bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthetic pluripotent bacterial stem cells
Authors: Molinari Sara , Shis David L. , Chappell James , Igoshin Oleg A. , Bennett Matthew R. 
Published: October 2018

AbstractA defining property of stem cells is their ability to differentiate via asymmetric cell division, in which a stem cell creates a differentiated daughter cell but retains its own phenotype. Here, we describe a synthetic genetic circuit for controlling asymmetrical cell division in Escherichia coli. Specifically, we engineered an inducible system that can bind and segregate plasmid DNA to a single position in the cell. Upon division, the co-localized plasmids are kept by one and only one of the daughter cells. The other daughter cell receives no plasmid DNA and is hence irreversibly differentiated from its sibling. In this way, we achieved asymmetric cell division though asymmetric plasmid partitioning. We also characterized an orthogonal inducible circuit that enables the simultaneous asymmetric partitioning of two plasmid species – resulting in pluripotent cells that have four distinct differentiated states. These results point the way towards engineering multicellular systems from prokaryotic hosts.
Metabolites associated with early cognitive changes implicated in Alzheimer’s disease
Authors: Darst Burcu F. , Jonaitis Erin M. , Koscik Rebecca L. , Clark Lindsay R. , Lu Qiongshi , Hogan Kirk J. , Johnson Sterling C. , Engelman Corinne D. 
Published: October 2018

AbstractWe investigated the metabolomics of early cognitive changes related to Alzheimer’s disease (AD) in order to better understand mechanisms that could contribute to early stages and progression of this disease. This investigation used longitudinal plasma samples from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), a cohort of participants who were dementia free at enrollment and enriched with a parental history of AD. Metabolomic profiles were quantified for 2,338 fasting plasma samples among 1,206 participants, each with up to three study visits. Of 1,097 metabolites tested, levels of seven were associated with executive function trajectories, including an amino acid and three fatty acids, but none were associated with delayed recall trajectories. Our time-varying metabolomic results suggest potential mechanisms that could contribute to the earliest signs of cognitive decline. In particular, fatty acids may be associated with cognition in a manner that is more complex than previously suspected.
Tomato fruit ripening factor NOR controls leaf senescence
Authors: Ma Xuemin , Balazadeh Salma , Mueller-Roeber Bernd 
Published: October 2018

AbstractNAC transcription factors (TFs) are important regulators of expressional reprogramming during plant development, stress responses and leaf senescence. NAC TFs also play important roles in fruit ripening. In tomato (Solanum lycopersicum), one of the best characterized NAC involved in fruit ripening is NON-RIPENING (NOR) and the non-ripening (nor) mutation has been widely used to extend fruit shelf life in elite varieties. Here, we show that NOR additionally controls leaf senescence. Expression of NOR increases with leaf age, and developmental as well as dark-induced senescence are delayed in the nor mutant, while overexpression of NOR promotes leaf senescence. Genes associated with chlorophyll degradation as well as senescence-associated genes (SAGs) show reduced and elevated expression, respectively, in nor mutants and NOR overexpressors. Overexpression of NOR also stimulates leaf senescence in Arabidopsis thaliana. In tomato, NOR supports senescence by directly and positively regulating the expression of several senescence-associated genes including, besides others, SlSAG15 and SlSAG113, SlSGR1 and SlYLS4. Finally, we find that another senescence control NAC TF, namely SlNAP2, acts upstream of NOR to regulate its expression. Our data support a model whereby NAC TFs have often been recruited by higher plants for both, the control of leaf senescence and fruit ripening.
Functional Connectivity of Corticostriatal Circuitry and Psychosis-Like Experiences in the General Community
Authors: Sabaroedin Kristina , Tiego Jeggan , Parkes Linden , Sforazzini Francesco , Finlay Amy , Johnson Beth , Pinar Ari , Cropley Vanessa , Harrison Ben J , Zalesky Andrew , Pantelis Christos , Bellgrove Mark , Fornito Alex 
Published: October 2018

AbstractBackgroundPsychotic symptoms are proposed lie on a continuum, ranging from isolated psychosis-like experiences (PLEs) in non-clinical populations to frank disorder. Here, we investigate neurobiological correlates of this symptomatologic continuum by examining whether functional connectivity of dorsal corticostriatal circuitry, which is disrupted in patients and high-risk individuals, is associated with the severity of subclinical PLEs.MethodsA community sample of 672 adults with no history of psychiatric or neurological illnesses completed a battery of seven questionnaires spanning various PLE domains. Principal component analysis (PCA) estimated major dimensions of PLEs from the questionnaires. PCA dimension scores were then correlated with whole-brain voxelwise functional connectivity (FC) maps of the striatum in a subset of 353 participants who completed a resting-state neuroimaging protocol.ResultsPCA identified two dimensions of PLEs accounting for 62.57% of variance in the measures, corresponding to positive and negative PLEs. Reduced FC between the dorsal striatum and prefrontal cortex correlated with higher positive PLEs. Negative PLEs correlated with increased FC between the dorsal striatum and visual and sensorimotor areas. In the ventral corticostriatal system, positive and negative PLEs were both associated with FC between the ventro-rostral putamen and sensorimotor cortices.ConclusionsConsistent with past findings in patients and high-risk individuals, subthreshold positive symptomatology is associated with reduced FC of the dorsal circuit. These findings suggest that the connectivity of this circuit tracks the expression of psychotic phenomena across a broad spectrum of severity, extending from the subclinical domain to clinical diagnosis.
Sequence Imputation from Low Density Single Nucleotide Polymorphism Panel in a Black Poplar Breeding population
Authors: Pégard Marie , Rogier Odile , Bérard Aurélie , Faivre-Rampant Patricia , Le Paslier Marie-Christine , Bastien Catherine , Jorge Véronique , Sánchez Leopoldo 
Published: October 2018

AbstractBackgroundGenomic selection accuracy increases with the use of high SNP (single nucleotide polymorphism) coverage. However, such gains in coverage come at high costs, preventing their operational implementation by breeders. Low density panels imputed to higher densities offer a cheaper alternative. Our study is one of the first to explore the imputation in a tree species: black poplar. About 1000 pure-breed Populus nigra trees corresponding to a subsample of the French breeding population were selected and genotyped with a 12K custom Infinium Bead-Chip. Forty-three of those individuals corresponding mostly to nodal trees in the pedigree were fully sequenced (reference), while the remaining majority (target) was imputed from 8K to 1.4 million SNPs using FImpute. Each SNP and individual was evaluated for imputation errors by leave-one-out cross validation in the training sample of 43 sequenced trees. Some summary statistics such as Hardy Weinberg Equilibrium exact test p-value, quality of sequencing, depth of sequencing per site and per individual, minor allele frequency, marker density ratio or SNP information redundancy were calculated. Principal component and Boruta analyses were used on all these parameters to rank the factors affecting the quality of imputation. Additionally, we characterize the impact of the relatedness between reference population and target population.ResultsDuring the imputation process, we used 7,540 SNPs from the chip to impute 1,438,827 SNPs from sequences along the 19 Chromosomes. At the individual level, imputation accuracy was very high with a proportion of SNPs correctly imputed between 0.84 and 0.99. The variation in accuracies was mostly due to differences in relatedness between individuals. At a SNP level, the imputation quality strongly depended on genotyped SNP density and to a lesser extent on the original minor allele frequency. The imputation did not appear to result in an increase of linkage disequilibrium. The genotype densification not only brought a better distribution of markers all along the genome, but also we did not detect any substantial bias in annotation categories.ConclusionsThis study shows that it is possible to impute low-density marker panels to whole genome sequence with good accuracy under certain conditions that could be common to many breeding populations.
Comprehensive Benchmarking and Integration of Tumour Microenvironment Cell Estimation Methods
Authors: Jiménez-Sánchez Alejandro , Cast Oliver , Miller Martin 
Published: October 2018

ABSTRACTThe tumour microenvironment comprises complex cellular compositions and interactions between cancer, immune, and stromal components which all play crucial roles in cancer. Various computational approaches have been developed during the last decade that estimate the relative abundance of different cell types in an unbiased manner using bulk tumour RNA data. However, a comparison that objectively evaluates the performance of these approaches against one another has not been conducted. Here we benchmarked six widely used tools and gene sets: Bindea et al. gene sets, Davoli et al. gene sets, CIBERSORT, MCP-counter, TIMER, and xCell. We also introduce ConsensusTME, a consensus approach that uses the union of genes that the six tools used for cell estimation, and corrects for tumour-type specificity. We benchmarked the seven tools using TCGA DNA-derived purity scores (33 tumour-types), methylation-derived leukocyte scores (30 tumour-types), and H&E deep learning derived lymphocyte counts (13 tumour types), and individual benchmark data sets (PBMCs and 2 tumour types). Although none of the seven tools outperformed others in every benchmark, ConsensusTME ranked consistently well in all cancer-related benchmarks making it the top performing method overall. Computational methods that provide robust and accurate estimates of non-cancerous cell populations in the tumour microenvironment from tumour bulk expression data are important tools that can advance our understanding of tumour, immune, and stroma interactions, with potential clinical application if high accuracy estimates are achieved.
Ontology based mining of pathogen-disease associations from literature
Authors: Kafkas Șenay , Hoehndorf Robert 
Published: October 2018

AbstractBackgroundInfectious diseases claim millions of lives especially in the developing countries each year, and resistance to drugs is an emerging threat worldwide. Identification of causative pathogens accurately and rapidly plays a key role in the success of treatment. To support infectious disease research and mechanisms of infection, there is a need for an open resource on pathogen-disease associations that can be utilized in computational studies. A large number of pathogen-disease associations is available from the literature in unstructured form and we need automated methods to extract the data.ResultsWe developed a text mining system designed for extracting pathogen-disease relations from literature. Our approach utilizes background knowledge from an ontology and statistical methods for extracting associations between pathogens and diseases. In total, we extracted a total of 3,420 pathogen-disease associations from literature. We integrated our literature-derived associations into a database which links pathogens to their phenotypes for supporting infectious disease research.ConclusionsTo the best of our knowledge, we present the first study focusing on extracting pathogen-disease associations from publications. We believe the text mined data can be utilized as a valuable resource for infectious disease research. All the data is publicly available from https://github.com/bio-ontology-research-group/padimi and through a public SPARQL endpoint from http://patho.phenomebrowser.net/.
Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding
Authors: Sundaresan Vaanathi , Zamboni Giovanna , Le Heron Campbell , Rothwell Peter M. , Husain Masud , Battaglini Marco , De Stefano Nicola , Jenkinson Mark , Griffanti Ludovica 
Published: October 2018

AbstractWhite matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurode-generative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions.In this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature, with respect to K-nearest neighbour algorithm currently used for lesion probability map estimation in BIANCA. Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort and a vascular cohort.We observed that including population-level parametric lesion probabilities with re-spect to age and using alternative machine learning techniques provided negligible im-provement. However, LOCATE provided a substantial improvement in the lesion segmentation performance when compared to the global thresholding currently used in BIANCA. We further validated LOCATE on a cohort of CADASIL (Cerebral autoso-mal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease characterised by extensive WMH burden, and healthy controls showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.
Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations
Authors: Hameri Tuure , Fengos Georgios , Ataman Meric , Miskovic Ljubisa , Hatzimanikatis Vassily 
Published: October 2018

AbstractLarge-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grownEscherichia coliinto a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of theE. coliaerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grownE. coli.
Genome-wide transcription during early wheat meiosis is independent of synapsis, ploidy level and thePh1locus
Authors: Martín A.C. , Borrill P. , Higgins J. , Alabdullah A.K. , RamÍrez-González R.H. , Swarbreck D. , Uauy C. , Shaw P. , Moore G. 
Published: October 2018

ABSTRACTPolyploidization is a fundamental process in plant evolution. One of the biggest challenges faced by a new polyploid is meiosis, particularly discriminating between multiple related chromosomes so that only homologous chromosomes synapse and recombine to ensure regular chromosome segregation and balanced gametes. Despite its large genome size, high DNA repetitive content and similarity between homoeologous chromosomes, hexaploid wheat completes meiosis in a shorter period than diploid species with a much smaller genome. Therefore, during wheat meiosis, mechanisms additional to the classical model based on DNA sequence homology, must facilitate more efficient homologous recognition. One such mechanism could involve exploitation of differences in chromosome structure between homologues and homoeologues at the onset of meiosis. In turn, these chromatin changes, can be expected to be linked to transcriptional gene activity. In this study, we present an extensive analysis of a large RNA-Seq data derived from six different genotypes: wheat, wheat-rye hybrids and newly synthesized octoploid triticale, both in the presence and absence of thePh1locus. Plant material was collected at early prophase, at the transition leptotene-zygotene, when the telomere bouquet is forming and synapsis between homologues is beginning. The six genotypes exhibit different levels of synapsis and chromatin structure at this stage; therefore, recombination and consequently segregation, are also different. Unexpectedly, our study reveals that neither synapsis, whole genome duplication nor the absence of thePh1locus are associated with major changes in gene expression levels during early meiotic prophase. Overall wheat transcription at this meiotic stage is therefore highly resilient to such alterations, even in the presence of major chromatin structural changes. This suggests that post-transcriptional and post-translational processes are likely to be more important. Thus, further studies will be required to reveal whether these observations are specific to wheat meiosis, and whether there are significant changes in post-transcriptional and post-translational modifications in wheat and other polyploid species associated with their polyploidisation.
Highly mutated memory cells drive an inefficient secondary antibody response to a variant protein
Authors: Tennant Richard K , Holzer Barbara , Love John , Tchilian Elma , White Harry N 
Published: October 2018

AbstractA powerful vaccine against mutable viruses might induce memory antibodies that either strongly bound antigenic variants or that could rapidly undergo secondary affinity maturation to achieve this. We have recently shown after secondary immunization of mice with a widely variant protein (Burton et al. 2018) that IgM+ memory B-cells with few mutations supported an efficient secondary germinal centre (GC) and serum response, superior to a primary response to the same protein. Here, boosting with more closely related proteins produced a GC response dominated by highly mutated B-cells that failed, not efficiently improving serum avidity even in the presence of extra adjuvant, and that was worse than a primary response. This supports a hypothesis that over certain antigenic differences, a cross reactive, mutated, memory B-cell compartment can be an impediment to affinity maturation.
Disease mortality in domesticated animals is predicted by host evolutionary relationships
Authors: Farrell Maxwell J. , Davies T. J. 
Published: October 2018

AbstractInfectious diseases of domesticated animals impact human well-being via food insecurity, loss of livelihoods, and human infections. While much research has focused on parasites that infect single host species, most parasites of domesticated mammals infect multiple species. The impact of multi-host parasites varies across hosts; some rarely result in death, whereas others are nearly always fatal. Despite their high ecological and societal costs, we currently lack theory for predicting the lethality of multi-host parasites. Here, using a global dataset of over 4000 case-fatality rates for 65 infectious diseases (caused by micro and macro-parasites) and 12 domesticated host species, we show that the average evolutionary distance from an infected host to other mammal host species is a strong predictor of disease-induced mortality. We find that as parasites infect species outside of their documented phy-1 logenetic host range, they are more likely to result in lethal infections, with the odds of death doubling for each additional 10 million years of evolutionary distance. Our results for domesticated animal diseases reveal patterns in the evolution of highly lethal parasites that are difficult to observe in the wild, and further suggest that the severity of infectious diseases may be predicted from evolutionary relationships among hosts.