Literature DB >> 34570326

Pre-existing Autoantibodies Neutralizing High Concentrations of Type I Interferons in Almost 10% of COVID-19 Patients Admitted to Intensive Care in Barcelona.

Xavier Solanich1,2, Raúl Rigo-Bonnin3,4, Victor-David Gumucio3,5, Paul Bastard6,7,8, Jérémie Rosain6,7, Quentin Philippot6,7, Xosé-Luis Perez-Fernandez3,5, Maria-Paz Fuset-Cabanes3,5, Miguel-Ángel Gordillo-Benitez3,5, Guillermo Suarez-Cuartin3,9, Enric Boza-Hernandez3,10, Antoni Riera-Mestre11,3,12, Alba Parra-Martínez13,14, Roger Colobran15,16,17, Arnau Antolí11,3, Sergio Navarro3,18, Gemma Rocamora-Blanch11,3, Mario Framil3,18, Laura Calatayud3,19, Xavier Corbella11,3,20, Jean-Laurent Casanova6,7,8,21, Francisco Morandeira3,18, Joan Sabater-Riera3,5.   

Abstract

BACKGROUND: It is important to predict which patients infected by SARS-CoV-2 are at higher risk of life-threatening COVID-19. Several studies suggest that neutralizing auto-antibodies (auto-Abs) against type I interferons (IFNs) are predictive of critical COVID-19 pneumonia.
OBJECTIVES: We aimed to test for auto-Abs to type I IFN and describe the main characteristics of COVID-19 patients admitted to intensive care depending on whether or not these auto-Abs are present.
METHODS: Retrospective analysis of all COVID-19 patients admitted to an intensive care unit (ICU) in whom samples were available, from March 2020 to March 2021, in Barcelona, Spain.
RESULTS: A total of 275 (70.5%) out of 390 patients admitted to ICU were tested for type I IFNs auto-antibodies (α2 and/or ω) by ELISA, being positive in 49 (17.8%) of them. Blocking activity of plasma diluted 1/10 for high concentrations (10 ng/mL) of IFNs was proven in 26 (9.5%) patients. Almost all the patients with neutralizing auto-Abs were men (92.3%). ICU patients with positive results for neutralizing IFNs auto-Abs did not show relevant differences in demographic, comorbidities, clinical features, and mortality, when compared with those with negative results. Nevertheless, some laboratory tests (leukocytosis, neutrophilia, thrombocytosis) related with COVID-19 severity, as well as acute kidney injury (17 [65.4%] vs. 100 [40.2%]; p = 0.013) were significantly higher in patients with auto-Abs.
CONCLUSION: Auto-Abs neutralizing high concentrations of type I IFNs were found in 9.5% of patients admitted to the ICU for COVID-19 pneumonia in a hospital in Barcelona. These auto-Abs should be tested early upon diagnosis of SARS-CoV-2 infection, as they account for a significant proportion of life-threatening cases.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  COVID-19; SARS-CoV-2; acute kidney injury; auto-antibodies; type I interferons

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Year:  2021        PMID: 34570326      PMCID: PMC8475877          DOI: 10.1007/s10875-021-01136-x

Source DB:  PubMed          Journal:  J Clin Immunol        ISSN: 0271-9142            Impact factor:   8.317


Introduction

In December 2019, an emerging disease (COVID-19), caused by a newly identified human coronavirus (SARS-CoV-2), was first recognized in Wuhan, China, and spread worldwide [1, 2]. The WHO declared the COVID-19 epidemic to be a pandemic on March 12, 2020 [3], and it continues to spread globally, causing considerable morbimortality and economic damage. Age is the greatest risk factor for life-threatening COVID-19 pneumonia [4], and other epidemiological risk factors (men gender, obesity, diabetes, common genetic variants…) can contribute but with a modest effects [5-8]. These conditions do not allow physicians to accurately predict which patients infected by SARS-CoV-2 are at risk to transit into the most severe stages of COVID-19. Type I interferons (IFNs) are a family of cytokines that mediate the early innate immune response to viral infections limiting viral spread. When SARS-CoV-2 enters human cells, its viral RNA is recognized by endosomal Toll-like receptors such as TLR3 and TLR7, as well as cytosolic MDA-5, which drive a pathway that leads to gene expression of type I IFNs [5, 9]. In the past months, several human genetic variants associated with higher viral binding and entry have been identified, as well as genes related to higher COVID-19 severity [10, 11]. In addition, rare deleterious variants impairing TLR3- and TLR7-driven type I IFNs induction via IRF7 and amplification via IFNAR1 have been identified in about 5% of life-threatening COVID-19 cases younger than 60 years [12, Asano in press]. Recently, an international consortium reported that 101 of 987 patients (10.2%) with life-threatening COVID-19 pneumonia had neutralizing auto-antibodies (auto-Abs) against type I IFNs (IFN-α2, IFN-ω, or both) [13]. All of the patients tested had low or undetectable serum IFN-α values during acute disease. Interestingly, these auto-Abs were present before SARS-CoV-2 pandemic in the patients tested. Nonetheless, these antibodies were absent in 663 individuals with asymptomatic or mild SARSCoV-2 infection. Half of these patients were over 65 years old, and notably, 95 (94%) of the 101 patients with auto-Abs were men. More recently, it was found that auto-abs neutralizing 100-fold lower concentrations of type I IFN were more frequent, found in about 15% of critical cases (Bastard in press). These findings may provide a first explanation for the excess of older men among patients with life-threatening COVID-19. Furthermore, they might also offer a means in identifying individuals at-risk of evolving into severe or critical stage of COVID-19 [5], as it has been replicated worldwide [14-17]. In addition, the detection of neutralizing auto-Abs against type I IFNs is technically straightforward and not expensive, so that it could be advantageous to apply in routine clinical practice. Finally, these findings might also pave the way for prevention and treatment by using plasmapheresis, plasmablast depletion, or recombinant type I IFNs not targeted by the auto-Abs (e.g., IFN-β) [18-20]. In the present study, we aimed to describe clinical, analytical, and evolutive data of life-threatening COVID-19 patients admitted to the ICU depending on whether or not auto-Abs neutralizing high concentrations of type I IFNs are present.

Methods

Study Design and Patients

This study was conducted at the Hospital Universitari de Bellvitge (HUB), a 750-bed tertiary-care public hospital for adults in Barcelona, Spain. HUB is the referral hospital for 2 million inhabitants with high-complexity diseases from the Southern area of Catalonia. We performed a retrospective study of COVID-19 patients admitted to the ICU during the first year of the pandemic (from March 2020 to March 2021) in whom samples were available. SARS-CoV-2 infection was confirmed by RT-PCR in all patients. Data were obtained from routine daily practice and anonymized. Personal and clinical data were collected in accordance with the Spanish Data Protection Act (Ley Orgánica 3/2018 de 5 de diciembre de Protección de Datos Personales). Informed consent was waived due to the study’s retrospective nature, and the mandatory isolation measures applied during in-hospital care. The protocol was approved by the Ethics Committee of the Hospital Universitari de Bellvitge (Barcelona, Spain; approval number PR40/21).

Clinical and Laboratory Variables

Demographic data and main comorbidities were collected from each patient. Laboratory data were registered at admission to the ICU. The WHO 8-point ordinal scale was calculated in each participant (https://www.who.int/blueprint/priority-diseases/key-action/COVID-19_Treatment_Trial_Design_Master_Protocol_synopsis_Final_18022020.pdf). Complications were documented as follows: (1) Thrombotic complications included deep vein thrombosis (DVT), pulmonary embolism (PE), myocardial infarction, mesenteric ischemia, lower limb ischemia, cerebral ischemic attack confirmed by an imaging study; (2) Hemorrhagic complications included major bleeding according to the definition of the International Society on Thrombosis and Haemostasis [21]; (3) Cardiovascular complications included no coronary heart disease (heart failure, arrhythmias, myocarditis); (4) Acute kidney injury (AKI) was defined using the Kidney Disease Improving Global Outcomes (KDIGO) staging. So, patients were classified as stage 1 if they present an increase of concentration of creatinine in plasma (CREA) of 26.5 μmol/L within 48 h, or increase in CREA ≥ 1.5 times baseline, which is known or presumed to have occurred within the prior 7 days; stage 2 AKI was considered when CREA increase 2.0 to 2.9 times baseline; and stage 3 AKI, when CREA increase ≥ 3.0 times baseline or increase in CREA to ≥ 353.6 μmol/L, or the initiation of renal replacement therapy (RRT), or in patients < 18 years a decrease in eGFR to < 35 mL/min/1.73m2 [22]; (5) Superinfection included a second infection with a bacterial agent at the time or during ICU admission; (6) Sepsis was defined as an increase in the Sequential (sepsis-related) Organ Failure Assessment (SOFA) score of 2 points or more with respect to baseline SOFA; and (7) Septic shock was identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mmHg or greater and serum lactate level greater than 2 mmol/L (> 18 mg/dL) in the absence of hypovolemia [23]; (8) Multiple organ failure was defined as the SOFA score alteration of two or more organs with a score of ≥ 3 [24]. Treatments specifically used to treat COVID-19, mechanical ventilation duration and other organ support during ICU stay as vasopressors, RRT, nitric oxide, and extracorporeal membrane oxygenation (ECMO) were also analyzed. Length of hospital and ICU stay and death during hospitalization were also recorded. All drugs and procedures were used according to HUB protocol which is detailed in the supplementary materials.

Auto-Abs Against Type I IFNs

Analysis of auto-Abs against type I IFNs (IFN-α2 and IFN-ω) were performed using an ELISA technique according to St. Giles procedure [13]. In brief, NUNC MaxiSorp™ high protein-binding capacity 96 well ELISA plates (Thermo Fisher Scientific Inc., Waltham, MA, USA) were coated with recombinant human IFN-α2 or IFN-ω by incubation of the diluted cytokine in 100 μL of coating buffer (1 mg/L) overnight at 4° C. Plates were washed three times with PBS, blocked by incubation with PBS supplemented with 5% nonfat milk powder 1 h at room temperature on an agitator, washed again with PBS-Tween 0.005% (v/v), and incubated with 100 μL of 1:50 dilution of serum samples from patients or controls in HPE dilution buffer (Sanquin, Amsterdam, The Netherlands) for 2 h at room temperature in the agitator. After wash, Fc-specific HRP-conjugated IgG fractions of polyclonal goat antiserum against human IgG (Nordic-MUbio, Susteren, The Netherlands) were added to a final concentration of 2 mg/L. Plates were incubated for 1 h at room temperature and washed. Then, substrate (TMB) was added and incubated 10 min. The reaction was stopped by adding H2SO4 0.18 M, and optical density at 450 nm was measured. We considered as positive results of both auto-Abs against type I IFNs any result greater than a cutoff value calculated as the mean value plus two standard deviations of a control group of healthy non-COVID-19 patients with a similar age and gender.

Neutralizing Auto-Abs Against Type I IFNs

The neutralizing ability in vitro of anti-Abs against IFN-α2 and anti-IFN-ω, i.e., their blocking activity, was determined by assessing a reporter luciferase activity [13]. Briefly, HEK293T cells were transfected with the firefly luciferase plasmids under the control of human ISRE promoters in the pGL4.45 backbone, and a constitutively expressing Renilla luciferase plasmid for normalization (pRL-SV40). Next, cells were transfected in the presence of the X-tremeGene 9 transfection reagent (MilliporeSigma, Burlington, MA, USA) for 36 h. Then, Dulbecco’s modified Eagle medium (DMEM, Thermo Fisher Scientific) medium supplemented with 10% healthy control or patient serum/plasma and were either left unstimulated or were stimulated with IFN-α2 or IFN-ω (10 ng/mL) for 16 h at 37 °C. Each sample was tested once. Finally, luciferase levels were measured with the Dual-Glo reagent, according to the manufacturer’s protocol (Promega Corp., Madison, WI, USA). Firefly luciferase values were normalized against Renilla luciferase values.

Statistical Analysis

Continuous variables were presented as the median and interquartile range (IQR) and categorical data as frequency rates and percentages. Comparisons of the cohorts were made using a chi-square test or Fisher’s exact test for categorical variables and a Mann–Whitney U test for continuous or ordinal variables. From June 2020, there were significant changes in the treatment of COVID-19 patients, and for this reason, it has been performed a subanalysis of these two periods (first wave vs. second/third wave in Spain). Statistical significance was defined as p-value < 0.05, and we also used odds ratios (OR) and their 95% confidence intervals (CI) for categorical variables. Calculations were performed with the statistical package SPSS version 19 (IBM Corp. Endicott, NY, USA).

Results

From March 10, 2020, to March 6, 2021, 3216 COVID-19 patients were hospitalized at our hospital, and 390 (12.1%) were admitted to the ICU due to respiratory failure. Of them, 275 (70.5%) ICU patients had frozen serum samples stored in the HUB immunology department, and type I IFNs auto-Abs could be tested. Main characteristics of all included patients are shown in Tables 1, 2, and 3. Patients included belonged to the different epidemic waves (first 125 [45.4%], second 23 [8.4%], and third 127 [46.2%]). Overall, the median age was 64 years old (IQR 55–71), and male gender represented 76.7% of all patients. The most prevalent pre-existing comorbidities were hypertension (53.1%), obesity (49.8%), dyslipidemia (49.1%), and diabetes mellitus (28.4%). The median number of days from the appearance of clinical symptoms to admission to the hospital was 8 (IQR 6–11), and later with a median of 2 (IQR 0–6) days, they were admitted to the ICU. The main laboratory parameters at ICU admission showed a median of 0.64 (IQR 0.38–0.96) lymphocytes × 109 cells/L, a median LDH of 471.5 (IQR 367.5–610.8) U/L, a median CRP of 136.1 (IQR 52.8–238.3) mg/L, a median ferritin of 1495 (874–2325) mg/L, and a median d-dimer of 879 (454–2862) μg/L. The median paO2/FiO2 at ICU admission was 116.5 (IQR 86–166) mmHg/%. Overall, 38 (13.8%) patients belonged to group 5 of the WHO 8-point ordinal scale, 78 (28.4%) to group 6, 16 (5.8%) to group 7, and 143 (52.0%) to group 8 (Table S1). Regarding the drugs administrated during their hospital stay, 92.0% of patients were treated with corticosteroids, 91.2% with enoxaparin, 30.5% with tocilizumab, 19.3% with remdesivir, and 10.5% with interferon beta 1. Most prevalent complications during ICU stay were superinfection 207 (75.3%), sepsis 134 (48.7%), and acute kidney injury 117 (42.5%). In hospital, all-cause mortality was 52.0%.
Table 1

Main demographic, comorbidities, clinical, and laboratory data of ICU patients with severe COVID-19 infection considering the presence of positive results of auto-Abs IFN-α2 or auto-Abs IFN-ω obtained by ELISA and luciferase activity techniques

VariableAll results for auto-Abs to type I IFNs(n = 275)Neutralizing positive results for some or both auto-Abs to type I IFNs(n = 26)Neutralizing negative results for both auto-Abs to type I IFNs(n = 249)p-valueOR(95% CI)
Pandemic wave
  First; n (%)125 (45.5)13 (50.0)112 (45.0)0.820n.a
  Second; n (%)23 (8.4)1 (3.8)22 (8.8)
  Third; n (%)127 (46.2)12 (46.2)115 (46.2)
Demographics
  Age; median (IQC)64 (55–71)63 (57–73)64 (55–71)0.712n.a
  Sex (male); n (%)211 (76.7)24 (92.3)187 (75.1)0.0483.979 (0.914–17.32)
Comorbidities
  Cancer; n (%)31 (11.3)2 (7.7)29 (11.6)0.7500.632 (0.142–2.815)
  Cardiac disease; n (%)44 (16.0)4 (15.4)40 (16.1)1.0000.950 (0.311–2.905)
  Chronic kidney disease; n (%)38 (13.8)3 (11.5)35 (14.1)1.0000.798 (0.227–2.798)
  Chronic liver disease; n (%)24 (8.7)3 (11.5)21 (8.4)0.4841.416 (0.392–5.111)
  Chronic obstructive pulmonary disease; n (%)45 (16.4)3 (11.5)42 (16.9)0.5900.643 (0.185–2.239)
  Diabetes; n (%)78 (28.4)7 (26.9)71 (28.5)0.8640.924 (0.372–2.293)
Dyslipidemia; n (%)135 (49.1)13 (50.0)122 (49.0)0.9221.041 (0.464–2.335)
  Hypertension; n (%)146 (53.1)13 (50.0)133 (53.4)0.7400.872 (0.389–1.957)
  Obesity; n (%)137 (49.8)11 (42.3)126 (50.6)0.4210.716 (0.316–1.620)
  Smoking; n (%)20 (7.3)0 (0.0)20 (8.0)0.233n.a
Symptom onset and admission
  Number of days from the appearance of clinical symptoms to admission to the hospital; median (IQR)8 (6–11)7 (6–8)8 (6–11)0.009n.a
  Number of days from the hospital admission to the ICU; median (IQR)2 (0–6)3.5 (1–7)2 (0–6)0.352n.a
Biological quantities at the first day in ICU
  LEU, × 109 cells/L; median (IQR)9.75 (8.59–14.3)13.7 (9.40–20.0)9.30 (6.65–13.5)0.001n.a
  NEU, × 109 cells/L; median (IQR)8.41 (5.72–12.7)12.7 (8.63–19.0)8.10 (5.65–11.9)0.001n.a
  LYM, × 109 cells/L; median (IQR)0.64 (0.38–0.96)0.51 (0.41–0.72)0.66 (0.37–0.98)0.067n.a
  PLT, × 109 cells/L; median (IQR)232 (173–303)260.5 (217–325)230 (168–298)0.038n.a
apH, 1; median (IQR)7.35 (7.29–7.43)7.35 (7.30–7.39)7.35 (7.29–7.43)0.800n.a
  paCO2, mmHg; median (IQR)46 (40–56.5)47 (40–53)46 (40–57)0.856n.a
  paO2, mmHg; median (IQR)96.5 (76–125)90 (73–127)97 (76–124.5)0.574n.a
  aSatO2, %; median (IQR)97.1 (94.5–98.7)96.7 (94.3–98.4)97.2 (94.5–98.7)0.420n.a
  ALB, g/L; median (IQR)31.6 (27.4–35.0)32.0 (26.4–35.0)31.5 (27.7–35.0)0.741n.a
  LDH, U/L; median (IQR)471.5 (367.5–610.8)444.5 (354–538)474.5 (370–613)0.395n.a
  ALT, U/L; median (IQR)34 (23–56.3)38.5 (28–61)34 (23–56)0.421n.a
  AST, U/L; median (IQR)45 (31–64.8)41 (27–52)45 (32–68)0.165n.a
  BIL, μmol/L; median (IQR)9.2 (6.5–13.9)10.4 (6.0–15.0)9.0 (6.7–13.7)0.819n.a
  CREA, μmol/L; median (IQR)81 (61–114)80 (61–117)81 (60–111)0.767n.a
  UREA, mmo/L; median (IQR)7.9 (5.2–11.5)8.1 (5.7–11.7)7.9 (5.2–11.4)0.588n.a
  TROP-T, ng/L; median (IQR)14.7 (9.4–28.2)11.3 (8.4–14.7)15.8 (9.8–30.9)0.121n.a
  DD, μg/L; median (IQR)879 (454–2862)963 (482–3507)878 (452–2811)0.671n.a
  PT, 1; median (IQR)1.16 (1.08–1.28)1.23 (1.11–1.25)1.15 (1.08–1.29)0.230n.a
  PROCAL, μg/L; median (IQR)0.26 (0.13–0.68)0.29 (0.14–0.51)0.26 (0.13–0.73)0.875n.a
  CRP, mg/L; median (IQR)136.1 (52.8–238.3)212.1 (62.2–366.3)130.1 (52.7–229.1)0.055n.a
  FERRI, mg/L; median (IQR)1495 (874–2325)1240 (919–2389)1498 (862–2291)0.664n.a
  IL6, ng/L; median (IQR)91.3 (19.5–455.2)40.4 (30.2–207.9)95.3 (19.7–474)0.778n.a

OR, odds-ratio; CI, confidence interval; ICU, intensive care unit; IQR, interquartilic range; n.a., not applicable; LEU, number concentration of leucocytes in blood; NEU, number concentration of neutrophils in blood; LYM, number concentration of lymphocytes in blood; PLT, number concentration of platelets in blood; apH, pH in arterial blood; paCO, partial pressure of carbon dioxide in arterial blood, paO, partial pressure of oxygen in arterial blood; aSatO, substance fraction of oxygen in arterial blood; ALB, mass concentration of albumin in plasma; LDH, catalytic concentration of lactate dehydrogenase in plasma; ALT, catalytic concentration of alanine transaminase in plasma; AST, catalytic concentration of aspartate transaminase in plasma; BIL, substance concentration of bilirubin in plasma; CREA, substance concentration of creatinine in plasma; UREA, substance concentration of urea in plasma; TROP-T, mass concentration of troponin T in plasma; DD, mass concentration of D-dimer in plasma; PT, relative time of prothrombin in plasma; PROCAL, mass concentration of procalcitonin in plasma; CRP, mass concentration of C-reactive protein in plasma; FERRI, mass concentration of ferritin in plasma; IL6, mass concentration of interleukin-6 in plasma

ALB, LDH, ALT, AST, BIL, CREA, UREA, TROP-T, PROCAL, CRP, FERRI, and IL6 were measured using a Cobas 6000 or Cobas 8000 analyzers (Roche Diagnostics, Risch-Rotkreuz, Switzerland). LEU, NEU, LYM, and PLT were measured using a Sysmex XN-2000 analyzer (Sysmex, Kobe, Japan), and DD, PT from ACL TOP 500 analyzer (Instrumentation Laboratory, Bedford, MA, USA). On the other hand, apH, paCO2, paO2, and aSatO2 were obtained from GEM Premier 5000 gasometers (Instrumentation Laboratory)

Numbers in bold indicate a p-value < 0.05

Table 2

Drugs, mechanical ventilation and other specific ICU treatments of severe COVID-19 patients admitted to ICU considering the presence of positive results of auto-Abs IFN-α2 or auto-Abs IFN-ω obtained by ELISA and luciferase activity techniques

VariableAll results for auto-Abs to type I IFNs(n = 275)Neutralizing positive results for some or both auto-Abs to type I IFNs(n = 26)Neutralizing negative results for both auto-Abs to type I IFNs(n = 249)p-valueOR (95% CI)
Specific ICU treatment and mechanical ventilation data
  Patients with CRRT; n (%)28 (10.2)3 (11.5)25 (10.0)0.7361.169 (0.328–4.170)
  Patients with ECMO; n (%)25 (9.1)2 (7.7)23 (9.2)1.0000.819 (0.182–3.688)
  paO2/FiO2, mmHg/%; median (IQR)116.5 (86–166)111 (85–153)120 (86.5–167)0.313n.a
  Patients treated with IMV; n (%)232 (84.4)22 (84.6)210 (84.3)1.0001.021 (0.334–3.127)
  Patients with nitric oxide administration during IMV; n (%)38 (13.8)4 (15.4)34 (13.7)0.7671.150 (0.373–3.542)
  Patients positioned in prone position during IMV; n (%)205 (74.5)18 (69.2)187 (75.1)0.5130.746 (0.309–1.800)
  Number of days with IMV; median (IQR)13 (4–27)11 (3–17)13 (4–28)0.291n.a
Drugs administration
  Patients treated with hydroxychloroquine; n (%)126 (45.8)13 (50.0)113 (45.4)0.6531.204 (0.536–2.701)
  Patients treated with lopinavir/ritonavir; n (%)85 (30.9)11 (42.3)74 (29.7)0.1861.734 (0.761–3.954)
  Patients treated with remdesivir; n (%)53 (19.3)5 (19.2)48 (19.3)0.9950.997 (0.358–2.778)
  Patients treated with azithromycin; n (%)69 (25.1)5 (19.2)64 (25.7)0.4690.688 (0.249–1.901)
  Patients treated with tocilizumab; n (%)84 (30.5)9 (34.6)75 (30.1)0.6361.228 (0.524–2.880)
  Patients treated with corticosteroids; n (%)253 (92.0)25 (96.2)228 (91.6)0.7052.303 (0.297–17.85)
  Patients treated with interferon beta 1; n (%)29 (10.5)3 (11.5)26 (10.4)0.7441.119 (0.314–3.983)
  Patients treated with enoxaparin; n (%)250 (91.2)26 (100.0)224 (90.3)0.144n.a

  Patients treated with anticoagulants

with prophylactic or therapeutic goal; n (%)

275 (100)26 (100.0)249 (100.0)n.an.a

OR, odds-ratio; CI, confidence interval; ICU, intensive care unit; IQR, interquartilic range; n.a., not applicable; CRRT, continuous renal replacement therapy; ECMO, extracorporeal membrane oxygenation; IMV, invasive mechanical ventilation; FiO, fraction of inspired oxygen; paO, partial pressure of oxygen in arterial blood

Numbers in bold indicate a p-value < 0.05

Table 3

Length of hospital and ICU stay, and complications of severe COVID-19 patients admitted to ICU considering the presence of positive results of auto-Abs IFN-α2 or auto-Abs IFN-ω obtained by ELISA and Luciferase activity techniques

VariableAll results for auto-Abs to type I IFNs(n = 275)Neutralizing positive results for some or both auto-Abs to type I IFNs(n = 26)Neutralizing negative results for both auto-Abs to type I IFNs(n = 249)p-valueOR (95% CI)
Length of hospital and ICU stay
  Number of admitted days to the ICU; median (IQR)15 (7–31)13.5 (4–24)15 (7–31)0.500n.a
  Number of admitted days to the hospital; median (IQR)29 (15–49)30.5 (14–46)29 (16–50)0.819n.a
Complications during ICU stay
  Patients with neurological complications; n (%)77 (28.0)5 (19.2)72 (28.9)0.2950.585 (0.213–1.612)
  Patients with thrombotic complications; n (%)50 (18.2)5 (19.2)45 (18.1)0.7951.079 (0.389–3.015)
  Patients with hemorrhagical complications; n (%)27 (9.8)4 (15.4)23 (9.2)0.3011.787 (0.567–5.634)
  Patients with cardiovascular complications; n (%)56 (20.4)5 (19.2)51 (20.5)0.8800.924 (0.332–2.570)
  Patients with acute kidney injury; n (%)117 (42.5)17 (65.4)100 (40.2)0.0132.814 (1.207–6.563)
  Patients with superinfection; n (%)207 (75.3)19 (73.1)188 (75.5)0.7850.881 (0.353–2.195)
  Patients with sepsis; n (%)134 (48.7)11 (42.3)123 (49.4)0.4910.751 (0.332–1.700)
  Patients with septic shock; n (%)70 (25.5)4 (15.4)66 (26.5)0.2150.504 (0.167–1.517)
  Patients with multiple organ failure; n (%)56 (20.4)5 (19.2)51 (20.5)0.8800.924 (0.332–2.570)
Final status
  Exitus; n (%)143 (52.0)12 (46.2)131 (52.6)0.5310.772 (0.343–1.736)

OR, odds-ratio; CI, confidence interval; ICU, intensive care unit; IQR, interquartilic range; n.a., not applicable

Numbers in bold indicate a p-value < 0.05

Main demographic, comorbidities, clinical, and laboratory data of ICU patients with severe COVID-19 infection considering the presence of positive results of auto-Abs IFN-α2 or auto-Abs IFN-ω obtained by ELISA and luciferase activity techniques OR, odds-ratio; CI, confidence interval; ICU, intensive care unit; IQR, interquartilic range; n.a., not applicable; LEU, number concentration of leucocytes in blood; NEU, number concentration of neutrophils in blood; LYM, number concentration of lymphocytes in blood; PLT, number concentration of platelets in blood; apH, pH in arterial blood; paCO, partial pressure of carbon dioxide in arterial blood, paO, partial pressure of oxygen in arterial blood; aSatO, substance fraction of oxygen in arterial blood; ALB, mass concentration of albumin in plasma; LDH, catalytic concentration of lactate dehydrogenase in plasma; ALT, catalytic concentration of alanine transaminase in plasma; AST, catalytic concentration of aspartate transaminase in plasma; BIL, substance concentration of bilirubin in plasma; CREA, substance concentration of creatinine in plasma; UREA, substance concentration of urea in plasma; TROP-T, mass concentration of troponin T in plasma; DD, mass concentration of D-dimer in plasma; PT, relative time of prothrombin in plasma; PROCAL, mass concentration of procalcitonin in plasma; CRP, mass concentration of C-reactive protein in plasma; FERRI, mass concentration of ferritin in plasma; IL6, mass concentration of interleukin-6 in plasma ALB, LDH, ALT, AST, BIL, CREA, UREA, TROP-T, PROCAL, CRP, FERRI, and IL6 were measured using a Cobas 6000 or Cobas 8000 analyzers (Roche Diagnostics, Risch-Rotkreuz, Switzerland). LEU, NEU, LYM, and PLT were measured using a Sysmex XN-2000 analyzer (Sysmex, Kobe, Japan), and DD, PT from ACL TOP 500 analyzer (Instrumentation Laboratory, Bedford, MA, USA). On the other hand, apH, paCO2, paO2, and aSatO2 were obtained from GEM Premier 5000 gasometers (Instrumentation Laboratory) Numbers in bold indicate a p-value < 0.05 Drugs, mechanical ventilation and other specific ICU treatments of severe COVID-19 patients admitted to ICU considering the presence of positive results of auto-Abs IFN-α2 or auto-Abs IFN-ω obtained by ELISA and luciferase activity techniques Patients treated with anticoagulants with prophylactic or therapeutic goal; n (%) OR, odds-ratio; CI, confidence interval; ICU, intensive care unit; IQR, interquartilic range; n.a., not applicable; CRRT, continuous renal replacement therapy; ECMO, extracorporeal membrane oxygenation; IMV, invasive mechanical ventilation; FiO, fraction of inspired oxygen; paO, partial pressure of oxygen in arterial blood Numbers in bold indicate a p-value < 0.05 Length of hospital and ICU stay, and complications of severe COVID-19 patients admitted to ICU considering the presence of positive results of auto-Abs IFN-α2 or auto-Abs IFN-ω obtained by ELISA and Luciferase activity techniques OR, odds-ratio; CI, confidence interval; ICU, intensive care unit; IQR, interquartilic range; n.a., not applicable Numbers in bold indicate a p-value < 0.05 We found that 49 (17.8%) of these 275 patients were positive for auto-Abs against type I IFNs (IFN-α2 and/or IFN-ω) by ELISA, of which 19 (6.9%) only against IFN-α2, 8 (2.9%) only against IFN-ω, and 22 (8.0%) against both. Next, we aimed to confirm the neutralizing activity of these auto-Abs. A blocking activity of 10 ng/mL was observed in 26 (53.1%) of these 49 patients with positive auto-Abs against IFNs results. Auto-Abs were neutralizing against both IFN-α2 and IFN-ω in 21 (80.8%) of these 26 patients, against only IFN-α2 in four patients (15.4%), and in only one patient (3.8%) for IFN-ω. We further assessed the clinical, analytical, and evolutive data of life-threatening COVID-19 patients admitted to the ICU depending on whether or not auto-Abs neutralizing high concentrations of type I IFNs are present (Tables 1, 2, and 3). Table S1 shows the same data but classifies ICU patients following the WHO 8-point ordinal scale. Almost all the patients with positive results of neutralizing auto-Abs were men, being statistically higher than in the group of patients showing negative results (24 [92.3%] vs. 187 [75.1]; p = 0.048). No relevant differences were observed in the main comorbidities between the two groups. The median number of days from the onset of symptoms to admission to the hospital was significantly lower in neutralizing auto-Abs group (7 [IQR 6–8] vs. 8 [IQR 6–11]; p = 0.009), while the number of days from the hospital admission to the ICU (3.5 [IQR 1–7] vs. 2 [IQR 0–6]; p = 0.352) was not different between the two groups. Overall, the median number of days admitted to the hospital was similar in both groups (30.5 [IQR 14–46] vs. 29 [IQR 16–50]; p = 0.819). The specific ICU treatment and mechanical ventilation data between both groups were not significantly different. Regarding analytical variables, those patients with neutralizing auto-Abs showed significantly higher median values of leukocytes (13.7109 cells/L [IQR 9.40–20.0] vs. 9.30 × 109 cells/L [IQR 6.65–13.5]; p = 0.001), neutrophils (12.7 × 109 cells/L [IQR 8.63–19.0] vs. 8.10 × 109 cells/L [IQR 5.65–11.9]; p = 0.001), platelets (260.5 × 109 cells/L [IQR 217–325] vs. 230 × 109 cells/L [IQR 168–298]; p = 0.038)) than negative neutralizing auto-Abs patients. Furthermore, median CRP values were numerically higher (212.1 mg/L [IQR 62.2–366.3] vs. 130.1 mg/L [IQR 52.7–229.1]; p = 0.055) in those patients with neutralizing auto-Abs. Drugs specifically used to treat COVID-19 at any time during admission were not different between the two groups. No significant association between the presence of neutralizing auto-Abs and mortality (12 [46.2%] vs. 131 [52.6%]; p = 0.531) or other complications was found (Table 3), except for acute kidney injury (AKI) (17 [65.4%] vs. 100 [40.2%]; p = 0.013). Patients with positive auto-Abs showed approximately three times more probability to present AKI (OR 2.814 [95%CI 1.207–6.563]) than those with negative results. Significant differences were observed in patients at KDIGO-AKI stages 1 (p < 0.001), 2 (p < 0.001), and 3 (p < 0.001) when they were compared with those patients with non AKI. AKI was significantly higher in neutralizing auto-Abs patients who finally died (12 [100%] vs. 60 [45.8%]; p < 0.001), but not in the rest of the 8-point ordinal scale groups (Table S1). When AKI-related variables were selected and a binary logistic regression analysis was performed, a higher risk of AKI was independently associated with the presence of type I IFNs neutralizing auto-Abs (multivariate OR 7.672 [95% CI 2.286–25.75]), as well as, a glomerular filtrate rate (GFR) < 60 mL/min/1.73m2 at hospital admission, the need for ECMO, the development of multiple organ failure, the seventh and eighth points of the ordinal scale, and the use of interferon beta 1 during ICU admission (Table S2).

Discussion

From March 2020 to March 2021, a sample of 275 ICU patients could be tested for type I IFNs auto-Abs (α2 and ω), representing 70.5% of all patients admitted to the ICU during the study period. One-fifth (49 (17.8%)) showed positive results, with blocking activity in half of them (26 (9.5%)). There were no relevant differences in the main demographic, comorbidities, and clinical data. Patients with positive neutralizing auto-Abs had a significantly higher leukocytes, neutrophils, and platelet values than negative ones. Interestingly, acute kidney injury was also significantly more frequent in positive patients. Overall, half of these patients (52.0%) died without significant differences between positive and negative neutralizing auto-Abs groups. A recent study by Koning et al. [14] showed that auto-Abs against IFN-α2 and IFN-ω tested by multiplex particle–based assay and ELISA were found in 35 (16.6%) out of 210 COVID-19 patients, of whom 6 (17.1%) out of 35 had neutralizing auto-Abs using STAT1 phosphorylation assay. Eighty-eight (41.9%) of these 210 COVID-19 patients were admitted to ICU, belonging all 6 patients with neutralizing auto-Abs to this group of greater severity. Accordingly, Bastard et al. [13] reported that auto-Abs against IFN-α2 and IFN-ω were detected in 135 (13.7%) out of 987 life-threatening COVID-19 patients, showing blocking activity in 101 (74.8%) of these 135 ones. Altogether, these findings suggest that the greater the severity, the higher the proportion of neutralizing antibodies, but even in the critically ill COVID-19 patients, it is important to determine the blocking activity against type I IFNs. In our cohort, half (53.1%) of auto-Abs determined by ELISA showed blocking activity for 10 ng/mL of IFNs using luciferase reporter assays. According to previous reports [13-17], type I IFN neutralizing auto-Abs may help physicians to identify patients at higher-risk to develop severe COVID-19, at the early stages of the disease. However, there is still limited data on whether characteristics of ICU patients with neutralizing IFN auto-Abs are different from those ICU patients without these auto-Abs. Our results did not show demographic, comorbidity or clinical differences between both groups, except for an excess of men in patients with auto-Abs positive results. It could be explained because an inadequate type I IFN response is a common feature in critical COVID-19 patients [5, 9, 25, 26] regardless of whether this defect is due to auto-Abs against type I IFNs [15, 17], rare inborn errors of immunity, or any other mechanism. However, some laboratory differences were detected in our COVID-19 patients admitted to ICU considering the presence of neutralizing IFN auto-Abs. Higher CRP values were close to statistical significance in the group of patients with neutralizing auto-Abs, as reported by Troya et al. in a smaller group of ICU patients [16]. In addition, our patients with auto-Abs positive results also showed significantly higher leukocytes, neutrophils, and platelet values. All these blood parameters have been used to stratify patients at higher risk for COVID-19 complications [8, 9] suggesting that positive neutralizing auto-Abs patients may develop more severe forms of COVID-19. In contrast with previously described in smaller cohorts [14, 16], mortality in our patients was not different between those ICU patients with and without neutralizing type I IFNs auto-Abs. Interestingly, we found a significant association between AKI and neutralizing type I IFNs auto-Abs. AKI can be caused by several mechanisms in critical COVID-19 patients [27], and it should be determined if these auto-Abs play a role in its pathogenesis. It is possible, but only speculative, that type I IFN auto-Abs predisposes to the formation of immune complexes that in turn activate complement. The abnormal presence of plasma-derived complement components in the tubular lumen leads to the assembly of the C5b-9 in the tubular epithelial cells, and it could be involved in the pathogenesis of tubulointerstitial damage. In this regard, a retrospective series of six post-mortem COVID-19 patients showed complement C5b-9 deposition on tubules in all kidneys examined [28]. Although, these findings have to be confirmed, neutralizing IFN auto-Abs might be a biomarker to identify those critical COVID-19 patients with greater risk of developing AKI, helping physicians to make earlier preventive and therapeutic decisions. Unlike other factors related to increased COVID-19 severity, detection of neutralizing type I IFNs auto-Abs in ICU patients may pave the way for specific therapeutic interventions. In this regard, plasmapheresis was recently reported to decrease the titers of blood auto-Abs in four hospitalized patients with life-threatening COVID-19 pneumonia, even though mortality still was 50% [19]. Little is also known whether the administration of IFN-β, B-cell depletion, or other therapies might be beneficial to treat these patients with auto-Abs against type I IFNs admitted to ICU [20]. Our study has several limitations that deserve further comment. First, it was not possible to obtain plasma samples from all the patients admitted to the ICU during the study period, although we were able to analyze more than 70% of them. Nevertheless, this was a representative group with little potential for bias. Second, we exclusively detected the most frequent type I IFNs (α2 and ω) by ELISA, and, therefore, it is possible that some study patients presented other antibodies that were not detected (i.e., auto-Abs against IFN-β). Third, we analyzed blocking activity for 10 ng/mL of IFNs according with previous reports [13-16], but blood IFN-α concentrations of mild/moderate COVID-19 patients typically range from 1 to 100 pg/mL, and they are even lower in severe and critical ones [25], so auto-Abs neutralizing concentrations of type I IFNs below 10 ng/mL may underlie life-threatening COVID-19 pneumonia in more than 9.5% of cases, as suggested by a recent study [Bastard in press]. Fourth, since our study was retrospective, confounders could be overlooked, and missing data might have altered some results. Fifth, the study design does not permit us to establish if the antibodies play a pathogenic role or are simply a biomarker of increased risk for developing renal failure among such patients. Finally, the present study does not allow assessing the usefulness of auto-Abs in those patients at earlier or milder stages of the disease. In summary, one-fifth of COVID-19 patients admitted to ICU presented auto-Abs against type I IFNs (IFN-α2 and/or IFN-ω), and blocking activity against 10 ng/mL of type I IFNs in half of them. In such life-threatening COVID-19 population, the presence of neutralizing IFNs auto-Abs was remarkably and statistically greater in men, associated with increased inflammatory laboratory parameters related to COVID-19 severity, and also related with a higher risk for developing acute kidney injury. Conversely, mortality between both groups was not different. Therefore, the early identification of these auto-Abs help to identify a significant proportion of patients at higher risk to develop critical COVID-19 pneumonia, its usefulness being more limited when patients are in the ICU. Further research is needed to assess the clinical and pathogenic role of neutralizing auto-Abs against type I IFNs in order to better select the most appropriate therapies. Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 138 KB)
  17 in total

1.  Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China.

Authors:  Chaomin Wu; Xiaoyan Chen; Yanping Cai; Jia'an Xia; Xing Zhou; Sha Xu; Hanping Huang; Li Zhang; Xia Zhou; Chunling Du; Yuye Zhang; Juan Song; Sijiao Wang; Yencheng Chao; Zeyong Yang; Jie Xu; Xin Zhou; Dechang Chen; Weining Xiong; Lei Xu; Feng Zhou; Jinjun Jiang; Chunxue Bai; Junhua Zheng; Yuanlin Song
Journal:  JAMA Intern Med       Date:  2020-07-01       Impact factor: 21.873

2.  Diverse functional autoantibodies in patients with COVID-19.

Authors:  Eric Y Wang; Tianyang Mao; Jon Klein; Yile Dai; John D Huck; Jillian R Jaycox; Feimei Liu; Ting Zhou; Benjamin Israelow; Patrick Wong; Andreas Coppi; Carolina Lucas; Julio Silva; Ji Eun Oh; Eric Song; Emily S Perotti; Neil S Zheng; Suzanne Fischer; Melissa Campbell; John B Fournier; Anne L Wyllie; Chantal B F Vogels; Isabel M Ott; Chaney C Kalinich; Mary E Petrone; Anne E Watkins; Charles Dela Cruz; Shelli F Farhadian; Wade L Schulz; Shuangge Ma; Nathan D Grubaugh; Albert I Ko; Akiko Iwasaki; Aaron M Ring
Journal:  Nature       Date:  2021-05-19       Impact factor: 49.962

Review 3.  COVID-19 one year into the pandemic: from genetics and genomics to therapy, vaccination, and policy.

Authors:  Giuseppe Novelli; Michela Biancolella; Ruty Mehrian-Shai; Vito Luigi Colona; Anderson F Brito; Nathan D Grubaugh; Vasilis Vasiliou; Lucio Luzzatto; Juergen K V Reichardt
Journal:  Hum Genomics       Date:  2021-05-10       Impact factor: 4.639

4.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.

Authors:  Chaolin Huang; Yeming Wang; Xingwang Li; Lili Ren; Jianping Zhao; Yi Hu; Li Zhang; Guohui Fan; Jiuyang Xu; Xiaoying Gu; Zhenshun Cheng; Ting Yu; Jiaan Xia; Yuan Wei; Wenjuan Wu; Xuelei Xie; Wen Yin; Hui Li; Min Liu; Yan Xiao; Hong Gao; Li Guo; Jungang Xie; Guangfa Wang; Rongmeng Jiang; Zhancheng Gao; Qi Jin; Jianwei Wang; Bin Cao
Journal:  Lancet       Date:  2020-01-24       Impact factor: 79.321

Review 5.  Immunology of COVID-19: Current State of the Science.

Authors:  Nicolas Vabret; Graham J Britton; Conor Gruber; Samarth Hegde; Joel Kim; Maria Kuksin; Rachel Levantovsky; Louise Malle; Alvaro Moreira; Matthew D Park; Luisanna Pia; Emma Risson; Miriam Saffern; Bérengère Salomé; Myvizhi Esai Selvan; Matthew P Spindler; Jessica Tan; Verena van der Heide; Jill K Gregory; Konstantina Alexandropoulos; Nina Bhardwaj; Brian D Brown; Benjamin Greenbaum; Zeynep H Gümüş; Dirk Homann; Amir Horowitz; Alice O Kamphorst; Maria A Curotto de Lafaille; Saurabh Mehandru; Miriam Merad; Robert M Samstein
Journal:  Immunity       Date:  2020-05-06       Impact factor: 31.745

6.  Beneficial effect of corticosteroids in preventing mortality in patients receiving tocilizumab to treat severe COVID-19 illness.

Authors:  Manuel Rubio-Rivas; Mar Ronda; Ariadna Padulles; Francesca Mitjavila; Antoni Riera-Mestre; Carlos García-Forero; Adriana Iriarte; Jose M Mora; Nuria Padulles; Monica Gonzalez; Xavier Solanich; Merce Gasa; Guillermo Suarez-Cuartin; Joan Sabater; Xose L Perez-Fernandez; Eugenia Santacana; Elisabet Leiva; Albert Ariza-Sole; Paolo D Dallaglio; Maria Quero; Antonio Soriano; Alberto Pasqualetto; Maylin Koo; Virginia Esteve; Arnau Antoli; Rafael Moreno-Gonzalez; Sergi Yun; Pau Cerda; Mariona Llaberia; Francesc Formiga; Marta Fanlo; Abelardo Montero; David Chivite; Olga Capdevila; Ferran Bolao; Xavier Pinto; Josep Llop; Antoni Sabate; Jordi Guardiola; Josep M Cruzado; Josep Comin-Colet; Salud Santos; Ramon Jodar; Xavier Corbella
Journal:  Int J Infect Dis       Date:  2020-10-06       Impact factor: 3.623

7.  Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications.

Authors:  Andrew T Levin; William P Hanage; Nana Owusu-Boaitey; Kensington B Cochran; Seamus P Walsh; Gideon Meyerowitz-Katz
Journal:  Eur J Epidemiol       Date:  2020-12-08       Impact factor: 8.082

Review 8.  Human genetic factors associated with susceptibility to SARS-CoV-2 infection and COVID-19 disease severity.

Authors:  Cleo Anastassopoulou; Zoi Gkizarioti; George P Patrinos; Athanasios Tsakris
Journal:  Hum Genomics       Date:  2020-10-22       Impact factor: 4.639

9.  Inborn errors of type I IFN immunity in patients with life-threatening COVID-19.

Authors:  Paul Bastard; Zhiyong Liu; Jérémie Le Pen; Marcela Moncada-Velez; Jie Chen; Masato Ogishi; Ira K D Sabli; Stephanie Hodeib; Cecilia Korol; Jérémie Rosain; Kaya Bilguvar; Junqiang Ye; Alexandre Bolze; Benedetta Bigio; Rui Yang; Andrés Augusto Arias; Qinhua Zhou; Yu Zhang; Richard P Lifton; Shen-Ying Zhang; Guy Gorochov; Vivien Béziat; Emmanuelle Jouanguy; Vanessa Sancho-Shimizu; Charles M Rice; Laurent Abel; Luigi D Notarangelo; Aurélie Cobat; Helen C Su; Jean-Laurent Casanova; Qian Zhang; Fanny Onodi; Sarantis Korniotis; Léa Karpf; Quentin Philippot; Marwa Chbihi; Lucie Bonnet-Madin; Karim Dorgham; Nikaïa Smith; William M Schneider; Brandon S Razooky; Hans-Heinrich Hoffmann; Eleftherios Michailidis; Leen Moens; Ji Eun Han; Lazaro Lorenzo; Lucy Bizien; Philip Meade; Anna-Lena Neehus; Aileen Camille Ugurbil; Aurélien Corneau; Gaspard Kerner; Peng Zhang; Franck Rapaport; Yoann Seeleuthner; Jeremy Manry; Cecile Masson; Yohann Schmitt; Agatha Schlüter; Tom Le Voyer; Taushif Khan; Juan Li; Jacques Fellay; Lucie Roussel; Mohammad Shahrooei; Mohammed F Alosaimi; Davood Mansouri; Haya Al-Saud; Fahd Al-Mulla; Feras Almourfi; Saleh Zaid Al-Muhsen; Fahad Alsohime; Saeed Al Turki; Rana Hasanato; Diederik van de Beek; Andrea Biondi; Laura Rachele Bettini; Mariella D'Angio'; Paolo Bonfanti; Luisa Imberti; Alessandra Sottini; Simone Paghera; Eugenia Quiros-Roldan; Camillo Rossi; Andrew J Oler; Miranda F Tompkins; Camille Alba; Isabelle Vandernoot; Jean-Christophe Goffard; Guillaume Smits; Isabelle Migeotte; Filomeen Haerynck; Pere Soler-Palacin; Andrea Martin-Nalda; Roger Colobran; Pierre-Emmanuel Morange; Sevgi Keles; Fatma Çölkesen; Tayfun Ozcelik; Kadriye Kart Yasar; Sevtap Senoglu; Şemsi Nur Karabela; Carlos Rodríguez-Gallego; Giuseppe Novelli; Sami Hraiech; Yacine Tandjaoui-Lambiotte; Xavier Duval; Cédric Laouénan; Andrew L Snow; Clifton L Dalgard; Joshua D Milner; Donald C Vinh; Trine H Mogensen; Nico Marr; András N Spaan; Bertrand Boisson; Stéphanie Boisson-Dupuis; Jacinta Bustamante; Anne Puel; Michael J Ciancanelli; Isabelle Meyts; Tom Maniatis; Vassili Soumelis; Ali Amara; Michel Nussenzweig; Adolfo García-Sastre; Florian Krammer; Aurora Pujol; Darragh Duffy
Journal:  Science       Date:  2020-09-24       Impact factor: 47.728

10.  Autoantibodies against type I IFNs in patients with life-threatening COVID-19.

Authors:  Paul Bastard; Lindsey B Rosen; Qian Zhang; Eleftherios Michailidis; Hans-Heinrich Hoffmann; Yu Zhang; Karim Dorgham; Quentin Philippot; Jérémie Rosain; Vivien Béziat; Steven M Holland; Guy Gorochov; Emmanuelle Jouanguy; Charles M Rice; Aurélie Cobat; Luigi D Notarangelo; Laurent Abel; Helen C Su; Jean-Laurent Casanova; Jérémy Manry; Elana Shaw; Liis Haljasmägi; Pärt Peterson; Lazaro Lorenzo; Lucy Bizien; Sophie Trouillet-Assant; Kerry Dobbs; Adriana Almeida de Jesus; Alexandre Belot; Anne Kallaste; Emilie Catherinot; Yacine Tandjaoui-Lambiotte; Jeremie Le Pen; Gaspard Kerner; Benedetta Bigio; Yoann Seeleuthner; Rui Yang; Alexandre Bolze; András N Spaan; Ottavia M Delmonte; Michael S Abers; Alessandro Aiuti; Giorgio Casari; Vito Lampasona; Lorenzo Piemonti; Fabio Ciceri; Kaya Bilguvar; Richard P Lifton; Marc Vasse; David M Smadja; Mélanie Migaud; Jérome Hadjadj; Benjamin Terrier; Darragh Duffy; Lluis Quintana-Murci; Diederik van de Beek; Lucie Roussel; Donald C Vinh; Stuart G Tangye; Filomeen Haerynck; David Dalmau; Javier Martinez-Picado; Petter Brodin; Michel C Nussenzweig; Stéphanie Boisson-Dupuis; Carlos Rodríguez-Gallego; Guillaume Vogt; Trine H Mogensen; Andrew J Oler; Jingwen Gu; Peter D Burbelo; Jeffrey I Cohen; Andrea Biondi; Laura Rachele Bettini; Mariella D'Angio; Paolo Bonfanti; Patrick Rossignol; Julien Mayaux; Frédéric Rieux-Laucat; Eystein S Husebye; Francesca Fusco; Matilde Valeria Ursini; Luisa Imberti; Alessandra Sottini; Simone Paghera; Eugenia Quiros-Roldan; Camillo Rossi; Riccardo Castagnoli; Daniela Montagna; Amelia Licari; Gian Luigi Marseglia; Xavier Duval; Jade Ghosn; John S Tsang; Raphaela Goldbach-Mansky; Kai Kisand; Michail S Lionakis; Anne Puel; Shen-Ying Zhang
Journal:  Science       Date:  2020-09-24       Impact factor: 63.714

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  24 in total

1.  Autoantibodies targeting cytokines and connective tissue disease autoantigens are common in acute non-SARS-CoV-2 infections.

Authors:  Allan Feng; Emily Yang; Andrew Moore; Shaurya Dhingra; Sarah Chang; Xihui Yin; Ruoxi Pi; Elisabeth Mack; Sara Völkel; Reinhard Geßner; Margrit Gundisch; Andreas Neubauer; Harald Renz; Sotirios Tsiodras; Paraskevi Fragkou; Adijat Asuni; Joseph Levitt; Jennifer Wilson; Michelle Leong; Jennifer Lumb; Rong Mao; Kassandra Pinedo; Jonasel Roque; Christopher Richards; Mikayla Stabile; Gayathri Swaminathan; Maria Salagianni; Vasiliki Triantafyllia; Wilhelm Bertrams; Catherine Blish; Jan Carette; Jennifer Frankovich; Eric Meffre; Kari C Nadeau; Upinder Singh; Taia Wang; Eline Luning Prak; Susanne Herold; Evangelos Andreakos; Bernd Schmeck; Chrysanthi Skevaki; Angela Rogers; Paul Utz
Journal:  Res Sq       Date:  2022-01-20

Review 2.  The intersection of COVID-19 and autoimmunity.

Authors:  Jason S Knight; Roberto Caricchio; Jean-Laurent Casanova; Alexis J Combes; Betty Diamond; Sharon E Fox; David A Hanauer; Judith A James; Yogendra Kanthi; Virginia Ladd; Puja Mehta; Aaron M Ring; Ignacio Sanz; Carlo Selmi; Russell P Tracy; Paul J Utz; Catriona A Wagner; Julia Y Wang; William J McCune
Journal:  J Clin Invest       Date:  2021-12-15       Impact factor: 14.808

3.  Patients with severe COVID-19 do not have elevated autoantibodies against common diagnostic autoantigens.

Authors:  Antigona Ulndreaj; Mingyue Wang; Salvia Misaghian; Louis Paone; George B Sigal; Martin Stengelin; Christopher Campbell; Logan R Van Nynatten; Antoninus Soosaipillai; Atefeh Ghorbani; Anu Mathew; Douglas D Fraser; Eleftherios P Diamandis; Ioannis Prassas
Journal:  Clin Chem Lab Med       Date:  2022-04-28       Impact factor: 8.490

4.  Z-RNA and the Flipside of the SARS Nsp13 Helicase: Is There a Role for Flipons in Coronavirus-Induced Pathology?

Authors:  Alan Herbert; Maria Poptsova
Journal:  Front Immunol       Date:  2022-06-17       Impact factor: 8.786

Review 5.  Mechanisms of Immune Dysregulation in COVID-19 Are Different From SARS and MERS: A Perspective in Context of Kawasaki Disease and MIS-C.

Authors:  Manpreet Dhaliwal; Rahul Tyagi; Pooja Malhotra; Prabal Barman; Sathish Kumar Loganathan; Jyoti Sharma; Kaushal Sharma; Sanjib Mondal; Amit Rawat; Surjit Singh
Journal:  Front Pediatr       Date:  2022-05-05       Impact factor: 3.569

6.  Human Inborn Errors of Immunity: 2022 Update on the Classification from the International Union of Immunological Societies Expert Committee.

Authors:  Stuart G Tangye; Waleed Al-Herz; Aziz Bousfiha; Charlotte Cunningham-Rundles; Jose Luis Franco; Steven M Holland; Christoph Klein; Tomohiro Morio; Eric Oksenhendler; Capucine Picard; Anne Puel; Jennifer Puck; Mikko R J Seppänen; Raz Somech; Helen C Su; Kathleen E Sullivan; Troy R Torgerson; Isabelle Meyts
Journal:  J Clin Immunol       Date:  2022-06-24       Impact factor: 8.542

7.  Vaccine breakthrough hypoxemic COVID-19 pneumonia in patients with auto-Abs neutralizing type I IFNs.

Authors:  Paul Bastard; Sara Vazquez; Jamin Liu; Matthew T Laurie; Chung Yu Wang; Adrian Gervais; Tom Le Voyer; Lucy Bizien; Colin Zamecnik; Quentin Philippot; Jérémie Rosain; Chun Jimmie Ye; Aurélie Cobat; Leslie M Thompson; Evangelos Andreakos; Qian Zhang; Mark S Anderson; Jean-Laurent Casanova; Joseph L DeRisi; Emilie Catherinot; Andrew Willmore; Anthea M Mitchell; Rebecca Bair; Pierre Garçon; Heather Kenney; Arnaud Fekkar; Maria Salagianni; Garyphallia Poulakou; Eleni Siouti; Sabina Sahanic; Ivan Tancevski; Günter Weiss; Laurenz Nagl; Jérémy Manry; Sotirija Duvlis; Daniel Arroyo-Sánchez; Estela Paz Artal; Luis Rubio; Cristiano Perani; Michela Bezzi; Alessandra Sottini; Virginia Quaresima; Lucie Roussel; Donald C Vinh; Luis Felipe Reyes; Margaux Garzaro; Nevin Hatipoglu; David Boutboul; Yacine Tandjaoui-Lambiotte; Alessandro Borghesi; Anna Aliberti; Irene Cassaniti; Fabienne Venet; Guillaume Monneret; Rabih Halwani; Narjes Saheb Sharif-Askari; Jeffrey Danielson; Sonia Burrel; Caroline Morbieu; Yurii Stepanovskyy; Anastasia Bondarenko; Alla Volokha; Oksana Boyarchuk; Alenka Gagro; Mathilde Neuville; Bénédicte Neven; Sevgi Keles; Romain Hernu; Antonin Bal; Antonio Novelli; Giuseppe Novelli; Kahina Saker; Oana Ailioaie; Arnau Antolí; Eric Jeziorski; Gemma Rocamora-Blanch; Carla Teixeira; Clarisse Delaunay; Marine Lhuillier; Paul Le Turnier; Yu Zhang; Matthieu Mahevas; Qiang Pan-Hammarström; Hassan Abolhassani; Thierry Bompoil; Karim Dorgham; Guy Gorochov; Cédric Laouenan; Carlos Rodríguez-Gallego; Lisa F P Ng; Laurent Renia; Aurora Pujol; Alexandre Belot; François Raffi; Luis M Allende; Javier Martinez-Picado; Tayfun Ozcelik; Sevgi Keles; Luisa Imberti; Luigi D Notarangelo; Jesus Troya; Xavier Solanich; Shen-Ying Zhang; Anne Puel; Michael R Wilson; Sophie Trouillet-Assant; Laurent Abel; Emmanuelle Jouanguy
Journal:  Sci Immunol       Date:  2022-06-14

Review 8.  Human genetic and immunological determinants of critical COVID-19 pneumonia.

Authors:  Qian Zhang; Paul Bastard; Aurélie Cobat; Jean-Laurent Casanova
Journal:  Nature       Date:  2022-01-28       Impact factor: 69.504

Review 9.  Type I interferons and SARS-CoV-2: from cells to organisms.

Authors:  Paul Bastard; Qian Zhang; Shen-Ying Zhang; Emmanuelle Jouanguy; Jean-Laurent Casanova
Journal:  Curr Opin Immunol       Date:  2022-01-25       Impact factor: 7.486

10.  Serum from COVID-19 patients early in the pandemic shows limited evidence of cross-neutralization against variants of concern.

Authors:  Amanda J Griffin; Kyle L O'Donnell; Kyle Shifflett; John-Paul Lavik; Patrick M Russell; Michelle K Zimmerman; Ryan F Relich; Andrea Marzi
Journal:  Sci Rep       Date:  2022-03-10       Impact factor: 4.379

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