Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans

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Immune profiling of COVID-19 patients

Coronavirus disease 2019 (COVID-19) has affected millions of people globally, yet how the human immune system responds to and influences COVID-19 severity remains unclear. Mathew et al. present a comprehensive atlas of immune modulation associated with COVID-19. They performed high-dimensional flow cytometry of hospitalized COVID-19 patients and found three prominent and distinct immunotypes that are related to disease severity and clinical parameters. Arunachalam et al. report a systems biology approach to assess the immune system of COVID-19 patients with mild-to-severe disease. These studies provide a compendium of immune cell information and roadmaps for potential therapeutic interventions.

Science, this issue p. [Related article:]eabc8511, p. 1210

Abstract

Immune responses of COVID-19 patients are cataloged and compared with those of healthy individuals.

Abstract

Coronavirus disease 2019 (COVID-19) represents a global crisis, yet major knowledge gaps remain about human immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We analyzed immune responses in 76 COVID-19 patients and 69 healthy individuals from Hong Kong and Atlanta, Georgia, United States. In the peripheral blood mononuclear cells (PBMCs) of COVID-19 patients, we observed reduced expression of human leukocyte antigen class DR (HLA-DR) and proinflammatory cytokines by myeloid cells as well as impaired mammalian target of rapamycin (mTOR) signaling and interferon-α (IFN-α) production by plasmacytoid dendritic cells. By contrast, we detected enhanced plasma levels of inflammatory mediators—including EN-RAGE, TNFSF14, and oncostatin M—which correlated with disease severity and increased bacterial products in plasma. Single-cell transcriptomics revealed a lack of type I IFNs, reduced HLA-DR in the myeloid cells of patients with severe COVID-19, and transient expression of IFN-stimulated genes. This was consistent with bulk PBMC transcriptomics and transient, low IFN-α levels in plasma during infection. These results reveal mechanisms and potential therapeutic targets for COVID-19.

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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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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

Zunyou Wu, Jennifer M. McGoogan (2020)

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Author and article information

Journal

Journal ID (nlm-ta): Science

Journal ID (iso-abbrev): Science

Journal ID (publisher-id): SCIENCE

Journal ID (hwp): science

Title: Science (New York, N.y.)

Publisher: American Association for the Advancement of Science

ISSN (Print): 0036-8075

ISSN (Electronic): 1095-9203

Publication date (Print): 04 September 2020

Publication date (Electronic): 11 August 2020

Volume: 369

Issue: 6508

Pages: 1210-1220

Affiliations

[1 ]Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA.

[2 ]HKU-Pasteur Research Pole, School of Public Health, HKU Li Ka Shing Faculty of Medicine, The University of Hong Kong (HKU), Hong Kong.

[3 ]Centre of Influenza Research, School of Public Health, HKU Li Ka Shing Faculty of Medicine, HKU, Hong Kong.

[4 ]Center for Biomedical Informatics, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.

[5 ]Hope Clinic of the Emory Vaccine Center, Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Decatur, GA 30030, USA.

[6 ]Infectious Diseases Centre, Princess Margaret Hospital, Hospital Authority of Hong Kong, Hong Kong.

[7 ]Stanford Functional Genomics Facility, Stanford University School of Medicine, Stanford, CA 94305, USA.

[8 ]Emory Vaccine Center, Yerkes National Primate Research Center, Atlanta, GA 30329, USA.

[9 ]Department of Pediatrics, Division of Infectious Disease, Emory University School of Medicine, Atlanta, GA 30322, USA.

[10 ]Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA 30329, USA.

[11 ]Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.

[12 ]Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA.

Author notes

[*]

These authors contributed equally to this work.

[†]

These authors contributed equally to this work.

[‡ ]Corresponding author. Email: bpulend@ 123456stanford.edu

Author information

Prabhu S. Arunachalam https://orcid.org/0000-0003-0090-5689

Florian Wimmers https://orcid.org/0000-0002-6482-2228

Chris Ka Pun Mok https://orcid.org/0000-0002-0525-6772

Ranawaka A. P. M. Perera https://orcid.org/0000-0003-3936-1535

Madeleine Scott https://orcid.org/0000-0001-9350-5451

Thomas Hagan https://orcid.org/0000-0002-5254-5251

Natalia Sigal https://orcid.org/0000-0003-2831-7053

Yupeng Feng https://orcid.org/0000-0001-8550-2787

Laurel Bristow https://orcid.org/0000-0001-9050-0057

Dhananjay Wagh https://orcid.org/0000-0002-6937-003X

Kathryn L. Pellegrini https://orcid.org/0000-0002-5089-5477

Ghina Alaaeddine https://orcid.org/0000-0001-9570-7989

Wai Shing Leung https://orcid.org/0000-0001-8377-085X

Thomas Shiu Hong Chik https://orcid.org/0000-0002-0481-1420

Chris Yau Chung Choi https://orcid.org/0000-0002-3695-2770

Evan Anderson https://orcid.org/0000-0002-1576-4420

Srilatha Edupuganti https://orcid.org/0000-0001-6566-9969

Amit A. Upadhyay https://orcid.org/0000-0002-0952-4164

Steve E. Bosinger https://orcid.org/0000-0002-2116-5061

Holden Terry Maecker https://orcid.org/0000-0003-0795-9946

Purvesh Khatri https://orcid.org/0000-0002-4143-4708

Malik Peiris https://orcid.org/0000-0001-8217-5995

Bali Pulendran https://orcid.org/0000-0001-6517-4333

Article

Publisher ID: abc6261

DOI: 10.1126/science.abc6261

PMC ID: 7665312

PubMed ID: 32788292

SO-VID: 85d79f9d-e46f-4659-9dc8-d2cd2f7c008a

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 05 May 2020

Date revision received : 10 July 2020

Date accepted : 04 August 2020

Funding

Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: U19AI090023

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Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans

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Immune profiling of COVID-19 patients

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Novel Coronavirus Disease COVID-19

Most cited references 41

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

limma powers differential expression analyses for RNA-sequencing and microarray studies

Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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Cited by 572

Most referenced authors 4,015

Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans

Read this article at

Novel Coronavirus Disease COVID-19

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

limma powers differential expression analyses for RNA-sequencing and microarray studies

Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention