Evidence map›Paper›PMID 31901116›Full record

ArticleFrontiers of medicine2020

High-throughput metabolomics reveals the perturbed metabolic pathways and biomarkers of Yang Huang syndrome as potential targets for evaluating the therapeutic effects and mechanism of geniposide.

Heng Fang, Aihua Zhang, Xiaohang Zhou, Jingbo Yu, Qi Song, Xijun Wang

Abstract read
PubMed Publisher
In one paragraph

Article in Frontiers of medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.0field-weighted citation impact, top 26% 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

7 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. 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

6 authors at 1 institution in 1 country.

Heng FangSino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, National Chinmedomics Research Center, Harbin, 150040, China.
Aihua ZhangSino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, National Chinmedomics Research Center, Harbin, 150040, China.
Xiaohang ZhouSino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, National Chinmedomics Research Center, Harbin, 150040, China.
Jingbo YuSino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, National Chinmedomics Research Center, Harbin, 150040, China.
Qi SongSino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, National Chinmedomics Research Center, Harbin, 150040, China.
Xijun WangSino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, National Chinmedomics Research Center, Harbin, 150040, China. xijunwangls@126.com.
Heilongjiang University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput metabolomics can clarify the underlying molecular mechanism of diseases via the qualitative and quantitative analysis of metabolites. This study used the established Yang Huang syndrome (YHS) mouse model to evaluate the efficacy of geniposide (GEN). Urine metabolic data were quantified by ultraperformance liquid chromatography-tandem mass spectrometry. The non-target screening of the massive biological information dataset was performed, and a total of 33 metabolites, including tyramine glucuronide, aurine, and L-cysteine, were identified relating to YHS. These differential metabolites directly participated in the disturbance of phase I reaction and hydrophilic transformation of bilirubin. Interestingly, they were completely reversed by GEN. While, as the auxiliary technical means, we also focused on the molecular prediction and docking results in network pharmacological and integrated analysis part. We used integrated analysis to communicate the multiple results of metabolomics and network pharmacology. This study is the first to report that GEN indirectly regulates the metabolite "tyramine glucuronide" through its direct effect on the target heme oxygenase 1 in vivo. Meanwhile, heme oxygenase-1, a prediction of network pharmacology, was the confirmed metabolic enzyme of phase I reaction in hepatocytes. Our study indicated that the combination of high-throughput metabolomics and network pharmacology is a robust combination for deciphering the pathogenesis of the traditional Chinese medicine (TCM) syndrome.

Indexed as

Drugs, Chinese HerbalMetabolomicsAnimalsBiomarkersChromatography, High Pressure LiquidIridoidsMetabolic Networks and PathwaysMiceBiomarkersDrugs, Chinese HerbalgeniposideIridoidsliquid chromatography-mass spectrometrymetabolic pathwaysmetabolitesmetabolomics

Identifiers

PMID31901116
OpenAlexW2998300154

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.