Evidence map›Paper›PMID 35602214›Full record

ArticleCommunications medicine2021

Characterization of SARS-CoV-2 and host entry factors distribution in a COVID-19 autopsy series.

Xiao-Ming Wang, Rahul Mannan, Lanbo Xiao, Eman Abdulfatah, Yuanyuan Qiao, Carol Farver, Jeffrey L Myers, Sylvia Zelenka-Wang, Lisa McMurry, Fengyun Su and 8 more

Open access · goldAbstract read
In one paragraph

Article in Communications medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.2field-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

16 citing papers in PubMed, 24 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

18 authors at 3 institutions in 1 country.

Xiao-Ming Wang *Department of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Rahul Mannan *Department of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.ORCID 0000-0002-6642-0468
Lanbo XiaoDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Eman AbdulfatahDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Yuanyuan QiaoDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Carol FarverDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Jeffrey L MyersDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Sylvia Zelenka-WangDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Lisa McMurryDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Fengyun SuMichigan Center for Translational Pathology, Ann Arbor, MI USA.
Rui WangMichigan Center for Translational Pathology, Ann Arbor, MI USA.
Liron PantanowitzDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Jeffrey JentzenDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Allecia WilsonDepartment of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Yuping ZhangMichigan Center for Translational Pathology, Ann Arbor, MI USA.
Xuhong CaoMichigan Center for Translational Pathology, Ann Arbor, MI USA.
Arul M Chinnaiyan *Department of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.
Rohit Mehra *Department of Pathology, University of Michigan Medical School, Ann Arbor, MI USA.ORCID 0000-0002-6955-8884
University of Michigan · USMichigan Center for Translational Pathology · USHoward Hughes Medical Institute · US

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 1988 to 2026
$178.2M
Tissue/InformaticsP50CA186786 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ganesh S Palapattu · 2014 to 2026
$27.6M
Exploring Precision Oncology: From Gene Fusions to lncRNAsR35CA231996 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHINNAIYAN, ARUL M · 2018 to 2024
$6.4M
NCI NIH HHS P30 CA046592NCI NIH HHS P50 CA186786NCI NIH HHS R35 CA231996
6 · The paper itself

Abstract

Background: SARS-CoV-2 is a highly contagious virus that causes the disease COVID-19. We have recently reported that androgens regulate the expression of SARS-CoV-2 host entry factors ACE2 and TMPRSS2, and androgen receptor (AR) in lung epithelial cells. We also demonstrated that the transcriptional repression of the AR enhanceosome inhibited SARS-CoV-2 infection in vitro. Methods: To better understand the various sites of SARS-CoV-2 infection, and presence of host entry factors, we extensively characterized the tissue distribution and localization of SARS-CoV-2 virus, viral replication, and host entry factors in various anatomical sites sampled via autopsy. We applied RNA Results: We detect SARS-CoV-2 virus and viral replication in pulmonary tissues by RNA-ISH and IHC and a variety of non-pulmonary tissues including kidney, heart, liver, spleen, thyroid, lymph node, prostate, uterus, and colon by qRT-PCR. We observe heterogeneity in viral load and viral cytopathic effects among various organ systems, between individuals and within the same patient. In a patient with a history of kidney transplant and under immunosuppressant therapy, we observe an unusually high viral load in lung tissue by RNA-ISH, IHC and qRT-PCR. SARS-CoV-2 virus is also detected in this patent's kidney, liver and uterus. We find Conclusions: This study portrays the impact of dispersed SARS-CoV-2 infection in diverse organ systems, thereby facilitating avenues for systematic therapeutic approaches.

Indexed as

Diagnostic markersGene expression analysisRespiratory distress syndromeViral infection

Identifiers

PMID35602214
PMCPMC9053209
OpenAlexW3195659226

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.