bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long-read sequencing identified a causal structural variant in an exome-negative case and enabled preimplantation genetic diagnosis
Authors: Miao Hefan , Zhou Jiapeng , Yang Qi , Liang Fan , Wang Depeng , Ma Na , Gao Bodi , Du Juan , Lin Ge , Wang Kai , Zhang Qianjun 
Published: May 2018

AbstractFor a proportion of individuals judged clinically to have a recessive Mendelian disease, only one pathogenic variant can be found from clinical whole exome sequencing (WES), posing a challenge to genetic diagnosis and genetic counseling. Here we describe a case study, where WES identified only one pathogenic variant for an individual suspected to have glycogen storage disease type Ia (GSD-Ia), which is an autosomal recessive disease caused by bi-allelic mutations in the G6PC gene. Through Nanopore long-read whole-genome sequencing, we identified a 7kb deletion covering two exons on the other allele, suggesting that complex structural variants (SVs) may explain a fraction of cases when the second pathogenic allele is missing from WES on recessive diseases. Both breakpoints of the deletion are within Alu elements, and we designed Sanger sequencing and quantitative PCR assays based on the breakpoints for preimplantation genetic diagnosis (PGD) for the family planning on another child. Four embryos were obtained after in vitro fertilization (IVF), and an embryo without deletion in G6PC was transplanted after PGD and was confirmed by prenatal diagnosis, postnatal diagnosis, and subsequent lack of disease symptoms after birth. In summary, we present one of the first examples of using long-read sequencing to identify causal yet complex SVs in exome-negative patients, which subsequently enabled successful personalized PGD.
Profiles of cooperative brains: A discriminant analysis of cleaner and client fish monoaminergic responses to different social contexts
Authors: Maximino Caio , Gomes Ana Cristina R. , de Abreu Murilo S. , Cardoso Sónia C. , Lima-Maximino Monica , Winberg Svante , Soares Marta C. 
Published: May 2018

AbstractVertebrate cognitive function requires a dynamic coordination of multiple specialized areas of the brain. The challenge here is to understand how these brain areas respond in dependence to the neurophysiological mechanisms in place, as to enable the successful processing of information. For instance, social and cooperative behaviour has been linked to the activation of some specific brain areas, mostly associated with reward processing. Here we evaluated a classic model system of cooperation between species of fish and compared datasets of brain monoaminergic response. We analysed by using multivariate discriminant analysis the exposure of cleaners, Labroides dimidiatus, to several social-related conditions, as well as the response of one client species, Naso elegans, to similar contexts. We demonstrate that the variable appraisal of each social challenge contributes to brain dopaminergic and serotonergic changes, in cleaners and clients, with both showing the diencephalon and optic tectum as main areas of metabolite response. The role of the serotoninergic system activation was mostly demonstrated at the diencephalon and cerebellum of cleaners, a response that was driven by mutualistic interaction, contact with client. Our current evidence is the first to jointly demonstrate the level of selective similarity in brain monoaminergic mechanisms that underlie fish mutualistic and social engagement, for both sides of these partnerships.
NECorr, a Tool to Rank Gene Importance in Biological Processes using Molecular Networks and Transcriptome Data
Authors: Liseron-Monfils Christophe , Olson Andrew , Ware Doreen 
Published: May 2018

AbstractThe challenge of increasing crop yield while decreasing plants’ susceptibility to various stresses can be lessened by understanding plant regulatory processes in a tissue-specific manner. Molecular network analysis techniques were developed to aid in understanding gene inter-regulation. However, few tools for molecular network mining are designed to extract the most relevant genes to act upon. In order to find and to rank these putative regulator genes, we generated NECorr, a computational pipeline based on multiple-criteria decision-making algorithms. With the objective of ranking genes and their interactions in a selected condition or tissue, NECorr uses the molecular network topology as well as global gene expression analysis to find hub genes and their condition-specific regulators. NECorr was applied to Arabidopsis thaliana flower tissue and identifies known regulators in the developmental processes of this tissue as well as new putative regulators. NECorr will accelerate translational research by ranking candidate genes within a molecular network of interest.
Overlooked roles of DNA damage and maternal age in generating human germline mutations
Authors: Gao Ziyue , Moorjani Priya , Sasani Thomas , Pedersen Brent , Quinlan Aaron , Jorde Lynn , Amster Guy , Przeworski Molly 
Published: May 2018

AbstractAlthough the textbook view is that most germline mutations arise from replication errors, when analyzing largede novomutation datasets in humans, we find multiple lines of evidence that call that understanding into question. Notably, despite the drastic increase in the ratio of male to female germ cell divisions after the onset of spermatogenesis, even young fathers contribute three times more mutations than young mothers, and this ratio barely increases with parental ages. This surprising finding points to a substantial contribution of damage-induced mutations. Indeed, C to G transversions and CpG transitions, which together constitute one third of all mutations, show genomic distributions and sex-specific age dependencies indicative of doublestrand break repair and methylation-associated damage, respectively. Moreover, the data indicate that maternal age at conception influences the mutation rate both because of the accumulation of damage in oocytes and potentially through an influence on the number of postzygotic mutations.
Pseudomonas aeruginosadisplays a dormancy phenotype during long-term survival in water
Authors: Lewenza Shawn , Abboud Jason , Poon Karen , Kobryn Madison , Humplik Istvan , Bell John Rainer , Mardan Laura , Reckseidler-Zenteno Shauna 
Published: May 2018

ABSTRACTPseudomonas aeruginosais capable of long-term survival in water, which may serve as a reservoir for infection. Although viable cell counts of PAO1 incubated in water remain stable throughout 8 weeks, LIVE/DEAD® staining indicated a high proportion of cells stained with propidium iodide (PI). The proportion of PI-stained cells increased by 4 weeks, then decreased again by 8 weeks, suggesting an adaptive response. This was also evident in an observed shift in cell morphology from a rod to a coccoid shape after 8 weeks. Fluorescence-activated cell sorting (FACS) was used to recover PI-stained cells, which were plated and shown to be viable, indicating that PI-stained cells were membrane-compromised but still cultivable. PAO1 mid-log cells in water were labeled with the dsDNA-binding dye PicoGreen® to monitor viability as well as DNA integrity, which demonstrated that the population remains viable and transitions towards increased dsDNA staining. Metabolic activity was found to decrease significantly in water by 4 weeks. The PAO1 outer membrane became less permeable and more resistant to polymyxin B damage in water, and the profile of total membrane lipids changed over time. None of the individual mutants within a library of ~2500 mapped, mini-Tn5-luxtransposon mutants were found to have decreased survival in water. Among the ~1400 transcriptionalluxfusions, gene expression in water revealed that the majority of genes were repressed, but subsets of genes were induced at particular time points. In summary, these results indicate thatP. aeruginosais dormant in water and this adaptation involves a complex pattern of gene regulation and changes to the cell to promote long-term survival and antibiotic tolerance. The approach ofP. aeruginosaincubated in water may be useful to study antibiotic tolerance and the mechanisms of dormancy and survival in nutrient limiting conditions.
Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference
Authors: Sundaresan Vaanathi , Griffanti Ludovica , Kindalova Petya , Alfaro-Almagro Fidel , Zamboni Giovanna , Rothwell Peter M. , Nichols Thomas E. , Jenkinson Mark 
Published: May 2018

AbstractWhite matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model.In this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework.We tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease.On simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27 × 10−5, which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.
Assessing 42 inflammatory markers in 321 control subjects and 887 major depressive disorder cases: BMI and other confounders and overall predictive ability for current depression
Authors: Powell Timothy R. , Gaspar Helena , Chung Raymond , Keohane Aoife , Gunasinghe Cerisse , Uher Rudolf , Aitchison Katherine J. , Souery Daniel , Mors Ole , Maier Wolfgang , Zobel Astrid , Rietschel Marcella , Henigsberg Neven , Dernovšek Mojca Zvezdana , Hauser Joanna , Frissa Souci , Goodwin Laura , Hotopf Matthew , Hatch Stephani L , Collier David A. , Wang Hong , Wang Hong 
Published: May 2018

AbstractInflammatory markers such as cytokines represent potential biomarkers for major depressive disorder (MDD). Many, generally small studies have examined the role of single markers and found significant associations. We assessed 42 inflammatory markers, namely cytokines, in the blood of 321 control subjects and 887 MDD cases. We tested whether individual inflammatory marker levels were significantly affected by MDD case/control status, current episode, or current depression severity, co-varying for age, sex, body mass index (BMI), smoking, current antidepressant use, ethnicity, assay batch and study effects. We further used machine learning algorithms to investigate if we could use our data to blindly discriminate MDD patients, or those in a current episode. We found broad and powerful influences of confounding factors on log-protein levels. Notably, IL-6 levels were very strongly influenced by BMI (p = 1.37 × 10−43, variance explained = 18%), while Interleukin-16 was the most significant predictor of current depressive episode (p = 0.003, variance explained = 0.9%, q < 0.1). No single inflammatory marker predicted MDD case/control status when a subject was not in a depressed episode, nor did any predict depression severity. Machine learning results revealed that using inflammatory marker data with clinical confounder information significantly increased precision for differentiating MDD patients who were in an episode. To conclude, a wide panel of inflammatory markers alongside clinical information may aid in predicting the onset of symptoms, but no single inflammatory protein is likely to represent a clinically useful biomarker for MDD diagnosis or prognosis. We note that the potential influence of physical health related and population stratification related confounders on inflammatory biomarker studies in psychiatry is considerable.
Generation and Selection of a Panel of Pan-Filovirus Single-Chain Antibodies using Cell-Free Ribosome Display
Authors: Kunamneni Adinarayana , Clarke Elizabeth C. , Ye Chunyan , Bradfute Steven B. , Durvasula Ravi 
Published: May 2018

AbstractFiloviruses, which include ebolaviruses and marburgvirus, can cause outbreaks of highly lethal hemorrhagic fever. This disease causes significant morbidity and mortality in humans and non-human primates, with human fatality rates reaching 90% during some outbreaks. Currently, there are a lack of licensed vaccines or antivirals for these viruses. Since early symptoms of filovirus infection mimic more common diseases, there is a strong unmet public health and biodefense need for broad-spectrum filovirus rapid diagnostics. We have generated a panel of mouse single-chain Fv-antibodies (scFvs) to filovirus glycoproteins (GPs) using cell-free ribosome display and determined their cross-reactivity profiles to all known filovirus species. Two scFvs (4-2 and 22-1) were able to detect all known Ebolavirus and Marburgvirus species. This is the first report on ribosome display scFvs that can detect a broad set of filovirus GPs, which demonstrates their potential use in the development of a new generation of rapid diagnostic immunoassays.
TheSaccharomyces cerevisiaeHrq1 and Pif1 DNA helicases synergistically modulate telomerase activityin vitro
Authors: Nickens David G. , Rogers Cody M. , Bochman Matthew L. 
Published: May 2018

ABSTRACTTelomere length homeostasis is vital to maintaining genomic stability and is regulated by multiple factors, including telomerase activity and DNA helicases. TheSaccharomyces cerevisiaePif1 helicase was the first discovered catalytic inhibitor of telomerase, but recent experimental evidence suggests that Hrq1, the yeast homolog of the disease-linked human RecQ-like helicase 4 (RECQL4), plays a similar role via an undefined mechanism. Using yeast extracts enriched for telomerase activity and anin vitroprimer extension assay, here we determined the effects of recombinant wild-type and inactive Hrq1 and Pif1 on total telomerase activity and telomerase processivity. We found that titrations of these helicases alone have equal-but-opposite biphasic effects on telomerase, with Hrq1 stimulating activity at high concentrations. When the helicases were combined in reactions, however, they synergistically inhibited or stimulated telomerase activity depending on which helicase was catalytically active. These results suggest that Hrq1 and Pif1 interact and that their concerted activities ensure proper telomere length homeostasisin vivo. We propose a model in which Hrq1 and Pif1 cooperatively contribute to telomere length homeostasis in yeast.
Carpenter ants use diverse antennae sampling strategies to track odor trails
Authors: Draft Ryan W. , McGill Matthew R. , Kapoor Vikrant , Murthy Venkatesh N. 
Published: May 2018

AbstractDirected and meaningful animal behavior depends on the ability to sense key features in the environment. Among the different environmental signals, olfactory cues are critically important for foraging, navigation, and social communication in many species, including ants. Ants use their two antennae to explore the olfactory world, but how they do so remains largely unknown. In this study, we use high resolution videography to characterize the antennae dynamics of carpenter ants (Camponotus pennsylvanicus). Antennae are highly active during both tracking and exploratory behavior. When tracking, ants used several distinct behavioral strategies with stereotyped antennae sampling patterns (which we call sinusoidal behavior, probing, and trail following). In all behaviors, left and right antennae movements were anti-correlated, and tracking ants exhibited biases in the use of left vs right antenna to sample the odor trail. These results suggest non-redundant roles for the two antennae. In one of the behavioral modules (trail following), ants used both antennae to detect trail edges and direct subsequent turns, suggesting a specialized form of tropotaxis. Lastly, removal of an antenna resulted not only in less accurate tracking but also in changes in the sampling pattern of the remaining antenna. Our quantitative characterization of odor trail tracking lays a foundation to build better models of olfactory sensory processing and sensorimotor behavior in terrestrial insects.Summary StatementHigh resolution imaging of antennae reveals distinct patterns of sampling with non-redundant roles in odor tracking.
VCPA: genomic variant calling pipeline and data management tool for Alzheimer’s Disease Sequencing Project
Authors: Leung Yuk Yee , Valladares Otto , Chou Yi-Fan , Lin Han-Jen , Kuzma Amanda B , Cantwell Laura , Qu Liming , Gangadharan Prabhakaran ,   Alzheimer’s Disease Sequencing Project (ADSP), Salerno William J , Schellenberg Gerard D. , Wang Li-San 
Published: May 2018

AbstractSummary: We report VCPA, our SNP/Indel Variant Calling Pipeline and data management tool used for analysis of whole genome and exome sequencing (WGS/WES) for the Alzheimer’s Disease Sequencing Project. VCPA consists of two independent but linkable components: pipeline and tracking database. The pipeline is coded in Workflow Description Language and is fully optimized for the Amazon elastic compute cloud environment. This includes steps for processing raw sequence reads including read alignment, and all the way up to variant calling using GATK. The tracking database allows users to dynamically view the statuses of jobs running and the quality metrics reported by the pipeline. Users can thus monitor the production process and diagnose if any problem arises during the procedure. All quality metrics (>100 collected per processed genome) are stored in the database, thus facilitating users to compare, share and visualize the results. To summarize, VCPA is functional equivalent to the CCDG/TOPMed pipeline. Together with the dockerized database (also available as Amazon Machine Image), users can easily process any WGS/WES data on Amazon cloud with minimal installation.Availability: VCPA is released under the MIT license and is available for academic and nonprofit use for free. The pipeline source code and step-by-step instructions are available from the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (http://www.niagads.org/VCPA).Contact:yyee@pennmedicine.upenn.edu or lswang@pennmedicine.upenn.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Positive reward prediction errors strengthen incidental memory encoding
Authors: Jang Anthony I. , Nassar Matthew R. , Dillon Daniel G. , Frank Michael J. 
Published: May 2018

AbstractThe dopamine system is thought to provide a reward prediction error signal that facilitates reinforcement learning and reward-based choice in corticostriatal circuits. While it is believed that similar prediction error signals are also provided to temporal lobe memory systems, the impact of such signals on episodic memory encoding has not been fully characterized. Here we develop an incidental memory paradigm that allows us to 1) estimate the influence of reward prediction errors on the formation of episodic memories, 2) dissociate this influence from other factors such as surprise and uncertainty, 3) test the degree to which this influence depends on temporal correspondence between prediction error and memoranda presentation, and 4) determine the extent to which this influence is consolidation-dependent. We find that when choosing to gamble for potential rewards during a primary decision making task, people encode incidental memoranda more strongly even though they are not aware that their memory will be subsequently probed. Moreover, this strengthened encoding scales with the reward prediction error, and not overall reward, experienced selectively at the time of memoranda presentation (and not before or after). Finally, this strengthened encoding is identifiable within a few minutes and is not substantially enhanced after twenty-four hours, indicating that it is not consolidation-dependent. These results suggest a computationally and temporally specific role for putative dopaminergic reward prediction error signaling in memory formation.
Spikeling: a low-cost hardware implementation of a spiking neuron for neuroscience teaching and outreach
Authors: Baden Tom , James Ben , Zimmermann Maxime JY , Bartel Phillip , Grijseels Dorieke , Lagnado Leon , Maravall Miguel 
Published: May 2018

SummaryUnderstanding of how neurons encode and compute information is fundamental to our study of the brain, but opportunities for hands-on experience with neurophysiological techniques on live neurons are scarce in science education. Here, we present Spikeling, an open source £25 in silico implementation of a spiking neuron that mimics a wide range of neuronal behaviours for classroom education and public neuroscience outreach. Spikeling is based on an Arduino microcontroller running the computationally efficient Izhikevich model of a spiking neuron. The microcontroller is connected to input ports that simulate synaptic excitation or inhibition, dials controlling current injection and noise levels, a photodiode that makes Spikeling light-sensitive and an LED and speaker that allows spikes to be seen and heard. Output ports provide access to variables such as membrane potential for recording in experiments or digital signals that can be used to excite other connected Spikelings. These features allow for the intuitive exploration of the function of neurons and networks. We also report our experience of using Spikeling as a teaching tool for undergraduate and graduate neuroscience education in Nigeria and the UK.