Coupling, membrane conductance, and ion channel mRNA profiles in the establishment and maintenance of network activity in the crustacean cardiac ganglion

Neural networks produce critical rhythmic behaviors throughout an animal's lifespan, despite growth, differing environments, and changes in physiological state. This requires networks which balance stability in their properties with the plasticity necessary to respond to altered demands or perturbations. Studying the mechanisms which confer these properties requires a well characterized system with a known network topology and identifiable neurons that are amenable to both electrophysiological and molecular characterization and manipulation. Here, we use two networks from Cancer borealis to explore activity dependent regulation of cell connectivity, changes in cell properties with prolonged perturbation, and reliability of gene expression as a means for cell identification. For the first two topics we use the cardiac ganglion alone. The cardiac ganglion consists of a kernel of four interneurons that drive five motor neurons (termed large cells, LCs) which innervate the heart musculature. LCs burst synchronously due to simultaneous stimulation and electrical coupling through gap junctions. Depolarizing pharmacological perturbations have been shown to result in hyperexcitability (Ransdell et al., 2012a) and disrupt synchrony between LCs (Lane et al., 2016) eliciting rapid plasticity in ionic currents and electrical coupling which restores synchrony and excitability (Ransdell et al., 2012a; Lane et al., 2016). The salient electrophysiological signal which elicits coupling plasticity has not been identified. Using voltage clamp we directly control LC depolarizations to vary amplitude and timing of activity between LCs. We find that timing between cells, rather than depolarization elicits plasticity with the direction, i.e., potentiation or depression, being determined by the degree of desynchronization. With dynamic clamp we artificially couple networks from two animals and show that strong coupling with sufficient desynchronization can compromise a cell's output. These results suggest that coupling strength is tuned promoting synchrony or baseline cellular activity in a degree dependent manner. While rapid compensatory plasticity to hyperexcitability has been shown, it is unknown whether the changes are solely post-transcriptional and whether the short-term changes persist over longer time scales. We perturb networks for one or twenty-four hours and compare LCs' excitability, membrane properties, and abundances of ion channel and gap junction transcripts. We find evidence of rapid transcriptional changes at one hour, which may be maintained or regress at twenty-four hours. Additionally, we find that membrane properties and excitability are not maintained from one to twenty-four hours, suggesting a failure to maintain homeostasis or that additional compensatory changes are occurring at the network level. To address our third topic, we use LCs in addition to neurons collected form the stomatogastric ganglion which coordinates mastication and filtering in the digestive track. Both systems allow for unambiguous identification of cells based on anatomy or neuronal projections. We use this to evaluate the efficacy of cluster estimation procedures, clustering methods, and classification algorithms to determine the number of cell types present, group like cells together, and identify cells based on gene expression alone. We use single cell RNA-seq and single cell qRT-PCR to measure all contigs or a select set of ion channel, receptor, and gap junction mRNAs. We find these methods do not reproduce the known number of cell types present. Furthermore, although clustering and classification both outperform chance, we are unable to recapitulate cell type with complete accuracy from these data. These results indicate that, while promising, determining cell type by molecular profiling should not be relied on as the sole metric of cell type determination.Includes bibliographical references (pages 159-173)

Characterizing the financial cycle: evidence from a frequency domain analysis

Abstract A growing body of literature argues that the financial cycle is considerably longer in duration and larger in amplitude than the business cycle and that its distinguishing features became more pronounced over time. This paper proposes an empirical approach suitable to test these hypotheses. We parametrically estimate the whole spectrum of financial and real variables to obtain a complete picture of their cyclical properties. We provide strong statistical evidence for the US and slightly weaker evidence for the UK validating the hypothesized features of the financial cycle. In Germany, however, the financial cycle is, if at all, much less visible

Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Northcutt, A. J., Kick, D. R., Otopalik, A. G., Goetz, B. M., Harris, R. M., Santin, J. M., Hofmann, H. A., Marder, E., & Schulz, D. J. Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis. Proceedings of the National Academy of Sciences of the United States of America, 116 (52) (2019): 26980-26990, doi: 10.1073/pnas.1911413116.Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.We thank members of the D.J.S., H.A.H., and E.M. laboratories for helpful discussions. We thank the Genomic Sequencing and Analysis Facility (The University of Texas [UT] at Austin) for library preparation and sequencing and the bioinformatics consulting team at the UT Austin Center for Computational Biology and Bioinformatics for helpful advice. This work was supported by National Institutes of Health grant R01MH046742-29 (to E.M. and D.J.S.) and the National Institute of General Medical Sciences T32GM008396 (support for A.J.N.) and National Institute of Mental Health grant 5R25MH059472-18 and the Grass Foundation (support for Neural Systems and Behavior Course at the Marine Biological Laboratory)

Yield prediction through integration of genetic, environment, and management data through deep learning

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model’s sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield—those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors