Comparison of efficacy of SHENQI compound and rosiglitazone in the treatment of diabetic vasculopathy analyzing multi-factor mediated disease-causing modules

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Abstract

Atherosclerosis-predominant vasculopathy is a common complication of diabetes with high morbidity and high mortality, which is ruining the patient's daily life. As is known to all, traditional Chinese medicine (TCM) SHENQI compound and western medicine rosiglitazone play an important role in the treatment of diabetes. In particular, SHENQI compound has a significant inhibitory effect on vascular lesions. Here, to explore and compare the therapeutic mechanism of SHENQI compound and rosiglitazone on diabetic vasculopathy, we first built 7 groups of mouse models. The behavioral, physiological and pathological morphological characteristics of these mice showed that SHENQI compound has a more comprehensive curative effect than rosiglitazone and has a stronger inhibitory effect on vascular lesions. While rosiglitazone has a more effective but no significant effect on hypoglycemic. Further, based on the gene expression of mice in each group, we performed differential expression analysis. The functional enrichment analysis of these differentially expressed genes (DEGs) revealed the potential pathogenesis and treatment mechanisms of diabetic angiopathy. In addition, we found that SHENQI compound mainly exerts comprehensive effects by regulating MCM8, IRF7, CDK7, NEDD4L by pivot regulator analysis, while rosiglitazone can rapidly lower blood glucose levels by targeting PSMD3, UBA52. Except that, we also identified some pivot TFs and ncRNAs for these potential disease-causing DEG modules, which may the mediators bridging drugs and modules. Finally, similar to pivot regulator analysis, we also identified the regulation of some drugs (e.g. bumetanide, disopyramide and glyburide etc.) which have been shown to have a certain effect on diabetes or diabetic angiopathy, proofing the scientific and objectivity of this study. Overall, this study not only provides an in-depth comparison of the efficacy of SHENQI compound and rosiglitazone in the treatment of diabetic vasculopathy, but also provides clinicians and drug designers with valuable theoretical guidance.

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Most cited references 46

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Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

Gordon K. Smyth (2004)

The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.

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miR-200a Prevents Renal Fibrogenesis Through Repression of TGF-β2 Expression

Bo Wang, Philip Koh, Catherine E. Winbanks … (2010)

OBJECTIVE Progressive fibrosis in the diabetic kidney is driven and sustained by a diverse range of profibrotic factors. This study examines the critical role of microRNAs (miRNAs) in the regulation of the key fibrotic mediators, TGF-β1 and TGF-β2. RESEARCH DESIGN AND METHODS Rat proximal-tubular epithelial cells (NRK52E) were treated with TGF-β1 and TGF-β2 for 3 days, and expression of markers of epithelial-to-mesenchymal transition (EMT) and fibrogenesis were assessed by RT-PCR and Western blotting. The expression of miR-141 and miR-200a was also assessed, as was their role as translational repressors of TGF-β signaling. Finally, these pathways were explored in two different mouse models, representing early and advanced diabetic nephropathy. RESULTS Both TGF-β1 and TGF-β2 induced EMT and fibrogenesis in NRK52E cells. TGF-β1 and TGF-β2 also downregulated expression of miR-200a. The importance of these changes was demonstrated by the finding that ectopic expression miR-200a downregulated smad-3 activity and the expression of matrix proteins and prevented TGF-β–dependent EMT. miR-200a also downregulated the expression of TGF-β2, via direct interaction with the 3′ untranslated region of TGF-β2. The renal expression of miR-141 and miR-200a was also reduced in mouse models representing early and advanced kidney disease. CONCLUSIONS miR-200a and miR-141 significantly impact on the development and progression of TGF-β–dependent EMT and fibrosis in vitro and in vivo. These miRNAs appear to be intricately involved in fibrogenesis, both as downstream mediators of TGF-β signaling and as components of feedback regulation, and as such represent important new targets for the prevention of progressive kidney disease in the context of diabetes.

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RAID v2.0: an updated resource of RNA-associated interactions across organisms

Qi-ying Yi, Yue Zhao, Chunhua Li … (2016)

With the development of biotechnologies and computational prediction algorithms, the number of experimental and computational prediction RNA-associated interactions has grown rapidly in recent years. However, diverse RNA-associated interactions are scattered over a wide variety of resources and organisms, whereas a fully comprehensive view of diverse RNA-associated interactions is still not available for any species. Hence, we have updated the RAID database to version 2.0 (RAID v2.0, www.rna-society.org/raid/) by integrating experimental and computational prediction interactions from manually reading literature and other database resources under one common framework. The new developments in RAID v2.0 include (i) over 850-fold RNA-associated interactions, an enhancement compared to the previous version; (ii) numerous resources integrated with experimental or computational prediction evidence for each RNA-associated interaction; (iii) a reliability assessment for each RNA-associated interaction based on an integrative confidence score; and (iv) an increase of species coverage to 60. Consequently, RAID v2.0 recruits more than 5.27 million RNA-associated interactions, including more than 4 million RNA–RNA interactions and more than 1.2 million RNA–protein interactions, referring to nearly 130 000 RNA/protein symbols across 60 species.

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Author and article information

Contributors

Hong Gao:

ORCID: http://orcid.org/0000-0002-3050-6462

Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Software

Yuhong Duan:

ORCID: http://orcid.org/0000-0002-3796-4116

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draft

Xiaoxu Fu:

ORCID: http://orcid.org/0000-0003-1089-8013

Role: Formal analysisRole: SoftwareRole: Writing – review & editing

Hongyan Xie:

ORCID: http://orcid.org/0000-0002-0153-9986

Role: ConceptualizationRole: VisualizationRole: Writing – review & editing

Ya Liu:

ORCID: http://orcid.org/0000-0002-1949-8430

Role: Formal analysisRole: VisualizationRole: Writing – review & editing

Haipo Yuan:

ORCID: http://orcid.org/0000-0001-7183-3139

Role: ValidationRole: Writing – original draftRole: Writing – review & editing

Mingyang Zhou:

ORCID: http://orcid.org/0000-0001-8002-1677

Role: Formal analysisRole: Writing – review & editing

Chunguang Xie:

ORCID: http://orcid.org/0000-0002-1038-9545

Role: ConceptualizationRole: Project administration

Dong Wang: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 6 December 2018

Publication date Collection: 2018

Volume: 13

Issue: 12

Electronic Location Identifier: e0207683

Affiliations

[1 ] Teaching Hospital, School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China

[2 ] Department Two of Endocrinology, Teaching Hospital, Shaanxi University of Traditional Chinese Medicine, Xianyang, China

Harbin Medical University, CHINA

Author notes

Competing Interests: The authors have declared that no competing interests exist.

‡ These authors are first authors on this work.

* E-mail: xcg899@ 123456163.com .

Author information

Hong Gao http://orcid.org/0000-0002-3050-6462

Yuhong Duan http://orcid.org/0000-0002-3796-4116

Xiaoxu Fu http://orcid.org/0000-0003-1089-8013

Hongyan Xie http://orcid.org/0000-0002-0153-9986

Ya Liu http://orcid.org/0000-0002-1949-8430

Haipo Yuan http://orcid.org/0000-0001-7183-3139

Mingyang Zhou http://orcid.org/0000-0001-8002-1677

Chunguang Xie http://orcid.org/0000-0002-1038-9545

Article

Publisher ID: PONE-D-18-22736

DOI: 10.1371/journal.pone.0207683

PMC ID: 6283585

PubMed ID: 30521536

SO-VID: b5e4bf60-6f52-4297-a86d-289dac8bc992

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 14 August 2018

Date accepted : 5 November 2018

Page count

Figures: 7, Tables: 1, Pages: 18

Funding

Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 81102589

Award Recipient :

ORCID: http://orcid.org/0000-0002-3050-6462

Hong Gao

This work was supported by National Natural Science Foundation of China ( http://www.nsfc.gov.cn/) (81102589 to H.G.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Comparison of efficacy of SHENQI compound and rosiglitazone in the treatment of diabetic vasculopathy analyzing multi-factor mediated disease-causing modules

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Most cited references 46

Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

miR-200a Prevents Renal Fibrogenesis Through Repression of TGF-β2 Expression

RAID v2.0: an updated resource of RNA-associated interactions across organisms

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