Browsing by Subject "Electrophysiology"

Cardiac arrhythmias such as atrial fibrillation and ventricular tachycardia are closely associated with microscopic fibrotic changes in cardiac structure that result in a heterogeneous myocardium. While the incidence of fibrosis is correlated with arrhythmia burden and recurrence, the mechanisms linking the two remain poorly understood. Previous experimental and simulation studies have identified changes in local conduction due to micron-scale structural heterogeneities. However, because of the limited ability to simultaneously study conduction over a range of spatial scales, it remains unclear how numerous microheterogeneities act in aggregate to alter conduction on the macroscopic scale. The overall objective of this dissertation is to elucidate and characterize the effect of microfibrosis on cardiac conduction, through the use of computational models and directly paired experimental studies.

The impact of fibrotic collagen deposition on reentrant conduction was first examined in a model of cardiac tissue. The presence of collagenous septa was shown to prolong the cycle length of reentry; the magnitude of reentry prolongation is correlated with the overall degree of fibrosis and the length of individual collagenous septa. Mechanistically, cycle length prolongation is caused by lengthening of the reentrant tip trajectory and alteration of restitution properties. An equivalent homogenized model of fibrosis is unable to recapitulate the observed cycle length prolongation, suggesting that the details of the microstructure greatly impact the observed macroscale behavior. A hybrid model generated by adding discrete heterogeneities to the coarse, homogenized model is able to partially reproduce cycle length prolongation by replicating the lengthened tip trajectory.

In order to examine the mechanisms by which cardiac microstructure influences global conduction, a new framework for paired computational and experimental studies using the engineered-excitable Ex293 cell line was developed. The Ex293 mathematical model incorporates several measures of variation in cellular and tissue electrophysiological properties, and is novel in its use of stochastic variation in a multidimensional model of tissue. Replicating the range of experimentally observed single-cell and macro-scale behavior requires introducing ionic conductance variation between individual cells and between tissues, as well as conductivity variation between tissues.

This framework was then utilized for paired studies in a geometry of idealized fibrosis to examine fibrosis-induced changes in micro- and macro-scale behavior. The presence of microscopic heterogeneities slows conduction and alters the curvature of the macroscopic wavefront. On the microscale, branching of tissue around heterogeneities leads to conduction slowing due to imbalances of electrical source and load, while wavefront collisions at sites of tissue convergence lead to acceleration of propagation. The observed macroscopic behavior is directly attributable to the combination of these microscopic effects and the tortuosity of propagation around heterogeneities. Under diseased conditions involving reduced excitability, alteration of these microscale behaviors leads to reversal of changes in wavefront curvature.

These findings advance our knowledge of the role of myocardial micro-heterogeneities in conduction. Further application of these techniques to examine how the effects of microstructure are dynamically modulated may help improve our understanding of the factors giving rise to cardiac arrhythmia.

Cardiovascular diseases cause the majority of deaths worldwide, increasing the need for physiologically accurate computational patient models to study these conditions. This project aimed to develop patient-specific models of the Left Atrial Posterior Wall (LAPW) using Point Cloud Data (PCD) and evaluate model performance using unipolar Electrogram (EGM) signals.Data recorded from 5 Paroxysmal and 5 Persistent Atrial Fibrillation (AFib) patients who were paced back to sinus rhythm was analyzed and used for this work. From this PCD data, all the EGMs from each different patient were characterized and classified into three different categories: smooth biphasic, complex multiphasic, and other. Differences were found in EGM characteristics and distribution of waveforms’ categories among patients of both types of AFib. Models for two patients were created using PCD LAPW points without further image segmentation. To evaluate model performance, a sample of EGMs coming from monodomain simulations with healthy tissue substrate was compared to measured EGMs classified as smooth biphasic. Overall, the comparison yielded mixed results, showing excellent matches for some EGMs while showing marked differences for others which origin remained undetermined. To evaluate EGM morphology changes under fibrotic tissue conditions, four patient models were generated using the same technique but with fibrotic tissue substrate. Different types and degrees of fibrosis were simulated, with simulation results showing increasing multiphasic morphology behavior as fibrosis degree increased. The comparison of these waveforms did not yield results strong enough to determine the specific tissue substrate present on each patient. Overall, the results show that PCD is sufficient to create patient-specific models of the LAPW. These models can simulate EGMs that are comparable to measured waveforms, whose different morphologies can potentially be used to determine atrial substrate modifications.

Radiofrequency ablation (RFA) is commonly used to treat cardiac arrhythmias, by generating a series of discrete RFA lesions in the myocardium to isolate arrhythmogenic conduction pathways. The size of each lesion is controlled by the temperature of the tissue at the surface or the duration of RF power delivery, but feedback on the extent and transmurality of the generated lesion are unavailable with current technology. Intracardiac Echocardiography (ICE) may provide a solution through Acoustic Radiation Force Impulse (ARFI) imaging or Shear Wave Imaging (SWI), which each generate images of local mechanical compliance from very small ultrasonically-induced waves. This work compares ARFI and SWI in an ex-vivo experiment for lesion boundary assessment and lesion gap resolution.

Voltage-gated sodium channels (VGSCs) enable generation and spread of action potentials in electrically excitable cells and tissues of all metazoans, from jellyfish to humans. The functional, pore-forming α-subunit of eukaryotic VGSCs is formed from a large polypeptide chain of ~2000 amino acids (~260 kDa), comprising four homologous domains. In humans, VGSC loss-of-function mutations are associated with various neuronal, cardiac, and skeletal muscle disorders characterized by a decrease or complete loss of tissue excitability. Similarly, permanent excitability loss due to acute tissue injuries (e.g. stroke, spinal cord injury, heart attack) could lead to long-term disability and death. Whilst an increase in sodium current through stable gene transfer could improve such conditions, eukaryotic VGSC genes are too large (>6 kbp) to be efficiently delivered to cells by existing viral vectors. In contrast, prokaryotic voltage-gated sodium channels (BacNav) consist of four identical subunits, individually transcribed and translated from single genes of only ~800 bp in size. Therefore, it is plausible that small BacNav genes can be efficiently packaged into viral vectors, either alone or with other ion channel genes, and used to stably introduce or modify electrical excitability of primary human cells. The objective of this thesis is thus to develop the methodology to screen, optimize, and assess BacNav channels as potential substitutes for eukaryotic VGSCs. Specifically, we sought to utilize engineered BacNav to create de novo excitable human tissues and to rescue impaired action potential conduction in vitro.

First, by using a monoclonal HEK293 line stably expressing the potassium channel Kir2.1 and gap junction channel Cx43, we were able to select, among various BacNav orthologs and variants, the channel NavRosD G217A that yielded action potential propagation with highest maximum capture rate. Lentiviral transduction of each of the three channels (NavRosD G217A, Kir2.1, and Cx43) into human fibroblasts yielded robust expression and expected electrical properties as confirmed by patch clamp recordings. By co-expressing all three channels, we were able for the first time to stably convert human fibroblasts into electrically excitable and actively conducting cells. However, the conduction velocity of engineered fibroblast tissue was low, largely due to the slow activation kinetics of NavRosD channel.

In order to improve the conduction properties of engineered fibroblasts, we shifted our focus to NavSheP channel, currently the fastest known BacNav ortholog. Due to the overly hyperpolarized voltage dependency of the wild-type NavSheP channel, we generated a library of NavSheP mutants exhibiting a wide range of shifts in voltage-dependent activation and inactivation and, with the guidance from computational modeling, identified three mutants that yielded ~2.5-fold increases in conduction velocity compared to NavRosD G217A. Importantly, we demonstrated that engineered fibroblasts retained stable functional properties despite extensive expansion or differentiation into myofibroblasts and exhibited strong viability while supporting AP propagation in 3D settings. Furthermore, in an in vitro model of interstitial fibrosis, engineered excitable and actively-conducting fibroblasts rescued impaired cardiac conduction to healthy level. These results strongly suggested that engineered fibroblasts could be used as a robust source for potential cell-based therapies for cardiac diseases.

In addition to the generation of excitable fibroblasts, BacNav channels could also serve as potential substitutes for impaired VGSC in various excitable tissue disorders. The channel NavSheP D60A (ShePA) was chosen for direct expression in mammalian excitable tissues as it yielded fastest conduction in previous studies. By performing codon optimization and adding appropriate endoplasmic-reticulum export signal, we were able to significantly improve membrane expression of ShePA channels. Expression of ShePA in excitable HEK293 tissue (Ex293) rescued impaired conduction upon membrane depolarization and decoupling. Furthermore, cultures of neonatal rat ventricular myocytes (NRVMs) transduced with ShePA virus exhibited enhanced conduction properties and increased resistance to conduction failure in an in vitro model of regional ischemia. Lastly, ShePA expression in highly-arrhythmogenic cardiomyocyte-fibroblast co-cultures led to significant reduction in incidence of reentry. Taken together, these results demonstrated the potential applications of engineered BacNav channels for cardiac gene therapies.

In summary, this dissertation presents the first experimental evidences supporting the use of prokaryotic sodium channels for the induction, control, and rescue of mammalian tissue excitability. The encouraging in vitro results shown in these studies will stimulate the development of BacNav-based therapies for the treatment of cardiac diseases. Furthermore, the experimental methodology developed in this work will serve as a useful framework for the screening, optimization, and assessment of engineered BacNav for specific therapeutic applications.

The ability to monitor, learn from, and respond to social information is essential for many highly social animals, including humans. Deficits to this capacity are associated with numerous psychopathologies, including autism spectrum disorders, social anxiety disorder, and schizophrenia. To understand the neural mechanisms supporting social information seeking behavior requires understanding this behavior in its natural context, and presenting animals with species-appropriate stimuli that will elicit the behavior in the laboratory. In this dissertation, I describe a novel behavioral paradigm I developed for investigating social information seeking behavior in rhesus macaques in a laboratory setting, with the use of naturalistic videos of freely-behaving conspecifics as stimuli. I recorded neural activity in the orbitofrontal and lateral prefrontal cortex of monkeys as they engaged in this task, and found evidence for a rich but sparse representation of natural behaviors in both areas, particularly in the orbitofrontal cortex. This sparse encoding of conspecifics' behaviors represents the raw material for social information foraging decisions.

Anticipation is a state of expectancy for something that will happen, and it allows us to use past learning to prepare for and make predictions about the future. Studies have shown that anticipation influences behavioral performance, learning, and memory, and studies implicate reward-related brain circuitry. However, few studies have investigated the neural underpinnings of anticipation on a brain-wide network scale . In this set of experiments, I take an interdisciplinary cross-species approach, using in-vivo electrophysiology in mice and functional magnetic resonance imaging (fMRI) in humans, to investigate brain networks underlying anticipation in motivated behavior. Using a data-driven machine learning approach, I characterize the anticipatory network in mice running through a T-maze, and show how it is affected by behavioral perturbation in the form of a task reversal, and circuit perturbation in the form of a genetic mutant mouse line. I also validate this network in a separate cohort of mice in a variation of the T-maze task that varies in difficulty, and show how activity in this network is modulated by task difficulty and intermediate instrumental goals. Finally, I investigate this network using fMRI in human subjects performing a trivia-based task to show how this network links curiosity, a more intrinsic form of motivation, to memory. The findings from these studies provide evidence at multiple levels and across multiple species for an anticipatory network that links motivational state to cognitive performance.

Mechanosensation, or the ability to sense mechanical forces, is critical for the survival of all organisms. For vertebrates, the ability to sense and respond to environmental stimuli, or somatosensation, is not well understood at the neural circuit level, and the molecular underpinnings for somatosensation, especially in regards to mechanosensation, remain elusive and unresolved. In recent years, Piezo ion channels were discovered as the first mammalian excitatory bona fide mechanosensitive ion channel to be the initial molecules in sensing gentle touch in the somatosensory neural circuitry. Although physiological roles of Piezo ion channels for both somatosensation and other non-neuronal processes have been identified, the mechanisms for how these ion channels directly sense mechanical forces and transduce electrical signals remains unknown.

With the use of biochemical, molecular, and electrophysiological methods, I first developed a novel technique in which electrophysiology could be performed on microsomes, or vesicles formed from ER fractions containing proteins that are not trafficked to the plasma membrane. This experiment revealed that wild-type Piezo1 ion channels retain stretch activation in microsomes, and therefore this technique could be utilized to characterize mutants channels that lack trafficking to the plasma membrane in order to identify which domains are involved in activation and inactivation of Piezo ion channels. I next generated two separate constructs that removed domains suggested to be involved in activation and inactivation of Piezo1. Removal of the proposed inactivation domain rendered a non-functional channel that could still trimerize, suggesting that this domain not only plays a role in inactivation but also is critical for activation of Piezo1 ion channels. Partial removal of the proposed membrane-spanning mechanosensor domains produced a channel that lacked the ability to conduct large macroscopic currents but formed a conducting pore with single channel openings. This finding suggests that membrane tension is not sensed and transduced to the pore by a single domain, but rather multiple domains in concert. Together the findings provide evidence that the mechanisms for both activation and inactivation require multiple domains moving in collaboration together, and will broadly be informative for continued studies of the molecular mechanisms of mechanosensitive ion channels.

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