bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification and characterisation of serotonin signalling in the potato cyst nematodeGlobodera pallidareveals new targets for crop protection
Authors: Crisford Anna , Calahorro Fernando , Ludlow Elizabeth , Marvin Jessica M.C. , Hibbard Jennifer K. , Lilley Catherine J. , Kearn James , Keefe Francesca , Harmer Rachael , Urwin Peter E. , O’Connor Vincent , Holden-Dye Lindy 
Published: June 2018

AbstractPlant parasitic nematodes are microscopic pests that invade plant roots and cause extensive damage to crops worldwide. To investigate mechanisms underpinning their parasitic behaviour we used a chemical biology approach: We discovered that reserpine, a plant alkaloid known for its antagonism of the mammalian vesicular monoamine transporter VMAT and ability to impart a global depletion of synaptic biogenic amines in the nervous system, potently impairs the ability of the potato cyst nematodeGlobodera pallidato enter the host plant root. We show that this effect of reserpine is mediated by an inhibition of serotonergic signalling that is essential for activation of the stylet, a lance-like organ that protrudes from the mouth of the worm and which is used to pierce the host root to gain access. Prompted by this we identified core molecular components ofG. pallidaserotonin signalling encompassing the target of reserpine, VMAT; the synthetic enzyme for serotonin, tryptophan hydroxylase; the G protein coupled receptor SER-7 and the serotonin-gated chloride channel MOD-1. We found that inhibitors of tryptophan hydroxylase, SER-7 and MOD-1 phenocopy the plant protecting action of reserpine. Thus targeting the serotonin signalling pathway presents a promising new route to control plant parasitic nematodes.SummaryIndian snakeroot, an herbal medicine prepared from the roots of the shrubRauwolfia serpentina, has been used for centuries for its calming action. The major active constituent is reserpine which works by depleting a specific class of mood regulating chemical in the brain, the biogenic amines. We have discovered a remarkable effect of reserpine on a pest of global concern, the plant parasitic nematodes. These microscopic worms invade the roots of crops presenting a severe threat to food production. We show that reserpine disables serotonin signalling in the worm’s ‘brain’ that regulates the rhythmic thrusting of the stylet: a lance-like structure that protrudes from its mouth to pierce the plant root and which is essential to its parasitic lifecycle. Thus, reserpine joins nicotine as another intriguing example of Nature evolving its own protection against pests. We have identified key components of the serotonin signalling pathway in the potato cyst nematodeGlobodera pallidaand show that chemicals that target these sites inhibit the ability of the nematode to invade its host plant. We conclude that biogenic amine transmitters are intimately involved in the worm’s parasitic behaviour and provide a new discrete route to crop protection.
Machine Learning in Multi-Omics Data to Assess Longitudinal Predictors of Glycaemic Health
Authors: Prélot Laurie , Draisma Harmen , Anasanti Mila D. , Balkhiyarova Zhanna , Wielscher Matthias , Yengo Loic , Balkau Beverley , Roussel Ronan , Sebert Sylvain , Ala-Korpela Mika , Froguel Philippe , Jarvelin Marjo-Riitta , Kaakinen Marika , Prokopenko Inga 
Published: June 2018

AbstractType 2 diabetes (T2D) is a global health burden that will benefit from personalised risk prediction and targeted prevention programmes. Omics data have enabled more detailed risk prediction; however, most studies have focussed on directly on the ability of DNA variants predicting T2D onset with less attention given to epigenetic regulation and glycaemic trait variability. By applying machine learning to the longitudinal Northern Finland Birth Cohort 1966 (NFBC 1966) at 31 (T1) and 46 (T2) years old, we predicted fasting glucose (FG) and insulin (FI), glycated haemoglobin (HbA1c) and 2-hour glucose and insulin from oral glucose tolerance test (2hGlu, 2hIns) at T2 in 513 individuals from 1,001 variables at T1 and T2, including anthropometric, metabolic, metabolomic and epigenetic variables. We further tested whether the information obtained by the machine learning models in NFBC could be used to predict glycaemic traits in the independent French study with 48 matching predictors (DESIR, N=769, age range 30-65 years at recruitment, interval between data collections: 9 years). In this study, FG and FI were best predicted, with average R2 values of 0.38 and 0.53. Sex, branched-chain and aromatic amino acids, HDL-cholesterol, glycerol, ketone bodies, blood pressure at T2 and measurements of adiposity at T1, as well as multiple methylation marks at both time points were amongst the top predictors. In the validation analysis, we reached R2 values of 0.41/0.55 for FG/FI when trained and tested in NFBC1966 and 0.17/0.30 when trained in NFBC1966 and tested in DESIR. We identified clinically relevant sets of predictors from a large multi-omics dataset and highlighted the potential of methylation markers and longitudinal changes in prediction.
A multi-model Framework for the Arabidopsis life cycle
Authors: Zardilis Argyris , Hume Alastair , Millar Andrew J. 
Published: June 2018

AbstractLinking our understanding of biological processes at different scales is a major conceptual challenge in biology, which is aggravated by differences in research methods. Modelling can be a useful approach to consolidating our understanding across traditional research domains. The laboratory model speciesArabidopsis thalianais very widely used to study plant growth processes and has also been tested more recently in eco-physiology and population genetics. However, approaches from crop modelling that might link these domains are rarely applied to Arabidopsis. Here, we combine plant growth models with phenology models from eco-physiology, using the agent-based modelling language Chromar. We introduce a simpler Framework Model of vegetative growth for Arabidopsis,FM-lite.By extending this model to include inflorescence and fruit growth and seed dormancy, we present a whole-life-cycle, multi-modelFM-life,which allows us to simulate at the population level in various genotype x environment scenarios. Environmental effects on plant growth distinguish between the simulated life history strategies that were compatible with previously-described Arabidopsis phenology. Our results simulate reproductive success that is founded on the broad range of physiological processes familiar from crop models and suggest an approach to simulate evolution directly in future.HighlightA whole-life-cycle multi-model forArabidopsis thalianacombines phenology and physical growth models to explain reproductive success in different genotype x environment scenarios.
The functional genomic circuitry of human glioblastoma stem cells
Authors: MacLeod Graham , Bozek Danielle A. , Rajakulendran Nishani , Monteiro Vernon , Ahmadi Moloud , Steinhart Zachary , Kushida Michelle M. , Yu Helen , Coutinho Fiona J. , Restall Ian , Hao Xiaoguang , Hart Traver , Luchman H. Artee , Weiss Samuel , Dirks Peter B. , Angers Stephane 
Published: June 2018

SummarySuccessful glioblastoma (GBM) therapies have remained elusive due to limitations in understanding mechanisms of growth and survival of the tumorigenic population. Using CRISPR-Cas9 approaches in patient-derived GBM stem cells to interrogate function of the coding genome, we identify diverse actionable pathways responsible for growth that reveal the gene-essential circuitry of GBM stemness. In particular, we describe the Sox developmental transcription factor family; H3K79 methylation by DOT1L; and ufmylation stress responsiveness programs as essential for GBM stemness. Additionally, we find mechanisms of temozolomide resistance and sensitivity that could lead to combination strategies with this standard of care treatment. By reaching beyond static genome analysis of bulk tumors, with a genome wide functional approach, we dive deep into a broad range of biological processes to provide new understanding of GBM growth and treatment resistance.SignificanceGlioblastoma (GBM) remains an incurable disease despite an increasingly thorough depth of knowledge of the genomic and epigenomic alterations of bulk tumors. Evidence from multiple approaches support that GBM reflects an aberrant developmental hierarchy, with GBM stem cells (GSCs), fueling tumor growth and invasion. The properties of this tumor subpopulation may also in part explain treatment resistance and disease recurrence. Unfortunately, we still have a limited knowledge of the molecular circuitry of these cells and progress has been slow as we have not been able, until recently, to interrogate function at the genome-wide scale. Here, using parallel genome-wide CRISPR-Cas9 screens, we identify the essential genes for GSC growth. Further, by screening in the presence of low and high dose temozolomide, we identify mechanisms of drug resistance and sensitivity. These functional screens in patient derived cells reveal new aspects of GBM biology and identify a diversity of actionable targets such as genes governing stem cell traits, epigenome regulation and the response to stress stimuli.
Modeling the growth of organisms validates a general relation between metabolic costs and natural selection
Authors: Ilker Efe , Hinczewski Michael 
Published: June 2018

AbstractMetabolism and evolution are closely connected: if a mutation incurs extra energetic costs for an organism, there is a baseline selective disadvantage that may or may not be compensated for by other adaptive effects. A long-standing, but to date unproven, hypothesis is that this disadvantage is equal to the fractional cost relative to the total resting metabolic expenditure. This hypothesis has found a recent resurgence as a powerful tool for quantitatively understanding the strength of selection among different classes of organisms. Our work explores the validity of the hypothesis from first principles through a generalized metabolic growth model, versions of which have been successful in describing organismal growth from single cells to higher animals. We build a mathematical framework to calculate how perturbations in maintenance and synthesis costs translate into contributions to the selection coefficient, a measure of relative fitness. This allows us to show that the hypothesis is an approximation to the actual baseline selection coefficient. Moreover we can directly derive the correct prefactor in its functional form, as well as analytical bounds on the accuracy of the hypothesis for any given realization of the model. We illustrate our general framework using a special case of the growth model, which we show provides a quantitative description of overall metabolic synthesis and maintenance expenditures in data collected from a wide array of unicellular organisms (both prokaryotes and eukaryotes). In all these cases we demonstrate that the hypothesis is an excellent approximation, allowing estimates of baseline selection coefficients to within 15% of their actual values. Even in a broader biological parameter range, covering growth data from multicellular organisms, the hypothesis continues to work well, always within an order of magnitude of the correct result. Our work thus justifies its use as a versatile tool, setting the stage for its wider deployment.
Differential Sustained and Transient Temporal Processing Across Visual Streams
Authors: Stigliani Anthony , Jeska Brianna , Grill-Spector Kalanit 
Published: June 2018

ABSTRACTHow do high-level visual regions process the temporal aspects of our visual experience? While the temporal sensitivity of early visual cortex has been studied with fMRI in humans, temporal processing in high-level visual cortex is largely unknown. By modeling neural responses with millisecond precision in separate sustained and transient channels, and introducing a flexible encoding framework that captures differences in neural temporal integration time windows and response nonlinearities, we predict fMRI responses across visual cortex for stimuli ranging from 33 ms to 20 s. Using this innovative approach, we discovered that lateral category-selective regions respond to visual transients associated with stimulus onsets and offsets but not sustained visual information. Thus, lateral category-selective regions compute moment-tomoment visual transitions, but not stable features of the visual input. In contrast, ventral category-selective regions respond to both sustained and transient components of the visual input. Responses to sustained stimuli exhibit adaptation, whereas responses to transient stimuli are surprisingly larger for stimulus offsets than onsets. This large offset transient response may reflect a memory trace of the stimulus when it is no longer visible, whereas the onset transient response may reflect rapid processing of new items. Together, these findings reveal previously unconsidered, fundamental temporal mechanisms that distinguish visual streams in the human brain. Importantly, our results underscore the promise of modeling brain responses with millisecond precision to understand the underlying neural computations.AUTHOR SUMMARYHow does the brain encode the timing of our visual experience? Using functional magnetic resonance imaging (fMRI) and a temporal encoding model with millisecond resolution, we discovered that visual regions in the lateral and ventral processing streams fundamentally differ in their temporal processing of the visual input. Regions in lateral temporal cortex process visual transients associated with stimulus onsets and offsets but not the unchanging aspects of the visual input. That is, they compute moment-to-moment changes in the visual input. In contrast, regions in ventral temporal cortex process both stable and transient components, with the former exhibiting adaptation. Surprisingly, in these ventral regions responses to stimulus offsets were larger than onsets. We suggest that the former may reflect a memory trace of the stimulus, when it is no longer visible, and the latter may reflect rapid processing of new items at stimulus onset. Together, these findings (i) reveal a fundamental temporal mechanism that distinguishes visual streams and (ii) highlight both the importance and utility of modeling brain responses with millisecond precision to understand the temporal dynamics of neural computations in the human brain.
Amyloidogenic Proteins Drive Hepatic Proteostasis Remodeling in an Induced Pluripotent Stem Cell Model of Systemic Amyloid Disease
Authors: Giadone Richard M. , Liberti Derek C. , Matte Taylor M. , Rosarda Jessica D. , Torres-Arancivia Celia , Ghosh Sabrina , Diedrich Jolene K. , Pankow Sandra , Skvir Nicholas , Jean J.C. , Yates John R. , Wilson Andrew A. , Connors Lawreen H. , Kotton Darrell N. , Wiseman R. Luke , Murphy George J. 
Published: June 2018

ABSTRACTSystemic amyloidosis represents a class of disorders in which misfolded proteins are secreted by effector organs and deposited as proteotoxic aggregates at downstream target tissues. Despite being well-described clinically, the contribution of effector organs such as the liver to the pathogenesis of these diseases is poorly understood. Here, we utilize a patient-specific induced pluripotent stem cell (iPSC)-based model of hereditary transthyretin (TTR) amyloid disease (ATTR amyloidosis) in order to define the contributions of hepatic cells to the distal proteotoxicity of secreted TTR. To this end, we employ a gene correction strategy to generate isogenic, ATTR amyloidosis patient-specific iPSCs expressing either amyloidogenic or wild-type TTR. We further utilize this gene editing strategy in combination with single cell RNAseq to identify multiple hepatic proteostasis factors, including many components of adaptive unfolded protein response (UPR) signaling pathways, whose expression correlates with the production of destabilized TTR variants in iPSC-derived hepatic cells. We further demonstrate that enhancing ER proteostasis within ATTR amyloidosis iPSC-derived hepatic lineages via stress-independent activation of aforementioned adaptive UPR signaling preferentially reduces the secretion of destabilized amyloidogenic TTR. Together, these results suggest the potential of the liver to chaperone-at-a-distance and impact pathogenesis at downstream target cells in the context of systemic amyloid disease, and further highlight the promise of UPR modulating therapeutics for the treatment of TTR-mediated and other amyloid diseases.
Breakdown of spatial coding and neural synchronization in epilepsy
Authors: Shuman Tristan , Aharoni Daniel , Cai Denise J. , Lee Christopher R. , Chavlis Spyridon , Taxidis Jiannis , Flores Sergio E. , Cheng Kevin , Javaherian Milad , Kaba Christina C. , Shtrahman Matthew , Bakhurin Konstantin I. , Masmanidis Sotiris , Khakh Baljit S. , Poirazi Panayiota , Silva Alcino J. , Golshani Peyman 
Published: June 2018

AbstractTemporal lobe epilepsy causes significant cognitive deficits in both human patients and rodent models, yet the specific circuit mechanisms that alter cognitive processes remain unknown. There is dramatic and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models, but the functional consequences of these changes on information processing at the neuronal population level have not been well characterized. To examine spatial representations of epileptic and control mice, we developed a novel wire-free miniature microscope to allow for unconstrained behavior during in vivo calcium imaging of neuronal activity. We found that epileptic mice running on a linear track had severely impaired spatial processing in CA1 within a single session, as place cells were less precise and less stable, and population coding was impaired. Long-term stability of place cells was also compromised as place cells in epileptic mice were highly unstable across short time intervals and completely remapped across a week. Because of the large-scale reorganization of inhibitory circuits in epilepsy, we hypothesized that degraded spatial representations were caused by dysfunctional inhibition. To test this hypothesis, we examined the temporal dynamics of hippocampal interneurons using silicon probes to simultaneously record from CA1 and dentate gyrus during head-fixed virtual navigation. We found that epileptic mice had a profound reduction in theta coherence between the dentate gyrus and CA1 regions and altered interneuron synchronization. In particular, dentate interneurons of epileptic mice had altered phase preferences to ongoing theta oscillations, which decorrelated inhibitory population firing between CA1 and dentate gyrus. To assess the specific contribution of desynchronization on spatial coding, we built a CA1 network model to simulate hippocampal desynchronization. Critically, we found that desynchronized inputs reduced the information content and stability of CA1 neurons, consistent with the experimental data. Together, these results demonstrate that temporally precise intra-hippocampal communication is critical for forming the spatial code and that desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice.
Mouse Models ofGNAO1-Associated Movement Disorder: Allele- and sex-specific differences in phenotypes
Authors: Feng Huijie , Larrivee Casandra L. , Demireva Elena , Xie Huirong , Leipprandt Jeff , Neubig Richard R. 
Published: June 2018

AbstractBackgroundInfants and children with dominantde novomutations inGNAO1exhibit movement disorders, epilepsy, or both. Children with loss-of-function (LOF) mutations exhibit Epileptiform Encephalopathy 17 (EIEE17). Gain-of-function (GOF) mutations or those with normal function are found in patients with Neurodevelopmental Disorder with Involuntary Movements (NEDIM). There is no animal model with a human mutantGNAO1allele.ObjectivesHere we develop a mouse model carrying a humanGNAO1mutation and determine whether clinical features of theGNAO1mutation including movement disorder would be evident in the mouse model.MethodsA mouseGnao1knock-in GOF mutation (G203R) was created by CRISPR/Cas9 methods. The resulting offspring and littermate controls were subjected to a battery of behavioral tests. A previously reported GOF mutant mouse knock-in (Gnao1+/G184S) was also studied for comparison.ResultsGnao1+/G203Rmutant mice are viable and gain weight comparably to controls. Homozygotes are non-viable. Grip strength was decreased in both males and females. MaleGnao1+/G203Rmice were strongly affected in movement assays (RotaRod and DigiGait) while females were not. MaleGnao1+/G203Rmice also showed enhanced seizure propensity in the pentylenetetrazole kindling test. Mice with a G184S GOF knock-in also showed movement-related behavioral phenotypes but females were more strongly affected than males.ConclusionsGnao1+/G203Rmice phenocopy children with heterozygousGNAO1G203R mutations, showing both movement disorder and a relatively mild epilepsy pattern. This mouse model should be useful in mechanistic and preclinical studies ofGNAO1-related movement disorders.
Validating genome-wide CRISPR-Cas9 function in the non-conventional yeast Yarrowia lipolytica
Authors: Schwartz Cory , Cheng Jan-Fang , Evans Robert , Schwartz Christopher A. , Wagner James M. , Anglin Scott , Beitz Adam , Pan Weihua , Lonardi Stefano , Blenner Mark , Alper Hal S. , Yoshikuni Yasuo , Wheeldon Ian 
Published: June 2018

AbstractGenome-wide mutational screens are central to understanding the genetic underpinnings of evolved and engineered phenotypes. The widespread adoption of CRISPR-Cas9 genome editing has enabled such screens in many organisms, but identifying functional sgRNAs still remains a challenge. To address this limitation, we developed a methodology to quantify the cutting efficiency of each sgRNA in a genome-scale library in the biotechnologically important yeast Yarrowia lipolytica. Screening in the presence and absence of native DNA repair enabled high-throughput quantification of sgRNA function leading to the identification of high efficiency sgRNAs that cover 94% of genes. Library validation enhanced the classification of essential genes by identifying inactive guides that create false negatives and mask the effects of successful disruptions. Quantification of guide effectiveness also creates a dataset from which functional determinants of CRISPR-Cas9 can be identified. Finally, application of the library identified mutations that led to high lipid accumulation and eliminated pseudohyphal morphology.