Meta-analysis of cardiomyopathy-associated variants in troponin genes identifies loci and intragenic hot spots that are associated with worse clinical outcomes

INTRODUCTION

Troponin (TNN)-encoded cardiac troponins (Tn) are critical for sensing calcium and triggering myofilament contraction. TNN variants are associated with development of cardiomyopathy; however, recent advances in genetic analysis have identified rare population variants. It is unclear how certain variants are associated with disease while others are tolerated.

OBJECTIVE

To compare probands with TNNT2, TNNI3, and TNNC1 variants and utilize high-resolution variant comparison mapping of pathologic and rare population variants to identify loci associated with disease pathogenesis.

METHODS

Cardiomyopathy-associated TNN variants were identified in the literature and topology mapping conducted. Clinical features were compiled and compared. Rare population variants were obtained from the gnomAD database. Signal-to-noise (S:N) normalized pathologic variant frequency against population variant frequency. Abstract review of clinical phenotypes was applied to "significant" hot spots.

RESULTS

Probands were compiled (N = 70 studies, 224 probands) as were rare variants (N = 125,748 exomes; 15,708 genomes, MAF <0.001). TNNC1-positive probands demonstrated the youngest age of presentation (20.0 years; P = .016 vs TNNT2; P = .004 vs TNNI3) and the highest death, transplant, or ventricular fibrillation events (P = .093 vs TNNT2; P = .024 vs TNNI3; Kaplan Meir: P = .025). S:N analysis yielded hot spots of diagnostic significance within the tropomyosin-binding domains, α-helix 1, and the N-Terminus in TNNT2 with increased sudden cardiac death and ventricular fibrillation (P = .004). The inhibitory region and C-terminal region in TNNI3 exhibited increased restrictive cardiomyopathy (P =.008). HCM and RCM models tended to have increased calcium sensitivity and DCM decreased sensitivity (P < .001). DCM and HCM studies typically showed no differences in Hill coefficient which was decreased in RCM models (P < .001). CM models typically demonstrated no changes to Fmax (P = .239).

CONCLUSION

TNNC1-positive probands had younger ages of diagnosis and poorer clinical outcomes. Mapping of TNN variants identified locations in TNNT2 and TNNI3 associated with heightened pathogenicity, RCM diagnosis, and increased risk of sudden death.

A stochastic model for interacting neurons in the olfactory bulb

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On two diffusion neuronal models with multiplicative noise: The mean first-passage time properties

Two diffusion processes with multiplicative noise, able to model the changes in the neuronal membrane depolarization between two consecutive spikes of a single neuron, are considered and compared. The processes have the same deterministic part but different stochastic components. The differences in the state-dependent variabilities, their asymptotic distributions, and the properties of the first-passage time across a constant threshold are investigated. Closed form expressions for the mean of the first-passage time of both processes are derived and applied to determine the role played by the parameters involved in the model. It is shown that for some values of the input parameters, the higher variability, given by the second moment, does not imply shorter mean first-passage time. The reason for that can be found in the complete shape of the stationary distribution of the two processes. Applications outside neuroscience are also mentioned.

On a Stochastic Leaky Integrate-and-Fire Neuronal Model

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On a pulsating Brownian motor and its characterization

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On some computational results for single neurons' activity modeling

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On a non-Markov neuronal model and its approximations

Single neuron's activity modeling is considered with reference to some earlier contributions in which a non-Markov Gaussian process is assumed to describe the time course of the neuron's membrane potential. After re-formulating the problem in a rigorous framework and pinpointing the limits of validity of such a model, the available results on the firing probability density are compared with those obtained by us by means of an ad hoc numerical algorithm implemented for the leaky integrator diffusion firing model and with some data constructed by a simulation procedure of non-Markov Gaussian processes with pre-assigned covariances. Throughout this paper, the notion of 'correlation time' plays a fundamental role for the neuronal coding process modeling.