Alex H. Williams

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.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

Many Parameter Sets in a Multicompartment Model Oscillator Are Robust to Temperature Perturbations

Neurons in cold-blooded animals remarkably maintain their function over a wide range of temperatures, even though the rates of many cellular processes increase twofold, threefold, or many-fold for each 10°C increase in temperature. Moreover, the kinetics of ion channels, maximal conductances, and Ca2+ buffering each have independent temperature sensitivities, suggesting that the balance of biological parameters can be disturbed by even modest temperature changes. In stomatogastric ganglia of the crab Cancer borealis, the duty cycle of the bursting pacemaker kernel is highly robust between 7 and 23°C (Rinberg et al., 2013). We examined how this might be achieved in a detailed conductance-based model in which exponential temperature sensitivities were given by Q10 parameters. We assessed the temperature robustness of this model across 125,000 random sets of Q10 parameters. To examine how robustness might be achieved across a variable population of animals, we repeated this analysis across six sets of maximal conductance parameters that produced similar activity at 11°C. Many permissible combinations of maximal conductance and Q10 parameters were found over broad regions of parameter space and relatively few correlations among Q10s were observed across successful parameter sets. A significant portion of Q10 sets worked for at least 3 of the 6 maximal conductance sets (∼11.1%). Nonetheless, no Q10 set produced robust function across all six maximal conductance sets, suggesting that maximal conductance parameters critically contribute to temperature robustness. Overall, these results provide insight into principles of temperature robustness in neuronal oscillators.

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.

Distinct or shared actions of peptide family isoforms: II. Multiple pyrokinins exert similar effects in the lobster stomatogastric nervous system

ABSTRACT Many neuropeptides are members of peptide families, with multiple structurally similar isoforms frequently found even within a single species. This raises the question of whether the individual peptides serve common or distinct functions. In the accompanying paper, we found high isoform specificity in the responses of the lobster (Homarus americanus) cardiac neuromuscular system to members of the pyrokinin peptide family: only one of five crustacean isoforms showed any bioactivity in the cardiac system. Because previous studies in other species had found little isoform specificity in pyrokinin actions, we examined the effects of the same five crustacean pyrokinins on the lobster stomatogastric nervous system (STNS). In contrast to our findings in the cardiac system, the effects of the five pyrokinin isoforms on the STNS were indistinguishable: they all activated or enhanced the gastric mill motor pattern, but did not alter the pyloric pattern. These results, in combination with those from the cardiac ganglion, suggest that members of a peptide family in the same species can be both isoform specific and highly promiscuous in their modulatory capacity. The mechanisms that underlie these differences in specificity have not yet been elucidated; one possible explanation, which has yet to be tested, is the presence and differential distribution of multiple receptors for members of this peptide family. Summary: Crustaceans typically possess multiple pyrokinins; in the lobster, all isoforms tested similarly activated the gastric mill rhythm, suggesting that the pyrokinin receptor(s) in the stomatogastric nervous system is relatively promiscuous.

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

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience

The ability to identify interpretable, low-dimensional features that capture the dynamics of large-scale neural recordings is a major challenge in neuroscience. Dynamics that include repeated temporal patterns (which we call sequences), are not succinctly captured by traditional dimensionality reduction techniques such as principal components analysis (PCA) and non-negative matrix factorization (NMF). The presence of neural sequences is commonly demonstrated using visual display of trial-averaged firing rates [15, 32, 19]. However, the field suffers from a lack of task-independent, unsupervised tools for consistently identifying sequences directly from neural data, and cross-validating these sequences on held-out data. We propose a tool that extends a convolutional NMF technique to prevent its common failure modes. Our method, which we call seqNMF, provides a framework for extracting sequences from a dataset, and is easily cross-validated to assess the significance of each extracted factor. We apply seqNMF to recover sequences in both a previously published dataset from rat hippocampus, as well as a new dataset from the songbird pre-motor area, HVC. In the hippocampal data, our algorithm automatically identifies neural sequences that match those calculated manually by reference to behavioral events [15, 32]. The second data set was recorded in birds that never heard a tutor, and therefore sang pathologically variable songs. Despite this variable behavior, seqNMF is able to discover stereotyped neural sequences. These sequences are deployed in an overlapping and disorganized manner, strikingly different from what is seen in tutored birds. Thus, by identifying temporal structure directly from neural data, seqNMF can enable dissection of complex neural circuits with noisy or changing behavioral readouts.

Dendritic trafficking faces fundamental 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, showing how 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.

Massively parallel dissection of human accelerated regions in human and chimpanzee neural progenitors

How mutations in gene regulatory elements lead to evolutionary changes remains largely unknown. Human accelerated regions (HARs) are ideal for exploring this question, because they are associated with human-specific traits and contain multiple human-specific variants at sites conserved across mammals, suggesting that they alter or compensate to preserve function. We performed massively parallel reporter assays on all human and chimpanzee HAR sequences in human and chimpanzee iPSC-derived neural progenitors at two differentiation stages. Forty-three percent (306/714) of HARs function as neuronal enhancers, with two-thirds (204/306) showing consistent changes in activity between human and chimpanzee sequences. These changes were almost all sequence dependent and not affected by cell species or differentiation stage. We tested all evolutionary intermediates between human and chimpanzee sequences of seven HARs, finding variants that interact both positively and negatively. This study shows that variants acquired during human evolution interact to buffer and amplify changes to enhancer function.