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Optogenetics-enabled assessment of viral gene and cell therapy for restoration of cardiac excitability.

Ambrosi CM, Boyle PM, Chen K, Trayanova NA, Entcheva E - Sci Rep (2015)

Bottom Line: Multiple cardiac pathologies are accompanied by loss of tissue excitability, which leads to a range of heart rhythm disorders (arrhythmias).Taken directly, these results can help guide optogenetic interventions for light-based control of cardiac excitation.More generally, our findings can help optimize gene therapy for restoration of cardiac excitability.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY.

ABSTRACT
Multiple cardiac pathologies are accompanied by loss of tissue excitability, which leads to a range of heart rhythm disorders (arrhythmias). In addition to electronic device therapy (i.e. implantable pacemakers and cardioverter/defibrillators), biological approaches have recently been explored to restore pacemaking ability and to correct conduction slowing in the heart by delivering excitatory ion channels or ion channel agonists. Using optogenetics as a tool to selectively interrogate only cells transduced to produce an exogenous excitatory ion current, we experimentally and computationally quantify the efficiency of such biological approaches in rescuing cardiac excitability as a function of the mode of application (viral gene delivery or cell delivery) and the geometry of the transduced region (focal or spatially-distributed). We demonstrate that for each configuration (delivery mode and spatial pattern), the optical energy needed to excite can be used to predict therapeutic efficiency of excitability restoration. Taken directly, these results can help guide optogenetic interventions for light-based control of cardiac excitation. More generally, our findings can help optimize gene therapy for restoration of cardiac excitability.

No MeSH data available.


Related in: MedlinePlus

Generalized relationships between transgene patterns and optical excitability.(a) Threshold irradiance rheobase (Ee,rheo) compared to central density metric (CDM) for light sensitive cell distribution in GD models. Along with transgene expression patterns considered in Figs 1, 2, 3, 4, 5, 6 (GD-I, GD-UL, and GD-UH), additional simulations were conducted in models with other combinations of D and C (Fig. S3). Simple linear regression on log-log transformed data revealed an apparent power law relationship. (b) Same as (a), but for CD models (see also: Fig. S4). (c) Ee,rheo compared to the interface metric Moran’s I (IM) for GD models. No overall trend was observed; however, dashed lines highlight general patterns in interface/excitability relationship (see text). (d) Same as (c) but for CD models. For this case, there was an apparent power law relationship as in (a,b).
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f7: Generalized relationships between transgene patterns and optical excitability.(a) Threshold irradiance rheobase (Ee,rheo) compared to central density metric (CDM) for light sensitive cell distribution in GD models. Along with transgene expression patterns considered in Figs 1, 2, 3, 4, 5, 6 (GD-I, GD-UL, and GD-UH), additional simulations were conducted in models with other combinations of D and C (Fig. S3). Simple linear regression on log-log transformed data revealed an apparent power law relationship. (b) Same as (a), but for CD models (see also: Fig. S4). (c) Ee,rheo compared to the interface metric Moran’s I (IM) for GD models. No overall trend was observed; however, dashed lines highlight general patterns in interface/excitability relationship (see text). (d) Same as (c) but for CD models. For this case, there was an apparent power law relationship as in (a,b).

Mentions: Linear regression on log-log transformed data (Fig. 7a,b) revealed apparent power law relationships between Ee,rheo and CDM for both GD (Ee,rheo = 0.0110 × CDM–0.72; correlation coefficient r = –0.975) and CD (Ee,rheo = 0.0164 × CDM–1.19; r = –0.968). Thus, for both delivery modes, functional efficiency of transgene expression improved monotonically (Ee,rheo decreased) with higher transgene density. In contrast, the relationships between functional efficiency and IM differed considerably for GD and CD (Fig. 7c,d). For CD (Fig. 7d), consolidation of transgene-expressing donor cells (as indicated by higher IM values) was associated with improved functional efficiency (lower Ee,rheo); this resulted in an apparent power law relationship (Ee,rheo = 0.0118 × IM–2.64; r = –0.902) similar to those observed for CDM above. For GD, the effect of transgene consolidation was more complex (Fig. 7c). Although no overall trend was observed, analysis revealed general sub-patterns in the Ee,rheo vs. IM relationship (dashed lines in Fig. 7c). When transgene expression density was high (D = 0.357), excitability was insensitive to changes in IM due to increased clustering (Fig. 7c, i). In contrast, when expression density was low (Fig. 7c, ii: D = 0.025), higher IM was associated with improved functional efficiency, with near order of magnitude difference between Ee,rheo values for the diffuse and clustered distributions. Finally, when the spatial distribution of light-sensitive cells was diffuse (Fig. 7c, iii: low IM due to C = 0.25), the functional efficiency of GD-transduced syncytia was exquisitely sensitive to transduction density.


Optogenetics-enabled assessment of viral gene and cell therapy for restoration of cardiac excitability.

Ambrosi CM, Boyle PM, Chen K, Trayanova NA, Entcheva E - Sci Rep (2015)

Generalized relationships between transgene patterns and optical excitability.(a) Threshold irradiance rheobase (Ee,rheo) compared to central density metric (CDM) for light sensitive cell distribution in GD models. Along with transgene expression patterns considered in Figs 1, 2, 3, 4, 5, 6 (GD-I, GD-UL, and GD-UH), additional simulations were conducted in models with other combinations of D and C (Fig. S3). Simple linear regression on log-log transformed data revealed an apparent power law relationship. (b) Same as (a), but for CD models (see also: Fig. S4). (c) Ee,rheo compared to the interface metric Moran’s I (IM) for GD models. No overall trend was observed; however, dashed lines highlight general patterns in interface/excitability relationship (see text). (d) Same as (c) but for CD models. For this case, there was an apparent power law relationship as in (a,b).
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4664892&req=5

f7: Generalized relationships between transgene patterns and optical excitability.(a) Threshold irradiance rheobase (Ee,rheo) compared to central density metric (CDM) for light sensitive cell distribution in GD models. Along with transgene expression patterns considered in Figs 1, 2, 3, 4, 5, 6 (GD-I, GD-UL, and GD-UH), additional simulations were conducted in models with other combinations of D and C (Fig. S3). Simple linear regression on log-log transformed data revealed an apparent power law relationship. (b) Same as (a), but for CD models (see also: Fig. S4). (c) Ee,rheo compared to the interface metric Moran’s I (IM) for GD models. No overall trend was observed; however, dashed lines highlight general patterns in interface/excitability relationship (see text). (d) Same as (c) but for CD models. For this case, there was an apparent power law relationship as in (a,b).
Mentions: Linear regression on log-log transformed data (Fig. 7a,b) revealed apparent power law relationships between Ee,rheo and CDM for both GD (Ee,rheo = 0.0110 × CDM–0.72; correlation coefficient r = –0.975) and CD (Ee,rheo = 0.0164 × CDM–1.19; r = –0.968). Thus, for both delivery modes, functional efficiency of transgene expression improved monotonically (Ee,rheo decreased) with higher transgene density. In contrast, the relationships between functional efficiency and IM differed considerably for GD and CD (Fig. 7c,d). For CD (Fig. 7d), consolidation of transgene-expressing donor cells (as indicated by higher IM values) was associated with improved functional efficiency (lower Ee,rheo); this resulted in an apparent power law relationship (Ee,rheo = 0.0118 × IM–2.64; r = –0.902) similar to those observed for CDM above. For GD, the effect of transgene consolidation was more complex (Fig. 7c). Although no overall trend was observed, analysis revealed general sub-patterns in the Ee,rheo vs. IM relationship (dashed lines in Fig. 7c). When transgene expression density was high (D = 0.357), excitability was insensitive to changes in IM due to increased clustering (Fig. 7c, i). In contrast, when expression density was low (Fig. 7c, ii: D = 0.025), higher IM was associated with improved functional efficiency, with near order of magnitude difference between Ee,rheo values for the diffuse and clustered distributions. Finally, when the spatial distribution of light-sensitive cells was diffuse (Fig. 7c, iii: low IM due to C = 0.25), the functional efficiency of GD-transduced syncytia was exquisitely sensitive to transduction density.

Bottom Line: Multiple cardiac pathologies are accompanied by loss of tissue excitability, which leads to a range of heart rhythm disorders (arrhythmias).Taken directly, these results can help guide optogenetic interventions for light-based control of cardiac excitation.More generally, our findings can help optimize gene therapy for restoration of cardiac excitability.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY.

ABSTRACT
Multiple cardiac pathologies are accompanied by loss of tissue excitability, which leads to a range of heart rhythm disorders (arrhythmias). In addition to electronic device therapy (i.e. implantable pacemakers and cardioverter/defibrillators), biological approaches have recently been explored to restore pacemaking ability and to correct conduction slowing in the heart by delivering excitatory ion channels or ion channel agonists. Using optogenetics as a tool to selectively interrogate only cells transduced to produce an exogenous excitatory ion current, we experimentally and computationally quantify the efficiency of such biological approaches in rescuing cardiac excitability as a function of the mode of application (viral gene delivery or cell delivery) and the geometry of the transduced region (focal or spatially-distributed). We demonstrate that for each configuration (delivery mode and spatial pattern), the optical energy needed to excite can be used to predict therapeutic efficiency of excitability restoration. Taken directly, these results can help guide optogenetic interventions for light-based control of cardiac excitation. More generally, our findings can help optimize gene therapy for restoration of cardiac excitability.

No MeSH data available.


Related in: MedlinePlus