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bioRxiv2019

Neuro-genetic plasticity of Caenorhabditis elegans behavioral thermal tolerance

BackgroundAnimal responses to thermal stimuli involve intricate contributions of genetics, neurobiology and physiology, with temperature variation providing a pervasive environmental factor for natural selection. Thermal behavior thus exemplifies a dynamic trait that requires non-trivial phenotypic summaries to appropriately capture the trait in response to a changing environment. To characterize the deterministic and plastic components of thermal responses, we developed a novel micro-droplet assay of nematode behavior that permits information-dense summaries of dynamic behavioral phenotypes as reaction norms in response to increasing temperature (thermal tolerance curves, TTC).\n\nResultsWe found that C. elegans TTCs shift predictably with rearing conditions and developmental stage, with significant differences between distinct wildtype genetic backgrounds. Moreover, after screening TTCs for 58 C. elegans genetic mutant strains, we determined that genes affecting thermosensation, including cmk-1 and tax-4, potentially play important roles in the behavioral control of locomotion at high temperature, implicating neural decision-making in TTC shape rather than just generalized physiological limits. However, expression of the transient receptor potential ion channel TRPA-1 in the nervous system is not sufficient to rescue rearing-dependent plasticity in TTCs conferred by normal expression of this gene, indicating instead a role for intestinal signaling involving TRPA-1 in the adaptive plasticity of thermal performance.\n\nConclusionsThese results implicate nervous system and non-nervous system contributions to behavior, in addition to basic cellular physiology, as key mediators of evolutionary responses to selection from temperature variation in nature.

genetics↗

bioRxiv2019

Ray tracing 3D spectral scenes through human optics models

Scientists and engineers have created computations and made measurements that characterize the first steps of seeing. ISETBio software integrates such computations and data into an open-source software package. The initial ISETBio implementations modeled image formation (physiological optics) for planar or distant scenes. The ISET3d software described here extends that implementation, simulating image formation for three-dimensional scenes. The software system relies on a quantitative computer graphics program that ray traces the scene radiance through the physiological optics to the retinal irradiance. We describe and validate the implementation for several model eyes. Then, we use the software to quantify the impact of several physiological optics parameters on three-dimensional image formation. ISET3d is integrated with ISETBio, making it straightforward to convert the retinal irradiance into cone excitations. These methods help the user compute the predictions of optics models for a wide range of spatially-rich three-dimensional scenes. They can also be used to evaluate the impact of nearby visual occlusion, the information available to binocular vision, or the retinal images expected from near-field and augmented reality displays.

neuroscience↗

bioRxiv2019

Quantitative Colour Pattern Analysis (QCPA): A Comprehensive Framework for the Analysis of Colour Patterns in Nature

O_LITo understand the function of colour signals in nature, we require robust quantitative analytical frameworks to enable us to estimate how animal and plant colour patterns appear against their natural background as viewed by ecologically relevant species. Due to the quantitative limitations of existing methods, colour and pattern are rarely analysed in conjunction with one another, despite a large body of literature and decades of research on the importance of spatiochromatic colour pattern analyses. Furthermore, key physiological limitations of animal visual systems such as spatial acuity, spectral sensitivities, photoreceptor abundances and receptor noise levels are rarely considered together in colour pattern analyses.\nC_LIO_LIHere, we present a novel analytical framework, called the Quantitative Colour Pattern Analysis (QCPA). We have overcome many quantitative and qualitative limitations of existing colour pattern analyses by combining calibrated digital photography and visual modelling. We have integrated and updated existing spatiochromatic colour pattern analyses, including adjacency, visual contrast and boundary strength analysis, to be implemented using calibrated digital photography through the Multispectral Image Analysis and Calibration (MICA) Toolbox.\nC_LIO_LIThis combination of calibrated photography and spatiochromatic colour pattern analyses is enabled by the inclusion of psychophysical colour and luminance discrimination thresholds for image segmentation, which we call Receptor Noise Limited Clustering, used here for the first time. Furthermore, QCPA provides a novel psycho-physiological approach to the modelling of spatial acuity using convolution in the spatial or frequency domains, followed by Receptor Noise Limited Ranked Filtering to eliminate intermediate edge artefacts and recover sharp boundaries following smoothing. We also present a new type of colour pattern analysis, the Local Edge Intensity Analysis (LEIA) as well as a range of novel psycho-physiological approaches to the visualisation of spatiochromatic data.\nC_LIO_LIQCPA combines novel and existing pattern analysis frameworks into what we hope is a unified, user-friendly, free and open source toolbox and introduce a range of novel analytical and data-visualisation approaches. These analyses and tools have been seamlessly integrated into the MICA toolbox providing a dynamic and user-friendly workflow.\nC_LIO_LIQCPA is a framework for the empirical investigation of key theories underlying the design, function and evolution of colour patterns in nature. We believe that it is compatible with, but more thorough than, other existing colour pattern analyses.\nC_LI

animal behavior and cognition↗

bioRxiv2019

Coordinating Receptor Expression and Wiring Specificity in Olfactory Receptor Neurons

The ultimate function of a neuron is determined by both its physiology and connectivity, but the transcriptional regulatory mechanisms that coordinate these two features are not well understood1-4. The Drosophila Olfactory receptor neurons (ORNs) provide an excellent system to investigate this question. As in mammals5, each Drosophila ORN class is defined by the expression of a single olfactory receptor or a unique combination thereof, which determines their odor responses, and by the single glomerulus to which their axons target, which determines how sensory signals are represented in the brain6-10. In mammals, the coordination of olfactory receptor expression and wiring specificity is accomplished in part by olfactory receptors themselves regulating ORN wiring specificity11-13. However, Drosophila olfactory receptors do not instruct axon targeting6, 14, raising the question as to how receptor expression and wiring specificity are coordinated. Using single-cell RNA-sequencing and genetic analysis, we identified 33 transcriptomic clusters for fly ORNs. We unambiguously mapped 17 to glomerular classes, demonstrating that transcriptomic clusters correspond well with anatomically and physiologically defined ORN classes. We found that each ORN expresses ~150 transcription factors (TFs), and identified a master TF that regulates both olfactory receptor expression and wiring specificity. A second TF plays distinct roles, regulating only receptor expression in one class and only wiring in another. Thus, fly ORNs utilize diverse transcriptional strategies to coordinate physiology and connectivity.

neuroscience↗

bioRxiv2019

A role for Hypoxia Inducible Factor 1a (HIF1a) in intermittent hypoxia-dependent changes in spatial memory and synaptic plasticity

Intermittent hypoxia (IH), a key feature of sleep apnea, increases the oxygen regulated transcription factor Hypoxia Inducible Factor 1a (HIF1a). Although recognized for its role in IH-dependent changes in cardio-respiratory physiology, it remains unclear how IH-dependent HIF1a signaling affects neurophysiology underlying learning and memory. This study examines how IH affects hippocampal associated learning and memory in wildtype mice and mice heterozygous for the HIF1a gene (HIF1a+/-). In wild-type mice, ten days of IH impaired performance in the Barnes maze increased hippocampal HIF1a and elevated protein carbonyls. These behavioral and biochemical effects of IH were accompanied by a decrease in the N-Methyl-D-Aspartate receptor (NMDAr) and an attenuation of long-term potentiation (LTP) in area CA1. In HIF1a+/-, IH did not impair Barnes maze performance, increase hippocampal HIF1a, or enhance protein carbonyl content. At the network level, IH neither led to a decrease in NMDAr nor impaired LTP. Concurrent antioxidant treatment during IH mitigated the IH-dependent effects on the Barnes maze performance and LTP in wildtype mice. Our findings indicate that IH-dependent HIF1a signaling leads to oxidative stress and reduces NMDAr to impair LTP in area CA1, which contributes to IH-dependent deficits in learning and memory associated with the hippocampus.\n\nSignificanceIntermittent Hypoxia is a hallmark of sleep apnea and decreases the threshold for cognitive deficit. We demonstrate that intermittent hypoxia-dependent HIF1a signaling contributes to impairments in hippocampal associated memory. This is co-incidental with HIF1a-mediated alternations in synaptic physiology and increased oxidative stress.\n\nKey pointsO_LIIntermittent hypoxia (IH) is a hallmark of sleep apnea and is known to cause learning and memory deficits.\nC_LIO_LIHypoxia Inducible Factor 1a (HIF1a), is associated with IH-dependent changes in physiology.\nC_LIO_LIIH exposure causes increased hippocampal HIF1a in wild type mice and is associated with elevated oxidative stress, impairments to spatial memory, and suppression of long term potentiation (LTP).\nC_LIO_LIIH-dependent suppression of LTP is co-incidental with diminished NMDA receptor contribution to glutamatergic transmission.\nC_LIO_LIFollowing IH, mice heterozygous for HIF1a (HIF1a+/-) do not show an increase in HIF1a and oxidative stress, or changes in either behavior or glutamatergic transmission.\nC_LI

neuroscience↗

bioRxiv2019

Disambiguating the role of blood flow and global signal with Partial Information Decomposition

In resting state functional magnetic resonance imaging (rs-fMRI) a common strategy to reduce the impact of physiological noise and other artifacts on the data is to regress out the global signal using global signal regression (GSR). Yet, GSR is one of the most controversial preprocessing techniques for rs-fMRI. It effectively removes non-neuronal artifacts, but at the same time it alters correlational patterns in unpredicted ways. Furthermore the global signal includes neural BOLD signal by construction, and is consequently related to neural and behavioral function. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proved to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improve denoising methods. Using GSR but not correcting for blood flow might selectively introduce physiological artifacts across intrinsic connectivity networks that distort the functional connectivity estimates.

neuroscience↗

bioRxiv2019

Visual and non-visual properties of filters manipulating short-wavelength light

Optical filters and tints manipulating short-wavelength light (so-called \"blue-blocking\" or \"blue-attenuating\") are used as a remedy for a range of ocular, retinal, neurological and psychiatric disorders. In many cases, the only available quantification of the optical effects of a given optical filter is the spectral transmittance, which specifies the amount of light transmitted as a function of wavelength. Here, we propose a novel physiologically relevant and retinally referenced framework for quantifying the visual and non-visual effects of these filters, incorporating the attenuation of luminance (luminance factor), the attenuation of melanopsin activation (melanopsin factor), the shift in colour, and the reduction of the colour gamut (gamut factor). We examined a novel data base of optical transmittance filters (n=120) which were digitally extracted from a variety of sources and find a large diversity in the alteration of visual and non-visual properties. We suggest that future studies and examinations of the physiological effects of optical filters quantify the visual and non-visual effects of the filters beyond the spectral transmittance, which will eventually aid in developing a mechanistic understanding of how different filters affect physiology.

neuroscience↗

bioRxiv2019

The emergence of sequence-dependent structural motifs in stretched, torsionally constrained DNA

The double-helical structure of DNA results from canonical base pairing and stacking interactions. However, variations from steady-state conformations result from mechanical perturbations in cells. These different topologies have physiological relevance but their dependence on sequence remains unclear. Here, we use molecular dynamics simulations to show that sequence differences result in markedly different structural motifs upon physiological twisting and stretching. We simulated overextension on four different sequences of DNA ((AA)12, (AT)12, (GG)12 and (GC)12) with supercoiling densities within the physiological range. We found that DNA denatures in the majority of stretching simulations, surprisingly including those with overtwisted DNA. GC-rich sequences were observed to be more stable than AT-rich, with the specific response dependent on base pair ordering. Furthermore, we found that (AT)12 forms stable periodic structures with non-canonical hydrogen bonds in some regions and non-canonical stacking in others, whereas (GC)12 forms a stacking motif of four base pairs independent of supercoiling density. Our results demonstrate that 20-30% DNA extension is sufficient for breaking B-DNA around and significantly above cellular supercoiling, and that the DNA sequence is crucial for understanding structural changes under mechanical stress. Our findings have important implications for the activities of protein machinery interacting with DNA in all cells.

biophysics↗

bioRxiv2019

Rapid characterisation of hERG channel kinetics II: temperature dependence

Ion channel behaviour can depend strongly on temperature, with faster kinetics at physiological temperatures leading to considerable changes in currents relative to room temperature. These temperature-dependent changes in voltage-dependent ion channel kinetics (rates of opening, closing and inactivating) are commonly represented with Q10 coefficients or an Eyring relationship. In this paper we assess the validity of these representations by characterising channel kinetics at multiple temperatures. We focus on the hERG channel, which is important in drug safety assessment and commonly screened at room temperature, so that results require extrapolation to physiological temperature. In Part I of this study we established a reliable method for high-throughput characterisation of hERG1a (Kv11.1) kinetics, using a 15 second information-rich optimised protocol. In this Part II, we use this protocol to study the temperature dependence of hERG kinetics using CHO cells over-expressing hERG1a on the Nanion SyncroPatch 384PE, a 384-well automated patch clamp platform, with temperature control. We characterise the temperature dependence of hERG gating by fitting the parameters of a mathematical model of hERG kinetics to data obtained at five distinct temperatures between 25 and 37 {degrees}C, and validate the models using different protocols. Our models reveal that activation is far more temperature sensitive than inactivation, and we observe that the temperature dependency of the kinetic parameters is not represented well by Q10 coefficients: it broadly follows a generalised, but not the standardly-used, Eyring relationship. We also demonstrate that experimental estimations of Q10 coefficients are protocol-dependent. Our results show that a direct fit using our 15 second protocol best represents hERG kinetics at any given temperature, and suggests that predictions from the Generalised Eyring theory may be preferentially used if no experimentally-derived data are available.\n\nStatement of SignificanceIon channel currents are highly sensitive to temperature changes. Yet because many experiments are performed more easily at room temperature, it is common to extrapolate findings to physiological temperatures through the use of Q10 coefficients or Eyring rate theory. By applying short, information-rich protocols that we developed in Part I of this study we identify how kinetic parameters change over temperature. We find that the commonly-used Q10 and Eyring formulations are incapable of describing the parameters temperature dependence, a more Generalised Eyring relationship works well, but remeasuring kinetics and refitting a model is optimal. The findings have implications for the accuracy of the many applications of Q10 coefficients in electrophysiology, and suggest that care is needed to avoid misleading extrapolations in their many scientific and industrial pharmaceutical applications.

biophysics↗

bioRxiv2019

Dynamic Response in the Larval Geoduck Clam Proteome to Elevated pCO2

Pacific geoduck clams (Panopea generosa) are found along the Northeast Pacific coast where they are significant components of coastal and estuarine ecosystems and the basis of a growing and highly profitable aquaculture industry. The Pacific coastline, however, is also the sight of rapidly changing ocean habitat, including significant reductions in pH. The impacts of ocean acidification on invertebrate bivalve larvae have been widely documented and it is well established that many species experience growth and developmental deficiencies when exposed to low pH. As a native of environments that have historically lower pH than the open ocean, it is possible that geoduck larvae are less impacted by these effects than other species. Over two weeks in larval development (days 6-19 post-fertilization) geoduck larvae were reared at pH 7.5 or 7.1 in a commercial shellfish hatchery. Larvae were sampled at six time points throughout the period for a in-depth proteomics analysis of developmental molecular physiology. Larvae reared at low pH were smaller than those reared at ambient pH, especially in the prodissoconch II phase of development. Competency for settlement was also delayed in larvae from the low pH conditions. A comparison of proteomic profiles over the course of development reveal that these differing phenotypic outcomes are likely due to environmental disruptions to the timing of molecular physiological events as suites of proteins showed differing profiles of abundance between the two pH environments. Ocean acidification likely caused an energetic stress on the larvae at pH 7.1, causing a shift in physiological prioritization with resulting loss of fitness.

molecular biology↗

bioRxiv2019

Machine learning predicts early-onset acute organ failure in critically ill patients with sickle cell disease

BackgroundSickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may progress to organ failure during disease exacerbations. Early detection of acute physiological deterioration leading to organ failure is not always attainable. Machine learning techniques that allow for prediction of organ failure may enable earlier identification and treatment, and potentially reduce mortality. We tested the hypothesis that machine learning physiomarkers could predict the development of organ dysfunction in an adult sample of patients with SCD admitted to intensive care units.\n\nMethods and FindingsWe studied 63 sequential SCD patients with 163 patient encounters, mean age 33.0{+/-}11.0 years, admitted to intensive care units, some of whom (6.7%) had pre-existing cardiovascular or kidney disease. A subset of these patient encounters (37; 23%) met sequential organ failure assessment (SOFA) criteria. The site of organ failure included: central nervous system (32), cardiovascular (11), renal (10), liver (7), respiratory (5) and coagulation (2) systems. Most (81.5%) of the patient encounters who experienced organ failure had single organ failure. The other 126 SCD patient encounters served as controls. A set of signal processing features (such as fast fourier transform, energy, continuous wavelet transform, etc.) derived from heart rate, blood pressure, and respiratory rate were identified to distinguish patients with SCD who developed acute physiological deterioration leading to organ failure, from SCD patients who did not meet the criteria. A random forest model accurately predicted organ failure up to six hours prior to onset, with a five-fold cross-validation accuracy of 94.57% (average sensitivity and specificity of 90.24% and 98.9% respectively).\n\nConclusionsThis study demonstrates the viability of using machine learning to predict acute physiological deterioration heralded organ failure among hospitalized adults with SCD. The discovery of salient physiomarkers through machine learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.

bioinformatics↗

bioRxiv2019

Cellular properties of intrinsically photosensitive retinal ganglion cells during postnatal development

BackgroundMelanopsin-expressing, intrinsically photosensitive retinal ganglion cells (ipRGCs) respond directly to light and have been shown to mediate a broad variety of visual behaviors in adult animals. ipRGCs are also the first light sensitive cells in the developing retina, and have been implicated in a number of retinal developmental processes such as pruning of retinal vasculature and refinement of retinofugal projections. However, little is currently known about the properties of the six ipRGC subtypes during development, and how these cells act to influence retinal development. We therefore sought to characterize the structure, physiology, and birthdate of the most abundant ipRGC subtypes, M1, M2, and M4, at discrete postnatal developmental timepoints.\n\nMethodsWe utilized whole cell patch clamp to measure the electrophysiological and morphological properties of ipRGC subtypes through postnatal development. We also used EdU labeling to determine the embryonic timepoints at which ipRGC subtypes terminally differentiate.\n\nResultsOur data show that ipRGC subtypes are distinguishable from each other early in postnatal development. Additionally, we find that while ipRGC subtypes terminally differentiate at similar embryonic stages, the subtypes reach adult-like morphology and physiology at different developmental timepoints.\n\nConclusionsThis work provides a broad assessment of ipRGC morphological and physiological properties during the postnatal stages at which they are most influential in modulating retinal development, and lays the groundwork for further understanding of the specific role of each ipRGC subtype in influencing retinal and visual system development.

neuroscience↗

bioRxiv2019

In situ measurement of absolute concentrations by Normalized Raman Imaging

Cell mass and its chemical composition are important aggregate cellular variables for physiological processes including growth control and tissue homeostasis. Despite their central importance, it has been difficult to quantitatively measure these quantities from single cells in intact tissue. Here, we introduce Normalized Raman Imaging (NoRI), a Stimulated Raman Scattering (SRS) microscopy method that provides the local concentrations of protein, lipid and water from live or fixed tissue samples with high spatial resolution. Using NoRI, we demonstrate that single cell protein, lipid and water concentrations are maintained in a tight range in cells under same physiological conditions and are altered in different physiological states such as cell cycle stages, attachment to substrates of different stiffness, or by entering senescence. In animal tissues, protein and lipid concentration varies with cell types, yet an unexpected cell-to-cell heterogeneity was found in cerebellar Purkinje cells. Protein and lipid concentration profile provides a new means to quantitatively compare disease-related pathology as demonstrated using models of Alzheimers disease. Our demonstration shows that NoRI is a broadly applicable tool for probing the biological regulation of protein mass, lipid mass and water in cellular and tissue growth, homeostasis, and disease.

bioengineering↗

bioRxiv2019

Retinal stabilization reveals limited influence of extraretinal signals on heading tuning in the medial superior temporal area

Heading perception in primates depends heavily on visual optic-flow cues. Yet during self-motion, heading percepts remain stable even though smooth-pursuit eye movements often distort optic flow. Electrophysiological studies have identified visual areas in monkey cortex, including the dorsal medial superior temporal area (MSTd), that signal the true heading direction during pursuit. According to theoretical work, self-motion can be represented accurately by compensating for these distortions in two ways: via retinal mechanisms or via extraretinal efference-copy signals, which predict the sensory consequences of movement. Psychophysical evidence strongly supports the efference-copy hypothesis, but physiological evidence remains inconclusive. Neurons that signal the true heading direction during pursuit are found in visual areas of monkey cortex, including the dorsal medial superior temporal area (MSTd). Here we measured heading tuning in MSTd using a novel stimulus paradigm, in which we stabilize the optic-flow stimulus on the retina during pursuit. This approach isolates the effects on neuronal heading preferences of extraretinal signals, which remain active while the retinal stimulus is prevented from changing. Our results demonstrate a significant but small influence of extraretinal signals on the preferred heading directions of MSTd neurons. Under our stimulus conditions, which are rich in retinal cues, we find that retinal mechanisms dominate physiological corrections for pursuit eye movements, suggesting that extraretinal cues, such as predictive efference-copy mechanisms, have a limited role under naturalistic conditions.\n\nSignificance StatementSensory systems discount stimulation caused by the animals own behavior. For example, eye movements cause irrelevant retinal signals that could interfere with motion perception. The visual system compensates for such self-generated motion, but how this happens is unclear. Two theoretical possibilities are a purely visual calculation or one using an internal signal of eye movements to compensate for their effects. Such a signal can be isolated by experimentally stabilizing the image on a moving retina, but this approach has never been adopted to study motion physiology. Using this method, we find that eye-movement signals have little influence on neural activity in visual cortex, while feed-forward visual calculation has a strong effect and is likely important under real-world conditions.

neuroscience↗

bioRxiv2019

Comparison of Escherichia coli surface attachment methods for single-cell, in vivo microscopy

For in vivo, single-cell imaging bacterial cells are commonly immobilised via physical confinement or surface attachment. Different surface attachment methods have been used both for atomic force and optical microscopy (including super resolution), and some have been reported to affect bacterial physiology. However, a systematic comparison of the effects these attachment methods have on the bacterial physiology is lacking. Here we present such a comparison for bacterium Escherichia coli, and assess the growth rate, size and intracellular pH of cells growing attached to different, commonly used, surfaces. We demonstrate that E. coli grow at the same rate, length and internal pH on all the tested surfaces when in the same growth medium. The result suggests that tested attachment methods can be used interchangeably when studying E. coli physiology.

systems biology↗

bioRxiv2019

Patterns of annual and seasonal immune investment in a temporal reproductive opportunist

Historically, investigations of how organisms investments in immunity fluctuate in response to environmental and physiological changes have focused on seasonally breeding organisms that confine reproduction to seasons with mild environmental conditions and abundant resources. Consequently, knowledge of how harsh environmental conditions and reproductive effort may interact to shape investment in immunity remains limited. The red crossbill, Loxia curvirostra, is a songbird that can breed on both short, cold and long, warm days if conifer seeds are abundant. This species provides an ideal system to investigate the influence of environmental fluctuations, reproductive investment, and their potential interactions on patterns of immune investment. In this study, we measured inter- and intra-annual immune variation in crossbills across four consecutive summers (2010-2013) and multiple seasons within one year (summer 2011-spring 2012) to explore how physiological and environmental factors impact this immune variation. Overall, the data suggest that immunity varies seasonally, among years, and in response to environmental fluctuations in food resources, precipitation, and temperature, but less in response to physiological measures such as reproduction. Collectively, this system demonstrates that a reproductively flexible organism may breed when conditions allow simultaneous investment in survival-related processes rather than at the expense of them.

ecology↗

bioRxiv2019

An analysis of avian vocal performance at the note and song levels

Sexual displays that require extreme feats of physiological performance have the potential to reliably indicate the signallers skill. The hypothesis that the structure of bird song is physiologically constrained remains controversial. We tested for evidence of performance constraints in Adelaides warblers (Setophaga adelaidae) songs. At the note level, we identified three trade-offs with well-defined limits. At the song level, we identified two trade-offs, but their limits were less well-defined than the note-level limits. Trade-offs at both levels suggest that song structure is constrained by limits to the speed of both frequency modulation (while vocalizing and between notes) and respiration. Individual males experience the same trade-offs that characterize the population, but the intensity of those trade-offs varies among individuals. Performance metrics derived from the observed limits to performance varied moderately among individuals and strongly among song types. Note-level performance metrics were positively skewed, as predicted by the hypothesis that this population has experienced positive selection for constrained performance. We conclude that physiological limits on frequency modulation and respiration constrain song structure in male Adelaides warblers. Further work is needed to determine whether receivers respond to natural levels of variation in performance, and whether performance correlates with singer quality.

animal behavior and cognition↗

bioRxiv2019

Identifying Novel Roles for Peptidergic Signaling in Mice

Despite accumulating evidence demonstrating the essential roles played by neuropeptides, it has proven challenging to use this information to develop therapeutic strategies. Peptidergic signaling can involve juxtacrine, paracrine, endocrine and neuronal signaling, making it difficult to define physiologically important pathways. One of the final steps in the biosynthesis of many neuropeptides requires a single enzyme, peptidylglycine -amidating monooxygenase (PAM), and lack of amidation renders most of these peptides biologically inert. PAM, an ancient integral membrane enzyme that traverses the biosynthetic and endocytic pathways, also affects cytoskeletal organization and gene expression. While mice, zebrafish and flies lacking Pam (PamKO/KO) are not viable, we reasoned that cell-type specific elimination of Pam expression would generate mice that could be screened for physiologically important and tissue-specific deficits. PamcKO/cKO mice, with loxP sites flanking the 2 exons deleted in the global PamKO/KO mouse, were indistinguishable from wildtype mice. Eliminating Pam expression in excitatory forebrain neurons reduced anxiety-like behavior, increased locomotor responsiveness to cocaine and improved thermoregulation in the cold. A number of amidated peptides play essential roles in each of these behaviors. Although atrial natriuretic peptide (ANP) is not amidated, Pam expression in the atrium exceeds levels in any other tissue. Eliminating Pam expression in cardiomyocytes increased anxiety-like behavior and improved thermoregulation. Atrial and serum levels of ANP fell sharply PamMyh6-cKO/cKO in mice and RNASeq analysis identified changes in gene expression in pathways related to cardiac function. Use of this screening platform should facilitate the development of new therapeutic approaches targeted to peptidergic pathways.\n\nSIGNIFICANCEPeptidergic signaling, which plays key roles in the many pathways that control thermoregulation, salt and water balance, metabolism, anxiety, pain perception and sexual reproduction, is essential for the maintenance of homeostasis. Despite the fact that peptides generally signal through G protein coupled receptors, it has proven difficult to use knowledge about peptide synthesis, storage and secretion to develop effective therapeutics. Our goal was to develop an in vivo bioassay system that would reveal physiologically meaningful deficits associated with disturbed peptidergic signaling. We did so by developing a system in which an enzyme essential for the production of many bioactive peptides could be eliminated in a tissue-specific manner.

neuroscience↗