38 research outputs found
The SEQC2 epigenomics quality control (EpiQC) study
BACKGROUND: Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group. RESULTS: Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. CONCLUSIONS: The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research
Electrophysiology of Heart Failure Using a Rabbit Model: From the Failing Myocyte to Ventricular Fibrillation.
Heart failure is a leading cause of death, yet its underlying electrophysiological (EP) mechanisms are not well understood. In this study, we use a multiscale approach to analyze a model of heart failure and connect its results to features of the electrocardiogram (ECG). The heart failure model is derived by modifying a previously validated electrophysiology model for a healthy rabbit heart. Specifically, in accordance with the heart failure literature, we modified the cell EP by changing both membrane currents and calcium handling. At the tissue level, we modeled the increased gap junction lateralization and lower conduction velocity due to downregulation of Connexin 43. At the biventricular level, we reduced the apex-to-base and transmural gradients of action potential duration (APD). The failing cell model was first validated by reproducing the longer action potential, slower and lower calcium transient, and earlier alternans characteristic of heart failure EP. Subsequently, we compared the electrical wave propagation in one dimensional cables of healthy and failing cells. The validated cell model was then used to simulate the EP of heart failure in an anatomically accurate biventricular rabbit model. As pacing cycle length decreases, both the normal and failing heart develop T-wave alternans, but only the failing heart shows QRS alternans (although moderate) at rapid pacing. Moreover, T-wave alternans is significantly more pronounced in the failing heart. At rapid pacing, APD maps show areas of conduction block in the failing heart. Finally, accelerated pacing initiated wave reentry and breakup in the failing heart. Further, the onset of VF was not observed with an upregulation of SERCA, a potential drug therapy, using the same protocol. The changes introduced at the cell and tissue level have increased the failing heart's susceptibility to dynamic instabilities and arrhythmias under rapid pacing. However, the observed increase in arrhythmogenic potential is not due to a steepening of the restitution curve (not present in our model), but rather to a novel blocking mechanism
From cells to ventricles: understanding the mechanisms of ventricular fibrillation through multiscale modeling
by Shankarjee Krishnamoorthi et al.
Transmural cable simulations.
<p>At PCL = 400ms, the APD gradient is apparent, especially in the normal cell cable. At PCL = 250ms a slight concordant alternans is visible in the normal cell cable, whereas discordant alternans is visible in the failing cell cable. At PCL = 200ms, the normal cell cable shows discordant alternans whereas the failing cell cable presents complete conduction block.</p
Membrane current changes from normal to failing cell model.
<p>Membrane current changes from normal to failing cell model.</p
Normal (left) and failing (right) transmural and apex-to-base action potentials.
<p>In the failing heart, we notice the longer action potential and the reduced transmural and apex-to-base gradients.</p
Normal (left) and failing (right) ECG at rest (PCL = 400ms).
<p>The failing heart ECG shows slight widening of QRS waves, lower T-wave peaks in all leads, and marked ST-segment depression in leads V5 and V6.</p
Calcium handling changes from normal to failing cell model.
<p>Calcium handling changes from normal to failing cell model.</p
Homogeneous cable simulations showing increased activation times in the failing cell cables.
<p>Discordant alternans is visible at PCL = 250ms and PCL = 200ms in the failing cell cable. In contrast, in the normal cell cable, moderate concordant alternans is visible at PCL = 250ms and discordant alternans appears at PCL = 200ms.</p