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      Duzhong Fang Attenuates the POMC-Derived Neuroinflammation in Parkinsonian Mice

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          Abstract

          Background

          Neuroinflammation and microglia reactivity are now recognized to be features of Parkinson’s disease (PD). Thus, microglia phenotype is a potential new target for developing treatments against PD. Duzhong Fang (DZF) is a traditional Chinese medicine (TCM) prescription. The theory of TCM argues that Duzhong Fang, nourishing yin and tonifying yang, may treat PD. However, its modern pharmacological studies and the underlying mechanisms are unclear.

          Methods

          First, MPTP was used to establish a parkinsonian mouse model, and behavioral testing was used to evaluate the locomotor dysfunction. Then, HPLC, immunohistochemical staining, and Western blot assays were performed to evaluate the survival of dopaminergic neurons. Molecular biological and immunofluorescence staining were used to evaluate the neuroinflammation and microglial activation. In addition, RNA-seq transcriptomics was used to analyze differentially expressed genes and verify by RT-PCR.

          Results

          In the present study, we first confirmed that DZF can alleviate neuroinflammation and ameliorate dyskinesia in parkinsonian mice. Then, further studies found that DZF can regulate microglial morphology and reactivity and act on the POMC gene. POMC is an upstream target for regulating inflammation and proinflammatory cytokines, and DZF can directly inhibit the POMC level and restore the homeostatic signature of microglia in parkinsonian mice.

          Conclusion

          This study found that POMC may have a potential role as a therapeutic target for PD. DZF may inhibit neuroinflammation and play an anti-PD effect by down-regulating the expression of POMC.

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          Most cited references72

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

            Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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              Epidemiology of Parkinson's disease.

              Parkinson's disease (PD) affects 1-2 per 1000 of the population at any time. PD prevalence is increasing with age and PD affects 1% of the population above 60 years. The main neuropathological finding is α-synuclein-containing Lewy bodies and loss of dopaminergic neurons in the substantia nigra, manifesting as reduced facilitation of voluntary movements. With progression of PD, Lewy body pathology spreads to neocortical and cortical regions. PD is regarded as a movement disorder with three cardinal signs: tremor, rigidity and bradykinesia. A recent revision of the diagnostic criteria excludes postural instability as a fourth hallmark and defines supportive criteria, absolute exclusion criteria and red flags. Non-motor symptoms in PD have gained increasing attention and both motor and non-motor signs are now included among the supportive criteria. The cause of PD is unknown in most cases. Genetic risk factors have been identified, including monogenetic causes that are rare in unselected populations. Some genetic factor can be identified in 5-10% of the patients. Several environmental factors are associated with increased risk of PD. Autopsy studies show that the clinical diagnosis of PD is not confirmed at autopsy in a significant proportion of patients. Revised diagnostic criteria are expected to improve the clinician´s accuracy in diagnosing PD. Increasing knowledge on genetic and environmental risk factors of PD will probably elucidate the cause of this disease within the near future.
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                Author and article information

                Journal
                J Inflamm Res
                J Inflamm Res
                jir
                jinres
                Journal of Inflammation Research
                Dove
                1178-7031
                23 July 2021
                2021
                : 14
                : 3261-3276
                Affiliations
                [1 ]State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine , Tianjin, 301617, People’s Republic of China
                [2 ]School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine , Tianjin, 301617, People’s Republic of China
                [3 ]College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine , Tianjin, 301617, People’s Republic of China
                Author notes
                Correspondence: Hong Guo; Yanjun Zhang Tianjin University of Traditional Chinese Medicine , Tianjin, 301617, People’s Republic of China Email cacti1983@163.com; zyjsunye@163.com
                [*]

                These authors contributed equally to this work

                Article
                316314
                10.2147/JIR.S316314
                8315774
                34326654
                876fbb42-6992-4eb1-bac3-36dd9ca24b2d
                © 2021 Li et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 17 April 2021
                : 01 July 2021
                Page count
                Figures: 8, Tables: 2, References: 72, Pages: 16
                Categories
                Original Research

                Immunology
                parkinson’s disease,microglia,inflammation,dzf,pomc
                Immunology
                parkinson’s disease, microglia, inflammation, dzf, pomc

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