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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,135 records · Page 63Linked to original sources

bioRxiv2017

CDC20B is required for deuterosome-mediated centriole production in multiciliated cells

Multiciliated cells (MCCs) harbour dozens to hundreds of motile cilia, which beat in a synchronized and directional manner, thus generating hydrodynamic forces important in animal physiology. In vertebrates, MCC differentiation critically depends on the synthesis and release of numerous centrioles by specialized structures called deuterosomes. Little is known about the composition, organization and regulation of deuterosomes. Here, single-cell RNA sequencing reveals that human deuterosome-stage MCCs are characterized by the expression of many cell cycle-related genes. We further investigated the uncharacterized vertebrate-specific cell division cycle 20B (CDC20B) gene, the host gene of microRNA-449abc. We show that the CDC20B protein associates to deuterosomes and is required for the release of centrioles and the subsequent production of cilia in mouse and Xenopus MCCs. CDC20B interacts with PLK1, which has been shown to coordinate centriole disengagement with the protease Separase in mitotic cells. Strikingly, over-expression of Separase rescued centriole disengagement and cilia production in CDC20B-deficient MCCs. This work reveals the shaping of a new biological function, deuterosome-mediated centriole production in vertebrate MCCs, by adaptation of canonical and recently evolved cell cycle-related molecules.

cell biology↗

bioRxiv2017

The unrealized potential of herbaria in global change biology

Plant and fungal specimens in herbaria are becoming primary resources for investigating how plant phenology and geographic distributions shift with climate change, greatly expanding inferences across spatial, temporal, and phylogenetic dimensions. However, these specimens contain a wealth of additional data--including nutrients, defensive compounds, herbivore damage, disease lesions, and signatures of physiological processes--that capture ecological and evolutionary responses to the Anthropocene but which are less frequently utilized. Here, we outline the diversity of herbarium data, global change topics to which they have been applied, and new hypotheses they could inform. We find that herbarium data have been used extensively to study impacts of climate change and invasive species, but that such data are less commonly used to address other drivers of biodiversity loss, including habitat conversion, pollution, and overexploitation. In addition, we note that fungal specimens are under-explored relative to vascular plants. To facilitate broader application of plant and fungal specimens in global change research, we outline the limitations of these data and modern sampling and statistical tools that may be applied to surmount challenges they present. Using a case study of insect herbivory, we illustrate how novel herbarium data may be employed to test hypotheses for which few data exist, despite potentially large biases. With the goal of positioning herbaria as hubs for global change research, we suggest future research directions and curation priorities.

plant biology↗

bioRxiv2017

3D label-free imaging and analysis of Pinus pollen grains using optical diffraction tomography

O_LIThe structure of pollen grains is related to the reproductive function of the plants. Here, three-dimensional (3D) refractive index maps were obtained for individual conifer pollen grains using optical diffraction tomography (ODT).\nC_LIO_LIThe 3D morphological features of pollen grains from pine trees were investigated using measured refractive index maps, in which distinct substructures were clearly distinguished and analyzed.\nC_LIO_LIMorphological and physiochemical parameters of the pollen grains were quantified from the obtained refractive index (RI) maps and used to quantitatively study the interspecific differences of pollen grains from different strains.\nC_LIO_LIOur results demonstrate that ODT can assess the structure of pollen grains. This label-free and rapid 3D imaging approach may provide a new platform for understanding the physiology of pollen grains.\nC_LI

plant biology↗

bioRxiv2017

Tracking wakefulness as it fades: micro-measures of Alertness

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method based on frequency and sleep graphoelements, which is capable of detecting micro variations in alertness. The validity of the automated method was verified by training and testing the algorithm using a dataset where participants are known to fall asleep. In addition, we tested generalizability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience.

neuroscience↗

bioRxiv2017

Variational Treatment of Trial-by-Trial Drift-Diffusion Models of Behaviour

The Full Drift Diffusion Model (DDM) is challenging to fit to behavioural data. Precision of the fits are usually poor for some if not all parameters, and computationally expensive to obtain. Moreover, inference at the trial level for each and every parameters, threshold included, has so far been considered as impossible. Most approaches rely on strong assumptions about the model structure, such as selection of the parameters that are subject to trial-to-trial variability or prior distribution of these parameters, that usually lack precise mathematical or empirical justifications. The fact that, in most versions of the DDM, the sequence of participants choices are considered as independent and identically distributed (i.i.d.), has been mainly overlooked so far. Our contribution to the field is threefold: first, we introduce Variational Bayes as a method to fit the full DDM. Second, we relax the i.i.d. assumption, and propose a data-driven algorithm based on a Recurrent Auto-Encoder, that estimates the local posterior probability of the DDM parameters at each trial based on the sequence of parameters and data preceding the data. Finally, we show that inference at the trial level can be achieved efficiently for each and every parameter of the DDM, threshold included. This data-driven approach is highly generic and self-contained, in the sense that no external input (e.g. regressors or physiological measure) is necessary to fit the data. Using simulations and real-world examples, we show that this method outperforms by several order of magnitude the i.i.d.-based ones, either Markov Chain Monte Carlo or i.i.d.-VB.

animal behavior and cognition↗

bioRxiv2017

A Multilevel Computational Characterization of Endophenotypes in Addiction

Substance use disorders are characterized by a profound intersubject (phenotypic) variability in the expression of addictive symptomatology and propensity to relapse following treatment. However, laboratory investigations have primarily focused on common neural substrates in addiction, and have not yet been able to identify mechanisms that can account for the multifaceted phenotypic behaviors reported in literature. To investigate this knowledge gap theoretically, here we simulated phenotypic variations in addiction symptomology and responses to putative treatments, using both a neural model based on cortico-striatal circuit dynamics, and an algorithmic model of reinforcement learning. These simulations rely on the widely accepted assumption that both the ventral, model-based, goal-directed system and the dorsal, model-free, habitual system are vulnerable to extra-physiologic dopamine reinforcements triggered by drug consumption. We found that endophenotypic differences in the balance between the two circuit or control systems resulted in an inverted U-shape in optimal choice behavior. Specifically, greater unbalance led to a higher likelihood of developing addiction and more severe drug-taking behaviors. Furthermore, endophenotypes with opposite asymmetrical biases among cortico-striatal circuits expressed similar addiction behaviors, but responded differently to simulated treatments, suggesting personalized treatment development could rely on endophenotypic rather than phenotypic differentiations. We propose our simulated results, confirmed across neural and algorithmic levels of analysis, inform on a fundamental and, to date, neglected quantitative method to characterize clinical heterogeneity in addiction.

neuroscience↗

bioRxiv2017

Ecological Insights from the Evolutionary History of Microbial Innovations

Bacteria and Archaea represent the base of the evolutionary tree of life and contain the vast majority of phylogenetic and functional diversity. Because these organisms and their traits directly impact ecosystems and human health, a focus on functional traits has become increasingly common in microbial ecology. These trait-based approaches have the potential to link microbial communities and their ecological function. But an open question is how, why, and in what order microorganisms acquired the traits we observe in the present day. To address this, we reconstructed the evolutionary history of microbial traits using genomic data to understand the evolution, selective advantage, and similarity of traits in extant organisms and provide insights into the composition of genomes and communities. We used the geological timeline and physiological expectations to provide independent evidence in support of this evolutionary history. Using this reconstructed evolutionary history, we explored hypotheses related to the composition of genomes. We showed that gene transition rates can be used to make predictions about the size and type of genes in a genome: generalist genomes comprise many evolutionarily labile genes while specialist genomes comprise more highly conserved functional genes. These findings suggest that generalist organisms do not build up and hoard an array of functions, but rather tend to experiment with functions related to environmental sensing, transport, and complex resource degradation. Our results provide a framework for understanding the evolutionary history of extant microorganisms, the origin and maintenanceof traits, and linking evolutionary relatedness and ecological function.

microbiology↗

bioRxiv2017

Improved genome assembly and annotation for the rock pigeon (Columba livia)

The domestic rock pigeon (Columba livia) is among the most widely distributed and phenotypically diverse avian species. This species is broadly studied in ecology, genetics, physiology, behavior, and evolutionary biology, and has recently emerged as a model for understanding the molecular basis of anatomical diversity, the magnetic sense, and other key aspects of avian biology. Here we report an update to the C. livia genome reference assembly and gene annotation dataset. Greatly increased scaffold lengths in the updated reference assembly, along with an updated annotation set, provide improved tools for evolutionary and functional genetic studies of the pigeon, and for comparative avian genomics in general.

genomics↗

bioRxiv2017

Single-cell quantitative analysis of skeletal muscle cell population dynamicsduring regeneration and ageing

The skeletal muscle is populated by a variety of different mononuclear cell types that actively contribute to tissue homeostasis. When the tissue is stressed by exercise or by an acute or chronic insult, the different cell types are activated, exchange signals and initiate a finely-orchestrated regeneration process to prevent the loss of muscle mass. This cell variety is exacerbated by an additional intra population heterogeneity, where the cell population boundaries often lose their significance. Here, we applied a high-dimensional single-cell mass cytometry analysis to solve the cellular and molecular complexity of the muscle tissue in different physiological and pathological conditions. Taken together, our results provide a comprehensive picture both of muscle cell population homeostasis during ageing and of the changes induced by a perturbation of the system, be it chronic (dystrophy) or acute (cardiotoxin).

systems biology↗

bioRxiv2017

Genetic architecture drives seasonal onset of hibernation in the 13-lined ground squirrel

Hibernation is a highly dynamic phenotype whose timing, for many mammals, is controlled by a circannual clock and accompanied by rhythms in body mass and food intake. When housed in an animal facility, 13-lined ground squirrels exhibit individual variation in the seasonal onset of hibernation, which is not explained by environmental or biological factors, such as body mass and sex. We hypothesized that underlying genetic architecture instead drives variation in this timing. After first increasing the contiguity of the genome assembly, we therefore employed a genotype-by-sequencing approach to characterize genetic variation in 153 13-lined ground squirrels. Combining this with datalogger records, we estimated high heritability (61-100%) for the seasonal onset of hibernation. After applying a genome-wide scan with 46,996 variants, we also identified 21 loci significantly associated with hibernation immergence, which alone accounted for 54% of the variance in the phenotype. The most significant marker (SNP 15, p=3.81x10-6) was located near prolactin-releasing hormone receptor (PRLHR), a gene that regulates food intake and energy homeostasis. Other significant loci were located near genes functionally related to hibernation physiology, including muscarinic acetylcholine receptor M2 (CHRM2), involved in the control of heart rate, exocyst complex component 4 (EXOC4) and prohormone convertase 2 (PCSK2), both of which are involved in insulin signaling and processing. Finally, we applied an expression quantitative loci (eQTL) analysis using existing transcriptome datasets, and we identified significant (q<0.1) associations for 9/21 variants. Our results highlight the power of applying a genetic mapping strategy to hibernation and present new insight into the genetics driving its seasonal onset.

genetics↗

bioRxiv2017

Bacterial adaptation to host diet is a key evolutionary force shaping host-microbe symbiosis

Life on Earth was dominated by bacteria for billions of years1. About 600 million years ago, animal life emerged and micro-organisms played a crucial role in shaping animal development, physiology and evolution2-4. The two partners committed to a symbiotic relationship that persists in nearly all animals today. Such beneficial interactions are pervasive throughout nature and have been extensively characterized5,6. However, the ecological and evolutionary forces that drive the emergence and evolution of the symbiont benefits to their animal hosts remain largely elusive. Here we show that the host nutritional environment, instead of the host, is a predominant driving force in this evolutionary process and we identify a mechanism resulting from the bacterial adaptation to the diet, which confers improved functional benefit to the host. By applying experimental evolution to a model of host-bacteria beneficial symbiosis: Drosophila melanogaster associated with Lactobacillus plantarum, one of its growth promoting symbiotic bacteria7,8, we found that the de novo mutations in the same acetate kinase (ackA) locus invariably emerge first, rapidly become fixed, and such evolution occurs with or without the host. Furthermore, we demonstrate that ackA mutations trigger the increased production of N-acetyl-glutamine, which is sufficient to confer improved host growth capabilities to the evolved bacterial strains. Our study therefore identifies a specific mechanism by which a symbiotic bacterium increases its benefit to its animal host and reveals that adaptation to the host diet is a foremost step in the determination of the evolutionary course of symbiosis between an animal and its gut microbes.

evolutionary biology↗

bioRxiv2017

The Control of Tonic Pain by Active Relief Learning

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning so that the cause of the pain can be reduced if possible. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system also uses an uncertainty ( associability) signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that allows reduction of ongoing pain when learning about potential relief.

neuroscience↗

bioRxiv2017

Cytoplasmic import and processing of mRNA amplify transcriptional bursts accounting for the majority of cellular noise

Transcription is an episodic process characterized by probabilistic bursts; but how these bursts are modulated by cellular physiology remains unclear and has implications for how selection may act on these fluctuations. Using simulations and single-molecule RNA counting, we examined how cellular processes influence cell-to-cell variability (noise). The results show that RNA noise is amplified in the cytoplasm compared to the nucleus in ~85% of genes across diverse promoters, genomic loci, and cell types (human and mouse). Surprisingly, measurements show further amplification of RNA noise in the cytoplasm, fitting a model of biphasic mRNA conversion between translation- and degradation-competent states. The multi-state translation-degradation of mRNA also causes substantial noise amplification in protein levels, ultimately accounting for ~74% of intrinsic protein variability in cell populations. Overall, the results demonstrate how transcriptional bursts are intrinsically amplified by mRNA processing and indicate mechanisms through which noise could act as a substrate for evolutionary selection.

cell biology↗

bioRxiv2017

LRX Proteins play a crucial role in pollen grain and pollen tube cell wall development

Leucine-rich repeat extensins (LRXs) are chimeric proteins containing an N-terminal leucine-rich repeat (LRR) and a C-terminal extensin domain. LRXs are involved in cell wall formation in vegetative tissues and required for plant growth. However, the nature of their role in these cellular processes remains to be elucidated. Here, we used a combination of molecular techniques, light microscopy, and transmission electron microscopy to characterize mutants of pollen-expressed LRXs in Arabidopsis thaliana. Mutations in multiple pollen-expressed lrx genes causes severe defects in pollen germination and pollen tube (PT) growth, resulting in a reduced seed set. Physiological experiments demonstrate that manipulating Ca2+ availability partially suppresses the PT growth defects, suggesting that LRX proteins influence Ca2+-related processes. Furthermore, we show that LRX protein localizes to the cell wall, and its LRR-domain (which likely mediates protein-protein interactions) is associated with the plasma membrane. Mechanical analyses by cellular force microscopy and finite element method-based modelling revealed significant changes in the material properties of the cell wall and the fine-tuning of cellular biophysical parameters in the mutants compared to the wild type. The results indicate that LRX proteins might play a role in cell wall-plasma membrane communication, influencing cell wall formation and cellular mechanics.

plant biology↗

bioRxiv2017

A new analysis of resting state connectivity and graph theory reveals distinctive short-term modulations due to whisker stimulation in rats.

Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions.

neuroscience↗

bioRxiv2017

Detecting past and ongoing natural selection among ethnically Tibetan women at high altitude in Nepal

Adaptive evolution in humans has rarely been characterized for its whole set of components, i.e. selective pressure, adaptive phenotype, beneficial alleles and realized fitness differential. We combined approaches for detecting selective sweeps and polygenic adaptations and for mapping the genetic bases of physiological and fertility phenotypes in approximately 1000 indigenous ethnically Tibetan women from Nepal, adapted to high altitude. We performed genome-wide association analysis and tests for polygenic adaptations which showed evidence of positive selection for alleles associated with more pregnancies and live births and evidence of negative selection for those associated with higher offspring mortality. Lower hemoglobin level did not show clear evidence for polygenic adaptation, despite its strong association with an EPAS1 haplotype carrying selective sweep signals.

genetics↗

bioRxiv2018

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism and model-guided re-engineering. Recent applications of metabolic models have also demonstrated their usefulness in unraveling cross-feeding within microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging far behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual cura-tion required to obtain good quality models and thus physiologically meaningful simulation results. Here, we present an automated tool - CarveMe - for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation-ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We build reconstructions for two model organisms, Escherichia coli and Bacillus subtillis, as well as a collection of human gut bacteria, and show that CarveMe models perform similarly to manually curated models in reproducing experimental phenotypes. Finally, we demonstrate the scalability of CarveMe through reconstructing 5587 bacterial models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

bioinformatics↗

bioRxiv2017

Evidence for, and Metabolic Consequences of, a Cardiac Mitochondrial KNa1.2 Channel

Controversy surrounds the molecular identity of mitochondrial K+ channels that are important for protection against cardiac ischemia-reperfusion injury. While KNa1.2 (Kcnt2 gene) is necessary for cardioprotection by volatile anesthetics, electrophysiologic evidence for a channel of this type in mitochondria is lacking. The endogenous physiologic role of a potential mito-KNa1.2 channel is also unclear. Herein, single channel patch-clamp of 27 independent cardiac mitochondrial inner membrane (mitoplast) preparations from wild type (WT) mice yielded 6 channels matching the known ion-sensitivity, ion-selectivity, pharmacology and conductance properties of KNa1.2 (slope conductance 138{+/-}1 pS). However, similar experiments on 40 preparations from Kcnt2-/- mice yielded zero such channels. The KNa opener bithionol uncoupled respiration in WT but not Kcnt2-/- cardiomyocytes. Furthermore, when oxidizing only fat as substrate, Kcnt2-/- cardiomyocytes and hearts were less responsive to increases in energetic demand. Kcnt2-/- mice also had elevated body fat, but no baseline differences in the cardiac metabolome. These data support the existence of a cardiac mitochondrial KNa1.2 channel, and a role for cardiac KNa1.2 in regulating metabolism under conditions of high energetic demand.

cell biology↗