bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reevaluation of the Role of ERK3 in Perinatal Survival and Post-Natal Growth Using New Genetically-Engineered Mouse Models
Authors: Soulez Mathilde , Saba-El-Leil Marc K. , Turgeon Benjamin , Mathien Simon , Coulombe Philippe , Klinger Sonia , Rousseau Justine , Lévesque Kim , Meloche Sylvain 
Published: November 2018

AbstractThe physiological functions of the atypical MAP kinase ERK3 remain poorly characterized. Previous analysis of mice with a targeted insertion of the lacZ reporter in the Mapk6 locus (Mapk6lacZ) showed that inactivation of ERK3 in Mapk6lacZ mice leads to perinatal lethality associated with intrauterine growth restriction, defective lung maturation, and neuromuscular anomalies. To further explore the role of ERK3 in physiology and disease, we generated novel mouse models expressing a catalytically-inactive (Mapk6KD) or conditional (Mapk6Δ) allele of ERK3. Surprisingly, we found that mice devoid of ERK3 kinase activity or expression survive the perinatal period without any observable lung or neuromuscular phenotype. ERK3 mutant mice reached adulthood, were fertile and showed no apparent health problem. However, analysis of growth curves revealed that ERK3 kinase activity is ncessary for optimal post-natal growth. To gain insight into the genetic basis underlying the discrepancy in phenotypes of different Mapk6 mutant mouse models, we analyzed the regulation of genes flanking the Mapk6 locus by quantitative PCR. We found that expression of several Mapk6 neighboring genes is deregulated in Mapk6lacZ mice, but not in Mapk6KD or Mapk6Δ mutant mice. Our genetic analysis suggests that off-target effects of the targeting construct on local gene expression are likely to be responsible for the perinatal lethality phenotype of Mapk6lacZ mutant mice.
Population genetic structure of the bank vole Myodes glareolus within its glacial refugium in peninsular Italy
Authors: Chiocchio Andrea , Colangelo Paolo , Aloise Gaetano , Amori Gianni , Bertolino Sandro , Bisconti Roberta , Castiglia Riccardo , Canestrelli Daniele 
Published: November 2018

AbstractIt is now well established that Southern European peninsulas have been major glacial refugia for temperate species during Pleistocene climatic oscillations. However, substantial environmental changes occurred also within these peninsulas throughout the Pleistocene, rising questions about the role and interplay of various of micro-evolutionary processes in shaping patterns of intraspecific diversity within these areas. Here, we investigate the patterns of genetic variation in the bank vole Myodes glareolus within the Italian peninsula. By using a panel of 13 microsatellite loci, we found more intra-specific variation than expected based on previous assessments. Indeed, both Bayesian and ordination-based clustering analyses of variation recovered five main geographic/genetic clusters along the peninsula, with three clusters geographically restricted to the southern portion of the study area. This pattern supports the occurrence of multiple sub-refugia for the bank vole in peninsular Italy, likely promoted by the major paleo-environmental changes which affected forested habitats within this area during the Pleistocene. Thus, our results support a scenario whereby the high levels of intraspecific diversity observed within major glacial refugia are better explained by dynamic micro-evolutionary processes occurred within these areas, rather than by long-term demographic stability of refugial population. Finally, the narrow and isolated distribution of some of the identified lineages, suggest the need for future assessments of their conservation and taxonomic status.
Bayesian estimation for stochastic gene expression using multifidelity models
Authors: Vo Huy D. , Fox Zachary , Baetica Ania , Munsky Brian 
Published: November 2018

AbstractThe finite state projection (FSP) approach to solving the chemical master equation has enabled successful inference of discrete stochastic models to predict single-cell gene regulation dynamics. Unfortunately, the FSP approach is highly computationally intensive for all but the simplest models, an issue that is highly problematic when parameter inference and uncertainty quantification takes enormous numbers of parameter evaluations. To address this issue, we propose two new computational methods for the Bayesian inference of stochastic gene expression parameters given single-cell experiments. We formulate and verify an Adaptive Delayed Acceptance Metropolis-Hastings (ADAMH) algorithm to utilize with reduced Krylov-basis projections of the FSP. We then introduce an extension of the ADAMH into a Hybrid scheme that consists of an initial phase to construct a reduced model and a faster second phase to sample from the approximate posterior distribution determined by the constructed model. We test and compare both algorithms to an adaptive Metropolis algorithm with full FSP-based likelihood evaluations on three example models and simulated data to show that the new ADAMH variants achieve substantial speedup in comparison to the full FSP approach. By reducing the computational costs of parameter estimation, we expect the ADAMH approach to enable efficient data-driven estimation for more complex gene regulation models.
Transcriptome responses to reduced dopamine in the Substantia Nigra Pars Compacta reveals a potential protective role for dopamine
Authors: Koltun M. , Cichewicz K. , Gibbs J.T. , Darvas M. , Hirsh J. 
Published: November 2018

AbstractParkinson’s Disease (PD), is a neurodegenerative disorder affecting both cognitive and motor functions. It is characterized by decreased brain dopamine (DA) and a selective and progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc), whereas dopaminergic neurons in the ventral tegmental area (VTA) show reduced vulnerability. The majority of animal models of PD are genetic lesion or neurotoxin exposure models that lead to death of dopaminergic neurons. Here we use a DAT:TH KO mouse model that by inactivation of the tyrosine hydroxylase (Th) gene in dopamine transporter-expressing neurons, causes selective depletion of striatal dopamine without affecting DA neuron survival. We analyzed transcriptome responses to decreased DA in both pre- and post-synaptic dopaminergic brain regions of DAT:TH KO animals. We detected only few differentially expressed genes in the post-synaptic regions as a function of DA deficiency. This suggests that the broad striatal transcriptional changes in neurodegeneration-based PD models are not direct effects of DA depletion, but are rather a result of DA neuronal death. However, we find a number of dopaminergic genes differentially expressed in SNc, and to a lesser extent in VTA, as a function of DA deficiency, providing evidence for a DA-dependent feedback loop. Of particular interest, expression ofNr4a2, a crucial transcription factor maintaining DA neuron identity, is significantly decreased in SNc, but not VTA, of DAT:TH KO mice, implying a potential protective role for DA in the SNc.
Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits
Authors: Barson D , Hamodi AS , Shen X , Lur G , Constable RT , Cardin JA , Crair MC , Higley MJ 
Published: November 2018

AbstractSpontaneous and sensory-evoked activity propagates across spatial scales in the mammalian cortex but technical challenges have generally precluded establishing conceptual links between the function of local circuits of neurons and brain-wide network dynamics. To solve this problem, we developed a method for simultaneous cellular-resolution two-photon calcium imaging of a local microcircuit and mesoscopic widefield calcium imaging of the entire cortical mantle in awake, behaving mice. Our method employs an orthogonal axis design whereby the mesoscopic objective is oriented downward directly above the brain and the two-photon objective is oriented horizontally, with imaging performed through a glass right angle microprism implanted in the skull. In support of this method, we introduce a suite of analysis tools for relating the activity of individual cells to distal cortical areas, as well as a viral method for robust and widespread gene delivery in the juvenile mouse brain. We use these methods to characterize the diversity of associations of individual, genetically-defined neurons with cortex-wide network motifs.
Comparative genomic analysis of the emerging pathogen Streptococcus pseudopneumoniae: novel insights into virulence determinants and identification of a novel species-specific molecular marker
Authors: Garriss Geneviève , Nannapaneni Priyanka , Simões Alexandra S. , Browall Sarah , Sá-Leão Raquel , Goossens Herman , de Lencastre Herminia , Henriques-Normark Birgitta 
Published: November 2018

AbstractStreptococcus pseudopneumoniae is a close relative of the major human pathogen S. pneumoniae. While initially considered as a commensal species, it has been increasingly associated with lower-respiratory tract infections and high prevalence of antimicrobial resistance (AMR). S. pseudopneumoniae is difficult to identify using traditional typing methods due to similarities with S. pneumoniae and other members of the mitis group (SMG). Using phylogenetic and comparative genomic analyses of SMG genomes, we identified a new molecular marker specific for S. pseudopneumoniae and absent from any other bacterial genome sequenced to date. We found that a large number of known virulence and colonization genes are present in the core S. pseudopneumoniae genome and we reveal the impressive number of known and new surface-exposed proteins encoded by this species. Phylogenetic analyses of S. pseudopneumoniae show that specific clades are associated with allelic variants of core proteins. Resistance to tetracycline and macrolides, the two most common resistances, were encoded by Tn916-like integrating conjugative elements and Mega-2. Overall, we found a tight association of genotypic determinants of AMR as well as phenotypic AMR with a specific lineage of S. pseudopneumoniae. Taken together, our results sheds light on the distribution in S. pseudopneumoniae of genes known to be important during invasive disease and colonization and provide insight into features that could contribute to virulence, colonization and adaptation.ImportanceS. pseudopneumoniae is an overlooked pathogen emerging as the causative agent of lower-respiratory tract infections and associated with chronic obstructive pulmonary disease (COPD) and exacerbation of COPD. However, much remains unknown on its clinical importance and epidemiology, mainly due to the lack of specific means to distinguish it from S. pneumoniae. Here, we provide a new molecular marker entirely specific for S. pseudopneumoniae. Furthermore, our research provides a deep analysis of the presence of virulence and colonization genes, as well as AMR determinants in this species. Our results provide crucial information and pave the way for further studies aiming at understanding the pathogenesis and epidemiology of S. pseudopneumoniae.
Healthy ageing reduces the precision of episodic memory retrieval
Authors: Korkki Saana M. , Richter Franziska R. , Jeyarathnarajah Priyanga , Simons Jon S. 
Published: November 2018

AbstractEpisodic memory declines with older age, but it is unresolved whether this decline reflects reduced probability of successfully retrieving information from memory, or decreased precision of the retrieved information. Here, we used continuous measures of episodic memory retrieval in combination with computational modelling of participants’ retrieval errors to distinguish between these two potential accounts of age-related memory deficits. In three experiments, young and older participants encoded stimuli displays consisting of everyday objects varying along different perceptual features (e.g., location, colour and orientation) in a circular space. At test, participants recreated the features of studied objects using a continuous response dial. Across all three experiments, we observed age-related declines in the precision of episodic memory retrieval, whereas age differences in retrieval success were limited to the most challenging task condition. Reductions in mnemonic precision were evident for retrieval of both item-based and contextual information, and persisted after controlling for age-related decreases in the fidelity of perception and working memory. The findings highlight impoverished precision of memory representations as one factor contributing to age-related episodic memory loss, and suggest that the cognitive and neural changes associated with older age can differentially affect distinct aspects of episodic retrieval.
SpitWorm, an herbivorous robot: Mechanical leaf wounding with simultaneous application of salivary components
Authors: Li Guanjun , Bartram Stefan , Guo Huijuan , Mithöfer Axel , Kunert Maritta , Boland Wilhelm 
Published: November 2018

AbstractInduction of plant defense against insect herbivory is initiated by a combination of both, mechanical wounding and chemical factors. In order to study both effects independently on plant defense induction, SpitWorm, a computer controlled device which mimics the damage pattern of feeding insect larvae on leaves and can additionally apply oral secretions (OS) or other solutions to the ‘biting site’ during ‘feeding’, was developed and evaluated. The amount of OS left by a Spodoptera littoralis larva during feeding on Phaseolus lunatus (lima bean) leaves was estimated by combining larval foregut volume, biting rate, and quantification of a fluorescent dye injected into the larvae’s foregut prior to feeding. For providing OS amounts by SpitWorm equivalent to larval feeding, dilution and delivery rate were optimized. The effectiveness of SpitWorm was tested by comparing volatile organic compounds (VOC) emissions of P. lunatus leaves treated with either SpitWorm, MecWorm or S. littoralis larvae. Identification and quantification of emitted VOCs revealed that SpitWorm induced a volatile bouquet that is qualitatively and quantitatively similar to herbivory. Additionally, RT-qPCR of four jasmonic acid responsive genes showed that SpitWorm, in contrast to MecWorm, induces the same regulation pattern as insect feeding. Thus, SpitWorm mimics insect herbivory almost identical to real larvae feeding.
Ischemic Preconditioning and Left Ventricular Dysfunction: A Novel Mechanism & Model for Pulseless Electrical Activity
Authors: Patil Kaustubha D , Tunin Richard S , Fink Sarah J , Tao Susumu , Halperin Henry R 
Published: November 2018

AbstractBackgroundPulseless electrical activity (PEA) is a very common rhythm in cardiac arrest and survival is ≈ 5%. Population data suggest coronary ischemia significantly contributes to PEA, but the mechanism is unknown.ObjectivesWe hypothesize ischemic preconditioning (IPC) in the setting of left ventricular (LV) dysfunction can convert ischemia-induced ventricular fibrillation (VF) into ischemia-induced PEA.MethodsUsing percutaneous coronary interventions in anesthetized swine, we studied the effect of IPC prior to ischemia on arrhythmic burden in normal animals and in animals with LV dysfunction. IPC protocol: four cycles of three minutes of coronary occlusion followed by seven minutes of reperfusion. Chronic LV dysfunction protocol: two serial infarcts in two coronary territories, separated by one week of recovery.ResultsIn normal animals, IPC prior to ischemia significantly reduced VF incidence (2/8 IPC vs. 7/8 control). In IPC animals with VF, the time to VF was significantly delayed (37.2 ± 7.3 min vs. 20.7 ± 4.9 min, p<0.005). In chronic LV dysfunction animals (EF 15% ± 5%), ischemia caused PEA in all animals (18/18). In non-preconditioned animals, VF followed PEA in all cases (12/12). In preconditioned animals, PEA sustained without VF in 2/6 animals. In 4/6 animals, PEA was prolonged and time to VF was significantly delayed compared to non-preconditioned animals (33.7 ± 7.8 min vs. 12.2 ± 5.0 min, p<0.0001).ConclusionIPC delays/prevents VF. IPC with LV dysfunction prior to ischemia produces prolonged PEA. IPC with LV dysfunction prior to ischemia is likely an important mechanism for human PEA arrest. This is the first animal model of ischemic pulseless electrical activity.
Maintenance of spatial gene expression by Polycomb-mediated repression after formation of a vertebrate body plan
Authors: Rougeot Julien , Chrispijn Naomi D. , Aben Marco , Elurbe Dei M. , Andralojc Karolina M. , Murphy Patrick J. , Jansen Pascal W.T.C. , Vermeulen Michiel , Cairns Bradley R. , Kamminga Leonie M. 
Published: November 2018

AbstractPolycomb group (PcG) proteins are transcriptional repressors that are important regulators of cell fate during embryonic development. Among them, Ezh2 is responsible for catalyzing the epigenetic repressive mark H3K27me3 and is essential for animal development. The ability of zebrafish embryos lacking both maternal and zygoticezh2to form a normal body plan provides a unique model to comprehensively study Ezh2 function during early development in vertebrates. By using a multi-omics approach, we found that Ezh2 is required for the deposition of H3K27me3 and is essential for the recruitment of Polycomb group protein Rnf2. However, and despite the complete absence of PcG-associated epigenetic mark and proteins, only minor changes in H3K4me3 deposition and gene and protein expression occurred. These changes were mainly due to local deregulation of transcription factors outside their normal expression boundaries. Altogether, our results in zebrafish show that Polycomb-mediated gene repression is important right after the body plan is formed to maintain spatially restricted expression profiles of transcription factors and highlight the differences that exist in the timing of PcG protein action between vertebrate species.Summary statementOur unique zebrafish model of maternal and zygotic mutant for thePolycombgroup geneezh2reveals major conserved and divergent mechanisms in epigenetic gene repression during vertebrate development.
A Bayesian framework for the analysis of systems biology models of the brain
Authors: Russell-Buckland Joshua , Barnes Chris P. , Tachtsidis Ilias 
Published: November 2018

AbstractSystems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain a more complete understanding of the parameter space as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a ‘healthy’ and ‘impaired’ state. By analysing both of these datasets, we show that different parameter spaces can be distinguished and compared between different physiological states or conditions. Finally, we analyse experimental data using the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the Bayesian approach provides a more complete understanding of the parameter space.Author summarySystems biology models are mathematical representations of biological processes that reproduce the overall behaviour of a biological system. They are comprised by a number of parameters representing biological information. We use them to understand the behaviour of biological systems, such as the brain. We do this by fitting the model’s parameter to observed or simulated data; and by looking at how these values change during the fitting process we investigate the mechanisms controlling the behaviour of our system. We are interested in understanding differences between a healthy and an injured brain. Here we outline a statistical framework that uses a Bayesian approach during the fitting process that can provide us with a distribution of parameters rather than single parameter number. We apply this method when simulating the physiological responses between a healthy and a vascular compromised brain to a drop in oxygenation. We then use experimental data that demonstrates the healthy brain response to an increase in arterial CO2 and fit our brain model predictions to the measurements. In both instances we show that our approach provides more information about the overlap between healthy and unhealthy brain states than a fitting process that provides a single value parameter estimate.
The Bayesian Superorganism: Collective Probability Estimation in Swarm Systems
Authors: Hunt Edmund R. , Franks Nigel R. , Baddeley Roland J. 
Published: November 2018

AbstractSuperorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to the abundance of such ‘swarm’ systems, as they effectively explore and exploit their environment collectively. We develop a Bayesian model of collective information processing in a decision-making task: choosing a nest site (a ‘multi-armed bandit’ problem). House-huntingTemnothoraxants are adept at discovering and choosing the best available nest site for their colony: we propose that this is possible via rapid, decentralized estimation of the probability that each choice is best. Viewed this way, their behavioral algorithm can be understood as a statistical method that anticipates recent advances in mathematics. Our nest finding model in-corporates insights from approximate Bayesian computation as a model of colony-level behavior; and particle filtering as a model ofTemnothorax‘tandem running’. Our framework suggests that the mechanisms of complex collective behavior can sometimes be explained as a spatial enactment of Bayesian inference. It facilitates the generation of quantitative hypotheses regarding individual and collective movement behaviors when collective decisions must be made. It also points to the potential for bioinspired statistical techniques. Finally, it suggests simple mechanisms for collective decision-making in engineered systems, such as robot swarms.