Binocular contrast vision at and above threshold

A fundamental problem for any visual system with binocular overlap is the combination of information from the two eyes. Electrophysiology shows that binocular integration of luminance contrast occurs early in visual cortex, but a specific systems architecture has not been established for human vision. Here, we address this by performing binocular summation and monocular, binocular, and dichoptic masking experiments for horizontal 1 cycle per degree test and masking gratings. These data reject three previously published proposals, each of which predict too little binocular summation and insufficient dichoptic facilitation. However, a simple development of one of the rejected models (the twin summation model) and a completely new model (the two-stage model) provide very good fits to the data. Two features common to both models are gently accelerating (almost linear) contrast transduction prior to binocular summation and suppressive ocular interactions that contribute to contrast gain control. With all model parameters fixed, both models correctly predict (1) systematic variation in psychometric slopes, (2) dichoptic contrast matching, and (3) high levels of binocular summation for various levels of binocular pedestal contrast. A review of evidence from elsewhere leads us to favor the two-stage model. © 2006 ARVO.

Neuronal network pharmacodynamics of GABAergic modulation in the human cortex determined using pharmaco-magnetoencephalography

Neuronal network oscillations are a unifying phenomenon in neuroscience research, with comparable measurements across scales and species. Cortical oscillations are of central importance in the characterization of neuronal network function in health and disease and are influential in effective drug development. Whilst animal in vitro and in vivo electrophysiology is able to characterize pharmacologically induced modulations in neuronal activity, present human counterparts have spatial and temporal limitations. Consequently, the potential applications for a human equivalent are extensive. Here, we demonstrate a novel implementation of contemporary neuroimaging methods called pharmaco-magnetoencephalography. This approach determines the spatial profile of neuronal network oscillatory power change across the cortex following drug administration and reconstructs the time course of these modulations at focal regions of interest. As a proof of concept, we characterize the nonspecific GABAergic modulator diazepam, which has a broad range of therapeutic applications. We demonstrate that diazepam variously modulates ? (4–7 Hz), a (7–14 Hz), ß (15–25 Hz), and ? (30–80 Hz) frequency oscillations in specific regions of the cortex, with a pharmacodynamic profile consistent with that of drug uptake. We examine the relevance of these results with regard to the spatial and temporal observations from other modalities and the various therapeutic consequences of diazepam and discuss the potential applications of such an approach in terms of drug development and translational neuroscience.

Characteristics of visual function in Asperger’s syndrome and the autism spectrum

Autism is a pervasive developmental disorder and Asperger’s syndrome is part of the spectrum of autism disorders. This thesis aims to: • Review and investigate current theories concerning visual function in individuals with Asperger’s syndrome and high functioning autism spectrum disorder and to translate the findings into clinical practice by developing a specific protocol for the eye examination of individuals of this population. • Investigate whether those with Asperger’s syndrome are more likely to suffer from Meares-Irlen syndrome and/or dyslexia. • Assess the integrity of the M-cell pathway in Asperger’s syndrome using perimetric tests available in optometric practice to investigate and also to describe the nature of any defects. • Evaluate eye movement strategies in Asperger’s whilst viewing both text and images. Also to evaluate the most appropriate methodology for investigating eye movements; namely optical digital eye tracking and electrophysiology methodologies. Findings of the investigations include • Eye examinations for individuals with Asperger’s syndrome should contain the same testing methods as for the general population, with special consideration for clear communication. • There is a depression of M-pathway visual field sensitivity in 57% (8/14) of people with Asperger’s syndrome, supporting previous evidence for an M-cell deficit in some individuals. • There is a raised prevalence of dyslexia in Asperger’s syndrome (26% of a sample of 31) but not necessarily of Meares-Irlen syndrome. • Gaze strategies are abnormal in Asperger’s syndrome, for both reading and viewing of images. With increased saccadic movement and decreased viewing of faces in comparison to background detail.

Electrophysiological studies of the visual pathways in Alzheimer's and Parkinson's disease

It is known that parallel pathways exist within the visual system. These have been described as magnocellular and parvocellular as a result of the layered organisation of the lateral geniculate nucleus and extend from the retina to the cortex. Dopamine (DA) and acetylcholine (ACH) are neurotransmitters that are present in the visual pathway. DA is present in the retina and is associated with the interplexiform cells and horizontal cells. ACH is also present in the retina and is associated with displaced amacrine cells; it is also present in the superior colliculus. DA is found to be significantly depleted in the brain of Parkinson's disease (PD) patients and ACH in Alzheimer's disease (AD) patients. For this reason these diseases were used to assess the function of DA and ACH in the electrophysiology of the visual pathway. Experiments were conducted on young normals to design stimuli that would preferentially activate the magnocellular or parvocellular pathway. These stimuli were then used to evoke visual evoked potentials (VEP) in patients with PD and AD, in order to assess the function of DA and ACH in the visual pathway. Electroretinograms (ERGs) were also measured in PD patients to assess the role of DA in the retina. In addition, peripheral ACH function was assessed by measuring VEPs, ERGs and contrast sensitivity (CS) in young normals following the topical instillation of hyoscine hydrobromide (an anticholinergic drug). The results indicate that the magnocellular pathway can be divided into two: a cholinergic tectal-association area pathway carrying luminance information, and a non-cholinergic geniculo-cortical pathway carrying spatial information. It was also found that depletion of DA had very little effect on the VEPs or ERGs, confirming a general regulatory function for this neurotransmitter.

Source localization methods

One of the most pressing demands on electrophysiology applied to the diagnosis of epilepsy is the non-invasive localization of the neuronal generators responsible for brain electrical and magnetic fields (the so-called inverse problem). These neuronal generators produce primary currents in the brain, which together with passive currents give rise to the EEG signal. Unfortunately, the signal we measure on the scalp surface doesn't directly indicate the location of the active neuronal assemblies. This is the expression of the ambiguity of the underlying static electromagnetic inverse problem, partly due to the relatively limited number of independent measures available. A given electric potential distribution recorded at the scalp can be explained by the activity of infinite different configurations of intracranial sources. In contrast, the forward problem, which consists of computing the potential field at the scalp from known source locations and strengths with known geometry and conductivity properties of the brain and its layers (CSF/meninges, skin and skull), i.e. the head model, has a unique solution. The head models vary from the computationally simpler spherical models (three or four concentric spheres) to the realistic models based on the segmentation of anatomical images obtained using magnetic resonance imaging (MRI). Realistic models – computationally intensive and difficult to implement – can separate different tissues of the head and account for the convoluted geometry of the brain and the significant inter-individual variability. In real-life applications, if the assumptions of the statistical, anatomical or functional properties of the signal and the volume in which it is generated are meaningful, a true three-dimensional tomographic representation of sources of brain electrical activity is possible in spite of the ‘ill-posed’ nature of the inverse problem (Michel et al., 2004). The techniques used to achieve this are now referred to as electrical source imaging (ESI) or magnetic source imaging (MSI). The first issue to influence reconstruction accuracy is spatial sampling, i.e. the number of EEG electrodes. It has been shown that this relationship is not linear, reaching a plateau at about 128 electrodes, provided spatial distribution is uniform. The second factor is related to the different properties of the source localization strategies used with respect to the hypothesized source configuration.

Human brain slices for epilepsy research:pitfalls, solutions and future challenges

Increasingly, neuroscientists are taking the opportunity to use live human tissue obtained from elective neurosurgical procedures for electrophysiological studies in vitro. Access to this valuable resource permits unique studies into the network dynamics that contribute to the generation of pathological electrical activity in the human epileptic brain. Whilst this approach has provided insights into the mechanistic features of electrophysiological patterns associated with human epilepsy, it is not without technical and methodological challenges. This review outlines the main difficulties associated with working with epileptic human brain slices from the point of collection, through the stages of preparation, storage and recording. Moreover, it outlines the limitations, in terms of the nature of epileptic activity that can be observed in such tissue, in particular, the rarity of spontaneous ictal discharges, we discuss manipulations that can be utilised to induce such activity. In addition to discussing conventional electrophysiological techniques that are routinely employed in epileptic human brain slices, we review how imaging and multielectrode array recordings could provide novel insights into the network dynamics of human epileptogenesis. Acute studies in human brain slices are ultimately limited by the lifetime of the tissue so overcoming this issue provides increased opportunity for information gain. We review the literature with respect to organotypic culture techniques that may hold the key to prolonging the viability of this material. A combination of long-term culture techniques, viral transduction approaches and electrophysiology in human brain slices promotes the possibility of large scale monitoring and manipulation of neuronal activity in epileptic microcircuits.

Fiber Scaffolds of Poly (glycerol-dodecanedioate) and its Derivative via Electrospinning for Neural Tissue Engineering

Peripheral nerves have demonstrated the ability to bridge gaps of up to 6 mm. Peripheral Nerve System injury sites beyond this range need autograft or allograft surgery. Central Nerve System cells do not allow spontaneous regeneration due to the intrinsic environmental inhibition. Although stem cell therapy seems to be a promising approach towards nerve repair, it is essential to use the distinct three-dimensional architecture of a cell scaffold with proper biomolecule embedding in order to ensure that the local environment can be controlled well enough for growth and survival. Many approaches have been developed for the fabrication of 3D scaffolds, and more recently, fiber-based scaffolds produced via the electrospinning have been garnering increasing interest, as it offers the opportunity for control over fiber composition, as well as fiber mesh porosity using a relatively simple experimental setup. All these attributes make electrospun fibers a new class of promising scaffolds for neural tissue engineering. Therefore, the purpose of this doctoral study is to investigate the use of the novel material PGD and its derivative PGDF for obtaining fiber scaffolds using the electrospinning. The performance of these scaffolds, combined with neural lineage cells derived from ESCs, was evaluated by the dissolvability test, Raman spectroscopy, cell viability assay, real time PCR, Immunocytochemistry, extracellular electrophysiology, etc. The newly designed collector makes it possible to easily obtain fibers with adequate length and integrity. The utilization of a solvent like ethanol and water for electrospinning of fibrous scaffolds provides a potentially less toxic and more biocompatible fabrication method. Cell viability testing demonstrated that the addition of gelatin leads to significant improvement of cell proliferation on the scaffolds. Both real time PCR and Immunocytochemistry analysis indicated that motor neuron differentiation was achieved through the high motor neuron gene expression using the metabolites approach. The addition of Fumaric acid into fiber scaffolds further promoted the differentiation. Based on the results, this newly fabricated electrospun fiber scaffold, combined with neural lineage cells, provides a potential alternate strategy for nerve injury repair.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Descending Control of Limb Movements in Drosophila melanogaster

Because the interactions between feedforward influences are inextricably linked during many motor outputs (including but not limited to walking), the contribution of descending inputs to the generation of movements is difficult to study. Here we take advantage of the relatively small number of descending neurons (DNs) in the Drosophila melanogaster model system. We first characterize the number and distribution of the DN populations, then present a novel load free preparation, which enables the study of descending control on limb movements in a context where sensory feedback can be is reduced while leaving the nervous system, musculature, and cuticle of the animal relatively intact. Lastly we use in-vivo whole cell patch clamp electrophysiology to characterize the role of individual DNs in response to specific sensory stimuli and in relationship to movement. We find that there are approximately 1100 DNs in Drosophila that are distributed across six clusters. Input from these DNs is not necessary for coordinated motor activity, which can be generated by the thoracic ganglion, but is necessary for the specific combinations of joint movements typically observed in walking. Lastly, we identify a particular cluster of DNs that are tuned to sensory stimuli and innervate the leg neuromeres. We propose that a multi-layered interaction between these DNs, other DNs, and motor circuits in the thoracic ganglia enable the diverse but well-coordinated range of motor outputs an animal might exhibit.

β-Arrestin-mediated Regulation of the Human Ether-a-go-go-Related Gene (hERG) Potassium Channel

The human ether-a-go-go-related gene (hERG) encodes the voltage-gated K+ channel, hERG (Kv11.1). This channel passes the rapidly-activating delayed rectifier K+ current (IKr), which is important for cardiac repolarization. A reduction in IKr due to loss-of-function mutations or drug interactions causes long QT syndrome (LQTS), which can lead to cardiac arrhythmias and sudden cardiac death. The density of hERG channels in the plasma membrane is a key determinant of normal physiological function, and is balanced by trafficking to and from the cell surface. Many LQTS-associated hERG mutations result in a trafficking deficiency of otherwise functional channels. Thus, elucidating mechanisms of hERG regulation at the plasma membrane is useful for the prevention and treatment of LQTS. We previously demonstrated that M3 muscarinic receptor activation increases mature hERG expression through a Gq protein-dependent protein kinase C (PKC) pathway. In addition to conventional Gq protein-coupling, M3 receptors recruit β-arrestins upon agonist binding. Traditionally known for their role in receptor desensitization and internalization, β-arrestins also act as adaptor proteins to facilitate G protein-independent signaling. In the present work, I investigated the exclusive effect of β-arrestin signaling on hERG expression by utilizing an arrestin-biased M3 designer receptor (M3D-arr) exclusively activated by clozapine-N-oxide (CNO). By expressing M3D-arr in hERG-HEK cells and treating with CNO under various conditions, I found that M3D-arr activation increased mature hERG expression and current. Within this paradigm, M3D-arr recruited β-arrestin to the plasma membrane, and promoted the PI3K-dependent activation of Akt. I further found that the activated Akt acted through phosphatidylinositol 3-phosphate 5-kinase (PIKfyve) and Rab11 to facilitate endosomal recycling of hERG channels to the plasma membrane.

Subfornical organ neurons integrate cardiovascular and metabolic signals

The subfornical organ (SFO) is a critical circumventricular organ involved in the control of cardiovascular and metabolic homeostasis. Despite the abundant literature clearly demonstrating the ability of SFO neurons to sense and respond to a plethora of circulating signals that influence various physiological systems, investigation of how simultaneously sensed signals interact and are integrated in the SFO is lacking. In this study, we use patch clamp techniques to investigate how the traditionally classified ‘cardiovascular’ hormone angiotensin II (ANG), ‘metabolic’ hormone cholecystokinin (CCK) and ‘metabolic’ signal glucose interact and are integrated in the SFO. Sequential bath-application of CCK (10nM) and ANG (10nM) onto dissociated SFO neurons revealed that: 63% of responsive SFO neurons depolarized to both CCK & ANG; 25% depolarized to ANG only; and 12% hyperpolarized to CCK only. We next investigated the effects of glucose by incubating and recording neurons in either hypo-, normo- or hyperglycemic conditions for a minimum of 24 hours and comparing the proportions of responses to ANG (n=55) or CCK (n=83) application in each condition. A hyperglycemic environment was associated with a larger proportion of depolarizing responses to ANG (X2, p<0.05), and a smaller proportion of depolarizing responses along with a larger proportion of hyperpolarizing responses to CCK (X2, p<0.01). These data demonstrate that SFO neurons excited by CCK are also excited by ANG, suggesting that CCK may influence fluid intake or blood pressure via the SFO, complementary to the well-understood actions of ANG at this site. Additionally, the demonstration that glucose environment affects the responsiveness of neurons to both these hormones highlights the ability of SFO neurons to integrate multiple metabolic and cardiovascular signals to affect transmission of information from the circulation to the brain, which has important implications for this structure’s critical role regulation of autonomic function.

Electrophysiological study of the effects of arginine vasopressin in the rat juxtacapsular nucleus of the bed nucleus of the stria terminalis

Arginine vasopressin (AVP), a nine amino acid neuropeptide (CYFQNCPRG- NH2) fulfills a dual function: (i) in the periphery, AVP acts as a peptide hormone and (ii) in the CNS, AVP is a neuromodulatory peptide. AVP produces its effects through 3 AVP receptors (AVPRs). AVPR1a and AVPR1b are expressed in the CNS and periphery, whilst AVPR2 is not found centrally but instead solely expressed in the kidneys. Recent evidence revealed a high density of AVP-binding sites in the juxtacapsular nucleus of the bed nucleus of the stria terminalis (jxBNST). While in other regions of the brain, AVP acts at AVPRs to regulate an array of biological processes, including male-typical social behaviours, social memory, stress adaptation, fear, anxiety, and fluid homeostasis, its role in the jxBNST remains elusive. Furthermore, the neurophysiological properties of AVP in the jxBNST are unknown so this study aimed to examine how AVP modulates synaptic transmission in the rat jxBNST. The BNST being one of the most notable sexually dimorphic brain regions and AVPR expression being influenced by gonadal steroids, we investigated the putative influence of sex on the modulatory effects of AVP in the jxBNST. Finally, due to AVP being released at a substantially higher concentration following periods of water deprivation, we examined changes in AVPs modulatory role following water deprivation. Male and female Long Evans rats were euthanized and brain slice whole-cell voltage-clamp electrophysiology was done in the jxBNST to measure the effects of AVP on synaptic transmission of GABA synapses. Exogenous application of AVP produced three responses; either postsynaptic long-term potentiation (LTP) of GABAA-inhibitory postsynaptic currents (IPSC), postsynaptic long-term depression (LTD) of GABAA-IPSC, or no change in GABAA-IPSC amplitudes. Interestingly, the proportion of neurons responding in each of these ways did not differ between sexes and within females was not estrous cycle-dependent. Finally, although not statistically significant, 24-hour water deprivation abolished GABAA-LTD, an effect that was not a consequence of social isolation. Taken together, our data show that AVP modulates GABAA synaptic transmission in the jxBNST in fluid homeostasis- but not sex-dependent manner.

Effects of menthol on rat tail artery mediated by TRPM8 and voltage-gated calcium channels

Transient receptor potential melastatin 8 (TRPM8) is the principal cold and menthol receptor channel. Characterized primarily for its cold sensing role in sensory neurons, it is expressed and functional in several non-neuronal tissues, including vasculature. We previously demonstrated that menthol causes vasoconstriction and vasodilatation in isolated arteries, depending on vascular tone. Here we investigated calcium's role in responses mediated by TRPM8 ligands in rat tail artery myocytes using patch-clamp electrophysiology and ratiometric Ca2+ recording. Isometric contraction studies examined actions of TRPM8 ligands in the presence/absence of L-type calcium channel blocker. Menthol (300 μM), a concentration typically used to induce TRPM8 currents, strongly inhibited L-type voltage-dependent Ca2+ current (L-ICa) in myocytes, especially it's sustained component, most relevant for depolarisation-induced vasoconstriction. In contraction studies, with nifedipine present (10 μM) to abolish L-ICa contribution to phenylephrine (PE)-induced vasoconstrictions of vascular rings, a marked increase in tone was observed with menthol. Menthol-induced increases in PE-induced vasoconstrictions were mediated predominantly by Ca2+-release from sarcoplasmic reticulum, since they were significantly inhibited by cyclopiazonic acid. Pre-incubation of vascular rings with a TRPM8 antagonist strongly inhibited menthol-induced increases in PE-induced vasoconstrictions, thus confirming specific role of TRPM8. Finally, two other common TRPM8 agonists, WS-12 and icilin, inhibited L-ICa. Thus, TRPM8 channels are functionally active in rat tail artery myocytes and play a distinct direct stimulatory role in control of vascular tone. However, indirect effects of TRPM8 agonists, which are unrelated to TRPM8, are mediated by inhibition of L-type Ca2+ channels, and largely obscure TRPM8-mediated vasoconstriction.

TRPV2 Channels Contribute to Stretch-Activated Cation Currents and Myogenic Constriction in Retinal Arterioles

Purpose: Activation of the transient receptor potential channels, TRPC6, TRPM4, and TRPP1 (PKD2), has been shown to contribute to the myogenic constriction of cerebral arteries. In the present study we sought to determine the potential role of various mechanosensitive TRP channels to myogenic signaling in arterioles of the rat retina.

Methods: Rat retinal arterioles were isolated for RT-PCR, Fura-2 Ca2+ microfluorimetry, patch-clamp electrophysiology, and pressure myography studies. In some experiments, confocal immunolabeling of wholemount preparations was used to examine the localization of specific mechanosensitive TRP channels in retinal vascular smooth muscle cells (VSMCs).

Results: Reverse transcription-polymerase chain reaction analysis demonstrated mRNA expression for TRPC1, M7, V1, V2, V4, and P1, but not TRPC6 or M4, in isolated retinal arterioles. Immunolabeling revealed plasma membrane, cytosolic and nuclear expression of TRPC1, M7, V1, V2, V4, and P1 in retinal VSMCs. Hypoosmotic stretch-induced Ca2+ influx in retinal VSMCs was reversed by the TRPV2 inhibitor tranilast and the nonselective TRPP1/V2 antagonist amiloride. Inhibitors of TRPC1, M7, V1, and V4 had no effect. Hypoosmotic stretch-activated cation currents were similar in Na+ and Cs+ containing solutions suggesting no contribution by TRPP1 channels. Direct plasma membrane stretch triggered cation current activity that was blocked by tranilast and specific TRPV2 pore-blocking antibodies and mimicked by the TRPV2 activator, Δ9-tetrahydrocannabinol. Preincubation of retinal arterioles with TRPV2 blocking antibodies prevented the development of myogenic tone.

Conclusions: Our results suggest that retinal VSMCs express a range of mechanosensitive TRP channels, but only TRPV2 appears to contribute to myogenic signaling in this vascular bed.