Longitudinal Assessment of Systemic Steroid Therapy on Hyperinflammatory Endothelial Biomarker Profiles and Serology Responses of COVID-19 Patients

doi:10.21203/rs.3.rs-1217324/v1

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Longitudinal Assessment of Systemic Steroid Therapy on Hyperinflammatory Endothelial Biomarker Profiles and Serology Responses of COVID-19 Patients

https://doi.org/10.21203/rs.3.rs-1217324/v1

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This first of its kind study provides objective context to the potential mechanism of action of corticosteroid use in COVID-19 patients from 3 separate European medical centers by connecting inflammatory biomarkers to IgG levels for the SARS-CoV-2 spike protein antigens and neutralization of ACE2 binding within infected individuals. CXCL9 is described herein as an important COVID-19 biomarker connecting disease severity with inflammatory biomarker and serology response profiles in corticosteroid-treated patients.

COVID-19

CXCL9

Cytokine

Dexamethasone

Inflammation

Serology

Patients and Samples

This study included a cohort of 28 patients requiring supplemental oxygen. 6 received corticosteroids (Steroids) prior to first sample collection, while 22 received a variety of standard of care (SOC) therapeutic regimen ranging from antibiotics to antivirals (Supplementary Figure 1A). The median Ordinal Scale (WHO) upon admission was OS5. No difference was observed in Ordinal scale at admission or mean time to symptom onset between Steroids or SOC groups (Supplementary Figure 1B, C). 12 age/sex-matched healthy controls were included for comparison.

ELISA and multiplex immunoassays

Protein Biomarker Assessments
Serum samples were analyzed with the Olink Inflammation I and Cardiovascular II Proseek multiplex assays (Uppsala, Sweden), a proximity extension assay (PEA) technology that utilizes oligonucleotide-labeled antibody probes, according to manufacturer’s specifications. The levels of 184 analyte-specific deoxyribonucleic acid (DNA) amplicons were quantified for each patient on the Fluidigm Biomark HD (San Francisco, CA). IL-19 was assessed as previously described [4].

Luminex Serology and Neutralization methodology

Fluorescently-dyed MagPlex-Microspheres (Luminex xMAP) were used to separate multiple conjugated protein antigens into spectrally distinct regions allowing for the simultaneous detection and quantitation of antibodies against these proteins. Additionally, endogenous neutralization of the spike protein in patient sample was measured by detecting the ability of a PE-labeled RBD to bind to a recombinant ACE2-conjugated bead.

For serological/neutralization testing, serum samples were titered in phosphate buffered saline-high salt solution (PBS-HS; 0.01 M PBS, 1% BSA, 0.02% Tween, 300 mM NaCl) and mixed with antigen-conjugated microspheres (4 SARS-CoV-2 antigens (NTD, RBD1, RBD2, and full length spike (ST4)), 5 mutant proteins (RBD-F490S, RBD-N460K, RBD-E484Q, RBD-Q493R, and full length spike D614G) and a seasonal coronavirus family member (NL63), as well as phycoerythrin (PE)-conjugated RBD antigen (50ng/mL)) for 45 minutes (Supplementary Table 2). Subsequently, beads were separated from supernatant using a magnet with 75uL transferred to a new plate containing ACE2-conjugated beads. The remaining beads were washed and combined with secondary PE-conjugated antibody (αIgG, #109-115-098, Jackson Labs). Both plates were incubated for 45 minutes at RT. Finally, plates were washed (3x) with PBS–low salt solution (PBS-LS; 0.01 M PBS, 1% BSA, 0.02% Tween) and read using a Luminex FlexMap3D System.

Statistical Analyses

Titer is commonly defined as the smallest dilution above the cut point or the dilution factor at the cut point based on an interpolation of assay values that straddle the cut point. In the serology assay, we used the latter method to calculate the titers. To calculate potency of the neutralization component of the Luminex assay, a 4-parameter logistic regression was used to calculate the absolute IC50. Samples with negative maximum percentage inhibition were excluded from further analysis, while IC50s of samples with maximum percentage inhibition less than 50% were imputed to a large number.

Spearman correlation coefficient was calculated between the titers, IC50s, protein biomarkers, and clinical measures accounting for multiplicity adjustment and false discovery rate as previously described [4]. A mixed-effect model was used to assess time effect of treatment accounting for age, sex, and multiplicity adjustments as previously described [4].

Ethics approval and consent to participate: All consent forms and protocols were approved by appropriate ethical review boards prior to study initiation.

Consent for publication: Not applicable.

Availability of data and materials: The data from the current study are available from the corresponding author on reasonable request.

Competing interests: Jonathan T. Sims, Venkatesh Krishnan, Ching-Yun Chang, Josh Poorbaugh, Montanea Daniels,Stephanie L. Beasley, Lin Zhang, & George H. Rodgers are employees and may be shareholders of Eli Lilly and Company. Fabio Lena, Leonardo G. Lacerenza, Bruno Sposato, Annabelle Dupont, Sophie Susen, Giacomo Casalini, and Mario Corbellino report no conflicts of interest. Justin Stebbing is editor-in-chief of Oncogene, sits on several scientific advisory boards, including Benevolent AI, and consults with Lansdowne partners, Vitruvian, and Eli Lilly and Company, and sits on the Board of Directors for BB Biotech Healthcare Trust.

Funding: The analytical measures using Olink within the study were funded by Eli Lilly and Company. The physicians and the COVID-19 patients did not receive any funding or reimbursement from Eli Lilly and Company.

Authors' contributions: J.T.S., V.K., J.S., M.C., F.L., and S.S. conceived the study. J.T.S., J.P., M.D., S.L.B., and G.H.R. conducted the laboratory experiments. J.T.S., C-Y.C., V.K., J.P. and L.Z. designed and conducted the main analyses and interpreted the results. J.P., G.H.R., F.L., L.G.L, B.S., A.D., S.S., G.C., M.C. and J.S. also contributed to the discussion of analyses. J.T.S. and V.K. wrote the first draft of the manuscript. J.T.S., J.P., J.S., and V.K. wrote the manuscript, with contributions and review by all other authors.

Acknowledgments: The authors would like to thank the continued dedication of healthcare workers across the world for their response to COVID-19. We are indebted to Julien Poissy, Carlotta Pucci, Michele Bindi, Cesira Nencioni, Leonardo Croci, and Maja Rossi for collaboration and assistance with this study. We thank Jeffrey Boyles and Denisa Foster for generating recombinant proteins for the serology assay. We thank Autum Auxier and Tomasz Izbicki for sample management and Robert Benschop for scientific input and support.

Ye Q, Wang B, Mao J. The pathogenesis and treatment of the `Cytokine Storm' in COVID-19. J Infect. 2020;80(6):607–13.
Jia H, Neptune E, Cui H. Targeting ACE2 for COVID-19 Therapy: Opportunities and Challenges. Am J Respir Cell Mol Biol. 2021;64(4):416–25.
Haljasmägi L, et al. Longitudinal proteomic profiling reveals increased early inflammation and sustained apoptosis proteins in severe COVID-19. Sci Rep. 2020;10(1):20533.
Sims JT, et al. Characterization of the cytokine storm reflects hyperinflammatory endothelial dysfunction in COVID-19. Journal of Allergy Clinical Immunology. 2021;147(1):107–11.

SuppTable1.jpg
SuppTable2.jpg
SuppFig1.jpg
A) Patient classification based on therapeutic decision prior to collection of first sample as either Steroids or SOC (Non-Steroid). B, C) Graphical box plot representations of the time (days) between a patient’s symptom onset and collection of first sample (B) or Ordinal Scale classification at admission (C) between Steroid and SOC patient groups.

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11 Jan, 2022
First submitted to journal
10 Jan, 2022

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Longitudinal Assessment of Systemic Steroid Therapy on Hyperinflammatory Endothelial Biomarker Profiles and Serology Responses of COVID-19 Patients

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