Robust Virus-Specific Adaptive Immunity in COVID-19 Patients with SARS-CoV-2 Δ382 Variant Infection

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Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) that have become dominant as the pandemic progresses bear the ORF8 mutation together with multiple spike mutations. A 382-nucleotide deletion (Δ382) in the ORF7b and ORF8 regions has been associated with milder disease phenotype and less systemic inflammation in COVID-19 patients. However, its impact on host immunity against SARS-CoV-2 remains undefined. Here, RNA-sequencing was performed to elucidate whole blood transcriptomic profiles and identify contrasting immune signatures between patients infected with either wildtype or Δ382 SARS-CoV-2 variant. Interestingly, the immune landscape of Δ382 SARS-CoV-2 infected patients featured an increased adaptive immune response, evidenced by enrichment of genes related to T cell functionality, a more robust SARS-CoV-2-specific T cell immunity, as well as a more rapid antibody response. At the molecular level, eukaryotic initiation factor 2 signaling was found to be upregulated in patients bearing Δ382, and its associated genes were correlated with systemic levels of T cell-associated and pro-inflammatory cytokines. This study provides more in-depth insight into the host–pathogen interactions of ORF8 with great promise as a therapeutic target to combat SARS-CoV-2 infection.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10875-021-01142-z.

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Most cited references 67

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Yoav Benjamini, Yosef Hochberg (1995)

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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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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Author and article information

Contributors

Lisa F. P. Ng: lisa_ng@IDLabs.a-star.edu.sg

Journal

Journal ID (nlm-ta): J Clin Immunol

Journal ID (iso-abbrev): J Clin Immunol

Title: Journal of Clinical Immunology

Publisher: Springer US (New York )

ISSN (Print): 0271-9142

ISSN (Electronic): 1573-2592

Publication date (Electronic): 30 October 2021

Publication date PMC-release: 30 October 2021

Pages: 1-16

Affiliations

[1 ]GRID grid.185448.4, ISNI 0000 0004 0637 0221, A*STAR Infectious Diseases Labs (A*STAR ID Labs), Agency for Science, Technology and Research (A*STAR), ; Singapore City, Singapore

[2 ]GRID grid.430276.4, ISNI 0000 0004 0387 2429, Singapore Immunology Network, Agency for Science, Technology and Research (A*STAR), ; Singapore City, Singapore

[3 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Department of Biochemistry, , Yong Loo Lin School of Medicine, National University of Singapore, ; Singapore City, Singapore

[4 ]GRID grid.508077.d, National Centre for Infectious Diseases, ; Singapore City, Singapore

[5 ]GRID grid.240988.f, ISNI 0000 0001 0298 8161, Department of Infectious Diseases, , Tan Tock Seng Hospital, ; Singapore City, Singapore

[6 ]GRID grid.59025.3b, ISNI 0000 0001 2224 0361, Lee Kong Chian School of Medicine, , Nanyang Technological University, ; Singapore City, Singapore

[7 ]GRID grid.412106.0, ISNI 0000 0004 0621 9599, Department of Medicine, , National University Hospital, ; Singapore City, Singapore

[8 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Infectious Diseases Translational Research Programme, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, ; Singapore City, Singapore

[9 ]GRID grid.163555.1, ISNI 0000 0000 9486 5048, Department of Infectious Diseases, , Singapore General Hospital, ; Singapore City, Singapore

[10 ]GRID grid.428397.3, ISNI 0000 0004 0385 0924, Programme in Emerging Infectious Diseases, Duke-NUS Medical School, ; Singapore City, Singapore

[11 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, ; Singapore City, Singapore

[12 ]GRID grid.10025.36, ISNI 0000 0004 1936 8470, NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, , University of Liverpool, ; Liverpool, UK

[13 ]GRID grid.10025.36, ISNI 0000 0004 1936 8470, Institute of Infection, Veterinary and Ecological Sciences, , University of Liverpool, ; Liverpool, UK

Article

Publisher ID: 1142

DOI: 10.1007/s10875-021-01142-z

PMC ID: 8556776

PubMed ID: 34716845

SO-VID: 11dd25fe-1d8a-4c6f-9521-33400b607e78

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/.

History

Date received : 21 June 2021

Date accepted : 17 September 2021

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Robust Virus-Specific Adaptive Immunity in COVID-19 Patients with SARS-CoV-2 Δ382 Variant Infection

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Novel Coronavirus Disease COVID-19

Most cited references 67

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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

STAR: ultrafast universal RNA-seq aligner.

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