bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ribosome profiling uncovers selective mRNA translation associated with eIF2 phosphorylation in erythroid progenitors
Authors: Paolini Nahuel A. , Moore Kat S. , di Summa Franca M. , Fokkema Ivo F.A.C. , ‘t Hoen Peter A.C. , von LIndern Marieke 
Published: November 2017

AbstractThe regulation of translation initiation factor 2 (eIF2) is important for erythroid survival and differentiation. Lack of iron, a critical component of heme and hemoglobin, activates Heme Regulated Inhibitor (HRI). This results in phosphorylation of eIF2 and reduced eIF2 availability, which inhibits protein synthesis. Translation of specific transcripts such as Atf4, however, is enhanced. Upstream open reading frames (uORFs) are key to this regulation. The aim of this study is to investigate how eIF2 phosphorylation affects mRNA translation in erythroblasts. Ribosome profiling combined with RNA sequencing was used to determine translation initiation sites and ribosome density on individual transcripts. Treatment of erythroblasts with Tunicamycin (Tm) increased phosphorylation of eIF2 2-fold. At a false discovery rate of 1%, ribosome density was increased for 147 transcripts, among which transcriptional regulators such as Atf4, Tis7/Ifrd1, Pnrc2, Gtf2h, Mbd3, JunB and Kmt2e. Translation of 337 transcripts decreased more than average, among which Dym and Csde1. Ribosome profiling following Harringtonine treatment uncovered novel translation initiation sites and uORFs. Surprisingly, translated uORFs did not predict eIF2-dependent translation efficiency, but uORF identity differs. The regulation of transcription and translation factors in reponse to eIF2 phosphorylation may explain the large overall response to iron deficiency in erythroblasts.- eif2 dependent translation in erythroblasts during proteotoxic stress determined by ribosome footprinting- identification of transcription factors upregulated in response to eIF2 phosphorylation- Advantages and disadvantages of translation initiation site determination using harringtonine- distinct uORF pattern in transcripts with enhanced, or more than average reduced translation upon proteotoxic stress
A robust and tunable mitotic oscillator in artificial cells
Authors: Guan Ye , Li Zhengda , Wang Shiyuan , Barnes Patrick M. , Liu Xuwen , Xu Haotian , Jin Minjun , Liu Allen P. , Yang Qiong 
Published: November 2017

ABSTRACTSingle-cell analysis is pivotal to deciphering complex phenomena like cellular heterogeneity, bistable switch, and oscillations, where a population ensemble cannot represent the individual behaviors. Bulk cell-free systems, despite having unique advantages of manipulation and characterization of biochemical networks, lack the essential single-cell information to understand a class of out-of-steady-state dynamics including cell cycles. Here we develop a novel artificial single-cell system by encapsulatingXenopusegg extracts in water-in-oil microemulsions to study mitotic dynamics. These “cells”, adjustable in sizes and periods, sustain oscillations for over 30 cycles, and function in forms from the simplest cytoplasmic-only to the more complicated ones involving nuclei dynamics, mimicking real mitotic cells. Such innate flexibility and robustness make it key to studying clock properties of tunability and stochasticity. Our result also highlights energy supply as an important regulator of cell cycles. We demonstrate a simple, powerful, and likely generalizable strategy of integrating strengths of single-cell approaches into conventionalin vitrosystems to study complex clock functions.
Reactive and pre-emptive vaccination strategies to control hepatitis E infection in emergency and refugee settings: a modelling study
Authors: Cooper Ben S , White Lisa J , Siddiqui Mahveen R 
Published: November 2017

AbstractBackgroundHepatitis E Virus (HEV) is an important cause of morbidity and mortality in emergency and refugee camp settings. Symptomatic infection is associated with case fatality rates of ~20% in pregnant women. However, its epidemiology is poorly understood and the potential impact of immunisation in outbreak settings uncertain. We aimed to estimate key epidemiological parameters for HEV and to evaluate the potential impact of both reactive vaccination (initiated in response to an epidemic) and pre-emptive vaccination.MethodsWe analysed data from one of the world’s largest recorded HEV epidemics, which occurred in refugee camps in Uganda (2007-2009), using transmission dynamic models to estimate epidemiological parameters and assess the potential impact of reactive and pre-emptive vaccination strategies.ResultsUnder baseline assumptions we estimated the basic reproduction number of HEV to range from 3.9 (95% CrI 2.8, 5.4) to 8.9 (5.4, 14.2). Mean latent and infectious periods were estimated to be 34 (28, 39) and 40 (23, 71) days respectively.Reactive two-dose vaccination of those aged 16-65 years excluding pregnant women (for whom vaccine is not licensed), if initiated after 50 reported cases, led to mean camp-specific reductions in mortality of 10 to 29%. Pre-emptive vaccination with two doses reduced mortality by 35 to 65%. Both strategies were more effective if coverage was extended to groups for whom the vaccine is not currently licensed. For example, two dose pre-emptive vaccination, if extended to include pregnant women, led to mean reductions in mortality of 66 to 82%.ConclusionsHEV has a high transmission potential in refugee camp settings. Substantial reductions in mortality through vaccination are expected, even if used reactively. There is potential for greater impact if vaccine safety and effectiveness can be established in pregnant women.FundingWellcome Trust (106491/Z/14/Z and 089275/Z/09/Z). BC: MRC/DfID (MR/K006924/1).
On the Calculation of Betweenness Centrality in Marine Connectivity Studies Using Transfer Probabilities
Authors: Costa Andrea , Petrenko Anne A. , Guizien Katell , Doglioli Andrea M. 
Published: November 2017

AbstractBetweenness has been used in a number of marine studies to identify portions of sea that sustain the connectivity of whole marine networks. Herein we highlight the need of methodological exactness in the calculation of betweenness when graph theory is applied to marine connectivity studies based on transfer probabilities. We show the inconsistency in calculating betweeness directly from transfer probabilities and propose a new metric for the node-to-node distance that solves it. Our argumentation is illustrated by both simple theoretical examples and the analysis of a literature data set.
The Basic Reproductive Number for Disease Systems with Multiple Coupled Heterogeneities
Authors: Lloyd Alun L. , Kitron Uriel , Perkins T. Alex , Vazquez-Prokopec Gonzalo M. , Waller Lance A. 
Published: November 2017

AbstractIn mathematical epidemiology, a well-known formula describes the impact of heterogeneity on the basic reproductive number,R0, for situations in which transmission is separable and for which there is one source of variation in susceptibility and one source of variation in infectiousness. This formula is written in terms of the magnitudes of the heterogeneities, as quantified by their coefficients of variation, and the correlation between them. A natural question to ask is whether analogous results apply when there are multiple sources of variation in susceptibility and/or infectiousness. In this paper we demonstrate that with three or more coupled heterogeneities,R0under separable transmission depends on details of the distribution of the heterogeneities in a way that is not seen in the well-known simpler situation. We provide explicit formulae for the cases of multivariate normal and multivariate log-normal distributions, showing thatR0can again be expressed in terms of the magnitudes of the heterogeneities and the pairwise correlations between them. The formulae, however, differ between the two multivariate distributions, demonstrating that no formula of this type applies generally when there are three or more coupled heterogeneities. We see that the results of the formulae are approximately equal when heterogeneities are relatively small and show that an earlier result in the literature (Koella, 1991) should be viewed in this light. We provide numerical illustrations of our results and discuss a setting in which coupled heterogeneities are likely to have a major impact on the value ofR0. We also describe a rather surprising result: in a system with three heterogeneities,R0can exhibit non-monotonic behavior with increasing levels of heterogeneity, in marked contrast to the familiar two heterogeneity setting in whichR0either increases or decreases with increasing heterogeneity.
Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA–sequencing data
Authors: Assefa Alemu Takele , De Paepe Katrijn , Everaert Celine , Mestdagh Pieter , Thas Olivier , Vandesompele Jo 
Published: November 2017

ABSTRACTBackgroundProtein-coding RNAs (mRNA) have been the primary target of most transcriptome studies in the past, but in recent years, attention has expanded to include long non-coding RNAs (lncRNA). lncRNAs are typically expressed at low levels, and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 14 popular tools for testing DE in RNA-seq data along with their normalization methods is comprehensively evaluated, with a particular focus on lncRNAs and low abundant mRNAs.ResultsThirteen performance metrics were used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets. Non-parametric procedures are used to simulate gene expression data in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, we kept track of the results for mRNA and lncRNA separately. All statistical models exhibited inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and analysis of benchmark RNA-seq datasets. No single tool uniformly outperformed the others.ConclusionOverall, the linear modeling with empirical Bayes moderation (limma) and the nonparametric approach (SAMSeq) showed best performance: good control of the false discovery rate (FDR) and reasonable sensitivity. However, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in a realistic clinical settings such as in cancer research. About half of the methods showed severe excess of false discoveries, making these methods unreliable for differential expression analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, http://statapps.ugent.be/tools/AppDGE/
RelTime relaxes the strict molecular clock throughout the phylogeny
Authors: Battistuzzi Fabia U. , Tao Qiqing , Jones Lance , Tamura Koichiro , Kumar Sudhir 
Published: November 2017

AbstractThe RelTime method estimates divergence times when evolutionary rates vary among lineages. Theoretical analyses show that RelTime relaxes the strict molecular clock throughout a molecular phylogeny, and it performs well in the analysis of empirical and computer simulated datasets in which evolutionary rates are variable. Lozano-Fernandez et al. (2017) found that the application of RelTime to one metazoan dataset (Erwin et al. 2011) produced equal rates for several ancient lineages, which led them to speculate that RelTime imposes a strict molecular clock for deep animal divergences. RelTime does not impose a strict molecular clock. The pattern observed by Lozano-Fernandez et al. (2017) was a result of the use of an option to assign the same rate to lineages in RelTime when the rates are not statistically significantly different. The median rate difference was 5% for many deep metazoan lineages for Erwin et al. (2011) dataset, so the rate equality was not rejected. In fact, RelTime analysis with and without the option to test rate differences produced very similar time estimates. We found that the Bayesian time estimates vary widely depending on the root priors assigned, and that the use of less restrictive priors produce Bayesian divergence times that are concordant with those from RelTime for Erwin et al. (2011) dataset. Therefore, it is prudent to discuss Bayesian estimates obtained under a range of priors in any discourse about molecular dating, including method comparisons.
Household Members Do Not Contact Each Other at Random: Implications for Infectious Disease Modelling
Authors: Goeyvaerts Nele , Santermans Eva , Potter Gail , Torneri Andrea , Van Kerckhove Kim , Willem Lander , Aerts Marc , Beutels Philippe , Hens Niel 
Published: November 2017

Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts and thus easily spread within households. Epidemic models, used to gain insight in infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now there was no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) in 2010-2011. We analyzed data from 318 households totaling 1266 individuals with household sizes ranging from 2 to 7 members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between father and child is smaller than for any other pair except for older siblings. Epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulation. However, ignoring the contact density when inferring from an epidemic model will result in biased estimates of within-household transmission rates. Further research on the implementation of within-household contact networks in epidemic models is necessary.Significance StatementHouseholds have a pivotal role in the spread of airborne infectious diseases. Households are bridging units between schools and workplaces, and social contacts within households are frequent and intimate, allowing for rapid disease spread. Infectious disease models typically assume that members of a household contact each other randomly. Until now there was no direct empirical evidence to support this assumption. In this paper, we present the first social contact survey specifically designed to study contact networks within households with young children. We investigate which factors drive contacts between household members on one particular day by means of a statistical model. Our results suggest the importance of connectedness within households over heterogeneity in number of contacts.
Per-sample immunoglobulin germline inference from B cell receptor deep sequencing data
Authors: Ralph Duncan K. , Matsen Frederick A. 
Published: November 2017

AbstractThe collection of immunoglobulin genes in an individual’s germline, which gives rise to B cell receptors via recombination, is known to vary significantly across individuals. In humans, for example, each individual has only a fraction of the several hundred known V alleles. Furthermore, the currently-accepted set of known V alleles is both incomplete (particularly for non-European samples), and contains a significant number of spurious alleles. The resulting uncertainty as to which immunoglobulin alleles are present in any given sample results in inaccurate B cell receptor sequence annotations, and in particular inaccurate inferred naive ancestors. In this paper we first show that the currently widespread practice of aligning each sequence to its closest match in the full set of IMGT alleles results in a very large number of spurious alleles that are not in the sample’s true set of germline V alleles. We then describe a new method for inferring each individual’s germline gene set from deep sequencing data, and show that it improves upon existing methods by making a detailed comparison on a variety of simulated and real data samples. This new method has been integrated into the partis annotation and clonal family inference package, available at https://github.com/psathyrella/partis, and is run by default without affecting overall run time.Author SummaryAntibodies are an important component of the adaptive immune system, which itself determines our response to both pathogens and vaccines. They are produced by B cells through somatic recombination of germline DNA, which results in a vast diversity of antigen binding affinities across the B cell repertoire. We typically learn about the development of this repertoire, and its history of interaction with antigens, by sequencing large numbers of the DNA sequences from which antibodies are derived. In order to understand such data, it is necessary to determine the combination of germline V, D, and J genes that was rearranged to form each such B cell receptor sequence. This is difficult, however, because the immunoglobulin locus exhibits an extraordinary level of diversity across individuals – encompassing both allelic variation and gene duplication, deletion, and conversion – and because the locus’s large size and repetitive structure make germline sequencing very difficult. In this paper we describe a new computational method that avoids this difficulty by inferring each individual’s set of immunoglobulin germline genes directly from expressed B cell receptor sequence data.
Analysis of Spounaviruses as a Case Study for the Overdue Reclassification of Tailed Bacteriophages
Authors: Barylski Jakub , Enault François , Dutilh Bas E. , Schuller Margo B.P. , Edwards Robert A. , Gillis Annika , Klumpp Jochen , Knezevic Petar , Krupovic Mart , Kuhn Jens H. , Lavigne Rob , Oksanen Hanna M. , Sullivan Matthew B. , Wittmann Johannes , Tolstoy Igor , Rodney Brister J. , Kropinski Andrew M. , Adriaenssens Evelien M. 
Published: November 2017

ABSTRACTIt is almost a cliché that tailed bacteriophages of the order Caudovirales are the most abundant and diverse viruses in the world. Yet, their taxonomy still consists of a single order with just three families: Myoviridae, Siphoviridae, and Podoviridae. Thousands of newly discovered phage genomes have recently challenged this morphology-based classification, revealing that tailed bacteriophages are genomically even more diverse than once thought. Here, we evaluate a range of methods for bacteriophage taxonomy by using a particularly challenging group as an example, the Bacillus phage SPO1-related viruses of the myovirid subfamily Spounavirinae. Exhaustive phylogenetic and phylogenomic analyses indicate that the spounavirins are consistent with the taxonomic rank of family and should be divided into at least five subfamilies. This work is a case study for virus genomic taxonomy and the first step in an impending massive reorganization of the tailed bacteriophage taxonomy.
Widespread Genotype-Phenotype Correlations in Intellectual Disability
Authors: Casanova Emily L. , Gerstner Zachary , Sharp Julia L. , Casanova Manuel F. , Feltus F. Alex 
Published: November 2017

ABSTRACTBackgroundLinking genotype to phenotype is a major aim of genetics research, yet many complex conditions continue to hide their underlying biochemical mechanisms. Recent research provides evidence that relevant gene-phenotype associations are discoverable in the study of intellectual disability (ID). Here we expand on that work, identifying distinctive gene interaction modules with unique enrichment patterns reflective of associated clinical features in ID.MethodsTwo hundred twelve forms of monogenic ID were curated according to comorbidities with autism and epilepsy. These groups were further subdivided according to secondary clinical symptoms of complex versus simple facial dysmorphia and neurodegenerative-like features due to their clinical prominence, modest symptom overlap, and probable etiological divergence. An aggregate gene interaction ID network for these phenotype subgroups was discovered using via a public database of known gene interactions: protein-protein, genetic, and mRNA coexpression. Additional annotation resources (Gene Ontology, Human Phenotype Ontology, TRANSFAC/JASPAR, and KEGG/WikiPathways) were utilized to assess functional and phenotypic enrichment modules within the full ID network.ResultsPhenotypic analysis revealed high rates of complex facial dysmorphia in ID with comorbid autism. In contrast, neurodegenerative-like features were overrepresented in ID with epilepsy. Network analysis subsequently showed that gene groups divided according to clinical features of interest resulted in distinctive interaction clusters, with unique functional enrichments according to module.ConclusionsThese data suggest that specific comorbid and secondary clinical features in ID are predictive of underlying genotype. In summary, ID form unique clusters, which are comprised of individual conditions with remarkable genotypic and phenotypic overlap.