Evidence map›Paper›PMID 30416760›Full record

ReviewSignal transduction and targeted therapy

Metabolite sensing and signaling in cell metabolism.

Yi-Ping Wang, Qun-Ying Lei

Open access · goldAbstract readReview
In one paragraph

Review in Signal transduction and targeted therapy. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
108citing papers in PubMed, 1 pooled it
6.9field-weighted citation impact, top 2% 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

108 citing papers in PubMed, 1 synthesis or guideline pooled it, 205 citations in OpenAlex.

  1. Pooled it
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  11. Tissue Immunometabolism in Autoimmunity.European journal of immunology · 2026
    Review
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48 more citing papers are in PubMed but not listed here.

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

2 authors at 1 institution in 1 country.

Yi-Ping WangCancer Institute, Fudan University Cancer Hospital and Cancer Metabolism Laboratory, Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, People's Republic of China.
Qun-Ying LeiCancer Institute, Fudan University Cancer Hospital and Cancer Metabolism Laboratory, Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, People's Republic of China.
Fudan University Shanghai Cancer Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolite sensing is one of the most fundamental biological processes. During evolution, multilayered mechanisms developed to sense fluctuations in a wide spectrum of metabolites, including nutrients, to coordinate cellular metabolism and biological networks. To date, AMPK and mTOR signaling are among the best-understood metabolite-sensing and signaling pathways. Here, we propose a sensor-transducer-effector model to describe known mechanisms of metabolite sensing and signaling. We define a metabolite sensor by its specificity, dynamicity, and functionality. We group the actions of metabolite sensing into three different modes: metabolite sensor-mediated signaling, metabolite-sensing module, and sensing by conjugating. With these modes of action, we provide a systematic view of how cells sense sugars, lipids, amino acids, and metabolic intermediates. In the future perspective, we suggest a systematic screen of metabolite-sensing macromolecules, high-throughput discovery of biomacromolecule-metabolite interactomes, and functional metabolomics to advance our knowledge of metabolite sensing and signaling. Most importantly, targeting metabolite sensing holds great promise in therapeutic intervention of metabolic diseases and in improving healthy aging.

Identifiers

PMID30416760
PMCPMC6224561
OpenAlexW2896863696

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.