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At least 19 recordsLinked to original sources

bioRxiv2018

Physiological predictors of reproductive performance in the European Starling (Sturnus vulgaris): I. Univariate analysis

Costs of reproduction are assumed to be widespread, and to have a physiological basis, but the mechanisms driving decreased survival and future fecundity remain elusive. Our overall goals were to assess whether physiological variables are related to workload ability or immediate fitness consequences and if they mediate future survival or reproductive success. We investigated individual variation in physiological state at two breeding stages (incubation, chick-rearing) in female European starlings (Sturnus vulgaris), for first-and second-broods over two years. Specifically, we measured a suite of 13 physiological traits related to aerobic/metabolic capacity, oxidative stress and muscle damage, intermediary metabolism and energy supply, and immune function. We tested for relationships to traits for workload (e.g. nest visit rate) and fitness (current reproduction, survival and future reproduction).\n\nThere was little co-variation among the 13 physiological traits, either in incubating or chick-rearing birds. There were some systematic differences in incubation versus chick-rearing physiology. Chick-rearing birds had lower hematocrit and plasma creatine kinase but higher hemoglobin, triglyceride and uric acid levels. Only plasma corticosterone was repeatable between incubation and chick-rearing. We assessed relationships in a univariate manner, and found very few significant relationships between incubation or chick-rearing physiology and measures of workload, current productivity, future fecundity or survival. Variability in ecological context may complicate the relationship between physiology and behavior. Additionally, individuals may regulate different aspects of their physiology independently, making detection of physiological mediators of cost of reproduction difficult. In a companion paper (Cohen et al. submitted) we explore the utility of multivariate analysis (Principal Components Analysis, Mahalanobis distance) of the same data to account for potentially complex physiological integration.

ecology↗

bioRxiv2018

Physiological predictors of reproductive performance in the European Starling (Sturnus vulgaris): II. Multivariate Analysis

Physiological variation is generally thought or supposed to underlie variation in fitness related traits in wild animals, including reproductive effort, reproductive success, and survival. However, physiological markers of individual quality have proven elusive. In this paper and its companion, we use data on 14 physiological parameters measured in 152 observations of 93 individual European starlings (Sturnus vulgaris) over 2 years in an attempt to understand how physiological variation relates to variation in current breeding productivity, future fecundity, and survival. The companion paper, focusing on univariate analysis, showed that individual physiological parameters have little relationship with these performance measures. Here, we used more sophisticated statistical approaches in an attempt to extract a multivariate signal from the biomarkers - physiological dysregulation as calculated via statistical distance, and a number of principal components analysis approaches. Broadly speaking, there was a surprising lack of association between physiology and performance: while some physiological summary measures were associated with some performance measures, the associations were not particularly strong or robust given the large number of statistical tests conducted. This implies either that there are relatively few links between physiology and performance, or, more likely, that the complexity of these relationships exceeds our ability to measure and model it, even using state-of-the-art statistical approaches. This is likely particularly true because our population was quite heterogeneous; we nonetheless urge caution regarding the over-interpretation of isolated significant findings in the literature.

ecology↗

bioRxiv2018

Genome-wide selection scans integrated with association mapping reveal mechanisms of physiological adaptation across a salinity gradient in killifish

Adaptive divergence between marine and freshwater environments is important in generating phyletic diversity within fishes, but the genetic basis of adaptation to freshwater habitats remains poorly understood. Available approaches to detect adaptive loci include genome scans for selection, but these can be difficult to interpret because of incomplete knowledge of the connection between genotype and phenotype. In contrast, genome wide association studies (GWAS) are powerful tools for linking genotype to phenotype, but offer limited insight into the evolutionary forces shaping variation. Here, we combine GWAS and selection scans to identify loci important in the adaptation of complex physiological traits to freshwater environments. We focused on freshwater (FW)-native and brackish water (BW)-native populations of the Atlantic killifish (Fundulus heteroclitus) as well as a population that is a natural admixture of these two populations. We measured phenotypes for multiple physiological traits that differ between populations and that may contribute to adaptation across osmotic niches (salinity tolerance, hypoxia tolerance, metabolic rate, and body shape) and used a reduced representation approach for genome-wide genotyping. Our results show patterns of population divergence in physiological capabilities that are consistent with local adaptation. Selection scans between BW-native and FW-native populations identified genomic regions that presumably aect fitness between BW and FW environments, while GWAS revealed loci that contribute to variation for each physiological trait. There was substantial overlap in the genomic regions putatively under selection and loci associated with the measured physiological traits, suggesting that these phenotypes are important for adaptive divergence between BW and FW environments. Our analysis also implicates candidate genes likely involved in physiological capabilities, some of which validate a priori hypotheses. Together, these data provide insight into the mechanisms that enable diversification of fishes across osmotic boundaries.\n\nAuthor SummaryIdentifying the genes that underlie adaptation is important for understanding the evolutionary process, but this is technically challenging. We bring multiple lines of evidence to bear for identifying genes that underlie adaptive divergence. Specifically, we integrate genotype-phenotype association mapping with genome-wide scans for signatures of natural selection to reveal genes that underlie phenotypic variation and that are adaptive in populations of killifish that are diverging between marine and freshwater environments. Because adaptation is likely manifest in multiple physiological traits, we focus on hypoxia tolerance, salinity tolerance, and metabolic rate; traits that are divergent between marine and freshwater populations. We show that each of these phenotypes is evolving by natural selection between environments; genetic variants that contribute to variation in these physiological traits tend to be evolving by natural selection between marine and freshwater populations. Furthermore, one of our top candidate genes provides a mechanistic explanation for previous hypotheses that suggest the adaptive importance of cellular tight junctions. Together, these data demonstrate a powerful approach to identify genes involved in adaptation and help to reveal the mechanisms enabling transitions of fishes across osmotic boundaries.

evolutionary biology↗

bioRxiv2017

The promiscuous estrogen receptor: evolution of physiological estrogens and response to phytochemicals and endocrine disruptors

Many actions of estradiol (E2), the principal physiological estrogen in vertebrates, are mediated by estrogen receptor- (ER) and ER{beta}. An important physiological feature of vertebrate ERs is their promiscuous response to several physiological steroids, including estradiol (E2), {Delta}5-androstenediol, 5-androstanediol, and 27-hydroxycholesterol. A novel structural characteristic of {Delta}5-androstenediol, 5-androstanediol, and 27-hydroxycholesterol is the presence of a C19 methyl group, which precludes the presence of an aromatic A ring with a C3 phenolic group that is a defining property of E2. The structural diversity of these estrogens can explain the response of the ER to synthetic chemicals such as bisphenol A and DDT, which disrupt estrogen physiology in vertebrates, and the estrogenic activity of a variety of plant-derived chemicals such as genistein, coumestrol, and resveratrol. Diversity in the A ring of physiological estrogens also expands potential structures of industrial chemicals that can act as endocrine disruptors. Compared to E2, synthesis of 27-hydroxycholesterol and {Delta}5-androstenediol is simpler, leading us, based on parsimony, to propose that one or both of these steroids or a related metabolite was a physiological estrogen early in the evolution of the ER, with E2 assuming this role later as the canonical estrogen. In addition to the well-studied role of the ER in reproductive physiology, the ER also is an important transcription factor in non-reproductive tissues such as the cardiovascular system, kidney, bone, and brain. Some of these ER actions in non-reproductive tissues appeared early in vertebrate evolution, long before mammals evolved.

physiology↗

bioRxiv2018

Motion and physiological noise effects on amygdala real-time fMRI neurofeedback learning

Real-time fMRI neurofeedback allows to learn control over activity in a localized brain region. However, with fMRI, physiological factors such as the cardiac cycle and respiration interfere with the measurement of brain activation. In conventional fMRI studies this is usually mitigated by inclusion of motion parameters and/or physiological parameters as nuisance regressors at the analysis stage, allowing to correct for and filter out such confounders. In real-time fMRI, however, such an approach is not routinely feasible due to the necessity to process all signals during the runtime of an experiment. The absence of on-line correction can therefore compromise real-time fMRI study outcomes reporting volitional self-regulation capability as BOLD signal changes. This is especially true for BOLD signal changes in subcortical regions situated close to blood vessels or air cavities, such as the amygdala. We therefore aimed to establish the effects of motion, heart rate, heart rate variability, and respiratory volume on learning effects, which means here an increase in BOLD signal change over NF training, in an amygdala neurofeedback experiment. Specifically, we investigate motion parameters from two emotion regulation studies - performed at 3T and 7T scanners - and additionally acquired physiological variance for the latter one. Our results revealed differences in these parameters between groups and especially between regulation and resting periods within each participant. However, strictly considering these parameters as nuisance regressors in data analysis revealed that the learning of volitional self-regulation of the amygdala is not driven by motion and physiological changes. As validation of our real-time findings, we compare them to the gold standard of assessment of motion and physiology from the Human Connectome Project. Based on this, we recommend to carefully report neurofeedback study results including physiological nuisance regression. To our knowledge, this is the first study investigating the effects of motion and physiological noise correction on neurofeedback BOLD effects.

neuroscience↗

bioRxiv2016

Inferring physiological energetics of loggerhead turtle (Caretta caretta) from existing data using a general metabolic theory

Loggerhead turtle is an endangered sea turtle species with a migratory lifestyle and worldwide distribution, experiencing markedly different habitats throughout its lifetime. Environmental conditions, especially food availability and temperature, constrain the acquisition and the use of available energy, thus affecting physiological processes such as growth, maturation, and reproduction. These physiological processes at the population level determine survival, fecundity, and ultimately the population growth rate--a key indicator of the success of conservation efforts. As a first step towards the comprehensive understanding of how environment shapes the physiology and the life cycle of a loggerhead turtle, we constructed a full life cycle model based on the principles of energy acquisition and utilization embedded in the Dynamic Energy Budget (DEB) theory. We adapted the standard DEB model using data from published and unpublished sources to obtain parameter estimates and model predictions that could be compared with data. The outcome was a successful mathematical description of ontogeny and life history traits of the loggerhead turtle. Some deviations between the model and the data existed (such as an earlier age at sexual maturity and faster growth of the post-hatchlings), yet probable causes for these deviations were found informative and discussed in great detail. Physiological traits such as the capacity to withstand starvation, trade-offs between reproduction and growth, and changes in the energy budget throughout the ontogeny were inferred from the model. The results offer new insights into physiology and ecology of loggerhead turtle with the potential to lead to novel approaches in conservation of this endangered species.

Zoology↗

bioRxiv2017

The Effects of Sex and Diet on Physiology and Liver Gene Expression in Diversity Outbred Mice

Inter-individual variation in metabolic health and adiposity is driven by many factors. Diet composition and genetic background and the interactions between these two factors affect adiposity and related traits such as circulating cholesterol levels. In this study, we fed 850 Diversity Outbred mice, half females and half males, with either a standard chow diet or a high fat, high sucrose diet beginning at weaning and aged them to 26 weeks. We measured clinical chemistry and body composition at early and late time points during the study, and liver transcription at euthanasia. Males weighed more than females and mice on a high fat diet generally weighed more than those on chow. Many traits showed sex- or diet-specific changes as well as more complex sex by diet interactions. We mapped both the physiological and molecular traits and found that the genetic architecture of the physiological traits is complex, with many single locus associations potentially being driven by more than one polymorphism. For liver transcription, we find that local polymorphisms affect constitutive and sex-specific transcription, but that the response to diet is not affected by local polymorphisms. We identified two loci for circulating cholesterol levels. We performed mediation analysis by mapping the physiological traits, given liver transcript abundance and propose several genes that may be modifiers of the physiological traits. By including both physiological and molecular traits in our analyses, we have created deeper phenotypic profiles to identify additional significant contributors to complex metabolic outcomes such as polygenic obesity. We make the phenotype, liver transcript and genotype data publicly available as a resource for the research community.

genetics↗

bioRxiv2018

Empirical Models for Anatomical and Physiological Changes in a Human Mother and Fetus During Pregnancy and Gestation

Many parameters treated as constants in traditional physiologically based pharmacokinetic models must be formulated as time-varying quantities when modeling pregnancy and gestation due to the dramatic physiological and anatomical changes that occur during this period. While several collections of empirical models for such parameters have been published, each has shortcomings. We sought to create a repository of empirical models for tissue volumes, blood flow rates, and other quantities that undergo substantial changes in a human mother and her fetus during the time between conception and birth, and to address deficiencies with similar, previously published repositories. We used maximum likelihood estimation to calibrate various models for the time-varying quantities of interest, and then used the Akaike information criterion to select an optimal model for each quantity. For quantities of interest for which time-course data were not available, we constructed composite models using percentages and/or models describing related quantities. In this way, we developed a comprehensive collection of formulae describing parameters essential for constructing a PBPK model of a human mother and her fetus throughout the approximately 40 weeks of pregnancy and gestation. We included models describing blood flow rates through various fetal blood routes that have no counterparts in adults. Our repository of mathematical models for anatomical and physiological quantities of interest provides a basis for PBPK models of human pregnancy and gestation, and as such, it can ultimately be used to support decision-making with respect to optimal pharmacological dosing and risk assessment for pregnant women and their developing fetuses. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.\n\nAUTHOR SUMMARYPhysiologically based pharmacokinetic modeling is a well-known technique for making predictions about internal time-course concentrations of a substance that has entered an organism. This tool is widely used in both pharmaceutical research and human health risk assessment because it harnesses one of the fundamental tenets of both pharmacology and toxicology: it is the concentrations of an active chemical that reach internal target tissues, rather than externally applied \"doses\", that govern the extent of the response (whether beneficial or adverse). Constructing physiologically based pharmacokinetic models for pregnancy and gestation presents a considerable challenge because many of the required parameters (such as blood flow rates or tissue volumes) that are typically assumed to be constant in adult models or short-duration simulations cannot be assumed to be constant when modeling pregnancy. Here we present models, stated as functions of gestational age, for anatomical and physiological changes that occur in a human mother and fetus during pregnancy and gestation. We evaluated and selected models by applying a consistent statistical technique, and where possible, we compared results produced by our models to those produced by previously-published models. The collection of pregnancy parameter models presented here represents the most comprehensive such collection to date.

developmental biology↗

bioRxiv2017

Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis

Inference from fMRI data faces the challenge that the hemodynamic system, that relates the underlying neural activity to the observed BOLD fMRI signal, is not known. We propose a new Bayesian model for task fMRI data with the following features: (i) joint estimation of brain activity and the underlying hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a Gaussian process (GP) prior guided by physiological information and (iii) the predicted BOLD is not necessarily generated by a linear time-invariant (LTI) system. We place a GP prior directly on the predicted BOLD time series, rather than on the hemodynamic response function as in previous literature. This allows us to incorporate physiological information via the GP prior mean in a flexible way. The prior mean function may be generated from a standard LTI system, based on a canonical hemodynamic response function, or a more elaborate physiological model such as the Balloon model. This gives us the nonparametric flexibility of the GP, but allows the posterior to fall back on the physiologically based prior when the data are weak. Results on simulated data show that even with an erroneous prior for the GP, the proposed model is still able to discriminate between active and non-active voxels in a satisfactory way. The proposed model is also applied to real fMRI data, where our Gaussian process model in several cases finds brain activity where previously proposed LTI models, parametric and nonparametric, does not.

neuroscience↗

bioRxiv2017

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis, that demonstrate the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals. Against the backdrop of the ubiquitous prevalence of degeneracy and its strong links to evolution, it is perhaps apt to add a corollary to Theodosius Dobzhanskys famous quote and state \"nothing in physiology makes sense except in the light of degeneracy\".\n\nHighlightsO_LIDegeneracy is the ability of structurally distinct elements to yield similar function\nC_LIO_LIWe postulate a critical role for degeneracy in the emergence of stable encoding systems\nC_LIO_LIWe catalog lines of evidence for the expression of degeneracy in the hippocampus\nC_LIO_LIWe suggest avenues for research to explore degeneracy in stable encoding systems\nC_LIO_LIDobzhansky wrote: \"nothing in biology makes sense except in the light of evolution\"\nC_LIO_LIA corollary: \"nothing in physiology makes sense except in the light of degeneracy\"\nC_LI

neuroscience↗

bioRxiv2018

Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice

Rationale: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage.\n\nObjective: Our primary goal was to develop machine learning models capable of predicting sepsis using streaming physiological data in real-time.\n\nMethods: A dataset consisting of high-frequency physiological data from 1,161 critically ill patients admitted to the intensive care unit (ICU) was analyzed in this IRB-approved retrospective observational cohort study. Of that total, 634 patients were identified to have developed sepsis. In this paper, we define sepsis as meeting the Systemic Inflammatory Response Syndrome (SIRS) criteria in the presence of the suspicion of infection. In addition to the physiological data, we include white blood cell count (WBC) to develop a model that can signal the future occurrence of sepsis. A random forest classifier was trained to discriminate between sepsis and non-sepsis patients using a total of 108 features extracted from 2-hour moving time-windows. The models were trained on 80% of the patients and were tested on the remaining 20% of the patients, for two observational periods of lengths 3 and 6 hours.\n\nResults: The models, respectively, resulted in F1 scores of 75% and 69% half-hour before sepsis onset and 79% and 76% ten minutes before sepsis onset. On average, the models were able to predict sepsis 210 minutes (3.5 hours) before the onset.\n\nConclusions: The use of robust machine learning algorithms, continuous streams of physiological data, and WBC, allows for early identification of at-risk patients in real-time with high accuracy.

bioinformatics↗

bioRxiv2018

Orchestration of Drosophila post-feeding physiology and behavior by the neuropeptide leucokinin

Behavior and physiology are orchestrated by neuropeptides acting as neuromodulators and/or circulating hormones. A central question is how these neuropeptides function to coordinate complex and competing behaviors. The neuropeptide leucokinin (LK) modulates diverse functions, including circadian rhythms, feeding, water homeostasis, and sleep, but the mechanisms underlying these complex interactions remain poorly understood. Here, we delineate the LK circuitry that governs homeostatic functions that are critical for survival. We found that impaired LK signaling affects diverse but coordinated processes, including regulation of stress, water homeostasis, locomotor activity, and metabolic rate. There are three different sets of LK neurons, which contribute to different aspects of this physiology. We show that the calcium activity of abdominal ganglia LK neurons (ABLKs) increases specifically following water consumption, but not under other conditions, suggesting that these neurons regulate water homeostasis and its associated physiology. To identify targets of LK peptide, we mapped the distribution of the LK receptor (Lkr), mined brain single-cell transcriptome dataset for genes coexpressed with Lkr, and utilized trans-synaptic labeling to identify synaptic partners of LK neurons. Lkr expression in the brain insulin-producing cells (IPCs), gut, renal tubules and sensory cells, and the post-synaptic signal in sensory neurons, correlates well with regulatory roles detected in the Lk and Lkr mutants. Furthermore, these mutants and flies with targeted knockdown of Lkr in IPCs displayed altered expression of insulin-like peptides (DILPs) in IPCs and modulated stress responses. Thus, some effects of LK signaling appear to occur via DILP action. Collectively, our data suggest that the three sets of LK neurons orchestrate the establishment of post-prandial homeostasis by regulating distinct physiological processes and behaviors such as diuresis, metabolism, organismal activity and insulin signaling. These findings provide a platform for investigating neuroendocrine regulation of behavior and brain-to-periphery communication.

neuroscience↗

bioRxiv2017

Non-contact measurement of emotional and physiological changes in heart rate from a webcam

Heart rate, measured in beats per minute (BPM), can be used as an index of an individuals physiological state. Each time the heart beats, blood is expelled and travels through the body. This blood flow can be detected in the face using a standard webcam that is able to pick up subtle changes in color that cannot be seen by the naked eye. Due to the light absorption spectrum of blood, we are able to detect differences in the amount of light absorbed by the blood traveling just below the skin (i.e., photoplethysmography). By modulating emotional and physiological stress--i.e., viewing arousing images and sitting vs. standing, respectively--to elicit changes in heart rate, we explored the feasibility of using a webcam as a psychophysiological measurement of autonomic activity. We found a high level of agreement between established physiological measures, electrocardiogram (ECG), and blood pulse oximetry, and heart rate estimates obtained from the webcam. We thus suggest webcams can be used as a non-invasive and readily available method for measuring psychophysiological changes, easily integrated into existing stimulus presentation software and hardware setups.

physiology↗

bioRxiv2017

Detecting pathogen exposure during the non-symptomatic incubation period using physiological data

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with high-resolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.

physiology↗

bioRxiv2018

Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2000 m a.s.l.

Measuring peripheral oxygen saturation (SpO2) with pulse oximeters at the point of care is widely established. However, since SpO2 is dependent on ambient atmospheric pressure, the distribution of SpO2 values in populations living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict SpO2 values for pediatric permanent residents living between 0 and 4000 m a.s.l. Based on a sensitivity analysis of oxygen transport parameters, we created an altitude-adaptive SpO2 model that takes physiological adaptation of permanent residents into account. From this model, we derived an altitude-adaptive abnormal SpO2 threshold using patient parameters from literature. We compared the obtained model and threshold against a previously proposed threshold derived statistically from data and two empirical datasets independently recorded from Peruvian children living at altitudes up to 4100 m a.s.l. Our model followed the trends of empirical data, with the empirical data having a narrower healthy SpO2 range below 2000 m a.s.l., but the medians did never differ more than 2.29% across all altitudes. Our threshold estimated abnormal SpO2 in only 17 out of 5981 (0.3%) healthy recordings, whereas the statistical threshold returned 95 (1.6%) recordings outside the healthy range. The strength of our parametrised model is that it is rooted in physiology-derived equations and enables customisation. Furthermore, as it provides a reference SpO2, it could assist practitioners in interpreting SpO2 values for diagnosis, prognosis, and oxygen administration at higher altitudes.\n\nNew & NoteworthyOur model describes the altitude-dependent decrease of SpO2 in healthy pediatric residents based on physiological equations and can be adapted based on measureable clinical parameters. The proposed altitude-specific abnormal SpO2 threshold might be more appropriate than rigid guidelines for administering oxygen that currently are only available for sea level patients. We see this as a starting point to discuss and adapt oxygen administration guidelines.

physiology↗

bioRxiv2014

Rapid transcriptional response to physiological neuronal activity in vivo revealed by transcriptome sequencing

Neuronal activity serves as a gateway between external stimulus from environment and the brain, often inducing gene expression changes. Alternative splicing (AS) is a widespread mechanism of increasing the number of transcripts produced from a single gene and has been shown to alter properties of neuronal genes, such as ion channels (Xie and Black 2001) and neurotransmitter receptors (Mu et al. 2003). Patterns of neural tissue-specific AS have been identified, often regulated by neuron-specific splicing factors that are essential for survival (Jensen et al. 2000; Li et al. 2007), demonstrating the importance of AS in neurons. In vitro studies of neuronal activity found AS changes in response to neuronal activity in addition to transcriptional ones, raising the question of whether such changes are recapitulated in vivo on behaviorally relevant timescales. We developed a paradigm for studying physiological neuronal activity through controlled stimulation of the olfactory bulb, and performed RNA-Seq transcriptome analysis of olfactory bulbs from odor-deprived and stimulated mice. We found that physiological stimulation induces large, rapid and reproducible changes in transcription in vivo, and that the activation of a core set of activity-regulated factors is recapitulated in an in vitro model of neuronal stimulation. However, physiological activity did not induce global changes in post-transcriptional mRNA processing, such as AS or alternative cleavage and polyadenylation. In contrast, analysis of RNA-Seq from in vitro stimulation models showed rapid activity-dependent changes in both transcription and mRNA processing. Our results provide the first genome-wide look at neuronal activity-dependent mRNA processing and suggest that rapid changes in AS might not be the dominant form of transcriptome alterations that take place during olfactory rodent behavior.

Neuroscience↗

bioRxiv2016

Deconstructing cell size control into physiological modules in Escherichia coli

It is generally assumed that the allocation and synthesis of total cellular resources in microorganisms are uniquely determined by the growth conditions. Adaptation to a new physiological state leads to a change in cell size via reallocation of cellular resources. However, it has not been understood how cell size is coordinated with biosynthesis and robustly adapts to physiological states. We show that cell size in Escherichia coli can be predicted for any steady-state condition by projecting all biosynthesis into three measurable variables representing replication initiation, replication-division cycle, and the global biosynthesis rate. These variables can be decoupled by selectively controlling their respective core biosynthesis using CRISPR interference and antibiotics, verifying our predictions that different physiological states can result in the same cell size. We performed extensive growth inhibition experiments, and discovered that cell size at replication initiation per origin, namely the initiation mass or \"unit cell,\" is remarkably invariant under perturbations targeting transcription, translation, ribosome content, replication kinetics, fatty acid and cell-wall synthesis, cell division, and cell shape. Based on this invariance and balanced resource allocation, we explain why the total cell size is the sum of all unit cells. These results provide an overarching framework with quantitative predictive power over cell size in bacteria.

microbiology↗

bioRxiv2017

Liver physiological T1rho dynamics associated with age and gender

PurposeUsing a single breathhold black blood sequence, the current study aims to understand the physiological ranges of liver T1rho relaxation for women and men.\n\nMaterials and MethodsThis volunteer study was conducted with institutional ethics committee approval, and included 62 females (age mean: 38.9 years; range: 18-75 years) and 34 males (age mean: 44.7 years, range: 24-80 years). MRI was conducted with a 3.0 T scanner, with six spin-lock times of 0, 10, 20, 25, 35, 50msec and a single breathhold of 12 seconds. Six slices were acquired for each examination.\n\nResultsFemale liver T1rho value ranged between 35.07 to 51.97ms, showed an age-dependent decrease with younger women had a higher measurement. Male Liver T1rho values ranged between 34.94 to 43.39 ms, and there was no evidential age-dependence. For females, there was a trend that liver T1rho value could be 4%-5% lower during menstrual phase than nonmenstrual phase. For both females and males, no evidential association was seen between body mass index and liver T1rho.\n\nConclusionLiver T1rho physiological value for males have relatively narrow distribution, while physiological value for females have wider distribution, and decreases with age.\n\nKey points1. Liver T1rho shows an age-dependency in women, with young women showing higher measurement. This age-dependency of liver T1rho measurement is not evidential in men. Post-menopausal women have similar liver T1rho value as men.\n\n2. Women at menstrual phase may have slight lower liver T1rho measurement.\n\n3. No association was noted between body mass index and liver T1rho\n\n4. When blood signal suppression sequence is used, in a population of 62 healthy women and 34 healthy men, the highest measured liver T1rho was 52 msec for young women, 44.7 msec for post-menopausal women, and 43.4 msec for men.

biophysics↗