, Paris, France, 6 Bordeaux University Hospital, Bordeaux, France, 7 University of Paris, Paris, France, 8 Assistance Publique – Hôpitaux de Paris, Paris, France Background: Long COVID (LC) is a newly defined syndrome linking COVID infection with an umbrella of long-lasting symptoms. Due to this variety of presentation and lack of understanding of the mechanism involved, no reliable

biomarkers have been found so far, and diagnostic uncertainty remains high. In the prospect of personalized medicine, there is a dire need for diagnostic and prognostic markers. To address this issue, we designed a multi-approach exploratory study on a well characterized cohort of LC patients using Resolved COVID infections (RC) and Uninfected Controls (UC). Methods: Plasma samples from 100 LC, 60 RC and 60 UC were analyzed by using state-of-the-art IVDr NMR allowing to harvest detailed lipidomic and metabolomic data and immune and mitochondrial stress markers explored by several multiplex ELISAs and electrochemiluminescent assays. On these data, we performed multivariate dimensionality reduction models (PCA, OPLS-DA) to visualize data distribution and discriminant variables between groups. Candidate biomarkers were validated with univariate analysis and ROC curve analysis, and then used to construct a machine deep learning model able to sort patients into diagnostic groups. Finally, LC symptoms were correlated with the most significant variables. Results: We identified several potential biomarkers related to platelet and neutrophil activation, inflammation, TCA cycle, vascularization, lipoproteins. Models fed by those biomarkers alone were sufficient to cluster the LC group from the two other groups. We also found that both previously infected groups (RC and LC) had a specific signature distinguishing them from UC. SARS-CoV-2 infection induced host immuno-metabolic signatures that persisted after full recovery (RC group). However, this signature was different in the LC group and was associated with LC pathophysiology. We created several machine learning models to test whether these biomarkers along with the other variables are enough to predict LC patients. Our model was able to correctly predict 99% of LC patients and had about 70% accuracy in distinguishing RC from UC patients. Conclusions: In this study, we identified potential biomarkers sufficient to diagnose LC with 99% accuracy. These biomarkers were associated with immune and metabolic dysregulation, as well as content symptoms, providing clues to future therapeutic targets. We are now testing these markers in a prospective setting to validate their use in clinical practice. Background: Post-Acute Sequelae of SARS-CoV-2 infection (PASC) or Long COVID describes the occurrence of persistent, relapsing, or new symptoms that are present after acute infection. SARS-CoV-2 entry occurs through its receptor binding domain to angiotensin converting enzyme 2 (ACE2), which regulates the renin angiotensin system (RAS). It has been shown that ACE2 shedding is associated with the development of autoantibodies (AA) that can cause RAS dysregulation and lead to pro-fibrotic sequelae seen in PASC. This study evaluated the levels of AA directed against ACE2 in the context of PASC. Methods: Participants were enrolled in the study following guidelines approved by the University of Hawaii Institutional Review Board into PASC (n=13), recovered from COVID-19 (n=15), and no history of COVID-19 (n=13) groups. ACE2 autoantibody levels were quantified in plasma (1/50 dilution) using a Luminex bead-based platform with recombinant ACE2 protein (Sino Biological, Wayne, PA) coupled to polystyrene microsphere beads (Luminex Corporation, Austin, TX). Antibodies were measured with the MagPix System using a 1/200 dilution of R-PE conjugated goat anti-human IgG (Jackson ImmunoResearch, West Grove, PA) in PBS. A standard curve was generated with an ACE2 monoclonal antibody (Sino Biological, Wayne, PA). A custom immunoassay kit (R&D Systems, Minneapolis, MN) was used for the detection and quantification of multiple soluble protein biomarkers. Statistical analyses were done with Prism Software (GraphPad, Boston, MA) using Wilcoxon rank sum and Kruskal-Wallis tests to compare between the groups. Results: A significant difference in AA levels were observed in the PASC group compared to the recovered from COVID-19 and no history of COVID-19 groups (p=0.02), Figure 1A. Additionally, significant MMP-9 level differences occurred between all groups with the PASC group having the highest levels (p<.001), Figure 1B. Conclusions: This is the first report of elevated ACE2 AA in relation to PASC. ACE2 AA likely affects the enzymatic activity of ACE2 which causes an accumulation of Ang II and decreased conversion to Ang (1-7) that may be responsible for pro-fibrotic activity. MMP-9 upregulation has been associated The figure, table, or graphic for this abstract has been removed. ACE2 Autoantibodies in Post-Acute Sequelae of SARS-CoV-2 Infection Melissa Agsalda-Garcia, Alan F. Garcia, Tiffany Shieh, Gehan Devendra, Dominic C. Chow, Cecilia M. Shikuma, Juwon Park University of Hawaii at Manoa, Honolulu, HI, USA

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CROI 2025 296