Dataset Information

Ventricular arrhythmias in mouse models of diabetic kidney disease.

ABSTRACT: Chronic kidney disease (CKD) affects more than 20 million people in the US, and it is associated with a significantly increased risk of sudden cardiac death (SCD). Despite the significance, the mechanistic relationship between SCD and CKD is not clear and there are few effective therapies. Using optical mapping techniques, we tested the hypothesis that mouse models of progressive diabetic kidney disease (DKD) exhibit enhanced ventricular arrhythmia incidence and underlying arrhythmia substrates. Compared to wild-type mice, both Lepr^db/db eNOS^-/- (2KO) and high fat diet plus low dose streptozotocin (HFD + STZ) mouse models of DKD experienced sudden death and greater arrhythmia inducibility, which was more common with isoproterenol than programmed electrical stimulation. 2KO mice demonstrated slowed conduction velocity, prolonged action potential duration (APD), and myocardial fibrosis; both 2KO and HFD + STZ mice exhibited arrhythmias and calcium dysregulation with isoproterenol challenge. Finally, circulating concentrations of the uremic toxin asymmetric dimethylarginine (ADMA) were elevated in 2KO mice. Incubation of human cardiac myocytes with ADMA prolonged APD, as also observed in 2KO mice hearts ex vivo. The present study elucidates an arrhythmia-associated mechanism of sudden death associated with DKD, which may lead to more effective treatments in the vulnerable DKD patient population.

SUBMITTER: Laurita KR

PROVIDER: S-EPMC8523538 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Ventricular arrhythmias in mouse models of diabetic kidney disease.

Laurita Kenneth R KR Khan Shenaz S McMahon Tracy T Dennis Adrienne T AT Li Vincent V Gaivin Robert R Sapa Hima H Fu Ji-Dong JD Schelling Jeffrey R JR

Scientific reports 20211018 1

Chronic kidney disease (CKD) affects more than 20 million people in the US, and it is associated with a significantly increased risk of sudden cardiac death (SCD). Despite the significance, the mechanistic relationship between SCD and CKD is not clear and there are few effective therapies. Using optical mapping techniques, we tested the hypothesis that mouse models of progressive diabetic kidney disease (DKD) exhibit enhanced ventricular arrhythmia incidence and underlying arrhythmia substrates. ...[more]

PMID: 34663875

Similar Datasets

Project description:BackgroundDiabetic nephropathy (DN) is a late complication in both type 1 diabetes mellitus (T1DM) and T2DM. Already at an early stage of DN morphological changes occur at the cell surface and in the extracellular matrix where the majority of the proteins carry N-linked glycosylations. These glycosylated proteins are highly important in cell adhesion and cell-matrix processes but not much is known about how they change in DN or whether the distinct etiology of T1DM and T2DM could have an effect on their abundances.MethodWe enriched for the N-glycosylated kidney proteome in db/db mice dosed with insulin or vehicle, in streptozotocin-induced (STZ) diabetic mice and healthy control mice dosed with vehicle. Glycopeptides were analyzed with label-free shotgun mass spectrometry and differential protein abundances identified in both mouse models were compared using multivariate analyses.ResultsThe majority of the N-glycosylated proteins were similarly regulated in both mouse models. However, distinct differences between the two mouse models were for example seen for integrin-β1, a protein expressed mainly in the glomeruli which abundance was increased in the STZ diabetic mice while decreased in the db/db mice and for the sodium/glucose cotransporter-1, mainly expressed in the proximal tubules which abundance was increased in the db/db mice but decreased in the STZ diabetic mice. Insulin had an effect on the level of both glomerular and tubular proteins in the db/db mice. It decreased the abundance of G-protein coupled receptor-116 and of tyrosine-protein phosphatase non-receptor type substrate-1 away from the level in the healthy control mice.ConclusionsOur finding of differences in the N-glycosylation protein profiles in the db/db and STZ mouse models suggest that the etiology of DN could give rise to variations in the cell adhesion and cell-matrix composition in T1DM and T2DM. Thus, N-glycosylated protein differences could be a clue to dissimilarities in T1DM and T2DM at later stages of DN. Furthermore, we observed insulin specific regulation of N-glycosylated proteins both in the direction of and away from the abundances in healthy control mice.

Project description:In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.

Dataset Information

Ventricular arrhythmias in mouse models of diabetic kidney disease.

Publications

Ventricular arrhythmias in mouse models of diabetic kidney disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets