bluuebunny/crossref_metadata_2025_split huggingface dataset:
Glycosylation-associated dysregulation of pyocyanin production inPseudomonas aeruginosa: Implications for quorum sensing regulation
Authors: McClinton Anna K. , Hamilton Caleb L. , Cioffi Donna L. , Cioffi Eugene A. 
Published: May 2018

AbstractPseudomonas aeruginosa(P. aeruginosa) is an important opportunistic pathogen associated with high mortality in pneumonia, sepsis, and cystic fibrosis. Lending to its ability to cause severe disease and death is its arsenal of virulence factors and host evasion tactics. In addition to various other regulatory systems, many ofP. aeruginosa’s virulence factors are regulated by a population density dependent regulatory network known as quorum sensing (QS). Many regulatory systems are impacted by post-translational modifications of proteins. An underexplored physiological aspect ofP. aeruginosais its ability to glycosylate proteins and the subsequent impact of glycosylation onP. aeruginosaphysiology and behavior. The goal of this study was to determine whetherP. aeruginosaQS is regulated by glycosylation. Here we demonstrate that disruption of glycosylation dysregulates QS phenotypes, notably pyocyanin production, inP. aeruginosaPAO1. In this study, it was initially observed that deletion of theP. aeruginosaneuraminidase, PaNA, caused an increased production of pyocyanin in LB-Lennox broth compared to wildtype bacteria at identical population densities. To confirm that the increased pyocyanin production was due to QS, we performed induction experiments using 10% cell-free media harvested from overnight cultures. To determine whether the QS phenotype observed is specific to pseudaminic acid, the target of PaNA, or if it is a reflection of global changes in glycosylation, we measured QS in a library of mutant bacteria generated in an MPAO1 background containing transposon insertions in various glycosyl-associated enzymes. The pattern of dysregulated QS held true in these mutant strains as well. Overall these data indicate that inP. aeruginosa, glycosylation is an important determinant of QS.
Mouse models of hereditary hemochromatosis do not develop early liver fibrosis in response to a high fat diet
Authors: Wagner John , Fillebeen Carine , Haliotis Tina , Mui Jeannie , Vali Hojatollah , Pantopoulos Kostas 
Published: May 2018

AbstractHepatic iron overload, a hallmark of hereditary hemochromatosis (HH), triggers progressive liver disease. There is also increasing evidence for a pathogenic role of iron in non-alcoholic fatty liver disease (NAFLD), which may progress to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis and hepatocellular cancer. Mouse models of HH and NAFLD can be used to explore potential interactions between iron and lipid metabolic pathways. Hfe−/− mice, a model of moderate iron overload, were reported to develop early liver fibrosis in response to a high fat diet. However, this was not the case with Hjv−/− mice, a model of severe iron overload. These data raised the possibility that the Hfe gene may protect against liver injury independently of its iron regulatory function. Herein, we addressed this hypothesis in a comparative study utilizing wild type, Hfe−/−, Hjv−/− and double Hfe−/−Hjv−/− mice. The animals, all in C57/BL6 background, were fed with a high fat diet for 14 weeks and developed hepatic steatosis, associated with mild iron overload. Hfe co-ablation did not sensitize steatotic Hjv-deficient mice to liver injury. Moreover, we did not observe any signs of liver inflammation or fibrosis even in single steatotic Hfe−/− mice. Ultrastructural studies revealed a reduced lipid and glycogen content in Hjv−/− hepatocytes, indicative of a metabolic defect. Interestingly, glycogen levels were restored in double Hfe−/−Hjv−/− mice, which is consistent with a metabolic function of Hfe. We conclude that hepatocellular iron excess does not aggravate diet-induced steatosis to steatohepatitis or early liver fibrosis in mouse models of HH, irrespectively of the presence or lack of Hfe.
Clustering of Type 2 Diabetes Genetic Loci by Multi-Trait Associations Identifies Disease Mechanisms and Subtypes
Authors: Udler Miriam S. , Kim Jaegil , von Grotthuss Marcin , Bonàs-Guarch Sílvia , Mercader Josep M , Cole Joanne B. , Chiou Joshua , Anderson Christopher D , Boehnke Michael , Laakso Markku , Atzmon Gil , Glaser Benjamin , Gaulton Kyle , Flannick Jason , Getz Gad , Florez Jose C. 
Published: May 2018

AbstractBackgroundType 2 diabetes (T2D) is a heterogeneous disease for which 1) disease-causing pathways are incompletely understood and 2) sub-classification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four independent cohorts of individuals with T2D.Methods and FindingsIn an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization clustering to genome-wide association results for 94 independent T2D genetic loci and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta-cell function, differing from each other by high vs. low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity-mediated (high BMI, waist circumference), “lipodystrophy-like” fat distribution (low BMI, adiponectin, HDL-cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster GRS’s were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease, and stroke risk. We evaluated the potential for clinical impact of these clusters in four studies containing participants with T2D (METSIM, N=487; Ashkenazi, N=509; Partners Biobank, N=2,065; UK Biobank N=14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with ~30% of all participants assigned to just one cluster top decile.ConclusionOur approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.
Cell type-dependent effects of ellagic acid on cellular metabolism
Authors: Boehning Alexandra L. , Essien Safia A. , Underwood Erica L. , Dash Pramod K. , Boehning Darren 
Published: May 2018

AbstractEllagic acid is a botanical polyphenol which has been shown to have numerous effects on cellular function. Ellagic acid can induce apoptosis and inhibit the proliferation of various cancer cell types in vitro and in vivo. As such, ellagic acid has attracted significant interest as a potential chemotherapeutic compound. One mechanism by which ellagic acid has been proposed to affect cellular physiology is by regulating metabolic pathways. Here we show the dose-dependent effects of ellagic acid on cellular energy production and downstream induction of the apoptotic program in HEK293, HeLa, MCF7, and HepG2 cells. At physiologically relevant doses, eallgic acid has pleiotropic and cell-type specific effects on mitochondrial function. At high doses ellagic acid can also influence glycolytic pathways and induce cell death. Our results demonstrate that ellagic acid can influence mitochondrial function at therapeutically relevant concentrations. The observed effects of ellagic acid on cellular respiration are complex and cell type-specific, which may limit the chemotherapeutic utility of this compound.
Global biogeographical regions of freshwater fish species
Authors: Leroy Boris , Dias Murilo S. , Giraud Emilien , Hugueny Bernard , Jézéquel Céline , Leprieur Fabien , Oberdorff Thierry , Tedesco Pablo A. 
Published: May 2018

AbstractAimTo define the major biogeographical regions and transition zones for freshwater fish species.TaxonStrictly freshwater species of actinopterygian fish (i.e., excluding marine and amphidromous fish families).MethodsWe based our bioregionalisation on a global database of freshwater fish species occurrences in drainage basins, which, after filtering, includes 11 295 species in 2 581 basins. On the basis of this dataset, we generated a bipartite (basin-species) network upon which we applied a hierarchical clustering algorithm (the Map Equation) to detect regions. We tested the robustness of regions with a sensitivity analysis. We identified transition zones between major regions with the participation coefficient, indicating the degree to which a basin has species from multiple regions.ResultsOur bioregionalisation scheme showed two major supercontinental regions (Old World and New World, 50% species of the world and 99.96% endemics each). Nested within these two supercontinental regions lie six major regions (Nearctic, Neotropical, Palearctic, Ethiopian, Sino-Oriental and Australian) with extremely high degrees of endemism (above 96% except for the Palearctic). Transition zones between regions were of limited extent compared to other groups of organisms. We identified numerous subregions with high diversity and endemism in tropical areas (e.g. Neotropical), and a few large subregions with low diversity and endemism at high latitudes (e.g. Palearctic).Main conclusionsOur results suggest that regions of freshwater fish species were shaped by events of vicariance and geodispersal which were similar to other groups, but with freshwater-specific processes of isolation that led to extremely high degrees of endemism (far exceeding endemism rates of other continental vertebrates), specific boundary locations, and limited extents of transition zones. The identified bioregions and transition zones of freshwater fish species reflect the strong isolation of freshwater fish faunas for the past 10 to 20 million years. The extremely high endemism and diversity of freshwater fish fauna raises many questions about the biogeographical consequences of current introductions and extinctions.
Aurora B is required for programmed variations of cytokinesis during morphogenesis in theC. elegansembryo
Authors: Bai Xiaofei , Lee Po-Yi , Chen Chin-Yi , Simmons James R. , Nebenfuehr Benjamin , Mitchell Diana , Klebanow Lindsey R. , Mattson Nicholas , Sorensen Turpin Christopher G. , Chen Bi-Chang , Betzig Eric , Bembenek Joshua N. 
Published: May 2018

AbstractWhile cytokinesis has been intensely studied, how it is executed during development is not well understood, despite a long-standing appreciation that various aspects of cytokinesis vary across cell and tissue types. To address this, we investigated cytokinesis during the invariantC. elegansembryo lineage and found several reproducibly altered parameters at different stages. During early divisions, furrow ingression asymmetry and midbody inheritance is consistent, suggesting specific regulation of these events. During morphogenesis, we find several unexpected alterations including migration of midbodies to the apical surface during epithelial polarization in different tissues. Aurora B kinase, which is essential for several aspects of cytokinesis, remains localized to the apical membrane after internalization of other midbody components. Inactivation of Aurora B causes cytokinesis failure, which disrupts polarization and tissue formation. Therefore, cytokinesis shows surprising diversity during development and is required during epithelial polarization to establish cellular architecture during morphogenesis.
Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism
Authors: Zhou Jian , Park Christopher Y. , Theesfeld Chandra L. , Yuan Yuan , Sawicka Kirsty , Darnell Jennifer C. , Scheckel Claudia , Fak John J , Tajima Yoko , Darnell Robert B. , Troyanskaya Olga G. 
Published: May 2018

AbstractWe address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts specific regulatory effects and deleterious disease impact of genetic variants. Applying this framework to 1,790 Autism Spectrum Disorder (ASD) simplex families reveals autism disease causality of noncoding mutations by demonstrating that ASD probands harbor transcriptional (TRDs) and post-transcriptional (RRDs) regulation-disrupting mutations of significantly higher functional impact than unaffected siblings. Importantly, we detect this significant noncoding contribution at each level, transcriptional and post-transcriptional, independently and after multiple hypothesis correction. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development, and reveals a convergent genetic landscape of coding and noncoding (TRD and RRD) de novo mutations in ASD. We demonstrate that sequences carrying prioritized proband de novo mutations possess transcriptional regulatory activity and drive expression differentially, and highlight a link between noncoding mutations and IQ heterogeneity in ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD, prioritizes high impact transcriptional and post-transcriptional regulatory mutations for further study, and is broadly applicable to complex human diseases.
Functional instability allows access to DNA in longer Transcription Activator-Like Effector (TALE) arrays
Authors: Geiger-Schuller Kathryn , Mitra Jaba , Ha Taekjip , Barrick Doug 
Published: May 2018

AbstractTranscription activator-like effectors (TALEs) bind DNA through an array of tandem 34-residue repeats. Here, we examine the kinetics of DNA binding for a set of TALE arrays with varying numbers of identical repeats using single molecule microscopy. Using a new deterministic modeling approach, we find evidence for conformational heterogeneity in both the free- and DNA-bound TALE arrays. Combined with previous work demonstrating populations of partly folded TALE states, our findings reveal a functional instability in TALE-DNA binding. For TALEs forming less than one superhelical turn around DNA, partly folded open states inhibit DNA binding. In contrast, for TALEs forming more than one turn, the partly folded open states facilitate DNA binding. Overall, we find that increasing repeat number results in significantly slower interconversion between the various DNA-free and DNA-bound states. These findings highlight the role of conformational heterogeneity and dynamics in facilitating macromolecular complex assembly.Impact StatementSingle molecule DNA-binding trajectories and deterministic modeling analyses demonstrate a functional role for high energy partly folded states in Transcription Activator-Like Effectors (TALEs) that could improve future TALEN design.
Genetic effects on promoter usage are highly context-specific and contribute to complex traits
Authors: Alasoo Kaur , Rodrigues Julia , Danesh John , Freitag Daniel F. , Paul Dirk S. , Gaffney Daniel J. 
Published: May 2018

AbstractGenetic variants regulating RNA splicing and transcript usage have been implicated in both common and rare diseases. Although transcript usage quantitative trait loci (tuQTLs) have now been mapped in multiple cell types and conditions, the molecular mechanisms through which these variants exert their effect have remained elusive. Specifically, changes in transcript usage could arise from promoter choice, alternative splicing or 3′ end choice, but current tuQTL studies have not been able to distinguish between them. Here, we performed comprehensive analysis of RNA-seq data from human macrophages exposed to a range of inflammatory stimuli (IFNγ, Salmonella, IFNγ + Salmonella) and a metabolic stimulus (acetylated LDL), obtained from up to 84 individuals. In addition to conventional gene-level and transcript-level analyses, we also developed an analytical approach to directly quantify promoter, internal exon and 3′ end usage. We found that although naive transcript-level analysis often links single genetic variants to multiple coupled changes on the transcriptome, this appears to be an artefact of incomplete transcript annotations. Most of this coupling disappears when promoters, splicing and 3′ end usage are quantified directly. Furthermore, promoter, splicing and 3′ end QTLs are each enriched in distinct genomic features, suggesting that they are predominantly controlled by independent regulatory mechanisms. We also find that promoter usage QTLs are 50% more likely to be context-specific than canonical splicing QTLs and constitute 25% of the transcript-level colocalisations with complex traits. Thus, promoter usage might be a previously underappreciated molecular mechanism mediating complex trait associations in a context-specific manner.
Cell-free DNA profiling of metastatic prostate cancer reveals microsatellite instability, structural rearrangements and clonal hematopoiesis
Authors: Mayrhofer Markus , De Laere Bram , Whitington Tom , Van Oyen Peter , Ghysel Christophe , Ampe Jozef , Ost Piet , Demey Wim , Hoekx Lucien , Schrijvers Dirk , Brouwers Barbara , Lybaert Willem , Everaert Els , De Maeseneer Daan , Strijbos Michiel , Bols Alain , Fransis Karen , Oeyen Steffi , van Dam Pieter-Jan , Van den Eynden Gert , Rutten Annemie , Aly Markus , Nordström Tobias , Van Laere Steven , Rantalainen Mattias , Rajan Prabhakar , Egevad Lars , Ullén Anders , Yachnin Jeffrey , Dirix Luc , Grönberg Henrik , Lindberg Johan 
Published: May 2018

AbstractBackgroundThere are multiple existing and emerging therapeutic avenues for metastatic prostate cancer, with a common denominator, which is the need for predictive biomarkers. Circulating tumor DNA (ctDNA) has the potential to cost-efficiently accelerate precision medicine trials to improve clinical efficacy and diminish costs and toxicity. However, comprehensive ctDNA profiling in metastatic prostate cancer to date has been limited.MethodsA combination of targeted- and low-pass whole genome sequencing was performed on plasma cell-free DNA and matched white blood cell germline DNA in 364 blood samples from 217 metastatic prostate cancer patients.ResultsctDNA was detected in 85.9% of baseline samples, correlated to line of therapy and was mirrored by circulating tumor cell enumeration of synchronous blood samples. Comprehensive profiling of the androgen receptor (AR) revealed a continuous increase in the fraction of patients with intra-ARstructural variation, from 15.4% during first line mCRPC therapy to 45.2% in fourth line, indicating a continuous evolution of AR during the course of the disease. Patients displayed frequent alterations in DNA repair deficiency genes (18.0%). Additionally, the microsatellite instability phenotype was identified in 3.81% of eligible samples (≥0.1 ctDNA fraction). Sequencing of non-repetitive intronic- and exonic regions ofPTEN, RB1andTP53detected biallelic inactivation in 47.5%, 20.3% and 44.1% of samples with ≥0.2 ctDNA fraction, respectively. Only one patient carried a clonal high-impact variant without a detectable second hit. Intronic high-impact structural variation was twice as common as exonic mutations inPTENand RB1. Finally, 14.6% of patients presented false positive variants due to clonal hematopoiesis, commonly ignored in commercially available assays.ConclusionsctDNA profiles appear to mirror the genomic landscape of metastatic prostate cancer tissue and may cost-efficiently provide somatic information in clinical trials designed to identify predictive biomarkers. However, intronic sequencing of the interrogated tumor suppressors challenge the ubiquitous focus on coding regions and is vital, together with profiling of synchronous white blood cells, to minimize erroneous assignments which in turn may confound results and impede true associations in clinical trials.
Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping
Authors: Campbell Malachy , Walia Harkamal , Morota Gota 
Published: May 2018

AbstractThe accessibility of high-throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, have provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study is to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice (Oryza sativa). An estimate of shoot biomass, projected shoot area (PSA), was recored over a period of 20 days for a panel of 357 diverse rice accessions using an image-based greenhouse phenotyping platform. A RR that included a fixed second-order Legendre polynomial, a random second-order Legendre polynomial for the additive genetic effect, a first-order Legendre polynomial for the environmental effect, and heterogeneous residual variances was used to model PSA trajectories. The utility of the RR model over a single time point (TP) approach, where PSA is fit at each time point independently, is shown through four prediction scenarios. In the first scenario, the RR and TP approaches were used to predict PSA for a set of lines lacking phenotypic data. The RR approach showed a 11.6% increase in prediction accuracy over the TP approach. Much of this improvement could be attributed to the greater additive genetic variance captured by the RR approach. The remaining scenarios focused forecasting future phenotypes using a subset of early time points for known lines with phenotypic data, as well new lines lacking phenotypic data. In all cases, PSA could be predicted with high accuracy (r: 0.79 to 0.89 and 0.55 to 0.58 for known and unknown lines, respectively). This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice, and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach.