Effect of Global Cardiac Ischemia on Human Ventricular Fibrillation: Insights from a Multi-scale Mechanistic Model of the Human Heart

Acute regional ischemia in the heart can lead to cardiac arrhythmias such as ventricular fibrillation (VF), which in turn compromise cardiac output and result in secondary global cardiac ischemia. The secondary ischemia may influence the underlying arrhythmia mechanism. A recent clinical study documents the effect of global cardiac ischaemia on the mechanisms of VF. During 150 seconds of global ischemia the dominant frequency of activation decreased, while after reperfusion it increased rapidly. At the same time the complexity of epicardial excitation, measured as the number of epicardical phase singularity points, remained approximately constant during ischemia. Here we perform numerical studies based on these clinical data and propose explanations for the observed dynamics of the period and complexity of activation patterns. In particular, we study the effects on ischemia in pseudo-1D and 2D cardiac tissue models as well as in an anatomically accurate model of human heart ventricles. We demonstrate that the fall of dominant frequency in VF during secondary ischemia can be explained by an increase in extracellular potassium, while the increase during reperfusion is consistent with washout of potassium and continued activation of the ATP-dependent potassium channels. We also suggest that memory effects are responsible for the observed complexity dynamics. In addition, we present unpublished clinical results of individual patient recordings and propose a way of estimating extracellular potassium and activation of ATP-dependent potassium channels from these measurements

Genetic variation and exercise-induced muscle damage: implications for athletic performance, injury and ageing.

Prolonged unaccustomed exercise involving muscle lengthening (eccentric) actions can result in ultrastructural muscle disruption, impaired excitation-contraction coupling, inflammation and muscle protein degradation. This process is associated with delayed onset muscle soreness and is referred to as exercise-induced muscle damage. Although a certain amount of muscle damage may be necessary for adaptation to occur, excessive damage or inadequate recovery from exercise-induced muscle damage can increase injury risk, particularly in older individuals, who experience more damage and require longer to recover from muscle damaging exercise than younger adults. Furthermore, it is apparent that inter-individual variation exists in the response to exercise-induced muscle damage, and there is evidence that genetic variability may play a key role. Although this area of research is in its infancy, certain gene variations, or polymorphisms have been associated with exercise-induced muscle damage (i.e. individuals with certain genotypes experience greater muscle damage, and require longer recovery, following strenuous exercise). These polymorphisms include ACTN3 (R577X, rs1815739), TNF (-308 G>A, rs1800629), IL6 (-174 G>C, rs1800795), and IGF2 (ApaI, 17200 G>A, rs680). Knowing how someone is likely to respond to a particular type of exercise could help coaches/practitioners individualise the exercise training of their athletes/patients, thus maximising recovery and adaptation, while reducing overload-associated injury risk. The purpose of this review is to provide a critical analysis of the literature concerning gene polymorphisms associated with exercise-induced muscle damage, both in young and older individuals, and to highlight the potential mechanisms underpinning these associations, thus providing a better understanding of exercise-induced muscle damage

Diazoxide Promotes Oligodendrocyte Precursor Cell Proliferation and Myelination

Several clinical conditions are associated with white matter injury, including periventricular white matter injury (PWMI), which is a form of brain injury sustained by preterm infants. It has been suggested that white matter injury in this condition is due to altered oligodendrocyte (OL) development or death, resulting in OL loss and hypomyelination. At present drugs are not available that stimulate OL proliferation and promote myelination. Evidence suggests that depolarizing stimuli reduces OL proliferation and differentiation, whereas agents that hyperpolarize OLs stimulate OL proliferation and differentiation. Considering that the drug diazoxide activates K(ATP) channels to hyperpolarize cells, we tested if this compound could influence OL proliferation and myelination.Studies were performed using rat oligodendrocyte precursor cell (OPC) cultures, cerebellar slice cultures, and an in vivo model of PWMI in which newborn mice were exposed to chronic sublethal hypoxia (10% O(2)). We found that K(ATP) channel components Kir 6.1 and 6.2 and SUR2 were expressed in oligodendrocytes. Additionally, diazoxide potently stimulated OPC proliferation, as did other K(ATP) activators. Diazoxide also stimulated myelination in cerebellar slice cultures. We also found that diazoxide prevented hypomyelination and ventriculomegaly following chronic sublethal hypoxia.These results identify KATP channel components in OLs and show that diazoxide can stimulate OL proliferation in vitro. Importantly we find that diazoxide can promote myelination in vivo and prevent hypoxia-induced PWMI

ProphNet: A generic prioritization method through propagation of information

This article has been published as part of BMC Bioinformatics Volume 15 Supplement 1, 2014: Integrated Bio-Search: Selected Works from the 12th International Workshop on Network Tools and Applications in Biology (NETTAB 2012).[Background] Prioritization methods have become an useful tool for mining large amounts of data to suggest promising hypotheses in early research stages. Particularly, network-based prioritization tools use a network representation for the interactions between different biological entities to identify novel indirect relationships. However, current network-based prioritization tools are strongly tailored to specific domains of interest (e.g. gene-disease prioritization) and they do not allow to consider networks with more than two types of entities (e.g. genes and diseases). Therefore, the direct application of these methods to accomplish new prioritization tasks is limited.[Results] This work presents ProphNet, a generic network-based prioritization tool that allows to integrate an arbitrary number of interrelated biological entities to accomplish any prioritization task. We tested the performance of ProphNet in comparison with leading network-based prioritization methods, namely rcNet and DomainRBF, for gene-disease and domain-disease prioritization, respectively. The results obtained by ProphNet show a significant improvement in terms of sensitivity and specificity for both tasks. We also applied ProphNet to disease-gene prioritization on Alzheimer, Diabetes Mellitus Type 2 and Breast Cancer to validate the results and identify putative candidate genes involved in these diseases.[Conclusions] ProphNet works on top of any heterogeneous network by integrating information of different types of biological entities to rank entities of a specific type according to their degree of relationship with a query set of entities of another type. Our method works by propagating information across data networks and measuring the correlation between the propagated values for a query and a target sets of entities. ProphNet is available at: http://genome2.ugr.es/prophnet webcite. A Matlab implementation of the algorithm is also available at the website.This work was part of projects P08-TIC-4299 of J. A., Sevilla and TIN2009-13489 of DGICT, Madrid. It was also supported by Plan Propio de Investigación, University of Granada

Monogenic diabetes in children and young adults: Challenges for researcher, clinician and patient

Monogenic diabetes results from one or more mutations in a single gene which might hence be rare but has great impact leading to diabetes at a very young age. It has resulted in great challenges for researchers elucidating the aetiology of diabetes and related features in other organ systems, for clinicians specifying a diagnosis that leads to improved genetic counselling, predicting of clinical course and changes in treatment, and for patients to altered treatment that has lead to coming off insulin and injections with no alternative (Glucokinase mutations), insulin injections being replaced by tablets (e.g. low dose in HNFα or high dose in potassium channel defects -Kir6.2 and SUR1) or with tablets in addition to insulin (e.g. metformin in insulin resistant syndromes). Genetic testing requires guidance to test for what gene especially given limited resources. Monogenic diabetes should be considered in any diabetic patient who has features inconsistent with their current diagnosis (unspecified neonatal diabetes, type 1 or type 2 diabetes) and clinical features of a specific subtype of monogenic diabetes (neonatal diabetes, familial diabetes, mild hyperglycaemia, syndromes). Guidance is given by clinical and physiological features in patient and family and the likelihood of the proposed mutation altering clinical care. In this article, I aimed to provide insight in the genes and mutations involved in insulin synthesis, secretion, and resistance, and to provide guidance for genetic testing by showing the clinical and physiological features and tests for each specified diagnosis as well as the opportunities for treatment

Exome Sequencing and Genetic Testing for MODY

Context: Genetic testing for monogenic diabetes is important for patient care. Given the extensive genetic and clinical heterogeneity of diabetes, exome sequencing might provide additional diagnostic potential when standard Sanger sequencing-based diagnostics is inconclusive. Objective: The aim of the study was to examine the performance of exome sequencing for a molecular diagnosis of MODY in patients who have undergone conventional diagnostic sequencing of candidate genes with negative results. Research Design and Methods: We performed exome enrichment followed by high-throughput sequencing in nine patients with suspected MODY. They were Sanger sequencing-negative for mutations in the HNF1A, HNF4A, GCK, HNF1B and INS genes. We excluded common, non-coding and synonymous gene variants, and performed in-depth analysis on filtered sequence variants in a pre-defined set of 111 genes implicated in glucose metabolism. Results: On average, we obtained 45 X median coverage of the entire targeted exome and found 199 rare coding variants per individual. We identified 0–4 rare non-synonymous and nonsense variants per individual in our a priori list of 111 candidate genes. Three of the variants were considered pathogenic (in ABCC8, HNF4A and PPARG, respectively), thus exome sequencing led to a genetic diagnosis in at least three of the nine patients. Approximately 91% of known heterozygous SNPs in the target exomes were detected, but we also found low coverage in some key diabetes genes using our current exome sequencing approach. Novel variants in the genes ARAP1, GLIS3, MADD, NOTCH2 and WFS1 need further investigation to reveal their possible role in diabetes. Conclusion: Our results demonstrate that exome sequencing can improve molecular diagnostics of MODY when used as a complement to Sanger sequencing. However, improvements will be needed, especially concerning coverage, before the full potential of exome sequencing can be realized