Evidence map›Paper›PMID 37292999›Full record

ArticlebioRxiv : the preprint server for biology2023

Longitudinal host transcriptional responses to SARS-CoV-2 infection in adults with extremely high viral load.

Vasanthi Avadhanula, Chad J Creighton, Laura Ferlic-Stark, Richard Sucgang, Yiqun Zhang, Divya Nagaraj, Erin G Nicholson, Anubama Rajan, Vipin Kumar Menon, Harshavardhan Doddapaneni and 7 more

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 2 institutions in 1 country.

Vasanthi AvadhanulaDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
Chad J CreightonDan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA.
Laura Ferlic-StarkDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
Richard SucgangCenter for Health Data Science and Analytics, Houston Methodist Research Institute, Houston, TX 77030, USA.
Yiqun ZhangDan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA.
Divya NagarajDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
Erin G NicholsonDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
Anubama RajanDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
Vipin Kumar MenonDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
Harshavardhan DoddapaneniDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
Donna Marie MuznyDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
Ginger MetcalfDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
Sara Joan Javornik CregeenDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
Kristi Louise HoffmanDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
Richard A GibbsDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
Joseph PetrosinoDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
Pedro A PiedraDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA.
Baylor College of Medicine · USHouston Methodist · US

Funding

Tumor BiologyP30CA125123 · NCI · BAYLOR COLLEGE OF MEDICINE · PI Suzanne AW Fuqua · 2007 to 2026
$73.9M
Viral Diversity and Pathogenicity in Mucosal Respiratory and Gastrointestinal DiseaseU19AI144297 · NIAID · BAYLOR COLLEGE OF MEDICINE · PI ESTES, MARY KOLB, GIBBS, RICHARD A · 2019 to 2024
$30.1M
NCI NIH HHS P30 CA125123NIAID NIH HHS U19 AI144297
6 · The paper itself

Abstract

Current understanding of viral dynamics of SARS-CoV-2 and host responses driving the pathogenic mechanisms in COVID-19 is rapidly evolving. Here, we conducted a longitudinal study to investigate gene expression patterns during acute SARS-CoV-2 illness. Cases included SARS-CoV-2 infected individuals with extremely high viral loads early in their illness, individuals having low SARS-CoV-2 viral loads early in their infection, and individuals testing negative for SARS-CoV-2. We could identify widespread transcriptional host responses to SARS-CoV-2 infection that were initially most strongly manifested in patients with extremely high initial viral loads, then attenuating within the patient over time as viral loads decreased. Genes correlated with SARS-CoV-2 viral load over time were similarly differentially expressed across independent datasets of SARS-CoV-2 infected lung and upper airway cells, from both in vitro systems and patient samples. We also generated expression data on the human nose organoid model during SARS-CoV-2 infection. The human nose organoid-generated host transcriptional response captured many aspects of responses observed in the above patient samples, while suggesting the existence of distinct host responses to SARS-CoV-2 depending on the cellular context, involving both epithelial and cellular immune responses. Our findings provide a catalog of SARS-CoV-2 host response genes changing over time.

Identifiers

PMID37292999
PMCPMC10245966
OpenAlexW4378388090

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.