Evidence map›Paper›PMID 36466858›Full record

ArticleFrontiers in immunology2022

Heterogeneity of neutrophils and inflammatory responses in patients with COVID-19 and healthy controls.

Jintao Xu, Bing He, Kyle Carver, Debora Vanheyningen, Brian Parkin, Lana X Garmire, Michal A Olszewski, Jane C Deng

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.6field-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

19 citing papers in PubMed, 27 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 3 institutions in 1 country.

Jintao XuResearch Service, LTC Charles S. Kettles Veterans Affairs Medical Center, Department of Veterans Affairs Health System, Ann Arbor, MI, United States.
Bing HeDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
Kyle CarverResearch Service, LTC Charles S. Kettles Veterans Affairs Medical Center, Department of Veterans Affairs Health System, Ann Arbor, MI, United States.
Debora VanheyningenResearch Service, LTC Charles S. Kettles Veterans Affairs Medical Center, Department of Veterans Affairs Health System, Ann Arbor, MI, United States.
Brian ParkinResearch Service, LTC Charles S. Kettles Veterans Affairs Medical Center, Department of Veterans Affairs Health System, Ann Arbor, MI, United States.
Lana X GarmireDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
Michal A OlszewskiResearch Service, LTC Charles S. Kettles Veterans Affairs Medical Center, Department of Veterans Affairs Health System, Ann Arbor, MI, United States.
Jane C DengResearch Service, LTC Charles S. Kettles Veterans Affairs Medical Center, Department of Veterans Affairs Health System, Ann Arbor, MI, United States.
University of Michigan–Ann Arbor · USMichigan Medicine · USVeterans Health Administration · US

Funding

Mechanisms to augment primary immunity in agingR01AG028082 · NIA · YALE UNIVERSITY · PI DENG, JANE C · 2007 to 2023
$6.9M
An Integrative Omics Approach to Identify Biomarkers Related to Preeclampsia and Breast Cancer RisksR01HD084633 · NICHD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2016 to 2020
$3.0M
DR. EPS: Drug Repurposing for Extended Patient SurvivalR01LM012907 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2019 to 2022
$1.0M
Neutrophil heterogeneity and function in host defense during pulmonary infectionI01BX004565 · VA · VETERANS HEALTH ADMINISTRATION · PI DENG, JANE C · 2019 to 2024
–
Immune mediated lung injury in COVID-19I01BX005447 · VA · VETERANS HEALTH ADMINISTRATION · PI DENG, JANE C · 2021 to 2022
–
BLRD VA I01 BX004565BLRD VA I01 BX005447NIA NIH HHS R01 AG028082NICHD NIH HHS R01 HD084633NLM NIH HHS R01 LM012907
6 · The paper itself

Abstract

Severe respiratory viral infections, including SARS-CoV-2, have resulted in high mortality rates despite corticosteroids and other immunomodulatory therapies. Despite recognition of the pathogenic role of neutrophils, in-depth analyses of this cell population have been limited, due to technical challenges of working with neutrophils. We undertook an unbiased, detailed analysis of neutrophil responses in adult patients with COVID-19 and healthy controls, to determine whether distinct neutrophil phenotypes could be identified during infections compared to the healthy state. Single-cell RNA sequencing analysis of peripheral blood neutrophils from hospitalized patients with mild or severe COVID-19 disease and healthy controls revealed distinct mature neutrophil subpopulations, with relative proportions linked to disease severity. Disruption of predicted cell-cell interactions, activated oxidative phosphorylation genes, and downregulated antiviral and host defense pathway genes were observed in neutrophils obtained during severe compared to mild infections. Our findings suggest that during severe infections, there is a loss of normal regulatory neutrophil phenotypes seen in healthy subjects, coupled with the dropout of appropriate cellular interactions. Given that neutrophils are the most abundant circulating leukocytes with highly pathogenic potential, current immunotherapies for severe infections may be optimized by determining whether they aid in restoring an appropriate balance of neutrophil subpopulations.

Indexed as

COVID-19Antiviral AgentsHumansNeutrophilsPatient AcuitySARS-CoV-2Antiviral AgentsCOVID-19immune responseneutrophil heterogeneitySARS – CoV – 2single-cell sequencing

Identifiers

PMID36466858
PMCPMC9709423
OpenAlexW4309311191

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.