Evidence map›Paper›PMID 30270561›Full record

ArticleBritish journal of pharmacology2019

Hispaglabridin B, a constituent of liquorice identified by a bioinformatics and machine learning approach, relieves protein-energy wasting by inhibiting forkhead box O1.

Zeng-Yan Huang, Ling-Jun Wang, Jia-Jia Wang, Wen-Jun Feng, Zhong-Qi Yang, Shi-Hao Ni, Yu-Sheng Huang, Huan Li, Yi Yang, Ming-Qing Wang and 5 more

Open access · bronzeAbstract read
In one paragraph

Article in British journal of pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.1field-weighted citation impact, top 14% 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

6 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
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  3. Plants (Basel, Switzerland) · 2021
    Review
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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

15 authors at 4 institutions in 2 countries.

Zeng-Yan HuangThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Ling-Jun WangThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Jia-Jia WangThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Wen-Jun FengThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Zhong-Qi YangThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Shi-Hao NiThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yu-Sheng HuangThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Huan LiThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yi YangThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Ming-Qing WangSchool of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China.
Rong HuSchool of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China.
Heng WanDepartment of Endocrinology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Chan-Juan WenDepartment of Radiology, Nan Fang Hospital of Southern Medical University, Guangzhou, China.
Shao-Xiang XianThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Lu LuThe First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.ORCID 0000-0002-9457-0164
Guangzhou University of Chinese Medicine · CNSouthern Medical University · CNFirst Affiliated Hospital of Guangzhou University of Chinese Medicine · CNThird Affiliated Hospital of Southern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeLiquorice is the root of Glycyrrhiza glabra, which is a popular food in Europe and China that has previously shown benefits for skeletal fatigue and nutrient metabolism. However, the mechanism and active ingredients remain largely unclear. The aim of this study was to investigate the active ingredients of liquorice for muscle wasting and elucidate the underlying mechanisms. EXPERIMENTAL APPROACH: RNA-Seq and bioinformatics analysis were applied to predict the main target of liquorice. A machine learning model and a docking tool were used to predict active ingredients. Isotope labelling experiments, immunostaining, Western blots, qRT-PCR, ChIP-PCR and luciferase reporters were utilized to test the pharmacological effects in vitro and in vivo. The reverse effects were verified through recombination-based overexpression. KEY

resultsThe liposoluble constituents of liquorice improved muscle wasting by inhibiting protein catabolism and fibre atrophy. We further identified FoxO1 as the target of liposoluble constituents of liquorice. In addition, hispaglabridin B (HB) was predicted as an inhibitor of FoxO1. Further studies determined that HB improved muscle wasting by inhibiting catabolism in vivo and in vitro. HB also markedly suppressed the transcriptional activity of FoxO1, with decreased expression of the muscle-specific E3 ubiquitin ligases MuRF1 and Atrogin-1. CONCLUSIONS AND IMPLICATIONS: HB can serve as a novel natural food extract for preventing muscle wasting in chronic kidney disease and possibly other catabolic conditions.

Indexed as

Computational BiologyMachine LearningAnimalsBenzopyransDose-Response Relationship, DrugForkhead Box Protein O1GlycyrrhizaMaleMiceMice, Inbred C57BLModels, MolecularMolecular StructureMuscle, SkeletalMuscular AtrophyPlant ExtractsPlant RootsBenzopyransForkhead Box Protein O1Hispaglabridin BPlant Extracts

Identifiers

PMID30270561
PMCPMC6295407
OpenAlexW2895511614

What OpenQuestion holds

Textmetadata
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.