bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low but genetically variable male remating ability in a tropicalDrosophiladespite substantial fitness benefits
Authors: Saxon Andrew D. , Jones Natalie E. , O’Brien Eleanor K. , Bridle Jon R. 
Published: December 2018

AbstractMating success is the main source of fitness variation in males, meaning that males should capitalise on all opportunities for mating. Strong selection on male mating success should also reduce genetic variation in male mating traits relative to other traits. We quantified mating latency, mating duration and productivity in males of the tropical fruitfly,Drosophila birchii, from 30 isofemale lines collected from across two elevational gradients, when they were given opportunities to mate with up to four females consecutively. Male remating rates were low compared to otherDrosophila(only 14 – 27% of males achieved a fourth mating), with mean mating durations approximately doubling across successive copulations. However, although successive remating produced progressively fewer offspring, it consistently increased overall male reproductive success, with males that mated four times more than doubling offspring number compared to males mating only once. We also found no reduction in the productivity of sons emerging from later matings, indicating a sustained cumulative fitness benefit to remating. Heritable variation was observed for most traits (H2= 0.035 – 0.292) except mating latency, but there was no divergence in trait means with elevation. The observed restricted remating ability of maleD. birchii, despite the clear benefits of remating, may be due to a low encounter rate with females in the field, leading to high investment per gamete (or ejaculate). However, it remains unclear why genetic variation in these traits is high, given we observe no variation in these traits across elevational gradients known to affect local population density.
doepipeline: a systematic approach to optimizing multi-level and multi-step data processing workflows
Authors: Svensson Daniel , Sjögren Rickard , Sundell David , Sjödin Andreas , Trygg Johan 
Published: December 2018

AbstractBackgroundSelecting the proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. To make the situation even more complex, multiple tools may be run in a sequential pipeline where the final output depends on the parameter configuration for each tool in the pipeline. Because of the complexity and difficulty of predicting outcomes, in practice parameters are often left at default settings or set based on personal or peer experience obtained in a trial and error fashion. To allow for the reliable and efficient selection of parameters for bioinformatic pipelines, a systematic approach is needed.ResultsWe presentdoepipeline, a novel approach to optimizing bioinformatic software parameters, based on core concepts of the Design of Experiments methodology and recent advances in subset designs. Optimal parameter settings are first approximated in a screening phase using a subset design that efficiently spans the entire search space, then optimized in the subsequent phase using response surface designs and OLS modeling.doepipelinewas used to optimize parameters in four use cases; 1) de-novo assembly, 2) scaffolding of a fragmented genome assembly, 3) k-mer taxonomic classification of Oxford Nanopore Technologies MinION reads, and 4) genetic variant calling. In all four cases,doepipelinefound parameter settings that produced a better outcome with respect to the characteristic measured when compared to using default values. Our approach is implemented and available in the Python packagedoepipeline.ConclusionsOur proposed methodology provides a systematic and robust framework for optimizing software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. Implementation indoepipelinemakes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code ofdoepipelineis available athttps://github.com/clicumu/doepipelineand it can be installed through conda-forge.
Measuring Granger-causal effects in multivariate time series by system editing
Authors: Pascual-Marqui Roberto D. , Biscay Rolando J. , Bosch-Bayard Jorge , Faber Pascal , Kinoshita Toshihiko , Kochi Kieko , Milz Patricia , Nishida Keiichiro , Yoshimura Masafumi 
Published: December 2018

1.AbstractWhat is the role of each node in a system of many interconnected nodes? This can be quantified by comparing the dynamics of the nodes in the intact system, with their modified dynamics in the edited system, where one node is deleted. In detail, the spectra are calculated from a causal multivariate autoregressive model for the intact system. Next, without re-estimation, one node is deleted from the model and the modified spectra at all other nodes are re-calculated. The change in spectra from the edited system to the intact system quantifies the role of the deleted node, giving a measure of its Granger-causal effects (CFX) on the system. A generalization of this novel measure is available for networks (i.e. for groups of nodes), which quantifies the role of each network in a system of many networks. For the sake of reproducible research, program codes (PASCAL), executable file, and toy data in human readable format are included in the supplementary material.
Predicting synergism of cancer drug combinations using NCI-ALMANAC data
Authors: Sidorov Pavel , Naulaerts Stefan , Ariey-Bonnet Jérémy , Pasquier Eddy , Ballester Pedro J. 
Published: December 2018

AbstractBackgroundDrug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs.In silicomodeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations).MethodsEach cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5000 pair-wise drug combinations, 60 cell lines, 4 types of models and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated.ResultsThe evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least two-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models.ConclusionsDespite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of requiredin vitrotests by predictingin silicowhich of the considered combinations are likely to be synergistic.
The regulation of HAD-like phosphatases by signaling pathways modulates cellular resistance to the metabolic inhibitor, 2-deoxyglucose
Authors: Defenouillère Quentin , Verraes Agathe , Laussel Clotilde , Friedrich Anne , Schacherer Joseph , Léon Sébastien 
Published: December 2018

AbstractCancer cells display an altered metabolism with an increased glycolysis and glucose uptake. Anti-cancer strategies targeting glycolysis through metabolic inhibitors have been considered. Particularly, the glucose analogue 2-deoxyglucose (2DG) is imported into cells and phosphorylated into 2DG-6-phosphate, a toxic by-product that inhibits glycolysis. Recent data suggest that 2DG has additional effects in the cell, and resistance to 2DG has also been observed. Using yeast as a model, we engaged an unbiased, mass-spectrometry-based approach to probe the cellular effects of 2DG on the proteome and study resistance mechanisms. This revealed that two 2DG-6-phosphate phosphatases, Dog1 and Dog2, are induced upon exposure to 2DG and participate in 2DG detoxication. 2DG induces Dog2 by upregulating several signaling pathways, such as the MAPK (Hog1/p38)-based stress-responsive pathway, the Unfolded Protein Response (UPR) triggered by 2DG-induced ER stress, and the MAPK (Slt2)-based Cell Wall Integrity pathway. Thus, 2DG-induced interference with cellular signaling rewires the expression of these endogenous phosphatases to promote 2DG resistance. Consequently, loss of the UPR or CWI pathways leads to hypersensitivity to 2DG. In contrast,DOG2is transcriptionally repressed by glucose availability in a Snf1/AMPK-dependent manner, and mutants impaired in this pathway are 2DG-resistant. The characterization and genome resequencing of spontaneous 2DG-resistant mutants revealed thatDOG2overexpression is a common strategy to achieve 2DG resistance. The human Dog2 orthologue, HDHD1, also displays 2DG-6-phosphate phosphatase activityin vitro, and its overexpression confers 2DG resistance in HeLa cells, which has important implications for potential future chemotherapies involving 2DG.
Embedding the Skin Conductance Response into the Brain Connectivity Framework: Monoaminergic Signaling Visible Through the Lenses of Computational Modeling
Authors: Branković Saša 
Published: December 2018

AbstractRelying on evidence for the functional, neurochemical, and spectral parallelism between the late event-related potentials, delta oscillatory brain responses, and the skin conductance response (SCR) system the hypotheses about the existence of the SCR-related brain oscillations and their connectivity with the SCR system have been here suggested. In contrast to classical approach to event-related oscillations which relies on either stimulus- or response-locked time reference, an approach assigned as “oscillatory process-related oscillations” has been introduced. The method enables us to overcome the variability of latency period of the SCR. The hypothesis about the existence of the SCR-related brain oscillations and their delta nature has been confirmed through the grand averaging method. An unexpected finding was the complex nature of the SCR-related oscillations: in addition to the two second EEG segment which was correlated with the SCR system signals they also comprised an initial 200 ms segment uncorrelated with the SCR. The hypothesis about the connectivity between the SCR system and the respective delta brain oscillatory response has been operationalized through a multiple time series regression model. The predictor set consists of the SCR, its first three derivatives, and their mutual interactions. The Monte Carlo test of the causal link between the SCR system signals and the related delta EEG signal demonstrated significance in more than half of the participants. The findings have been considered from the standpoints of the segmental structure of the EEG, monoaminergic signaling and recently emerged the “brain-body dynamic syncytium” hypothesis.
Population-wide copy number variation calling using variant call format files from 6,898 individuals
Authors: Png Grace , Suveges Daniel , Park Young-Chan , Walter Klaudia , Kundu Kousik , Ntalla Ioanna , Tsafantakis Emmanouil , Karaleftheri Maria , Dedoussis George , Zeggini Eleftheria , Gilly Arthur 
Published: December 2018

MotivationCopy number variants (CNVs) are large deletions or duplications at least 50 to 200 base pairs long. They play an important role in multiple disorders, but accurate calling of CNVs remains challenging. Most current approaches to CNV detection use raw read alignments, which are computationally intensive to process.ResultsWe use a regression tree-based approach to call CNVs from whole-genome sequencing (WGS, > 18x) variant call-sets in 6,898 samples across four European cohorts, and describe a rich large variation landscape comprising 1,320 CNVs. 61.8% of detected events have been previously reported in the Database of Genomic Variants. 23% of high-quality deletions affect entire genes, and we recapitulate known events such as theGSTM1andRHDgene deletions. We test for association between the detected deletions and 275 protein levels in 1,457 individuals to assess the potential clinical impact of the detected CNVs. We describe the LD structure and copy number variation underlying the association between levels of the CCL3 protein and a complex structural variant (MAF = 0.15, p = 3.6×10-12) affectingCCL3L3, a paralog of theCCL3gene. We also identify acis-association between a low-frequencyNOMO1deletion and the protein product of this gene (MAF = 0.02, p = 2.2×10-7), for which nocis-ortrans-single nucleotide variant-driven protein quantitative trait locus (pQTL) has been documented to date. This work demonstrates that existing population-wide WGS call-sets can be mined for CNVs with minimal computational overhead, delivering insight into a less well-studied, yet potentially impactful class of genetic variant.AvailabilityThe regression tree based approach, UN-CNVc, is available as an R and bash executable on GitHub athttps://github.com/agilly/un-cnvc.Contacteleftheria.zeggini@helmholtz-muenchen.de;arthur.gilly@helmholtz-muenchen.deSupplementary InformationSupplementary information is appended.
An event-based paradigm for analyzing fluorescent astrocyte activity uncovers novel single-cell and population-level physiology
Authors: Wang Yizhi , DelRosso Nicole V. , Vaidyanathan Trisha , Reitman Michael , Cahill Michelle K. , Mi Xuelong , Yu Guoqiang , Poskanzer Kira E. 
Published: December 2018

AbstractRecent work examining astrocytic physiology centers on fluorescence imaging approaches, due to development of sensitive fluorescent indicators and observation of spatiotemporally complex calcium and glutamate activity. However, the field remains hindered in fully characterizing these dynamics, both within single cells and at the population-level, because of the insufficiency of current region-of-interest-based approaches to describe activity that is often spatially unfixed, size-varying, and propagative. Here, we present a paradigm-shifting analytical framework that releases astrocyte biologists from ROI-based tools. Astrocyte Quantitative Analysis (AQuA) software enables users to take an event-based approach to accurately capture and quantify the irregular activity observed in astrocyte imaging datasets. We apply AQuA to a range of ex vivo and in vivo imaging data, and uncover previously undescribed physiological phenomena in each. Since AQuA is data-driven and based on machine learning principles, it can be applied across model organisms, fluorescent indicators, experimental modes, and imaging resolutions and speeds, enabling researchers to elucidate fundamental astrocyte physiology.
PSI controls tim splicing and circadian period in Drosophila
Authors: Foley Lauren , Ling Jinli , Joshi Radhika , Evantal Naveh , Kadener Sebastian , Emery Patrick 
Published: December 2018

AbstractThe Drosophila circadian pacemaker consists of transcriptional feedback loops subjected to both post-transcriptional and post-translational regulation. While post-translational regulatory mechanisms have been studied in detail, much less is known about circadian post-transcriptional control. To have a better understanding of the role and mechanisms of circadian post-transcriptional regulation, we targeted 364 RNA binding and RNA associated proteins with RNA interference. Among the 43 genes we identified was the alternative splicing regulator P-element somatic inhibitor (PSI). PSI downregulation shortens the period of circadian rhythms both in the brain and in peripheral tissues. Interestingly, we found that PSI regulates the thermosensitive alternative splicing of timeless (tim), promoting splicing events favored at warm temperature over those increased at cold temperature. Moreover, the period of circadian behavior was insensitive to PSI downregulation when flies could produce functional TIM proteins only from a transgene that cannot form the thermosensitive splicing isoforms. Therefore, we conclude that PSI regulates the period of Drosophila circadian rhythms through its modulation of the tim splicing pattern.
Convergent Allostery in Ribonucleotide Reductase
Authors: Thomas William C. , Brooks F. Phil , Burnim Audrey A. , Bacik John-Paul , Stubbe JoAnne , Kaelber Jason T. , Chen James Z. , Ando Nozomi 
Published: December 2018

AbstractRibonucleotide reductases (RNRs) use a conserved radical-based mechanism to catalyze the conversion of ribonucleotides to deoxyribonucleotides. Within the RNR family, class Ib RNRs are notable for being largely restricted to bacteria, including many pathogens, and for lacking an evolutionarily mobile ATP-cone domain that allosterically controls overall activity. In this study, we report the emergence of a new and unexpected mechanism of activity regulation in the sole RNR of the model organism Bacillus subtilis. Using a hypothesis-driven structural approach that combines the strengths of small-angle X-ray scattering (SAXS), crystallography, and cryo-electron microscopy (cryo-EM), we describe the reversible interconversion of six unique structures, including a flexible, active tetramer and two novel, inhibited filaments. These structures reveal the conformational gymnastics necessary for RNR activity and the molecular basis for its control via an evolutionarily convergent form of allostery.
Plasticity and the role of mass-scaling in allocation, morphology and anatomical trait responses to above and belowground resource limitation in cultivated sunflower (Helianthus annuus L.)
Authors: Wang Yan , Donovan Lisa A. , Temme Andries A. 
Published: December 2018

AbstractIn the face of resource limitations, plants show plasticity in multiple trait categories, including biomass allocation, morphology and anatomy, yet inevitably also grow less. The extent to which passive mass-scaling plays a role in trait responses that contribute to increased potential for resource acquisition are poorly understood. Here we assessed the role of mass-scaling on the direction, magnitude and coordination of trait plasticity to light and/or nutrient limitation in cultivated sunflower (Helianthus annuus).We grew seedlings of ten sunflower genotypes for three weeks in a factorial of light (50% shade) and nutrient (10% supply) limitation in the greenhouse and measured a suite of allocational, morphological and anatomical traits for leaves, stems, fine roots, and tap roots.Under resource limitation, plants were smaller and more biomass was allocated to the organ capturing the most limiting resource, as expected. Traits varied in the magnitude of plasticity and the extent to which the observed response was passive (scaled with plant mass) and/or had an additional active component. None of the allocational responses were primarily passive. Plastic changes to specific leaf area and specific root length were primarily active, and adjusted towards more acquisitive trait values under light and nutrient limitation, respectively. For many traits, the observed response was a mixture of active and passive components, and for some traits the active adjustment was antagonistic to the direction of passive adjustment, e.g. stem height, and tap root and stem theoretical hydraulic conductance. Passive scaling with size played a major role in the coordinated response to light, but correcting for mass clarified that the active responses to both limitations were more similar in magnitude, although still resource and organ specific.Our results demonstrate that both passive plasticity and active plasticity can contribute to increased uptake capacity for limiting resources in a manner that is resource, organ and trait specific. Indeed, passive adjustments (scaling with mass) of traits due to resource stress extends well beyond just mass allocation traits. For a full understanding of plants response to environmental stress both passive and active plasticity needs to be taken into account.
Differential dominance of an allele of the Drosophila tßh gene challenges standard genetic techniques
Authors: Damrau Christine , Colomb Julien , Brembs Björn 
Published: December 2018

AbstractThe biogenic amine octopamine (OA) and its precursor tyramine (TA) are involved in controlling a plethora of different physiological and behavioral processes. Thetyramine-ß-hydroxylase(tßh) gene encodes the enzyme catalyzing the last synthesis step from TA to OA. Here, we report differential dominance (from recessive to overdominant) of the putative nulltßhnM18allele in two behavioral measures in Buridan’s paradigm (walking speed and stripe deviation) and a proboscis extension assay in the fruit flyDrosophila melanogaster. The behavioral analysis of transgenictßhexpression experiments in mutant and wild type flies as well as of OA- and TA-receptor mutants revealed a complex interaction of both aminergic systems. Our analysis suggests that the different neuronal networks responsible for the three phenotypes show differential sensitivity totßhgene expression levels. The evidence suggests that this sensitivity is brought about by a TA/OA opponent system modulating the involved neuronal circuits. This conclusion entails important implications for standard transgenic techniques, commonly used in functional genetics.
Elongated cells drive morphogenesis in a surface-wrapped finite element model of germband retraction
Authors: McCleery W. T. , Veldhuis J. , Brodland G. W. , Bennett M. E. , Hutson M. S. 
Published: December 2018

ABSTRACTDuringDrosophilaembryogenesis, the germband first extends to curl around the posterior end of the embryo, and then retracts back; however, retraction is not simply the reversal of extension. At a tissue level, extension is coincident with ventral furrow formation, and at a cellular level, extension occurs via convergent cell neighbor exchanges in the germband while retraction involves only changes in cell shape. To understand how cell shapes, tissue organization and cellular forces drive germband retraction, we investigate this process using a whole-embryo, surface-wrapped cellular finite element model. This model represents two key epithelial tissues – amnioserosa and germband – as adjacent sheets of 2D cellular finite elements that are wrapped around an ellipsoidal 3D approximation of an embryo. The model reproduces the detailed kinematics ofin vivoretraction by fitting just one free model parameter, the tension along germband cell interfaces; all other cellular forces are constrained to follow ratios inferred from experimental observations. With no additional parameter adjustments, the model also reproduces failures of retraction when amnioserosa cells are removed to mimic U-shaped mutants or laser-microsurgery experiments. Surprisingly, retraction in the model is robust to changes in cellular force values, but is critically dependent on starting from a configuration with highly elongated amnioserosa cells. Their extreme cellular elongation is established during the prior process of germband extension and is then used to drive retraction. The amnioserosa is the one tissue whose cellular morphogenesis is reversed in germband extension and retraction – serving as a store of morphological information that coordinates the forces needed to retract the germband back to its pre-extension position and shape. In this case, and perhaps more generally, cellular force strengths are less important than the carefully established cell shapes that direct them.