Evidence map›Paper›PMID 33083801›Full record

ArticlebioRxiv : the preprint server for biology2021

Reconstructed signaling and regulatory networks identify potential drugs for SARS-CoV-2 infection.

Jun Ding, Jose Lugo-Martinez, Ye Yuan, Jessie Huang, Adam J Hume, Ellen L Suder, Elke Mühlberger, Darrell N Kotton, Ziv Bar-Joseph

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

9 authors.

Jun DingMeakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, Quebec, H4A 3J1, Canada.
Jose Lugo-MartinezDepartment of Computer Science, University of Puerto Rico, San Juan, Puerto Rico, 00925, USA.
Ye YuanInstitute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
Jessie HuangCenter for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA.
Adam J HumeCenter for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA.
Ellen L SuderCenter for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA.
Elke MühlbergerNational Emerging Infectious Diseases Laboratory (NEIDL), Boston University, Boston, MA 02118, USA.
Darrell N KottonCenter for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA.
Ziv Bar-JosephComputational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA.

Funding

Regulatory Compliance CoreUC7AI095321 · NIAID · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Kevin Michael Tuohey · 2014 to 2026
$159.3M
Project-005UL1TR001430 · NCATS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI BAIR-MERRITT, MEGAN H, CENTER, DAVID M. · 2015 to 2024
$52.3M
BIOLOGY OF THE LUNG--MULTIDISCIPLINARY PROGRAMT32HL007035 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Darrell N. Kotton, JOSEPH P MIZGERD · 1985 to 2026
$24.3M
A National iPS Cell Network with Deep Phenotyping for Translational ResearchU01TR001810 · NCATS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI GILAD, YOAV, KOTTON, DARRELL N. · 2016 to 2020
$4.4M
NATIONAL BIOREPOSITORY OF LUNG DISEASE-SPECIFIC HUMAN INDUCED PLURIPOTENT STEM CELLS:THE PURPOSE OF THIS RESOURCE IS TO BUILD AND SUPPORT A CRITICAL75N92020C00005 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI N/A, N/A · 2021 to 2024
$2.6M
Reconstructing regulatory networks from time series single cell dataR01GM122096 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI BAR-JOSEPH, ZIV · 2017 to 2020
$1.4M
NCATS NIH HHS U01 TR001810NCATS NIH HHS UL1 TR001430NHLBI NIH HHS 75N92020C00005NHLBI NIH HHS T32 HL007035NIAID NIH HHS UC7 AI095321NIGMS NIH HHS R01 GM122096
6 · The paper itself

Abstract

Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of sequence, functional and interaction data to reconstruct networks and pathways activated by the virus in host cells. We identify key proteins in these networks and further intersect them with genes differentially expressed at conditions that are known to impact viral activity. Several of the top ranked genes do not directly interact with virus proteins. We experimentally tested treatments for a number of the predicted targets. We show that blocking one of the predicted indirect targets significantly reduces viral loads in stem cell-derived alveolar epithelial type II cells (iAT2s).

Indexed as

COVID-19data integrationdynamic networks

Identifiers

PMID33083801
PMCPMC7574259

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.