Evidence map›Paper›PMID 32514000›Full record

ArticleNature communications2020

Sequential modification of bacterial chemoreceptors is key for achieving both accurate adaptation and high gain.

Bernardo A Mello, Anderson B Beserra, Yuhai Tu

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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  4. Review
  5. 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

3 authors.

Bernardo A MelloIBM T. J. Watson Research Center, Yorktown Heights, New York, NY, 10598, USA.ORCID http://orcid.org/0000-0002-9261-8013
Anderson B BeserraPhysics Institute, University of Brasilia, Brasilia, 70919-970, Brazil.
Yuhai TuIBM T. J. Watson Research Center, Yorktown Heights, New York, NY, 10598, USA. yuhai@us.ibm.com.ORCID http://orcid.org/0000-0002-4589-981X

Funding

Quantitative Modeling of Signal Transduction in Bacterial ChemotaxisR01GM081747 · NIGMS · IBM THOMAS J. WATSON RESEARCH CENTER · PI TU, YUHAI · 2007 to 2018
$2.6M
Molecular Mechanisms and Biochemical Circuits for Adaptation in Biological SystemsR35GM131734 · NIGMS · IBM THOMAS J. WATSON RESEARCH CENTER · PI TU, YUHAI · 2019 to 2024
$1.8M
NIGMS NIH HHS R01 GM081747NIGMS NIH HHS R35 GM131734
6 · The paper itself

Abstract

Many regulatory and signaling proteins have multiple modification sites. In bacterial chemotaxis, each chemoreceptor has multiple methylation sites that are responsible for adaptation. However, whether the ordering of the multisite methylation process affects adaptation remains unclear. Furthermore, the benefit of having multiple modification sites is also unclear. Here, we show that sequentially ordered methylation/demethylation is critical for perfect adaptation; adaptation accuracy decreases as randomness in the multisite methylation process increases. A tradeoff between adaptation accuracy and response gain is discovered. We find that this accuracy-gain tradeoff is lifted significantly by having more methylation sites, but only when the multisite modification process is sequential. Our study suggests that having multiple modification sites and a sequential modification process constitute a general strategy to achieve both accurate adaptation and high response gain simultaneously. Our theory agrees with existing data and predictions are made to help identify the molecular mechanism underlying ordered covalent modifications.

Indexed as

Adaptation, PhysiologicalBacterial ProteinsChemoreceptor CellsDemethylationMethylationModels, BiologicalBacterial Proteins

Identifiers

PMID32514000
PMCPMC7280522

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.