bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel and Direct Metamobilome Approach improves the Detection of Larger-sized Circular Elements across Kingdoms
Authors: Alanin Katrine Skov , Jørgensen Tue Sparholt , Browne Patrick , Petersen Bent , Riber Leise , Kot Witold , Hansen Lars Hestbjerg 
Published: September 2019

AbstractMobile genetic elements (MGEs) are instrumental in natural prokaryotic genome editing, permitting genome plasticity and allowing microbes to accumulate immense genetic diversity. MGEs include DNA elements such as plasmids, transposons and Insertion Sequences (IS-elements), as well as bacteriophages (phages), and they serve as a vast communal gene pool. These mobile DNA elements represent a human health risk as they can add new traits, such as antibiotic resistance or virulence, to a bacterial strain. Sequencing libraries targeting circular MGEs, referred to as mobilomes, allows the expansion of our current understanding of the mechanisms behind the mobility, prevalence and content of these elements. However, metamobilomes from bacterial communities are not studied to the same extent as metagenomics, partly because of methodological biases arising from multiple displacement amplification (MDA), often used in previous metamobilome publications. In this study, we show that MDA is detrimental to the detection of larger-sized plasmids if small plasmids are present by comparing the abundances of reads mapping to plasmids in a wastewater sample spiked with a mock community of selected plasmids with and without MDA. Furthermore, we show that it is possible to produce samples consisting almost exclusively of circular MGEs and obtain a catalog of larger, complete, circular MGEs from complex samples without the use of MDA.ImportanceMobile genetic elements (MGEs) can transport genetic information between genomes in different bacterial species, adding new traits, potentially generating dangerous multidrug-resistant pathogens. In fact, plasmids and circular MGEs can encode bacterial genetic specializations such as virulence, resistance to metals, antimicrobial compounds, and bacteriophages, as well as the degradation of xenobiotics. For this reason, circular MGEs are crucial to investigate, but they are often missed in metagenomics and ecological studies. In this study, we present, for the first time, an improved method, which reduces the bias towards small MGEs and we demonstrate that this method can unveil larger, complete circular MGEs from complex samples without the use of multiple displacement amplification. This method may result in the detection of larger-sized plasmids that have hitherto remained unnoticed and therefore has the potential to reveal novel accessory genes, acting as possible targets in the development of preventive strategies directed at pathogens.
Extensive Intratumor Proteogenomic Heterogeneity Revealed by Multiregion Sampling in High-Grade Serous Ovarian Tumor Specimens
Authors: Hunt Allison L. , Bateman Nicholas W. , Barakat Waleed , Makohon-Moore Sasha , Hood Brian L. , Conrads Kelly A. , Zhou Ming , Calvert Valerie , Pierobon Mariaelena , Loffredo Jeremy , Litzi Tracy J. , Oliver Julie , Mitchell Dave , Gist Glenn , Rojas Christine , Blanton Brian , Robinson Emma L. , Odunsi Kunle , Sood Anil K. , Casablanca Yovanni , Darcy Kathleen M. , Shriver Craig D. , Petricoin Emanuel F. , Rao Uma N. M. , Maxwell G. Larry , Conrads Thomas P. 
Published: September 2019

AbstractEnriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten primary high-grade serous ovarian tumors and analyzed using proteomics (mass spectrometry and reverse phase protein microarray) and RNA-sequencing analyses. Comparative analyses of transcript and protein abundances revealed independent clustering of enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering between purified collections, driven by overall tumor purity. Comparison of historic prognostic molecular subtypes for HGSOC revealed protein and transcript expression from tumor epithelium correlated most strongly with the differentiated molecular subtype, whereas stromal proteins and transcripts most strongly correlated with mesenchymal subtype. Protein and transcript abundance in tumor epithelium and stromal collections from neighboring sections exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial protein and transcript expression heterogeneity within the tumor microenvironment that directly bears on prognostic signatures and underscore the need to enrich cellular subpopulations for expression profiling.
Antipsychotic behavioral phenotypes in the mouse Collaborative Cross recombinant inbred inter-crosses (RIX)
Authors: Giusti-Rodríguez Paola , Xenakis James G. , Crowley James J. , Nonneman Randal J. , DeCristo Daniela M. , Ryan Allison , Quackenbush Corey R. , Miller Darla R. , Shaw Ginger D. , Zhabotynsky Vasyl , Sullivan Patrick F. , de Villena Fernando Pardo-Manuel , Zou Fei 
Published: September 2019

ABSTRACTSchizophrenia is an idiopathic disorder that affects approximately 1% of the human population, and presents with persistent delusions, hallucinations, and disorganized behaviors. Antipsychotics are the standard pharmacological treatment for schizophrenia, but are frequently discontinued by patients due to inefficacy and/or side effects. Chronic treatment with the typical antipsychotic haloperidol causes tardive dyskinesia (TD), which manifests as involuntary and often irreversible orofacial movements in around 30% of patients. Mice treated with haloperidol develop many of the features of TD, including jaw tremors, tongue protrusions, and vacuous chewing movements (VCMs). In this study, we used genetically diverse Collaborative Cross (CC) recombinant inbred inter-cross (RIX) mice to elucidate the genetic basis of antipsychotic-induced adverse drug reactions (ADRs). We performed a battery of behavioral tests in 840 mice from 73 RIX lines (derived from 62 CC strains) treated with haloperidol or placebo in order to monitor the development of ADRs. We used linear mixed models to test for strain and treatment effects. We observed highly significant strain effects for almost all behavioral measurements investigated (p<0.001). Further, we observed strong strain-by-treatment interactions for most phenotypes, particularly for changes in distance traveled, vertical activity, and extrapyramidal symptoms (EPS). Estimates of overall heritability ranged from 0.21 (change in body weight) to 0.4 (VCMs and change in distance traveled) while the portion attributable to the interactions of treatment and strain ranged from 0.01 (for change in body weight) to 0.15 (for change in EPS). Interestingly, close to 30% of RIX mice exhibited VCMs, a sensitivity to haloperidol exposure, approximately similar to the rate of TD in humans chronically exposed to haloperidol. Understanding the genetic basis for the susceptibility to antipsychotic ADRs may be possible in mouse, and extrapolation to humans could lead to safer therapeutic approaches for schizophrenia.
Complex genetic admixture histories reconstructed with Approximate Bayesian Computations
Authors: Fortes-Lima Cesar A. , Laurent Romain , Thouzeau Valentin , Toupance Bruno , Verdu Paul 
Published: September 2019

ABSTRACTAdmixture is a fundamental evolutionary process that has influenced genetic patterns in numerous species. Maximum-likelihood approaches based on allele frequencies and linkage-disequilibrium have been extensively used to infer admixture processes from dense genome-wide datasets mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct, especially when large datasets are unavailable. We develop an Approximate Bayesian Computations (ABC) framework to reconstruct complex admixture histories from independent genetic markers. We built the software packageMetHisto simulate independent SNPs in a two-way admixed population for scenarios with multiple admixture pulses, or monotonically decreasing or increasing admixture at each generation; drawing model-parameter values from prior distributions set by the user. For each simulated dataset, we calculate 24 summary statistics describing genetic diversity and moments of individual admixture fraction. We coupledMetHiswith existing ABC algorithms and investigate the admixture history of an African American and a Barbadian population. Results show that Random-Forest ABC scenario-choice, followed by Neural-Network ABC posterior parameter estimation, can distinguish most complex admixture scenarios and provide accurate model-parameter estimations. For both admixed populations, we find that monotonically decreasing contributions over time, from the European and African sources, explain the observed data more accurately than multiple admixture pulses. Furthermore, we find contrasted trajectories of introgression decay from the European and African sources between the two admixed populations. This approach will allow for reconstructing detailed admixture histories in numerous populations and species, particularly when maximum-likelihood methods are intractable.
Identification of key determinants ofStaphylococcus aureusvaginal colonization
Authors: Deng Liwen , Schilcher Katrin , Burcham Lindsey R. , Kwiecinski Jakub M. , Johsnon Paige M. , Head Steven R. , Heinrichs David E. , Horswill Alexander R. , Doran Kelly S. 
Published: September 2019

ABSTRACTStaphylococcus aureusis an important pathogen responsible for nosocomial and community acquired infections in humans, and methicillin-resistantS. aureus(MRSA) infections have continued to increase despite wide-spread preventative measures.S. aureuscan colonize the female vaginal tract and reports have suggested an increase in MRSA infections in pregnant and postpartum women as well as outbreaks in newborn nurseries. Currently, little is known about specific factors that promote MRSA vaginal colonization and subsequent infection. To studyS. aureuscolonization of the female reproductive tract in a mammalian system, we developed a mouse model ofS. aureusvaginal carriage and demonstrated that both hospital-associated and community-associated MRSA isolates can colonize the murine vaginal tract. Immunohistochemical analysis revealed an increase in neutrophils in the vaginal lumen during MRSA colonization. Additionally, we observed that a mutant lacking fibrinogen binding adhesins exhibited decreased persistence within the mouse vagina. To further identify novel factors that promote vaginal colonization, we performed RNA-sequencing to determine the transcriptome of MRSA growingin vivoduring vaginal carriage at 5 hours, 1-day, and 3-days post-inoculation. Over 25% of bacterial genes were differentially regulated at all time points during colonization compared to laboratory cultures. The most highly induced genes were those involved in iron acquisition, including the Isd system and siderophore transport systems. Mutants deficient in these pathways did not persist as well duringin vivocolonization. These results reveal that fibrinogen binding as well as the capacity to overcome host nutritional limitation are important determinants of MRSA vaginal colonization.IMPORTANCEStaphylococcus aureusis an opportunistic pathogen able to cause a wide variety of infections in humans. Recent reports have suggested an increasing prevalence of MRSA in pregnant and postpartum women, coinciding with the increased incidence of MRSA infections in the NICU and newborn nurseries. Vertical transmission from mothers to infants at delivery is a likely route of MRSA acquisition by the newborn, however, essentially nothing is known about host and bacterial factors that influence MRSA carriage in the vagina. Here, we established a mouse model of vaginal colonization and observed that multiple MRSA strains can persist in the vaginal tract. Additionally, we determined that MRSA interactions with fibrinogen as well as iron uptake can promote vaginal persistence. This study is the first to identify molecular mechanisms which govern vaginal colonization by MRSA, the critical initial step preceding infection and neonatal transmission.
Neuro-transcriptomic signatures for mood disorder morbidity and suicide mortality
Authors: Jabbi Mbemba , Arasappan Dhivya , Eickhoff Simon B. , Strakowski Stephen M. , Nemeroff Charles B. , Hofmann Hans A. 
Published: September 2019

ABSTRACTSuicidal behaviors are strongly linked with mood disorders, but the specific neurobiological and functional gene-expression correlates for this linkage remain elusive. We performed neuroimaging-guided RNA-sequencing in two studies to test the hypothesis that imaging-localized gray matter volume (GMV) loss in mood disorders, harbors gene-expression changes associated with disease morbidity and related suicide mortality in an independent postmortem cohort. To do so, first, we conducted study 1 using an anatomical likelihood estimation (ALE) MRI meta-analysis including a total of 47 voxel-based morphometry (VBM) publications (i.e. 26 control>major depressive disorder (MDD) studies, and 21 control>bipolar disorder (BD) studies) in 2387 (living) participants. Study 1 meta-analysis identified a selective anterior insula cortex (AIC) GMV loss in mood disorders. We then used this results to guide study 2postmortemtissue dissection and RNA-Sequencing of 100 independent donor brain samples with a life-time history of MDD (N=30), BD (N=37) and control (N=33). In study 2, exploratory factor-analysis identified a higher-order factor representing number of Axis-1 diagnoses (e.g. substance use disorders/psychosis/anxiety, etc.), referred to here asmorbidityand suicide-completion referred to asmortality. Comparisons of case-vs-control, and factor-analysis defined higher-order-factor contrast variables revealed that the imaging-identified AIC GMV loss sub-region harbors differential gene-expression changes in high morbidity-&-mortalityversuslow morbidity-&-mortality cohorts in immune, inflammasome, and neurodevelopmental pathways. Weighted gene co-expression network analysis further identified co-activated gene modules for psychiatric morbidity and mortality outcomes. These results provide evidence that AIC anatomical signature for mood disorders are possible correlates for gene-expression abnormalities in mood morbidity and suicide mortality.
Dynamic Price and Breeder’s equations for variable environments
Authors: Coulson Tim , Potter Tomos , Felmy Anja 
Published: September 2019

ABSTRACTThe Breeder’s and Price equations are static models of evolution. Recent work has demonstrated how components of these equations, such as selection differentials and heritabilities, can be calculated for each time-step from dynamic, evolutionarily-explicit, structured population models. These dynamic models consist of functions that describe how environmental factors impact associations of genotypes and phenotypic traits with survival, reproduction, development, and non-genetic inheritance. Because evolutionarily explicit versions of these structured models can include feedbacks and can capture all the routes via which environmental variation can impact evolution, they i) provide a more powerful predictive tool for modelling eco-evolution in natural settings than existing approaches, and ii) reveal how key evolutionary parameters incorporated into static models change with time as evolution, or environmental change, proceeds.
Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation
Authors: Varghese Abhishek , Drovandi Christopher , Mengersen Kerrie , Mira Antonietta 
Published: September 2019

AbstractThe Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to the agricultural sector. Current models of BBTV are largely deterministic, limited by an incomplete understanding of interactions in complex natural systems, and the appropriate identification of parameters. A stochastic network-based Susceptible-Infected model has been created which simulates the spread of BBTV across the subsections of a banana plantation, parameterising nodal recovery, neighbouring and distant infectivity across summer and winter. Findings from posterior results achieved through Markov Chain Monte Carlo approach to approximate Bayesian computation suggest seasonality in all parameters, which are influenced by correlated changes in inspection accuracy, temperatures and aphid activity. This paper demonstrates how the model may be used for monitoring and forecasting of various disease management strategies to support policy-level decision making.Author summaryThe Banana Bunchy Top Virus (BBTV) poses one of the greatest threats to the food security of developing nations and the banana industry throughout the Asia-Pacific Basin. Decision-makers face significant challenges in mitigating BBTV spread in banana plantations due to the vector-borne spread of this disease, which is significantly influenced by a vast array of external environmental factors that are unique to each plantation.We propose a flexible network-based model that describes the spread of BBTV in a real banana plantation through a random process while accounting for individual plantation characteristics and utilise a principled methodology for estimating model parameters. Our findings quantify the effect of seasonal changes and plantation configuration on BBTV spread and predict for high-risk areas in this plantation. We believe that our model might be used by decision-makers to evaluate the effectiveness of current disease management strategies and explore opportunities for improvements.
Predicting neuropsychiatric symptoms of Parkinson’s disease with measures of striatal dopaminergic deficiency
Authors: Bishnoi Ram , Badir Marina C. , Surya Sandarsh , Youssef Nagy A. 
Published: September 2019

ABSTRACTThe role of nigrostriatal dopaminergic neurons degeneration is well established in the pathophysiology of Parkinson’s disease. However, it is unclear if and how the degeneration of the dopamine pathways affects the manifestation of the neuropsychiatric symptoms (NPS) of Parkinson’s disease (PD). Dopamine transporter (DAT) imaging, a technique to measure the reduction in the dopamine transporters is increasingly used as a tool in the diagnosis of PD. In this study, we examine if the baseline dopamine transporter density in the striatum measured by striatal binding ratio (SBR) is associated with the longitudinal onset and/or progression of NPS in PD as measured by the part 1 of Movement Disorder Society - Unified Parkinson’s Disease Rating Scale, over four years. Data of patients with PD and an abnormal screening present on 123I-ioflupane single-proton emission computed tomography were obtained from Parkinson’s Progression Markers Initiative (PPMI) database. Latent Growth Modeling (LGM), a statistical technique that can model the change over time while considering the variability in rate of change at the individual level was used to examine the progression of NPS over time. The results indicate the SBR did not correlate with the baseline NPS but did correlate with the rate of change of NPS (p<0.001) over the next four years, even after eliminating age related variance which can be a significant confounding factor. In conclusion, this study showed gradual worsening in NPS in patients with Parkinson’s disease which inversely correlates with the density of the dopamine transporters as measured by SBR at baseline.
Repression of Viral Gene Expression and Replication by the Unfolded Protein Response Effector XBP1u
Authors: Hinte Florian , van Anken Eelco , Tirosh Boaz , Brune Wolfram 
Published: September 2019

AbstractThe unfolded protein response (UPR) is a cellular homeostatic circuit regulating protein synthesis and processing in the ER by three ER-to-nucleus signaling pathways. One pathway is triggered by the inositol-requiring enzyme 1 (IRE1), which splices the X-box binding protein 1 (XBP1) mRNA, thereby enabling expression of XBP1s. Another UPR pathway activates the activating transcription factor 6 (ATF6). Here we show that murine cytomegalovirus (MCMV), a prototypic β-herpesvirus, harnesses the UPR to regulate its own life cycle. MCMV activates the IRE1-XBP1 pathway early post infection to relieve repression by XBP1u, the product of the unspliced XBP1 mRNA. XBP1u inhibits viral gene expression and replication by blocking the activation of the viral major immediate-early promoter by XBP1s and ATF6. These findings reveal a redundant function of XBP1s and ATF6 as activators of the viral life cycle, and an unexpected role of XBP1u as a potent repressor of both XBP1s and ATF6-mediated activation.
Single Volume Image Generator and Deep Learning-based ASD Classification
Authors: Ahmed Md Rishad , Zhang Yuan , Inan Omer T. , Liao Hongen 
Published: September 2019

AbstractAutism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by social deficits and repetitive behaviors. Associated ASD biomarkers can be supportive of apprehending the underlying roots of the disease and lead the targeted diagnosis as well as treatment. Although deep learning approaches have been applied in functional magnetic resonance imaging (fMRI) based clinical or behavioral identification of ASD, most erstwhile models are inadequate in their capacity to exploit the data richness. Classification techniques often solely rely on region-based summary and/or functional connectivity analysis of one pipeline or unique site dataset. Besides these, biomedical data modeling to analyze big data related to ASD is still perplexing due to its complexity and heterogeneity. Single volume image consideration has not been previously investigated in classification purposes. By deeming these challenges, in this work, firstly, we design an image generator to generate single volume brain images from the whole-brain image of each subject. Secondly, the single volume images are analyzed by evaluating four deep learning approaches comprising one amended volume base Convolutional Neural Network framework to classify ASD and typical control participants. Thirdly, we propose a novel deep ensemble learning classifier using VGG16 as feature extractor to ensure further classification performance. Then, to evaluate the classifier performance across the inter sites, we apply the proposed method on each site individually and validate our findings by comparing literature reports. We showcase our approaches on large-scale multi-site brain imaging dataset (ABIDE) by considering four preprocessing pipelines, and the outcome demonstrates the state-of-the-art performance compared with the literature findings; hence, which are robust and consistent.