Evidence map›Paper›PMID 34952917›Full record

ArticleScientific reports2021

Elimination of negative feedback in TLR signalling allows rapid and hypersensitive detection of microbial contaminants.

Clovis H T Seumen, Urte Tomasiunaite, Daniel F Legler, Christof R Hauck

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 52% 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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

4 authors at 2 institutions in 2 countries.

Clovis H T SeumenLehrstuhl für Zellbiologie, Fachbereich Biologie, Maildrop 621, Universität Konstanz, Universitätsstrasse 10, 78457, Konstanz, Germany.
Urte TomasiunaiteLehrstuhl für Zellbiologie, Fachbereich Biologie, Maildrop 621, Universität Konstanz, Universitätsstrasse 10, 78457, Konstanz, Germany.
Daniel F LeglerKooperatives Promotionskolleg "InViTe", Universität Konstanz, 78457, Konstanz, Germany.
Christof R HauckLehrstuhl für Zellbiologie, Fachbereich Biologie, Maildrop 621, Universität Konstanz, Universitätsstrasse 10, 78457, Konstanz, Germany. christof.hauck@uni-konstanz.de.
University of Konstanz · DEBiotechnology Institute Thurgau · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The exquisite specificity of Toll-like receptors (TLRs) to sense microbial molecular signatures is used as a powerful tool to pinpoint microbial contaminants. Various cellular systems, from native human blood cells to transfected cell lines exploit TLRs as pyrogen detectors in biological preparations. However, slow cellular responses and limited sensitivity have hampered the replacement of animal-based tests such as the rabbit pyrogen test or lipopolysaccharide detection by Limulus amoebocyte lysate. Here, we report a novel human cell-based approach to boost detection of microbial contaminants by TLR-expressing cells. By genetic and pharmacologic elimination of negative control circuits, TLR-initiated cellular responses to bacterial molecular patterns were accelerated and significantly elevated. Combining depletion of protein phosphatase PP2ACA and pharmacological inhibition of PP1 in the optimized reporter cells further enhanced the sensitivity to allow detection of bacterial lipoprotein at 30 picogram/ml. Such next-generation cellular monitoring is poised to replace animal-based testing for microbial contaminants.

Indexed as

EndotoxinsHumansProtein BindingTHP-1 CellsToll-Like ReceptorsEndotoxinsToll-Like Receptors

Identifiers

PMID34952917
PMCPMC8709846
OpenAlexW4200155227

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.