Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection

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Abstract

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious respiratory virus which is responsible for the coronavirus disease 2019 (COVID-19) pandemic. It is increasingly clear that recovered individuals, even those who had mild COVID-19, can suffer from persistent symptoms for many months after infection, a condition referred to as “long COVID”, post-acute sequelae of COVID-19 (PASC), post-acute COVID-19 syndrome, or post COVID-19 condition. However, despite the plethora of research on COVID-19, relatively little is known about the molecular underpinnings of these long-term effects.

Methods

We have undertaken an integrated analysis of immune responses in blood at a transcriptional, cellular, and serological level at 12, 16, and 24 weeks post-infection (wpi) in 69 patients recovering from mild, moderate, severe, or critical COVID-19 in comparison to healthy uninfected controls. Twenty-one of these patients were referred to a long COVID clinic and > 50% reported ongoing symptoms more than 6 months post-infection.

Results

Anti-Spike and anti-RBD IgG responses were largely stable up to 24 wpi and correlated with disease severity. Deep immunophenotyping revealed significant differences in multiple innate (NK cells, LD neutrophils, CXCR3+ monocytes) and adaptive immune populations (T helper, T follicular helper, and regulatory T cells) in convalescent individuals compared to healthy controls, which were most strongly evident at 12 and 16 wpi. RNA sequencing revealed significant perturbations to gene expression in COVID-19 convalescents until at least 6 months post-infection. We also uncovered significant differences in the transcriptome at 24 wpi of convalescents who were referred to a long COVID clinic compared to those who were not.

Conclusions

Variation in the rate of recovery from infection at a cellular and transcriptional level may explain the persistence of symptoms associated with long COVID in some individuals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12916-021-02228-6.

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Most cited references 94

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

Chaolin Huang, Yeming Wang, Xingwang Li … (2020)

Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.

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Trimmomatic: a flexible trimmer for Illumina sequence data

Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au

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Author and article information

Contributors

Simon C. Barry: simon.barry@adelaide.edu.au

Branka Grubor-Bauk: branka.grubor@adelaide.edu.au

David J. Lynn: david.lynn@sahmri.com

Journal

Journal ID (nlm-ta): BMC Med

Journal ID (iso-abbrev): BMC Med

Title: BMC Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1741-7015

Publication date (Electronic): 14 January 2022

Publication date PMC-release: 14 January 2022

Publication date Collection: 2022

Volume: 20

Electronic Location Identifier: 26

Affiliations

[1 ]GRID grid.430453.5, ISNI 0000 0004 0565 2606, Precision Medicine Theme, South Australian Health and Medical Research Institute, ; Adelaide, SA 5001 Australia

[2 ]GRID grid.431036.3, Women’s and Children’s Health Network, ; North Adelaide, SA Australia

[3 ]GRID grid.1010.0, ISNI 0000 0004 1936 7304, Molecular Immunology, Robinson Research Institute, , University of Adelaide, ; Adelaide, SA Australia

[4 ]GRID grid.488717.5, Viral Immunology Group, Adelaide Medical School, , University of Adelaide and Basil Hetzel Institute for Translational Health Research, ; Adelaide, SA Australia

[5 ]GRID grid.1010.0, ISNI 0000 0004 1936 7304, Gene Silencing and Expression Core Facility, Adelaide Health and Medical Sciences, Robinson Research Institute, , University of Adelaide, ; Adelaide, SA Australia

[6 ]GRID grid.416075.1, ISNI 0000 0004 0367 1221, Infectious Diseases Department, , Royal Adelaide Hospital, Central Adelaide Local Health Network, ; Adelaide, SA Australia

[7 ]GRID grid.1010.0, ISNI 0000 0004 1936 7304, Intensive Care Unit, Royal Adelaide Hospital, Central Adelaide Local Health Network and Adelaide Medical School, , University of Adelaide, ; Adelaide, SA Australia

[8 ]GRID grid.414733.6, ISNI 0000 0001 2294 430X, Microbiology and Infectious Diseases Department, , SA Pathology, ; Adelaide, SA Australia

[9 ]GRID grid.1014.4, ISNI 0000 0004 0367 2697, Department of Renal Medicine, Flinders Medical Centre, , Flinders University, ; Bedford Park, SA 5042 Australia

[10 ]GRID grid.1014.4, ISNI 0000 0004 0367 2697, Flinders Health and Medical Research Institute, , Flinders University, ; Bedford Park, SA 5042 Australia

[11 ]GRID grid.1010.0, ISNI 0000 0004 1936 7304, Research Centre for Infectious Diseases, School of Biological Sciences, , University of Adelaide, ; Adelaide, SA 5005 Australia

Article

Publisher ID: 2228

DOI: 10.1186/s12916-021-02228-6

PMC ID: 8758383

PubMed ID: 35027067

SO-VID: 09fecb6f-f33e-4dac-93f3-773dd313df1e

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 16 December 2021

Date accepted : 29 December 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100013060, European Molecular Biology Laboratory;

Funded by: FundRef http://dx.doi.org/10.13039/100008017, Flinders Medical Centre Foundation;

Funded by: The Women’s and Children’s Hospital Foundation

Funded by: The Hospital Research Foundation

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: sars-cov-2,covid-19,immunity,rna-seq,t cell,antibody responses,convalescent patients,immunophenotyping,long covid,post-acute sequelae of covid-19 (pasc),post covid-19 condition,infection

Data availability:

ScienceOpen disciplines: Medicine

Keywords: sars-cov-2, covid-19, immunity, rna-seq, t cell, antibody responses, convalescent patients, immunophenotyping, long covid, post-acute sequelae of covid-19 (pasc), post covid-19 condition, infection

Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection

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Abstract

Background

Methods

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Conclusions

Supplementary Information

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Novel Coronavirus Disease COVID-19

Most cited references 94

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

Trimmomatic: a flexible trimmer for Illumina sequence data

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

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