Regional 4D Cardiac Magnetic Resonance Strain Predicts Cardiomyopathy Progression in Duchenne Muscular Dystrophy.

Background: Cardiomyopathy (CMP) is the leading cause of death in Duchenne muscular dystrophy (DMD). Characterization of disease trajectory can be challenging, especially in the early stage of CMP where onset and clinical progression may vary. Traditional metrics from cardiovascular magnetic resonance (CMR) imaging such as LVEF (left ventricular ejection fraction) and LGE (late gadolinium enhancement) are often insufficient for assessing disease trajectory. We hypothesized that strain patterns from a novel 4D (3D+time) CMR regional strain analysis method can be used to predict the rate of DMD CMP progression. Methods: We compiled 115 short-axis cine CMR image stacks for n=40 pediatric DMD patients (13.6±4.2 years) imaged yearly for 3 consecutive visits and computed regional strain metrics using custom-built feature tracking software. We measured regional strain parameters by determining the relative change in the localized 4D endocardial surface mesh using end diastole as the initial reference frame. Results: We first separated patients into two cohorts based on their initial CMR: LVEF≥55% (n=28, normal cohort) and LVEF<55% (n=12, abnormal cohort). Using LVEF decrease measured two years following the initial scan, we further subclassified these cohorts into slow (ΔLVEF%≤5) or fast (ΔLVEF%>5) progression groups for both the normal cohort (n=12, slow; n=15, fast) and the abnormal cohort (n=8, slow; n=4, fast). There was no statistical difference between the slow and fast progression groups in standard biomarkers such as LVEF, age, or LGE status. However, basal circumferential strain (Ecc) late diastolic strain rate and basal surface area strain (Ea) late diastolic strain rate magnitude were significantly decreased in fast progressors in both normal and abnormal cohorts (p<0.01, p=0.04 and p<0.01, p=0.02, respectively). Peak Ea and Ecc magnitudes were also decreased in fast progressors, though these only reached statistical significance in the normal cohort (p<0.01, p=0.24 and p<0.01, p=0.18, respectively). Conclusion: Regional strain metrics from 4D CMR can be used to differentiate between slow or fast CMP progression in a longitudinal DMD cohort. These results demonstrate that 4D CMR strain is useful for early identification of CMP progression in patients with DMD. Clinical Perspective: Cardiomyopathy is the number one cause of death in Duchenne muscular dystrophy, but the onset and progression of the disease are variable and heterogeneous. In this study, we used a novel 4D cardiovascular magnetic resonance regional strain analysis method to evaluate 40 pediatric Duchenne patients over three consecutive annual visits. From our analysis, we found that peak systolic strain and late diastolic strain rate were early indicators of cardiomyopathy progression. This method offers promise for early detection and monitoring, potentially improving patient outcomes through timely intervention and management.

Isolation of Mitochondrial Lipids and Mass Spectrometric Analysis.

Mitochondria participate in many important metabolic processes in the body. The lipid profile of mitochondria is especially important in membrane regulation and pathway signaling. The isolation and study of these lipids can provide unparalleled information about the mechanisms behind these cellular processes. In this chapter, we describe a protocol to isolate mitochondrial lipids from homogenized murine optic nerves. The lipid extraction was performed using butanol-methanol (BUME) and subsequently analyzed using liquid chromatography-mass spectrometry. Further analysis of the raw data was conducted using LipidSearch™ and MetaboAnalyst 4.0.

Analysis of Brain Metabolites Using Two Complementary Ultrahigh-Performance Liquid Chromatography-Mass Spectrometry Methods.

Metabolomics continues to progress, but obstacles remain. The preservation of metabolites in the target tissue and gathering information on the current metabolic state of the organism of interest proves challenging. Robustness, reproducibility, and reliable quantification are necessary for confident metabolite identification and should always be considered for effective biomarker discovery. Recent advancements in analytical platforms, techniques, and data analysis make metabolomics a promising omics for significant research. However, there is no single approach to effectively capturing the metabolome. Coupling separation techniques may improve the power of the analysis and facilitate confident metabolite identification, especially when performing untargeted metabolomics. In this chapter, we will present an untargeted metabolomic analysis of brain tissue from C57BL/6 mice using two UHPLC-MS methods based on reversed-phase and HILIC chromatography.

Metabolomics dataset of mouse optogenetic axon regeneration after optic nerve crush.

This metabolite dataset was collected from transgenic murine retinal ganglion cells (RGC) expressing bacterial channelrhodopsin labeled with fluorescent protein. Mice were subjected to optic nerve crush (ONC) with subsequent RGC stimulation of channelrhodopsin with blue light (promoting regeneration) or non-stimulation (control). ONC induces retinal ganglion cell degeneration over time with progressive loss of axons. In transgenic bacterial channelrhodopsin expressing RGC cells, light stimulation promotes regeneration of ONC axons. Genetically matched wild-type uninjured optic nerves were analyzed as controls for comparison. Metabolites were carefully extracted from finely minced optic nerve tissue using a solvent system (initial separation using 1:1 methanol and H2O and second extraction using 8:1:1 of acetonitrile:acetone:methanol). Untargeted liquid chromatography-mass spectrometry profiling was performed using fractionation on a Vanquish Horizon Binary UHPLC. Subsequent analyses were performed on an inline coupled Q-Exactive Orbitrap instrument. Metabolites were identified using Compound DiscovererTM software. Statistical analysis was performed using MetaboAnalyst 5.0. This data is available on Metabolomics Workbench, Study ID ST002111.