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Autoimmune anti-DNA and anti-phosphatidylserine antibodies predict development of severe COVID-19

Claudia Gomes, Marisol Zuniga, View ORCID ProfileKelly A Crotty, Kun Qian, Nubia Catalina Tovar, Lawrence Hsu Lin, Kimon V Argyropoulos, Robert Clancy, Peter Izmirly, Jill Buyon, David C Lee, View ORCID ProfileMaria Fernanda Yasnot-Acosta, Huilin Li, Paolo Cotzia, View ORCID ProfileAna Rodriguez  Correspondence email
Claudia Gomes
1Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA
Roles: Data curation, Investigation, Methodology
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Marisol Zuniga
1Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA
Roles: Data curation, Investigation, Methodology
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Kelly A Crotty
1Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA
Roles: Data curation, Investigation, Methodology
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  • ORCID record for Kelly A Crotty
Kun Qian
2Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
Roles: Formal analysis
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Nubia Catalina Tovar
1Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA
3Universidad de Córdoba, Montería, Córdoba, Colombia
4Universidad Del Sinú, Montería, Córdoba, Colombia
Roles: Data curation, Investigation, Methodology
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Lawrence Hsu Lin
5Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
Roles: Resources, Data curation, Investigation, Methodology, Patient recruitment and sample collection
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Kimon V Argyropoulos
5Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
Roles: Resources, Patient recruitment and sample collection, Writing—review and editing
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Robert Clancy
6Division of Rheumatology, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
Roles: Resources
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Peter Izmirly
6Division of Rheumatology, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
Roles: Resources
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Jill Buyon
6Division of Rheumatology, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
Roles: Resources
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David C Lee
7Department of Emergency Medicine, New York University Grossman School of Medicine, New York, NY, USA
Roles: Conceptualization, Formal analysis
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Maria Fernanda Yasnot-Acosta
3Universidad de Córdoba, Montería, Córdoba, Colombia
Roles: Resources
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  • ORCID record for Maria Fernanda Yasnot-Acosta
Huilin Li
2Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
Roles: Formal analysis
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Paolo Cotzia
5Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
Roles: Patient recruitment and sample collection, writing—review and editing
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Ana Rodriguez
1Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA
Roles: Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Writing—original draft, review, and editing
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  • ORCID record for Ana Rodriguez
  • For correspondence: ana.rodriguez@nyumc.org
Published 9 September 2021. DOI: 10.26508/lsa.202101180
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    Figure 1. COVID-19 patients present higher levels of IgG autoimmune antibodies than uninfected controls.

    (A, B, C) Analysis of plasma samples from 115 COVID-19, 38 systemic lupus erythematosus (SLE), and 101 Plasmodium vivax malaria patients for levels of IgG, anti-RBCL (A), anti-PS (B), and anti-DNA (C) by ELISA. Control samples were collected in the same area as patient samples, New York City for COVID-19 and SLE, and Tierralta (Colombia) for P. vivax malaria. Samples were considered positive for autoantibodies if the relative units > the mean plus three times the standard deviation of the controls. Percentage of positive samples for each group is indicated. The cut-off is indicated by the dashed horizontal line. Average of duplicated values for each sample is shown. *P < 0.05, **P < 0.001, ***P < 0.0001, ****P < 0.0001, Man–Whitney test.

    Source data are available for this figure.

    Source Data for Figure S1[LSA-2021-01180_SdataF1.xlsx]

  • Figure 2.
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    Figure 2. Anti-DNA antibodies from COVID-19 patients recognize ssDNA, dsDNA, and CpG.

    (A, B, C D) Plasma samples from COVID-19 (n = 14) and systemic lupus erythematosus (n = 20) patients and controls from New York City (n = 14), and Plasmodium vivax malaria patients (n = 14) and controls from Tierralta (Colombia) (n = 14) were tested for levels of IgG to ssDNA (A), dsDNA (B), DNA as used in Fig 1 (C), and CpG (D) by ELISA. Average of duplicated values for each sample with standard deviation is shown. ***P < 0.0001, ****P < 0.0001, Mann–Whitney test.

    Source data are available for this figure.

    Source Data for Figure S2[LSA-2021-01180_SdataF2.xlsx]

  • Figure 3.
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    Figure 3. Autoantibody levels increase with severity of disease.

    (A, B, C) Comparison of controls and COVID-19 patients stratified by severity of disease for levels of IgG, anti-RBCL (A), anti-PS (B), and anti-DNA (C). Average of duplicated values for each sample with standard deviation is shown. *P < 0.05, **P < 0.001, ***P < 0.0001, ****P < 0.0001, Mann–Whitney test.

    Source data are available for this figure.

    Source Data for Figure S3[LSA-2021-01180_SdataF3.xlsx]

  • Figure 4.
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    Figure 4. COVID-19 patients do not present higher levels of immune complex.

    Analysis of 115 plasma samples of COVID-19 patients stratified by severity of disease and 40 controls for levels of C1q-binding immune complex by ELISA. Average of duplicated values for each sample with standard deviation are shown. *P < 0.05, Mann–Whitney test.

    Source data are available for this figure.

    Source Data for Figure S4[LSA-2021-01180_SdataF4.xlsx]

  • Figure 5.
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    Figure 5. Correlation analysis of autoantibodies and maximum values of clinical tests identified significant associations mainly for anti-DNA antibodies.

    Heat map listing 118 clinical tests correlation with the autoantibodies and immune complex. Color scale indicates ρ values for each pair in the two tailed Spearman correlation test. Corresponding P-value (with false discovery rate correction) is indicated if P < 0.001 (**P < 0.001, ***P < 0.0001).

    Source data are available for this figure.

    Source Data for Figure S5[LSA-2021-01180_SdataF5.xlsx]

Tables

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    Table 1.

    Demographic and clinical characteristics of patients (n = 115).

    Age yr, median (IQR)66 (20–101)
    Female sex, n (%)35 (30.4)
    Race, n (%)
     White68 (59.1)
     Black14 (12.2)
     Asian8 (7.0)
     Other25 (21.7)
    Disease severity, n (%)
     Non-severe40 (34.8)
     Severe that survived42 (36.5)
     Severe that died33 (28.7)
    • View popup
    Table 2.

    Correlationa among autoantibody and immune complex levels.

    COVID-19 (n = 115)Anti-PSAnti-DNAImmune complex
    ρP-valueρP-valueρP-value
    Anti-RBCL0.61<0.00010.50<0.00010.41<0.0001
    Anti-PS--0.46<0.00010.36<0.0001
    Anti-DNA----0.230.0151
    Systemic lupus erythematosus (n = 40)
     Anti-RBCL0.370.0200.520.0008
     Anti-PS--0.430.0070
    Malaria (n = 101)
     Anti-RBCL0.46<0.00010.370.0001
     Anti-PS--0.54<0.0001
    • ↵Bold indicates correlations with p > 0.3 and P < 0.01.

    • ↵a Spearman correlation test.

    • View popup
    Table 3.

    Correlation analysis between autoantibodies and severity/living status of hospitalized patients.

    Odds ratioaP-valueb
    Living statusc
     Anti-RBCL3.9210.137
     Anti-PS2.0830.161
     Anti-DNA1.9250.161
     Immune complex4.7590.137
    Severityc
     Anti-RBCL2.7410.119
     Anti-PS5.7650.043
     Anti-DNA7.2190.006
     Immune complex3.4770.119
    • ↵Bold indicates Odds ratio > 5 and P < 0.05.

    • ↵a 1 U = 0.5 relative unit.

    • b False discovery rate adjusted P.

    • c Regression analysis adjusted for age, sex, and race.

    • View popup
    Table 4.

    Autoantibodies positive predictive value for disease severity in hospitalized patients.

    Positive predictive value, % (positives/total)a
    Anti-RBCL66.6 (28/42)
    Anti-PS92.8 (13/14)
    Anti-DNA85.7 (6/7)
    Immune complex0 (0/0)
    • ↵a Patients that develop severe disease from total autoantibody or immune complex positive at day 0.

Supplementary Materials

  • Figures
  • Tables
  • Table S1. Correlation analysis of autoantibodies and values of clinical tests at days 0–3. [LSA-2021-01180_TableS1.xlsx]

  • Table S2. Correlation analysis of autoantibodies and minimum values of clinical tests during hospital stay. [LSA-2021-01180_TableS2.xlsx]

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Autoantibodies predict COVID-19 severity
Claudia Gomes, Marisol Zuniga, Kelly A Crotty, Kun Qian, Nubia Catalina Tovar, Lawrence Hsu Lin, Kimon V Argyropoulos, Robert Clancy, Peter Izmirly, Jill Buyon, David C Lee, Maria Fernanda Yasnot-Acosta, Huilin Li, Paolo Cotzia, Ana Rodriguez
Life Science Alliance Sep 2021, 4 (11) e202101180; DOI: 10.26508/lsa.202101180

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Autoantibodies predict COVID-19 severity
Claudia Gomes, Marisol Zuniga, Kelly A Crotty, Kun Qian, Nubia Catalina Tovar, Lawrence Hsu Lin, Kimon V Argyropoulos, Robert Clancy, Peter Izmirly, Jill Buyon, David C Lee, Maria Fernanda Yasnot-Acosta, Huilin Li, Paolo Cotzia, Ana Rodriguez
Life Science Alliance Sep 2021, 4 (11) e202101180; DOI: 10.26508/lsa.202101180
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Volume 4, No. 11
November 2021
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