Evidence map›Paper›PMID 36810114›Full record

ArticleVirology journal2023

The detectable anti-interferon-γ autoantibodies in COVID-19 patients may be associated with disease severity.

Po-Ku Chen, Kai-Jieh Yeo, Shih-Hsin Chang, Tsai-Ling Liao, Chia-Hui Chou, Joung-Liang Lan, Ching-Kun Chang, Der-Yuan Chen

Open access · goldAbstract read
In one paragraph

Article in Virology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Po-Ku ChenRheumatology and Immunology Center, China Medical University Hospital, No. 2, Yude Road, Taichung, 40447, Taiwan.
Kai-Jieh YeoRheumatology and Immunology Center, China Medical University Hospital, No. 2, Yude Road, Taichung, 40447, Taiwan.
Shih-Hsin ChangRheumatology and Immunology Center, China Medical University Hospital, No. 2, Yude Road, Taichung, 40447, Taiwan.
Tsai-Ling LiaoPh.D. Program in Translational Medicine and Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung, Taiwan.
Chia-Hui ChouCollege of Medicine, China Medical University, Taichung, Taiwan.
Joung-Liang LanRheumatology and Immunology Center, China Medical University Hospital, No. 2, Yude Road, Taichung, 40447, Taiwan.
Ching-Kun ChangRheumatology and Immunology Center, China Medical University Hospital, No. 2, Yude Road, Taichung, 40447, Taiwan.
Der-Yuan ChenRheumatology and Immunology Center, China Medical University Hospital, No. 2, Yude Road, Taichung, 40447, Taiwan. dychen1957@gmail.com.
China Medical University Hospital · TWChina Medical University · TWNational Chung Hsing University · TWTaichung Veterans General Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNeutralizing anti-interferon (IFN)-γ autoantibodies are linked to adult-onset immunodeficiency and opportunistic infections.

methodsTo explore whether anti-IFN-γ autoantibodies are associated with disease severity of coronavirus disease 2019 (COVID-19), we examined the titers and functional neutralization of anti-IFN-γ autoantibodies in COVID-19 patients. In 127 COVID-19 patients and 22 healthy controls, serum titers of anti-IFN-γ autoantibodies were quantified using enzyme-linked immunosorbent assay, and the presence of autoantibodies was verified with immunoblotting assay. The neutralizing capacity against IFN-γ was evaluated with flow cytometry analysis and immunoblotting, and serum cytokines levels were determined using the MULTIPLEX platform.

resultsA higher proportion of severe/critical COVID-19 patients had positivity for anti-IFN-γ autoantibodies (18.0%) compared with non-severe patients (3.4%, p < 0.01) or healthy control (HC) (0.0%, p < 0.05). Severe/critical COVID-19 patients also had higher median titers of anti-IFN-γ autoantibodies (5.01) compared with non-severe patients (1.33) or HC (0.44). The immunoblotting assay could verify the detectable anti-IFN-γ autoantibodies and revealed more effective inhibition of signal transducer and activator of transcription (STAT1) phosphorylation on THP-1 cells treated with serum samples from anti-IFN-γ autoantibodies-positive patients compared with those from HC (2.21 ± 0.33 versus 4.47 ± 1.64, p < 0.05). In flow-cytometry analysis, sera from autoantibodies-positive patients could also significantly more effectively suppress the STAT1 phosphorylation (median,67.28%, interquartile range [IQR] 55.2-78.0%) compared with serum from HC (median,106.7%, IQR 100.0-117.8%, p < 0.05) or autoantibodies-negative patients (median,105.9%, IQR 85.5-116.3%, p < 0.05). Multivariate analysis revealed that the positivity and titers of anti-IFN-γ autoantibodies were significant predictors of severe/critical COVID-19. Compared with non-severe COVID-19 patients, we reveal that a significantly higher proportion of severe/critical COVID-19 patients are positive for anti-IFN-γ autoantibodies with neutralizing capacity.

conclusionOur results would add COVID-19 to the list of diseases with the presence of neutralizing anti-IFN-γ autoAbs. Anti-IFN-γ autoantibodies positivity is a potential predictor of severe/critical COVID-19.

Indexed as

AutoantibodiesCOVID-19AdultCytokinesHumansInterferon-gammaPatient AcuityAutoantibodiesCytokinesInterferon-gammaAnti-interferon-γ auto-AbsCOVID-19Disease severitySARS-CoV-2Titers

Identifiers

PMID36810114
PMCPMC9942050
OpenAlexW4321455330

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.