bluuebunny/crossref_metadata_2025_split huggingface dataset:
SF3B1ness score: screeningSF3B1mutation status from over 60,000 transcriptomes based on a machine learning approach
Authors: Shiraishi Yuichi , Chiba Kenichi , Okada Ai 
Published: March 2019

AbstractIn precision oncology, genomic evidence is used to determine the optimal treatment for each patient. However, identification of somatic mutations from genome sequencing data is often technically difficult and functional significance of somatic mutations is inconclusive in many cases. In this paper, to seek for an alternative approach, we tackle the problem of predicting functional mutations from transcriptome sequencing data. Focusing onSF3B1, a key splicing factor gene, we develop SF3B1ness score for classifying functional mutation status using a combination of Naive Bayes classifier and zero-inflated beta-binomial modeling (R package is available at (https://github.com/friend1WS/SF3B1ness). Using 8,992 TCGA exome and RNA sequencing data for evaluation, we show that the classifier based on SF3B1ness score is able to (1) attain very high precision (>93%) and sensitivity (>95%), (2) rescue several somatic mutations not identified by exome sequence analysis especially due to low variant allele frequencies, and (3) successfully measure functional importance for somatic mutation whose significance has been unknown. Furthermore, to demonstrate that the SF3B1ness score is highly robust and can be extensible to the cohorts outside training data, we performed a functionalSF3B1mutation screening on 51,577 additional transcriptome sequencing data. We have detected 135 samples with putativeSF3B1functional mutations including those that are rarely registered in the somatic mutation database (e.g., G664C, L747W, and R775G). Moreover, we could identify two cases withSF3B1mutations from normal tissues, implying that SF3B1ness score can be used for detecting clonal hematopoiesis.
Thermal preference influences depth use but not biomass of predatory fishes in response to lake morphometry
Authors: Bartley Timothy J. , Guzzo Matthew M. , Cazelles Kevin , Verville Alex , McMeans Bailey C. , McCann Kevin S. 
Published: March 2019

ABSTRACTTop predators’ responses to environmental conditions shape food web architecture and influence ecosystem structure and stability. Yet the impacts of fundamental properties like ecosystem size and morphometry on top predators’ behaviour are poorly understood. We examined how lake morphometry impacts the behaviour (inferred by depth use) of three key fish top predators—the cold-adapted lake trout, the cool-adapted walleye, and the warm-adapted smallmouth bass— which can each strongly impact local food web structure. We used catch-per-unit-effort data from nearly 500 boreal lakes of Ontario, Canada to evaluate the role of thermal preference in dictating mean depth of capture and biomass index in response to lake morphometry. We found evidence that thermal preferences influence how species’ depth use and biomass changed with lake size, proportion of littoral area, and maximum lake depth, although we found no relationship with lake shape. However, found no strong evidence that lake morphology influences these species’ biomasses, despite theory that predicts such a relationship. Our results suggest that some aspects of lake morphometry can alter habitat accessibility and productivity in ways that influence the behaviour and biomass of these top predator species depending on their thermal preferences. Our results have implications for how lake food webs expand and contract with lake morphometry and other key abiotic factors. We argue that several key abiotic factors likely drive top predator depth use in ways that may shape local food web structure and play an important role in determining the ultimate fate of ecosystems with environmental change.
Cytokinin functions as an asymmetric and anti-gravitropic signal in lateral roots
Authors: Waidmann Sascha , Rosquete Michel Ruiz , Schöller Maria , Lindner Heike , LaRue Therese , Sarkel Elizabeth , Petřík Ivan , Dünser Kai , Martopawiro Shanice , Sasidharan Rashmi , Novak Ondrej , Wabnik Krzysztof , Dinneny José R. , Kleine-Vehn Jürgen 
Published: March 2019

AbstractDirectional organ growth allows the plant root system to strategically cover its surroundings. Intercellular auxin transport is aligned with the gravity vector in the primary root tips, facilitating downward organ bending at the lower root flank. Here we show that cytokinin signaling functions as a lateral root specific anti-gravitropic component, promoting the radial distribution of the root system. We performed a genome-wide association study and revealed that signal peptide processing of Cytokinin Oxidase 2 (CKX2) affects its enzymatic activity and, thereby, determines the degradation of cytokinins in naturalArabidopsis thalianaaccessions. Cytokinin signaling interferes with growth at the upper lateral root flank and thereby prevents downward bending. Our interdisciplinary approach revealed that two phytohormonal cues at opposite organ flanks counterbalance each other’s negative impact on growth, suppressing organ growth towards gravity and allow for radial expansion of the root system.
Corticotropin-Releasing Hormone Signaling in the Oval Bed Nucleus of the Stria Terminalis Mediates Chronic Stress-Induced Negative Valence Behaviors Associated with Anxiety
Authors: Hu Pu , Maita Isabella , Kwok Christopher , Gu Edward , Gergues Mark , Liu Ji , Pang Zhiping , Swaab Dick F. , Lucassen Paul J. , Roepke Troy A. , Samuels Benjamin A. 
Published: March 2019

AbstractThe bed nucleus of stria terminalis (BNST) is a forebrain region highly sensitive to stress that expresses corticotropin-releasing hormone (CRH) neuropeptide at high levels and regulates negative valence behaviors associated with anxiety. However, how chronic stress modulates CRH signaling and neuronal activity in BNST remains unknown. We subjected C57BL6/J mice to a 6-week chronic variable mild stress (CVMS) paradigm and investigated the effects on behavior, BNST cellular neurophysiology, and BNST CRH signaling. We also utilized pharmacological infusions and optogenetics to decipher and mimic the effects of CVMS on BNST cellular neurophysiology and behavior. CVMS elevated plasma corticosterone levels, induced negative valence behaviors associated with anxiety, diminished M-currents (voltage-gated K+currents that stabilize membrane potential and regulate neuronal excitability), and increased mEPSC amplitude in ovBNST. Additionally, the number of c-fos+, CRH+, and CRH activator pituitary adenylate cyclase-activating polypeptide (PACAP)+cells were increased while CRH inhibitor striatal-enriched protein tyrosine phosphatase (STEP)+cells were decreased in ovBNST. These expression data were confirmed with qPCR. CVMS also activated PKA in BNST and the electrophysiological and behavioral effects of CVMS were reversed by ovBNST infusion of the PKA-selective antagonist H89. Moreover, optogenetic activation of ovBNST directly induced negative valence behaviors associated with anxiety, mimicking the effects of CVMS. CVMS mediates effects on negative valence behaviors associated with anxiety by activating CRH signaling components and cellular excitability in ovBNST Our findings decipher an important CRH-associated stress molecular signature in BNST and advance our understanding of the neural circuitry underlying stress-induced disorders.
Analytic Model of fMRI BOLD Signals for Separable Metrics of Neural and Metabolic Activity
Authors: Schaper Charles D. 
Published: March 2019

AbstractThe applications of fMRI (functional magnetic resonance imaging) are broad covering diagnostic and clinical extents of brain function, which involves the analysis of BOLD (blood oxygen level-dependent) contrast signals. The BOLD signals are sourced from both neural and metabolic functions, and thus to enable a detailed examination of fMRI studies, methods are sought to separate the neural and metabolic functions, such that the neural component, which is often the metric of interest, can be independently examined, especially in relation to neural connectivity. In this work, a modeling approach is developed that separates the neural and metabolic functions from the overall BOLD signal. The newly developed model is initially developed within a linear framework and demonstrates excellent comparison in data fit at 97.4% to the three Gamma function, which has been widely used to characterize fMRI BOLD experimental data. The neural component of the model formulation is comprised of a proper transfer function of two poles and two zeros, and characterizes the salient features of the BOLD signal, including the initial dip, peak, undershoot, and stabilization period. The linear model is extended to characterize nonlinear fMRI BOLD signal responses through the integration of saturation functions to both the leading and trailing edges of the signal. The nonlinear model representation not only explains the muted response in amplitude and oscillations, but also explains nuanced oscillations during the hold and settling phases of fMRI BOLD responses as exemplified in comparison to published data of sensorimotor responses. Further, the newly developed decomposition is derived within a framework for modeling neurovascular coupling environments, and thus lends credibility to the modeling framework. In developing the decomposition of the neural and metabolic transfer functions, it is a conclusion that the BOLD signal correlates very well with the fast dynamics associated with neural response to external stimuli.Graphical AbstractThe normalized impulse response of the BOLD signal and the corresponding neural activity according to the newly developed model. There is a correspondence of the critical points for the oscillatory response of the neural function and metabolic reactivity, including the initial dip, peak and subsequent undershoot. Thus, the BOLD signal is a correlated representation of the underlying neural response.
Genome-wide association study and low-cost genomic predictions for growth and fillet yield in Nile tilapia (Oreochromis niloticus)
Authors: Yoshida Grazyella M. , Lhorente Jean P. , Correa Katharina , Soto Jose , Salas Diego , Yáñez José M. 
Published: March 2019

ABSTRACTFillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in tilapia breeding programs.
Large-scale chemical-genetics of the human gut bacteriumBacteroides thetaiotaomicron
Authors: Liu Hualan , Price Morgan N. , Carlson Hans K. , Chen Yan , Ray Jayashree , Shiver Anthony L. , Petzold Christopher J. , Huang Kerwyn Casey , Arkin Adam P. , Deutschbauer Adam M. 
Published: March 2019

AbstractThe genomic catalogue of the human microbiota has expanded dramatically in recent years, and insights derived from human microbiota genomics has vast potential to generate treatments for human diseases. However, predictably harnessing the microbiota for beneficial outcomes is currently limited by our lack of understanding of the physiology of the constituent bacteria. For instance, the functions of most of their genes are not known. Here, we systematically measure mutant phenotypes for genes from the gut commensalBacteroides thetaiotaomicron. Using a barcoded transposon mutant library, we measured the fitness of 4,055B. thetaiotaomicrongenes across 492 experiments, including growth on 45 carbon substrates and in the presence of 57 stress-inducing compounds. Our data is in strong agreement with previous studies, and more importantly also uncovers the biological roles of poorly annotated genes. We identified 497 genes with a specific phenotype in only one or a handful of conditions, thus enabling informed predictions of gene function for a subset of these genes. For example, we identified a glycoside hydrolase important for growth on type I rhamnogalacturonan, a DUF4861 protein for glycosaminoglycan utilization, a DUF1080 protein for disaccharide utilization, and a tripartite multidrug resistance system specifically important for bile salt tolerance. Our approach can be applied to other members of the human microbiota to experimentally characterize their genes.
Retaining pluripotency and exogenous mRNA introduction in planarian stem cell culture
Authors: Lei Kai , Zhang Wenya , Chen Jiajia , McKinney Sean A. , Ross Eric J. , Lee Heng-Chi , Alvarado Alejandro Sánchez 
Published: March 2019

AbstractPlanarians possess naturally occurring pluripotent adult somatic stem cells (neoblasts) required for homeostasis and whole-body regeneration. However, no reliable neoblast culture methods are currently available, hindering mechanistic studies of pluripotency and the development of transgenic tools. We report the first robust methods for neoblast culture and delivery of exogenous mRNAs. We identify optimal culture media for maintaining neoblasts in vitro, and show via transplantation that cultured stem cells retain pluripotency for two days. We developed a new procedure that significantly improves neoblast yield and purity by modifying standard flow cytometry methods. These methods enable the introduction and expression of exogenous mRNAs in neoblasts, overcoming a key hurdle impeding the application of transgenics in planarians. The advances in cell culture reported here create new opportunities for mechanistic studies of planarian adult stem cell pluripotency, and provide a systematic framework to develop cell culture techniques in other emerging research organisms.
A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual
Authors: Bucholc Magda , Ding Xuemei , Wang Haiying , Glass David H. , Wang Hui , Prasad Girijesh , Maguire Liam P. , Bjourson Anthony J. , McClean Paula L. , Todd Stephen , Finn David P. , Wong-Lin KongFatt ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: March 2019

AbstractComputerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on the group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer’s disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and k-Nearest Neighbor (kNNreg) for regression and Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (kNNclass) for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to be consistently higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e.,R2= 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.
MTH1 inhibitor TH588 induces mitosis-dependent accumulation of genomic 8-oxodG and disturbs mitotic progression
Authors: Rudd Sean G , Gad Helge , Amaral Nuno , Hagenkort Anna , Groth Petra , Ström Cecilia E , Mortusewicz Oliver , Berglund Ulrika Warpman , Helleday Thomas 
Published: March 2019

ABSTRACTReactive oxygen species (ROS) oxidise nucleotide triphosphate pools (e.g., 8-oxodGTP), which may kill cells if incorporated into DNA. Whether cancers avoid poisoning from oxidised nucleotides by preventing incorporation via the oxidised purine diphosphatase MTH1 remains under debate. Also, little is known about DNA polymerases incorporating oxidised nucleotides in cells or how oxidised nucleotides in DNA become toxic. We show replacement of one of the main DNA replicases in human cells, DNA polymerase delta (Pol δ), to an error-prone variant allows increased 8-oxodG accumulation into DNA following treatment with the MTH1 inhibitor (MTH1i) TH588. The resulting elevated genomic 8-oxodG correlates with increased cytotoxicity of TH588. Interestingly, no substantial perturbation of replication fork progression is observed, but rather mitotic progression is impaired and mitotic DNA synthesis triggered. Reducing mitotic arrest by reversin treatment prevents accumulation of genomic 8-oxodG and reduces cytotoxicity of TH588, in line with the notion that mitotic arrest is required for ROS build-up and oxidation of the nucleotide pool. Furthermore, we demonstrate delayed mitosis and increased mitotic cell death following TH588 treatment in cells expressing the error-prone Pol δ variant, which is not observed following treatments with anti-mitotic agents, thus linking incorporation of oxidised nucleotides and disturbed mitotic progression.
Investigation of the validity of two Bayesian ancestral state reconstruction models for estimatingSalmonellatransmission during outbreaks
Authors: Bloomfield Samuel J. , Vaughan Timothy G. , Benschop Jackie , Marshall Jonathan C. , Hayman David T. S. , Biggs Patrick J. , Carter Philip E. , French Nigel P. 
Published: March 2019

AbstractAncestral state reconstruction models use genetic data to characterize a group of organisms’ common ancestor. These models have been applied to salmonellosis outbreaks to estimate the number of transmissions between different animal species that share similar geographical locations, with animal host as the state. However, as far as we are aware, no studies have validated these models for outbreak analysis. In this study, salmonellosis outbreaks were simulated using a stochastic Susceptible-Infected-Recovered model, and the host population and transmission parameters of these simulated outbreaks were estimated using Bayesian ancestral state reconstruction models (discrete trait analysis (DTA) and structured coalescent (SC)). These models were unable to accurately estimate the number of transmissions between the host populations or the amount of time spent in each host population. The DTA model was inaccurate because it assumed the number of isolates sampled from each host population was proportional to the number of individuals infected within each host population. The SC model was inaccurate possibly because it assumed that each host population’s effective population size was constant over the course of the simulated outbreaks. This study highlights the need for phylodynamic models that can take into consideration factors that influence the characteristics and behavior of outbreaks, e.g. changing effective population sizes, variation in infectious periods, intra-population transmissions, and disproportionate sampling of infected individuals.
Extent, heritability, and functional relevance of single cell expression variability in highly homogeneous populations of human cells
Authors: Osorio Daniel , Yu Xue , Zhong Yan , Li Guanxun , Yu Peng , Serpedin Erchin , Huang Jianhua , Cai James J. 
Published: March 2019

AbstractBecause of recent technological developments, single-cell assays such as single-cell RNA sequencing (scRNA-seq) have become much more widely available and have achieved unprecedented resolution in revealing cell heterogeneity. The extent of intrinsic cell-to-cell variability in gene expression, orsingle cell expression variability(scEV), has thus been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what its implications are for multi-cellular organisms. We therefore analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs). For each of the three cell types, we estimated scEV in homogeneous populations of cells; we identified 465, 466, and 291 highly variable genes (HVGs), respectively. These HVGs were enriched with specific functions precisely relevant to the cell types, from which the scRNA-seq data used to identify HVGs were generated—e.g., HVGs identified in lymphoblastoid cells were enriched in cytokine signaling pathways, LAECs collagen formation, and DFs keratinization. HVGs were deeply embedded in gene regulatory networks specific to corresponding cell types. We also found that scEV is a heritable trait, partially determined by cell donors’ genetic makeups. Furthermore, across genes, especially immune genes, levels of scEV and between-individual variability in gene expression were positively correlated, suggesting a potential link between the two variabilities measured at different organizational levels. Taken together, our results support the “variation is function” hypothesis, which postulates that scEV is required for higher-level system function. Thus, we argue that quantifying and characterizing scEV in relevant cell types may deepen our understating of normal as well as pathological cellular processes.