bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes
Authors: Viñuela Ana , Varshney Arushi , van de Bunt Martijn , Prasad Rashmi B. , Asplund Olof , Bennett Amanda , Boehnke Michael , Brown Andrew , Erdos Michael R. , Fadista João , Hansson Ola , Hatem Gad , Howald Cédric , Iyengar Apoorva K. , Johnson Paul , Krus Ulrika , MacDonald Patrick E. , Mahajan Anubha , Manning Fox Jocelyn E. , Narisu Narisu , Nylander Vibe , Orchard Peter , Oskolkov Nikolay , Panousis Nikolaos I. , Payne Anthony , Stitzel Michael L. , Vadlamudi Swarooparani , Welch Ryan , Collins Francis S. , Mohlke Karen L. , Gloyn Anna L. , Scott Laura J. , Dermitzakis Emmanouil T. , Groop Leif , Parker Stephen C.J. , McCarthy Mark I. 
Published: May 2019

AbstractMost signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.
Viral and bacterial infection elicit distinct changes in plasma lipids in febrile children
Authors: Wang Xinzhu , Nijman Ruud , Camuzeaux Stephane , Sands Caroline , Jackson Heather , Kaforou Myrsini , Emonts Marieke , Herberg Jethro , Maconochie Ian , Carrol Enitan D , Paulus Stephane C , Zenz Werner , Van der Flier Michiel , de Groot Ronald , Martinon-Torres Federico , Schlapbach Luregn J , Pollard Andrew J , Fink Colin , Kuijpers Taco T , Anderson Suzanne , Lewis Matthew , Levin Michael , McClure Myra ,   on behalf of EUCLIDS consortium
Published: May 2019

AbstractFever is the most common reason that children present to Emergency Departments in the UK. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. As a result, many children are prescribed antibiotics often unnecessarily, while others with life-threatening bacterial infections can remain untreated. The ‘omics’ approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n=20) and confirmed viral infection (n=20). We show for the first time that bacterial and viral infection elicit distinct changes in the host lipidome. Glycerophosphoinositol, sphingomyelin, lysophosphotidylcholine and cholesterol sulfate were increased in the confirmed virus infected group, while fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were increased in cases with confirmed bacterial infection. A combination of three lipids achieved the area under the receiver operating characteristic (ROC) curve of 0.918 (95% CI 0.835 to 1). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics.
Probing mechanisms of transcription elongation through cell-to-cell variability of RNA polymerase
Authors: Ali Md. Zulfikar , Choubey Sandeep , Das Dipjyoti , Brewster Robert C. 
Published: May 2019

AbstractThe process of transcription initiation and elongation are primary points of control in the regulation of gene expression. While biochemical studies have uncovered the mechanisms involved in controlling transcription at each step, how these mechanisms manifestin vivoat the level of individual genes is still unclear. Recent experimental advances have enabled single-cell measurements of RNAP molecules engaged in the process of transcribing a gene of interest. In this manuscript, we use Gillespie simulations to show that measurements of cell-to-cell variability of RNAP numbers and inter-polymerase distances can reveal the prevailing mode of regulation of a given gene. Mechanisms of regulation at each step, from initiation to elongation dynamics, produce qualitatively distinct signatures which can further be used to discern between them. Intriguingly, depending on the initiation kinetics, stochastic elongation can either enhance or suppress cell-to-cell variability at the RNAP level. To demonstrate the value of this framework, we analyze RNAP number distribution data for ribosomal genes in S. cerevisiae from three previously published studies and show that this approach provides crucial mechanistic insights into the transcriptional regulation of these genes.Author SummaryThe process of transcription comprises many distinct steps and understanding the regulation of each of these steps provides insight into how the levels of gene expression are controlled in the cell. In this manuscript, we use stochastic simulations to explore how regulation at the level of elongation and initiation together influences the distribution of actively transcribing RNAP on a gene. We find that each of these steps of regulation leaves a distinct imprint on the gene-to-gene variability of number of actively transcribing RNAP on the gene and their inter-RNAP distances. Using these results, we analyze recent experimental data of transcribing RNAP distributions and find that the perturbations in these studies primarily impact the transcription initiation dynamics of the genes.
Inference based PICRUSt accuracy varies across sample types and functional categories
Authors: Sun Shan , Jones Roshonda B. , Fodor Anthony A. 
Published: May 2019

AbstractBackgroundDespite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition, and PICRUSt is the most widely used of these techniques. In this study, we evaluated the performance of PICRUSt by comparing the significance of the differential abundance of functional gene profiles predicted with PICRUSt to those from shotgun metagenome sequencing across different environments.ResultsWe selected 7 datasets of human, non-human animal and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between gene compositions predicted with PICRUSt and measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the sample labels were shuffled. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of algorithms like PICRUSt. As an alternative, we compared the performance of PICRUSt predicted genes to metagenome genes in inference models associated with metadata within each dataset. With this method, we found reasonable performance for human datasets, with PICRUSt performing better for inference on genes related to “house-keeping” functions. However, the performance of PICRUSt degraded sharply outside of human datasets when used for inference.ConclusionWe conclude that the utility of PICRUSt for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted.
A Compositional Model to Assess Expression Changes from Single-Cell Rna-Seq Data
Authors: Ma By Xiuyu , Korthauer Keegan , Kendziorski Christina , Newton Michael A. 
Published: May 2019

AbstractOn the problem of scoring genes for evidence of changes in the distribution of single-cell expression, we introduce an empirical Bayesian mixture approach and evaluate its operating characteristics in a range of numerical experiments. The proposed approach leverages cell-subtype structure revealed in cluster analysis in order to boost gene-level information on expression changes. Cell clustering informs gene-level analysis through a specially-constructed prior distribution over pairs of multinomial probability vectors; this prior meshes with available model-based tools that score patterns of differential expression over multiple subtypes. We derive an explicit formula for the posterior probability that a gene has the same distribution in two cellular conditions, allowing for a gene-specific mixture over subtypes in each condition. Advantage is gained by the compositional structure of the model, in which a host of gene-specific mixture components are allowed, but also in which the mixing proportions are constrained at the whole cell level. This structure leads to a novel form of information sharing through which the cell-clustering results support gene-level scoring of differential distribution. The result, according to our numerical experiments, is improved sensitivity compared to several standard approaches for detecting distributional expression changes.
VSEPRnet: Physical structure encoding of sequence-based biomolecules for functionality prediction: Case study with peptides
Authors: Rath Siddharth , Francis-Landau Jonathan , Lu Ximing , Nakano-Baker Oliver , Rodriguez Jacob , Ustundag Burak Berk , Sarikaya Mehmet 
Published: May 2019

AbstractPredicting structure-dependent functionalities of biomolecules is crucial for accelerating a wide variety of applications in drug-screening, biosensing, disease-diagnosis, and therapy. Although the commonly used structural “fingerprints” work for biomolecules in traditional informatics implementations, they remain impractical in a wide range of machine learning approaches where the model is restricted to make data-driven decisions. Although peptides, proteins, and oligonucleotides have sequence-related propensities, representing them as sequences of letters, e.g., in bioinformatics studies, causes a loss of most of their structure-related functionalities. Biomolecules lacking sequence, such as polysaccharides, lipids, and their peptide conjugates, cannot be screened with models using the letter-based fingerprints. Here we introduce a new fingerprint derived from valence shell electron pair repulsion structures for small peptides that enables construction of structural feature-maps for a given biomolecule, regardless of the sequence or conformation. The feature-map introduced here uses a simple encoding derived from the molecular graph - atoms, bonds, distances, bond angles, etc., that make up each of the amino acids in the sequence, allowing a Residual Neural network model to take greater advantage of information in molecular structure. We make use of the short peptides binding to Major-Histocompatibility-Class-I protein alleles that are encoded in terms of their extended structures to predict allele-specific binding-affinities of test-peptides. Predictions are consistent, without appreciable loss in accuracy between models for different length sequences, marking an improvement over the current models. Biological processes are heterogeneous interactions, which justifies encoding all biomolecules universally in terms of structures and relating them to their functionality. The capabilities facilitated by the model expands the paradigm in establishing structure-function correlations among small molecules, short and longer sequences including large biomolecules, and genetic conjugates that may include polypeptides, polynucleotides, RNAs, lipids, peptidoglycans, peptido-lipids, and other biomolecules that could be implemented in a wide range of medical and nanobiotechnological applications in the future.
Mitochondrial Genome Diversity in the Central Siberian Plateau with Particular Reference to Prehistory of Northernmost Eurasia
Authors: Dryomov S. V. , Nazhmidenova A. M. , Starikovskaya E. B. , Shalaurova S. A. , Rohland N. , Mallick S. , Bernardos R. , Derevianko A. P. , Reich D. , Sukernik R. 
Published: May 2019

AbstractThe Central Siberian Plateau was last geographic area in Eurasia to become habitable by modern humans after the Last Glacial Maximum (LGM). Through comprehensive mitochondrial DNA genomes retained in indigenous Siberian populations, the Ket, Tofalar, and Todzhi - we explored genetic links between the Yenisei-Sayan region and Northeast Eurasia over the last 10,000 years. Accordingly, we generated 218 new complete mtDNA sequences and placed them into compound phylogenies along with 7 newly obtained and 70 published ancient mt genomes. Our findings reflect the origins and expansion history of mtDNA lineages that evolved in South-Central Siberia, as well as multiple phases of connections between this region and distant parts of Eurasia. Our result illustrates the importance of jointly sampling modern and prehistoric specimens to fully measure the past genetic diversity and to reconstruct the process of peopling of the high latitudes of the Siberian subcontinent.
A reversible memory switch for plant synthetic biology based on the phage PhiC31 integration system
Authors: Joan Miquel Bernabé-Orts , Alfredo Quijano-Rubio , Javier Mancheño-Bonillo , Victor Moles-Casas , Sara Selma , Antonio Granell , Diego Orzaez 
Published: May 2019

ABSTRACTPlant synthetic biology aims to contribute to global food security by engineering plants with new or improved functionalities. In recent years, synthetic biology has rapidly advanced from the setup of basic genetic devices to the design of increasingly complex gene circuits able to provide organisms with novel functions. While many bacterial, fungal and mammalian unicellular chassis have been extensively engineered, this progress has been delayed in plants due to their complex multicellular nature and the lack of reliable DNA devices that allow an accurate design of more sophisticated biological circuits. Among these basic devices, gene switches are crucial to deploying new layers of regulation into the engineered organisms. Of special interest are bistable genetic toggle switches, which allow a living organism to exist in two alternative states and switch between them with a minimal metabolic burden. Naturally occurring toggle switches control important decision-making processes such as cell fate and developmental events. We sought to engineer whole plants with an orthogonal genetic toggle switch to be able to regulate artificial functions with minimal interference with their natural pathways. Here we report a bistable toggle memory switch for whole plants based on the phage PhiC31 serine integrase and its cognate recombination directionality factor (RDF). This genetic device was designed to control the transcription of two genes of interest by inversion of a central DNA regulatory element. Each state of the device is defined by one transcriptionally active gene of interest, while the other one remains inactive. The state of the switch can be reversibly modified by the action of the recombination actuators, which were administered externally (e.g. via agroinfiltration), or produced internally in response to an inducible chemical stimulus. We extensively characterized the kinetics, memory, and reversibility of this genetic switch in Nicotiana benthamiana through transient and stable transformation experiments using transgenic plants and hairy roots. Finally, we coupled the integrase expression to an estradiol-inducible promoter as a proof of principle of inducible activation of the switch.
Yet another de novo genome assembler
Authors: Vaser Robert , Šikić Mile 
Published: May 2019

AbstractAdvances in sequencing technologies have pushed the limits of genome assemblies beyond imagination. The sheer amount of long read data that is being generated enables the assembly for even the largest and most complex organism for which efficient algorithms are needed. We present a new tool, called Ra, for de novo genome assembly of long uncorrected reads. It is a fast and memory friendly assembler based on sequence classification and assembly graphs, developed with large genomes in mind. It is freely available at https://github.com/lbcb-sci/ra.This work has been supported in part by the Croatian Science Foundation under the project Single genome and metagenome assembly (IP-2018-01-5886), and in part by the European Regional Development Fund under the grant KK.01.1.1.01.0009 (DATACROSS). In addition, M.Š. is partly supported by funding from A*STAR, Singapore.
Factors affecting the biology of Pachycrepoideus vindemmiae (Hymenoptera: Pteromalidae) on spotted-wing drosophila (Drosophila suzukii)
Authors: Bezerra Da Silva Cherre S. , Price Briana E. , Soohoo-Hui Alexander , Walton Vaughn M. 
Published: May 2019

AbstractPachycrepoideus vindemmiae is a parasitoid of the invasive spotted-wing drosophila (SWD, Drosophila suzukii) in the U.S. Few studies have addressed interactions between these two species and little is known about the potential of this parasitoid as a biocontrol agent of SWD. Here, we investigated the impact of extrinsic and intrinsic factors on life-history traits of P. vindemmiae. Both constant (entire adulthood) and limited (30 minutes) supply of water + honey, honey, or host increased parasitoid survival compared to controls (water or fasting). Water + honey caused the highest parasitoid survivals (35-60 days), independent of supply period, sex, and host availability. Females were intrinsically more resistant to water and honey scarcity than males, and host-feeding elevated such resistance even higher. Constant supply of honey supported the highest host-killing capacity (ca. 600 SWD pupae/wasp). However, in young females (4-9 days old), such honey effect was insignificant while water deprivation (either with or without honey) resulted in the highest host-killing potential. This indicate that although sugar becomes a critical nutritional resource as females age, young females depend more on water than sugar. No effect of water nor honey was observed on the sex ratio of young females, but when we considered the entire adulthood honey supply produced the lowest proportion of females (0.50), independent of water availability. Such reduction derived from sperm depletion, likely caused by both lack of re-mating and higher fecundity in honey-fed wasps. Neither water nor honey affected parasitoid emergence rate (0.97), independent of female age. Based on survival and host-killing capacity, we conclude that P. vindemmiae has a tremendous biocontrol potential against SWD. Both limited and constant supply of water, sugar, and host increase parasitoid survival, while constant supply of water and/or honey enhance its host-killing potential and decrease sex ratio depending on mother age.
Interpreting neural decoding models using grouped model reliance
Authors: Valentin Simon , Harkotte Maximilian , Popov Tzvetan 
Published: May 2019

AbstractThe application of machine learning algorithms for decoding psychological constructs based on neural data is becoming increasingly popular. However, there is a need for methods that allow to interpret trained decoding models, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches. The present study demonstrates grouped model reliance as a model-agnostic permutation-based approach to this problem. Grouped model reliance indicates the extent to which a trained model relies on conceptually related groups of variables, such as frequency bands or regions of interest in electroencephalographic (EEG) data. As a case study to demonstrate the method, random forest and support vector machine models were trained on within-participant single-trial EEG data from a Sternberg working memory task. Participants were asked to memorize a sequence of digits (0–9), varying randomly in length between one, four and seven digits, where EEG recordings for working memory load estimation were taken from a 3-second retention interval. Present results confirm previous findings in so far, as both random forest and support vector machine models relied on alpha-band activity in most subjects. However, as revealed by further analyses, patterns in frequency and particularly topography varied considerably between individuals, pointing to more pronounced inter-individual differences than reported previously.Author summaryModern machine learning algorithms currently receive considerable attention for their predictive power in neural decoding applications. However, there is a need for methods that make such predictive models interpretable. In the present work, we address the problem of assessing which aspects of the input data a trained model relies upon to make predictions. We demonstrate the use of grouped model-reliance as a generally applicable method for interpreting neural decoding models. Illustrating the method on a case study, we employed an experimental design in which a comparably small number of participants (10) completed a large number of trials (972) over multiple electroencephalography (EEG) recording sessions from a Sternberg working memory task. Trained decoding models consistently relied on alpha frequency activity, which is in line with existing research on the relationship between neural oscillations and working memory. However, our analyses also indicate large inter-individual variability with respect to the relation between activity patterns and working memory load in frequency and topography. Taken together, we argue that grouped model reliance provides a useful tool to better understand the workings of (sometimes otherwise black-box) decoding models.
Contesting commensuration
Authors: Gerdin Jonas , Englund Hans 
Published: May 2019

PurposeThe purpose of this paper is to explore how actors subjected to public performance evaluations may “contest commensuration,” i.e. may seek to influence how such ratings and rankings will be construed among important stakeholders.Design/methodology/approachA qualitative study of press releases, and interviews with department heads, is used as a basis for the analysis.FindingsThe empirically derived taxonomy of public responses to a state-initiated performance evaluation of educational programs shows that actors may mobilize an array of commensuration management tactics so as to maintain or improve one’s relative positional status. Such tactics may have at least three different foci, namely, on the comparison object (i.e. on the new grouping of actors), the comparison dimension (i.e. the standardized format for comparison) and the comparison rate (i.e. the rate received), respectively. The authors also find that not only are threats to positional status likely to spur commensuration management tactics, but also the opportunity to exploit a good rate.Originality/valueThe paper augments recent research that has problematized the so-called “reactive conformance thesis” by focusing on how evaluated organizations may directly try to influence external stakeholders through public responses. The study is also one of the first that analytically disentangles how they may skillfully exploit different forms of “plasticity” that are inherent in any type of commensuration.