Timothy O'Leary

Neuromodulation of Circuits with Variable Parameters: Single Neurons and Small Circuits Reveal Principles of State-Dependent and Robust Neuromodulation

Neuromodulation underlies many behavioral states and has been extensively studied in small circuits. This has allowed the systematic exploration of how neuromodulatory substances and the neurons that release them can influence circuit function. The physiological state of a network and its level of activity can have profound effects on how the modulators act, a phenomenon known as state dependence. We provide insights from experiments and computational work that show how state dependence can arise and the consequences it can have for cellular and circuit function. These observations pose a general unsolved question that is relevant to all nervous systems: How is robust modulation achieved in spite of animal-to-animal variability and degenerate, nonlinear mechanisms for the production of neuronal and network activity?

Dynamin I phosphorylation by GSK3 controls activity-dependent bulk endocytosis of synaptic vesicles

Glycogen synthase kinase 3 (GSK3) is a critical enzyme in neuronal physiology; however, it is not yet known whether it has any specific role in presynaptic function. We found that GSK3 phosphorylates a residue on the large GTPase dynamin I (Ser-774) both in vitro and in primary rat neuronal cultures. This was dependent on prior phosphorylation of Ser-778 by cyclin-dependent kinase 5. Using both acute inhibition with pharmacological antagonists and silencing of expression with short hairpin RNA, we found that GSK3 was specifically required for activity-dependent bulk endocytosis (ADBE) but not clathrin-mediated endocytosis. Moreover we found that the specific phosphorylation of Ser-774 on dynamin I by GSK3 was both necessary and sufficient for ADBE. These results demonstrate a presynaptic role for GSK3 and they indicate that a protein kinase signaling cascade prepares synaptic vesicles for retrieval during elevated neuronal activity.

Correlations in ion channel expression emerge from homeostatic tuning rules

Significance A deep puzzle in neuroscience is how neurons maintain their electrical properties despite continuous ion channel turnover and activity perturbations. Previous work proposed that activity-dependent homeostatic rules ensure robust development of excitability by regulating channel density, although it is not understood how these rules shape the distribution of ion channel types nor how finely tuned these rules must be. We show that generic homeostatic regulation rules impose correlations in the steady-state distribution of ion channels, as has been recently observed experimentally. Specific correlations depend on relative expression rates, and the regulation rules themselves are far more robust than previously thought. Experimental observations reveal that the expression levels of different ion channels vary across neurons of a defined type, even when these neurons exhibit stereotyped electrical properties. However, there are robust correlations between different ion channel expression levels, although the mechanisms that determine these correlations are unknown. Using generic model neurons, we show that correlated conductance expression can emerge from simple homeostatic control mechanisms that couple expression rates of individual conductances to cellular readouts of activity. The correlations depend on the relative rates of expression of different conductances. Thus, variability is consistent with homeostatic regulation and the structure of this variability reveals quantitative relations between regulation dynamics of different conductances. Furthermore, we show that homeostatic regulation is remarkably insensitive to the details that couple the regulation of a given conductance to overall neuronal activity because of degeneracy in the function of multiple conductances and can be robust to “antihomeostatic” regulation of a subset of conductances expressed in a cell.

Neuronal homeostasis: time for a change?

Abstract  Homeostatic processes that regulate electrical activity in neurones are now an established aspect of physiology and rest on a large body of experimental evidence that points to roles in development, learning and memory, and disease. However, the concepts underlying homeostasis are too often summarized in ways that restrict their explanatory power and obviate important subtleties. Here, we present a review of the underlying theory of homeostasis – control theory – in an attempt to reconcile some existing conceptual problems in the context of neuronal physiology. In addition to clarifying the underlying theory, this review highlights the remaining challenges posed when analysing homeostatic phenomena that underlie the regulation of neuronal excitability. Moreover, we suggest approaches for future experimental and computational work that will further our understanding of neuronal homeostasis and the fundamental neurophysiological functions it serves.

SynGAP isoforms exert opposing effects on synaptic strength

Alternative promoter usage and alternative splicing enable diversification of the transcriptome. Here we demonstrate that the function of Synaptic GTPase-Activating Protein (SynGAP), a key synaptic protein, is determined by the combination of its amino-terminal sequence with its carboxy-terminal sequence. 5′ rapid amplification of cDNA ends and primer extension show that different N-terminal protein sequences arise through alternative promoter usage that are regulated by synaptic activity and postnatal age. Heterogeneity in C-terminal protein sequence arises through alternative splicing. Overexpression of SynGAP α1 versus α2 C-termini-containing proteins in hippocampal neurons has opposing effects on synaptic strength, decreasing and increasing miniature excitatory synaptic currents amplitude/frequency, respectively. The magnitude of this C-terminal-dependent effect is modulated by the N-terminal peptide sequence. This is the first demonstration that activity-dependent alternative promoter usage can change the function of a synaptic protein at excitatory synapses. Furthermore, the direction and degree of synaptic modulation exerted by different protein isoforms from a single gene locus is dependent on the combination of differential promoter usage and alternative splicing.

The neuromuscular transform of the lobster cardiac system explains the opposing effects of a neuromodulator on muscle output.

Motor neuron activity is transformed into muscle movement through a cascade of complex molecular and biomechanical events. This nonlinear mapping of neural inputs to motor behaviors is called the neuromuscular transform (NMT). We examined the NMT in the cardiac system of the lobster Homarus americanus by stimulating a cardiac motor nerve with rhythmic bursts of action potentials and measuring muscle movements in response to different stimulation patterns. The NMT was similar across preparations, which suggested that it could be used to predict muscle movement from spontaneous neural activity in the intact heart. We assessed this possibility across semi-intact heart preparations in two separate analyses. First, we performed a linear regression analysis across 122 preparations in physiological saline to predict muscle movements from neural activity. Under these conditions, the NMT was predictive of contraction duty cycle but was unable to predict contraction amplitude, likely as a result of uncontrolled interanimal variability. Second, we assessed the ability of the NMT to predict changes in motor output induced by the neuropeptide C-type allatostatin. Wiwatpanit et al. (2012) showed that bath application of C-type allatostatin produced either increases or decreases in the amplitude of the lobster heart contractions. We show that an important component of these preparation-dependent effects can arise from quantifiable differences in the basal state of each preparation and the nonlinear form of the NMT. These results illustrate how properly characterizing the relationships between neural activity and measurable physiological outputs can provide insight into seemingly idiosyncratic effects of neuromodulators across individuals.

Mapping Neural Activation onto Behavior in an Entire Animal

A combination of technologies reveals which neurons constitute circuits for specific behaviors in Drosophila larvae. [Also see Research Article by Vogelstein et al.] For almost a century, neuroscientists have tried to understand how patterns of neuronal activity generate behavior. Many of the early studies turned to the “simple” systems of invertebrates in the hope of discovering the components of circuits and their connections. A striking finding was the existence of “command neurons” in arthropods and molluscs that produce complex and coordinated movements when stimulated (1, 2). The challenge was then to identify the connections between these neurons and other neurons important for those behaviors. This approach was always limited to examining only a few neurons from the tens or hundreds of thousands in the animal (3). On page 386 of this issue, Vogelstein et al. (4) usher in a new era of integrated methods for deciphering how an entire nervous system generates behavior.

Dendritic trafficking faces physiologically critical speed-precision tradeoffs

Nervous system function requires intracellular transport of channels, receptors, mRNAs, and other cargo throughout complex neuronal morphologies. Local signals such as synaptic input can regulate cargo trafficking, motivating the leading conceptual model of neuron-wide transport, sometimes called the ‘sushi-belt model’ (Doyle and Kiebler, 2011). Current theories and experiments are based on this model, yet its predictions are not rigorously understood. We formalized the sushi belt model mathematically, and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies. However, the model also predicts an unavoidable, morphology dependent tradeoff between speed, precision and metabolic efficiency of cargo transport. With experimental estimates of trafficking kinetics, the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree. These findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons. DOI: http://dx.doi.org/10.7554/eLife.20556.001

Single-channel properties of N-methyl-D-aspartate receptors containing chimaeric GluN2A/GluN2D subunits

Subtypes of NMDARs (N-methyl-D-aspartate receptors) display differences in their pharmacological and biophysical properties. The differences are, to a large extent, determined by the identities of the GluN2 (glutamate-binding) NMDAR subunits that are co-expressed with GluN1 (glycine-binding) subunits, which form the final tetrameric NMDAR assembly. Of the four GluN2 subunits that exist (termed A-D), NMDARs composed of GluN1/GluN2A and GluN1/GluN2D subunits display the greatest differences in their sensitivities to a variety of agonists, antagonists and channel blockers as well as showing marked differences in their single-channel conductances and deactivation kinetics. Here, we describe a series of experiments where we have generated and studied two chimaeric GluN2A/GluN2D subunits. The first chimaera, referred to as GluN2A(2D-M1M2M3), replaces the membrane-associated regions M1, M2 and M3 of the GluN2A subunit with the corresponding regions found in the GluN2D subunit. The second chimaera, GluN2A(2D-S1M1M2M3S2), replaces the same three membrane-associated regions of the GluN2A subunit plus the LBD (ligand-binding domain) with the corresponding regions of the GluN2D subunit. Our results show that the identity of the GluN2 LBD not only controls glutamate potency, but also influences the potency of the NMDAR co-agonist glycine, whereas the single-channel conductance and the duration of single activations of ion channels can be predicted by the identities of the M1-M3 regions and the LBD.

Neuronal behaviors: A control perspective

The purpose of this tutorial is to introduce and analyze models of neurons from a control perspective and to show how recently developed analytical tools help to address important biological questions. A first objective is to review the basic modeling principles of neurophysiology in which neurons are modeled as equivalent nonlinear electrical circuits that capture their excitable properties. The specific architecture of the models is key to the tractability of their analysis: in spite of their high-dimensional and nonlinear nature, the model properties can be understood in terms of few canonical positive and negative feedback motifs localized in distinct timescales. We use this insight to shed light on a key problem in experimental neurophysiology, the challenge of understanding the sensitivity of neuronal behaviors to underlying parameters in empirically-derived models. Finally, we show how sensitivity analysis of neuronal excitability relates to robustness and regulation of neuronal behaviors.