The genetics of cardiac amyloidosis.

Heritable cardiac amyloidosis (CA) is an underrecognized cause of morbidity and mortality in the USA. It results from the accumulation of the misfolded protein transthyretin within the myocardium, resulting in amyloid transthyretin-associated cardiomyopathy (ATTR-CM). Over 150 different pathologic point mutations within the transthyretin gene have been identified, each carrying variable clinical phenotypes and penetrance. In the USA, the most common cause of hereditary ATTR is the Val122Ile point mutation, with a prevalence of 3.4-4.0% in North Americans of African and Caribbean descent. Among Caucasians with hereditary ATTR-CM, the V30M mutation is the most commonly identified variant. Overall, the incidence of ATTR disease in the USA has been increasing, likely due to an increase in practitioner awareness, utilization of new non-invasive imaging technologies for ATTR diagnosis, and the growth of multidisciplinary amyloid programs across the country. Yet significant numbers of patients with evidence of left ventricular thickening on cardiac imaging, senile aortic stenosis, and/or symptoms of heart failure with preserved ejection fraction likely have undiagnosed CA, especially within the African American population. With the emergence of new disease-modifying therapies for ATTR, recognition and the prompt diagnosis of CA is important for patients and their potentially affected progeny. Herein, we review the genetics of heritable CA as well as the importance of genetic counseling and testing for patients and their families.

Somatic events modify hypertrophic cardiomyopathy pathology and link hypertrophy to arrhythmia.

Sarcomere protein gene mutations cause hypertrophic cardiomyopathy (HCM), a disease with distinctive histopathology and increased susceptibility to cardiac arrhythmias and risk for sudden death. Myocyte disarray (disorganized cell-cell contact) and cardiac fibrosis, the prototypic but protean features of HCM histopathology, are presumed triggers for ventricular arrhythmias that precipitate sudden death events. To assess relationships between arrhythmias and HCM pathology without confounding human variables, such as genetic heterogeneity of disease-causing mutations, background genotypes, and lifestyles, we studied cardiac electrophysiology, hypertrophy, and histopathology in mice engineered to carry an HCM mutation. Both genetically outbred and inbred HCM mice had variable susceptibility to arrhythmias, differences in ventricular hypertrophy, and variable amounts and distribution of histopathology. Among inbred HCM mice, neither the extent nor location of myocyte disarray or cardiac fibrosis correlated with ex vivo signal conduction properties or in vivo electrophysiologically stimulated arrhythmias. In contrast, the amount of ventricular hypertrophy was significantly associated with increased arrhythmia susceptibility. These data demonstrate that distinct somatic events contribute to variable HCM pathology and that cardiac hypertrophy, more than fibrosis or disarray, correlates with arrhythmic risk. We suggest that a shared pathway triggered by sarcomere gene mutations links cardiac hypertrophy and arrhythmias in HCM.

Epilepsy in small-world networks.

In hippocampal slice models of epilepsy, two behaviors are seen: short bursts of electrical activity lasting 100 msec and seizure-like electrical activity lasting seconds. The bursts originate from the CA3 region, where there is a high degree of recurrent excitatory connections. Seizures originate from the CA1, where there are fewer recurrent connections. In attempting to explain this behavior, we simulated model networks of excitatory neurons using several types of model neurons. The model neurons were connected in a ring containing predominantly local connections and some long-distance random connections, resulting in a small-world network connectivity pattern. By changing parameters such as the synaptic strengths, number of synapses per neuron, proportion of local versus long-distance connections, we induced "normal," "seizing," and "bursting" behaviors. Based on these simulations, we made a simple mathematical description of these networks under well-defined assumptions. This mathematical description explains how specific changes in the topology or synaptic strength in the model cause transitions from normal to seizing and then to bursting. These behaviors appear to be general properties of excitatory networks.