Evidence map›Paper›PMID 35309492›Full record

ArticleACS omega2022

Metabolomics Based on Peripheral Blood Mononuclear Cells to Dissect the Mechanisms of Chaigui Granules for Treating Depression.

Dehua Huang, Liwen Wang, Yanfei Wu, Xuemei Qin, Guanhua Du, Yuzhi Zhou

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2022. 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
0.8field-weighted citation impact, top 35% 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.

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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

6 authors at 2 institutions in 1 country.

Dehua HuangModern Research Center for Traditional Chinese Medicine, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University, 92 Wucheng Road, Xiaodian District, Taiyuan 030006, Shanxi, P. R. China.
Liwen WangModern Research Center for Traditional Chinese Medicine, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University, 92 Wucheng Road, Xiaodian District, Taiyuan 030006, Shanxi, P. R. China.
Yanfei WuDepartment of Traditional Chinese Medicine, First Hospital of Shanxi Medical University, Yingze District, Taiyuan 030001, Shanxi, China.
Xuemei QinModern Research Center for Traditional Chinese Medicine, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University, 92 Wucheng Road, Xiaodian District, Taiyuan 030006, Shanxi, P. R. China.ORCID https://orcid.org/0000-0002-0020-3364
Guanhua DuModern Research Center for Traditional Chinese Medicine, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University, 92 Wucheng Road, Xiaodian District, Taiyuan 030006, Shanxi, P. R. China.
Yuzhi ZhouModern Research Center for Traditional Chinese Medicine, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University, 92 Wucheng Road, Xiaodian District, Taiyuan 030006, Shanxi, P. R. China.ORCID https://orcid.org/0000-0002-2034-5626
Shanxi University · CNShanxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chaigui granules were a traditional Chinese medicine (TCM) preparation with antidepressant effects derived from a famous antidepressant prescription. It was of great significance to clarify the antidepressant mechanism of Chaigui granules for the clinical application of this drug. In this study, a chronic unpredictable mild stress (CUMS) depression model was successfully established, and behavioral indicators were used to evaluate the antidepressant effect. Second, the CD4+, CD8+, and CD4+/CD8+ levels were detected in peripheral blood. Meanwhile, the amount of inflammatory cytokines was determined in serum. Correspondingly, LC/MS-based peripheral blood mononuclear cell (PBMC) metabolomics was used to investigate vital metabolic pathways participating in the antidepressive effects of Chaigui granules. Finally, bioinformatics technology was further employed to discover the potential antidepressant mechanism of Chaigui granules regulating the immune system. The results suggested that the administration of Chaigui granules significantly improved CUMS-induced depressive symptoms. Chaigui granules could improve immune function by regulating T lymphocyte subsets, increasing anti-inflammatory cytokine levels of IL-2 and IL-10, and reducing pro-inflammatory cytokine levels of TNF-α, IL-1β, and IL-6. In addition, metabolomics results of PBMCs showed that Chaigui granules improved 14 of the 25 potential biomarkers induced by CUMS. Metabolic pathway analyses indicated that purine metabolism was the critical metabolic pathway regulated by Chaigui granules. Furthermore, correlation analysis indicated that 13 key biomarkers were related to immune-related indicators. The metabolite-gene network of 13 key biomarkers was investigated by using bioinformatics. The investigation showed that 10 targets (5'-nucleotidase ecto; 5'-nucleotidase, cytosolic IB; 5'-nucleotidase, cytosolic II; etc.), mainly belong to the purine metabolism, might be potential targets for Chaigui granules to exert their antidepressant effects by improving immune function impairment. Together, our results suggested that Chaigui granules might exert antidepressant effects by improving immune function and regulating the purine metabolic pathway in PBMCs. This work used PBMCs metabolomics as an entry point to study the antidepressant mechanism of Chaigui granules, which provided a new way to elucidate the mechanism of a traditional Chinese medicine prescription.

Identifiers

PMID35309492
PMCPMC8928523
OpenAlexW4220947982

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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.