Dataset Information

Inhibition of MyD88 by a novel inhibitor reverses two-thirds of the infarct area in myocardial ischemia and reperfusion injury.

ABSTRACT: Cardiomyocytes, macrophages, and fibroblasts play important roles in inflammation and repair during myocardial ischemia/reperfusion injury (MIRI). Myeloid differentiation primary response 88 (MyD88) is upregulated in immunocytes, cardiomyocytes, and fibroblasts during MIRI. MyD88 induces the secretion of proinflammatory cytokines, including interleukin (IL)-1β, IL-6, and tumor necrosis factor alpha (TNF-α), while fibroblasts are recruited and activated to mediate cardiac remodeling. The aim of this study was to assess the anti-MIRI effect and mode of action of the novel MyD88 inhibitor TJ-M2010-5. We synthesized TJ-M2010-5 and identified its target by co-immunoprecipitation, after which we established a murine MIRI model and tested the protective effect of TJ-M2010-5 by immunohistochemistry, flow cytometry, real-time PCR, and western blotting. Neonatal rat cardiomyocytes subjected to anoxia/reoxygenation were also isolated and their supernatants used to stimulate cardiac macrophagocytes and fibroblasts in vitro. MyD88 was found upregulated during the early and late phases after MIRI. The MyD88 inhibitor considerably improved cardiac function, reduced cardiomyocyte apoptosis, reduced IL-1β, IL-6, and TNF-α secretion, and inhibited CD80+CD86+MHCII+ macrophage and fibroblast migration. Moreover, TJ-M2010-5 markedly inhibited Toll-like receptor/MyD88 signaling in vivo and in vitro. Thus, our findings highlight TJ-M2010-5 as a potential therapeutic agent for MIRI treatment.

SUBMITTER: Miao Y

PROVIDER: S-EPMC7540094 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Inhibition of MyD88 by a novel inhibitor reverses two-thirds of the infarct area in myocardial ischemia and reperfusion injury.

Miao Yan Y Ding Zuochuan Z Zou Zhimiao Z Yang Yang Y Yang Min M Zhang Xiaoqian X Li Zeyang Z Zhou Liang L Zhang Limin L Zhang Xue X Du Dunfeng D Jiang Fengchao F Zhou Ping P

American journal of translational research 20200915 9

Cardiomyocytes, macrophages, and fibroblasts play important roles in inflammation and repair during myocardial ischemia/reperfusion injury (MIRI). Myeloid differentiation primary response 88 (MyD88) is upregulated in immunocytes, cardiomyocytes, and fibroblasts during MIRI. MyD88 induces the secretion of proinflammatory cytokines, including interleukin (IL)-1β, IL-6, and tumor necrosis factor alpha (TNF-α), while fibroblasts are recruited and activated to mediate cardiac remodeling. The aim of t ...[more]

PMID: 33042411

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Project description:Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on cardioprotection. The gold standard for IS quantification in ischemia/reperfusion (I/R) experiments is triphenyl tetrazolium chloride (TTC) staining, typically done manually. This study aimed to determine if automation through deep learning segmentation is a time-saving and valid alternative to standard IS quantification. High-resolution images from TTC-stained, macroscopic heart slices were retrospectively collected from pig experiments (n = 390) with I/R without/with cardioprotection to cover a wide IS range. Existing IS data from pig experiments, quantified using a standard method of manual and subsequent digital labeling of film-scan annotations, were used as reference. To automate the evaluation process with the aim to be more objective and save time, a deep learning pipeline was implemented; the collected images (n = 3869) were pre-processed by cropping and labeled (image annotations). To ensure their usability as training data for a deep learning segmentation model, IS was quantified from image annotations and compared to IS quantified using the existing film-scan annotations. A supervised deep learning segmentation model based on dynamic U-Net architecture was developed and trained. The evaluation of the trained model was performed by fivefold cross-validation (n = 220 experiments) and testing on an independent test set (n = 170 experiments). Performance metrics (Dice similarity coefficient [DSC], pixel accuracy [ACC], average precision [mAP]) were calculated. IS was then quantified from predictions and compared to IS quantified from image annotations (linear regression, Pearson's r; analysis of covariance; Bland-Altman plots). Performance metrics near 1 indicated a strong model performance on cross-validated data (DSC: 0.90, ACC: 0.98, mAP: 0.90) and on the test set data (DSC: 0.89, ACC: 0.98, mAP: 0.93). IS quantified from predictions correlated well with IS quantified from image annotations in all data sets (cross-validation: r = 0.98; test data set: r = 0.95) and analysis of covariance identified no significant differences. The model reduced the IS quantification time per experiment from approximately 90 min to 20 s. The model was further tested on a preliminary test set from experiments in isolated, saline-perfused rat hearts with regional I/R without/with cardioprotection (n = 27). There was also no significant difference in IS between image annotations and predictions, but the performance on the test set data from rat hearts was lower (DSC: 0.66, ACC: 0.91, mAP: 0.65). IS quantification using a deep learning segmentation model is a valid and time-efficient alternative to manual and subsequent digital labeling.

Dataset Information

Inhibition of MyD88 by a novel inhibitor reverses two-thirds of the infarct area in myocardial ischemia and reperfusion injury.

Publications

Inhibition of MyD88 by a novel inhibitor reverses two-thirds of the infarct area in myocardial ischemia and reperfusion injury.

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