Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

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Abstract

Background

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.

Methods

In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.

Results

Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

Conclusions

Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-020-00823-5.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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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

Thomas Ulas:

ORCID: http://orcid.org/0000-0002-9785-4197

t.ulas@uni-bonn.de

Journal

Journal ID (nlm-ta): Genome Med

Journal ID (iso-abbrev): Genome Med

Title: Genome Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1756-994X

Publication date (Electronic): 13 January 2021

Publication date PMC-release: 13 January 2021

Publication date Collection: 2021

Volume: 13

Electronic Location Identifier: 7

Affiliations

[1 ]GRID grid.424247.3, ISNI 0000 0004 0438 0426, Systems Medicine, , German Center for Neurodegenerative Diseases (DZNE), ; Bonn, Germany

[2 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, PRECISE Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn, ; Bonn, Germany

[3 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, , University of Bonn, ; Bonn, Germany

[4 ]GRID grid.10417.33, ISNI 0000 0004 0444 9382, Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), , Radboud University Medical Center, ; Nijmegen, The Netherlands

[5 ]GRID grid.5216.0, ISNI 0000 0001 2155 0800, 4th Department of Internal Medicine, , National and Kapodistrian University of Athens, Medical School, ; Athens, Greece

[6 ]GRID grid.15090.3d, ISNI 0000 0000 8786 803X, Department I of Internal Medicine, , University Hospital of Bonn (UKB), ; Bonn, Germany

[7 ]GRID grid.5216.0, ISNI 0000 0001 2155 0800, 1st Department of Pulmonary Medicine and Intensive Care Unit, , National and Kapodistrian University of Athens, Medical School, ; Athens, Greece

[8 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, West German Genome Center (WGGC), , University of Bonn, ; Bonn, Germany

[9 ]GRID grid.10417.33, ISNI 0000 0004 0444 9382, Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), , Radboud University Medical Center, ; Nijmegen, The Netherlands

[10 ]GRID grid.411327.2, ISNI 0000 0001 2176 9917, Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, , Heinrich Heine University Düsseldorf, ; Düsseldorf, Germany

[11 ]GRID grid.424247.3, ISNI 0000 0004 0438 0426, Population Health Sciences, , German Center for Neurodegenerative Diseases (DZNE), ; Bonn, Germany

[12 ]GRID grid.15090.3d, ISNI 0000 0000 8786 803X, Department of Internal Medicine II, Section of Pneumology, , University Hospital of Bonn (UKB), ; Bonn, Germany

[13 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, Department of Neurology, Faculty of Medicine, , University of Bonn, ; Bonn, Germany

[14 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, Immunology & Metabolism, Life and Medical Sciences (LIMES) Institute, , University of Bonn, ; Bonn, Germany

[15 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, , University of Bonn, ; Bonn, Germany

[16 ]GRID grid.452463.2, German Center for Infection Research (DZIF), ; Bonn, Germany

Author information

Thomas Ulas http://orcid.org/0000-0002-9785-4197

Article

Publisher ID: 823

DOI: 10.1186/s13073-020-00823-5

PMC ID: 7805430

PubMed ID: 33441124

SO-VID: d449b669-ace3-41de-8e85-757953a29fe2

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 : 15 July 2020

Date accepted : 18 December 2020

Custom metadata

ScienceOpen disciplines: Molecular medicine

Keywords: covid-19,blood transcriptomics,transcriptome,co-expression analysis,stratification,molecular disease phenotypes,granulocytes,neutrophils,drug repurposing

Data availability:

ScienceOpen disciplines: Molecular medicine

Keywords: covid-19, blood transcriptomics, transcriptome, co-expression analysis, stratification, molecular disease phenotypes, granulocytes, neutrophils, drug repurposing

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

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Novel Coronavirus Disease COVID-19

Most cited references 125

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

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

STAR: ultrafast universal RNA-seq aligner.

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