bluuebunny/crossref_metadata_2025_split huggingface dataset:
NMR Analysis of the Correlation of Metabolic Changes in Blood and Cerebrospinal Fluid in Alzheimer Model Male and Female Mice
Authors: Stojanovic Filip , Taktek Mariam , Khieu Nam Huan , Huang Junzhou , Jiang Susan , Rennie Kerry , Chakravarthy Balu , Costain Will J. , Cuperlovic-Culf Miroslava 
Published: May 2020

AbstractThe development of effective therapies as well as early, molecular diagnosis of Alzheimer’s disease is impeded by the lack of understanding of the underlying pathological mechanisms. Metabolomics studies of body fluids as well as brain tissues have shown major changes in metabolic profiles of Alzheimer’s patients. However, with analysis performed at the late stages of the disease it is not possible to distinguish causes and consequence. The mouse model APP/PS1 expresses a mutant amyloid precursor protein resulting in early Amyloid β (Aβ) accumulation as well as many resulting physiological changes including changes in metabolic profile and metabolism. Analysis of metabolic profile of cerebrospinal fluid (CSF) and blood of APP/PS1 mouse model can provide information about metabolic changes in these body fluids caused by Aβ accumulation. Using our novel method for analysis of correlation and mathematical ranking of significant correlations between metabolites in CSF and blood, we have explored changes in metabolite correlation and connectedness in APP/PS1 and wild type mice. Metabolites concentration and correlation changes in CSF, blood and across the blood brain barrier determined in this work are affected by the production of amyloid plaque. Metabolite changes observed in the APP/PS1 mouse model are the response to the mutation causing plaque formation, not the cause for the plaque suggesting that they are less relevant in the context of early treatment and prevention then the metabolic changes observed only in humans.
Genome-wide Association Studies Reveal Important Candidate Genes for theBacillus pumilusTUAT-1-Arabidopsis thalianaInteraction
Authors: Cotta Marina Soneghett , do Amaral Fernanda Plucani , Cruz Leonardo Magalhães , Wassem Roseli , de Oliveira Pedrosa Fábio , Yokoyama Tadashi , Stacey Gary 
Published: May 2020

ABSTRACTThe plant growth promoting bacterium (PGPB)Bacillus pumilusTUAT-1 is an indole acetic acid producer that can increase plant growth. Inoculation with this strain has been shown to confer greater plant tolerance to drought and saline conditions. Although the ability of TUAT-1 to enhance plant growth is well documented, little is known about what mechanisms underlie the plant response to this bacterium. Applying genome-wide association study (GWAS), we evaluated the interaction between TUAT-1 andArabidopsis thaliana, screening 288 plant ecotypes for root architecture traits comparing non-inoculated and inoculated plants. Most of the ecotypes were significantly affected by TUAT-1 inoculation (66.7%) for at least one of the root traits measured. For example, some ecotypes responded positively increasing root growth while others showed reduced growth upon inoculation. A total of 96 ecotypes (33.3%) did not respond significantly to PGPB inoculation. These results are consistent with the widely reported strain-genotype specificity shown in many plant-microbe interactions. The GWAS analysis revealed significant SNPs associated to specific root traits leading to identification of several genes putatively involved in enabling theBacillus pumilusTUAT-1 andA. thalianaassociation and contributing to plant growth promotion. Our results show that root architecture features are genetic separable traits associated with plant growth in association with TUAT-1. Our findings validate previous reported genes involved inBacillus spp.-plant interaction, growth promotion and highlight potential genes involved in plant microbe interaction. We suggest that plant-bacterial interaction and the plant growth promotion are quantitative and multigenic traits. This knowledge expands our understanding of the functional mechanisms driving plant growth promotion by PGPB.
Why scaling up uncertain predictions to higher levels of organisation will underestimate change
Authors: Orr James A. , Piggott Jeremy J. , Jackson Andrew , Arnoldi Jean-François 
Published: May 2020

AbstractUncertainty is an irreducible part of predictive science, causing us to over- or underestimate the magnitude of change that a system of interest will face. In a reductionist approach, we may use predictions at the level of individual system components (e.g. species biomass), and combine them to generate predictions for system-level properties (e.g. ecosystem function). Here we show that this process of scaling up uncertain predictions to higher levels of organization has a surprising consequence: it will systematically underestimate the magnitude of system-level change, an effect whose significance grows with the system’s dimensionality. This stems from a geometrical observation: in high dimensions there are more ways to be more different, than ways to be more similar. This general remark applies to any complex system. Here we will focus on ecosystems thus, on ecosystem-level predictions generated from the combination of predictions at the species-level. In this setting, the ecosystem’s dimensionality is a measure of its diversity. We explain why dimensional effects do not play out when predicting change of a single linear aggregate property (e.g. total biomass), yet are revealed when predicting change of non-linear properties (e.g. absolute biomass change, stability or diversity), and when several properties are considered at once to describe the ecosystem, as in multi-functional ecology. Our findings highlight and describe the counter-intuitive effects of scaling up uncertain predictions, effects that will occur in any field of science where a reductionist approach is used to generate predictions.
Anipose: a toolkit for robust markerless 3D pose estimation
Authors: Karashchuk Pierre , Rupp Katie L. , Dickinson Evyn S. , Walling-Bell Sarah , Sanders Elischa , Azim Eiman , Brunton Bingni W. , Tuthill John C. 
Published: May 2020

AbstractQuantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals live and move in 3D. Here, we introduce Anipose, a Python toolkit for robust markerless 3D pose estimation. Anipose is built on the popular 2D tracking method DeepLabCut, so users can easily expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on four datasets: a moving calibration board, fruit flies walking on a treadmill, mice reaching for a pellet, and humans performing various actions. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. We believe this open-source software and accompanying tutorials (anipose.org) will facilitate the analysis of 3D animal behavior and the biology that underlies it.
Identification of HIV-Transmitting Sub-Epithelial Mononuclear Phagocytes in Human Anogenital and Colorectal Tissues
Authors: Rhodes Jake W , Botting Rachel A , Bertram Kirstie M , Rana Hafsa , Baharlou Heeva , Longmuir-Vine Erica E , Vegh Peter , Fletcher James , O’Neil Thomas R , Parnell Grant P , Graham J Dinny , Nasr Najla , Lim Jake J K , Barnouti Laith , Haertsch Peter , Gosselink Martijn P , Di Re Angelina , Ctercteko Grahame , Jenkins Gregory J , Brooks Andrew J , Patrick Ellis , Byrne Scott N , Haniffa Muzlifah A , Cunningham Anthony L , Harman Andrew N 
Published: May 2020

AbstractTissue mononuclear phagocytes (MNP) are specialised in pathogen detection and antigen presentation. They are the first cells of the immune system to encounter HIV and play a key role in transmission as they deliver the virus to CD4 T cells, which are the primary HIV target cell in which the virus undergoes replication. Most studies have investigated the role that epithelial MNPs play in HIV transmission but, as mucosal trauma and inflammation are strongly associated with HIV transmission, it is also important to examine the role that sub-epithelial MNPs play. Sub-epithelial MNPs are present in a diverse array of subsets which differ in their function and the pathogens they detect. Understanding how specific subsets interact with HIV and deliver the virus to CD4 T cells is therefore of key importance to vaccine and microbicide development. In this study we have shown that, after topical application, HIV can penetrate to interact with sub-epithelial resident myeloid cells in anogenital explants and defined the full array of MNP subsets that are present in all the human anogenital and colorectal sub-epithelial tissues that HIV may encounter during sexual transmission. In doing so we have identified two subsets that preferentially take up HIV, become infected and transmit the virus to CD4 T cells; CD14+CD1c+CD11c+monocyte-derived dendritic cells and langerin-expressing dendritic cells 2 (DC2).
Inference of Diagnostic Markers and Therapeutic Targets from CSF proteomics for the Treatment of Hydrocephalus
Authors: Horowitz Arie , Saugier-Veber Pascale , Gilard Vianney 
Published: May 2020

ABSTRACTThe purpose of this mini-review is to examine if publicly available cerebrospinal fluid (CSF) proteomics data sets can be exploited to provide insight into the etiology of hydrocephalus, into the character of the injury inflicted on the parenchyma by ventriculomegaly, and into the response of the brain to this condition. While this undertaking was instigated by reanalysis of recent comparative proteomics of CSF collected from the brain of healthy andMpdzknockout (KO) mice (Yang et al., 2019), it is an opportunity to survey previously published CSF proteomics data sets to determine if they can be pooled together to that end. The overabundance of extracellular matrix (ECM) proteins, complement factors, and apolipoproteins in the CSF ofMpdzKO mice was taken to indicate that the hydrocephalic brain underwent ischemia, inflammation, and demyelination. The overabundance of five cytokine-binding proteins could be linked uniquely to insulin-like growth factor (IGF) secretion and signaling. The overabundance of two serpins, angiotensinogen and pigment epithelium-derived factor (PEDF) was considered as a biomarker of anti-angiogenic negative-feedback mechanisms to reduce CSF production. These findings raise the intriguing propositions that CSF proteomics can identify biomarkers of case-specific injuries, and that IGF signaling and angiogenesis pathways can serve as therapeutic targets. It appears, however, that the currently available proteomics data is not amenable to comparison of CSF from normal and hydrocephalic patients and cannot be used test the premise of those propositions.
A Rarefaction-Based Extension of the LDM for Testing Presence-Absence Associations in the Microbiome
Authors: Hu Yi-Juan , Lane Andrea , Satten Glen A. 
Published: May 2020

AbstractBackgroundMany methods for testing association between the microbiome and covariates of interest (e.g., clinical outcomes, environmental factors) assume that these associations are driven by changes in the relative abundance of taxa. However, these associations may also result from changes in which taxa are present and which are absent. Analyses of such presence-absence associations face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known thatrarefaction(subsampling to a common library size) controls this bias, but at the potential cost of information loss as well as the introduction of a stochastic component into the analysis. Currently, there is a need for robust and efficient methods for testing presence-absence associations in the presence of such confounding, both at the community level and at the individual-taxon level, that avoid the drawbacks of rarefaction.MethodsWe have previously developed the linear decomposition model (LDM) that unifies the community-level and taxon-level tests into one framework. Here we present an extension of the LDM for testing presence-absence associations. The extended LDM is a non-stochastic approach that repeatedly applies the LDM toallrarefied taxa count tables, averages the residual sum-of-squares (RSS) terms over the rarefaction replicates, and then forms anF-statistic based on these average RSS terms. We show that this approach compares favorably to averaging theF-statistic fromRrarefaction replicates, which can only be calculated stochastically. The flexible nature of the LDM allows discrete or continuous traits or interactions to be tested while allowing confounding covariates to be adjusted for.ResultsOur simulations indicate that our proposed method is robust to any systematic differences in library size and has better power than alternative approaches. We illustrate our method using an analysis of data on inflammatory bowel disease (IBD) in which case samples have systematically smaller library sizes than controls.ConclusionsThe rarefaction-based extension of the LDM performs well for testing presenceabsence associations and should be adopted even when there is no obvious systematic variation in library size.
Characterization of accessory genes in coronavirus genomes
Authors: Michel Christian J. , Mayer Claudine , Poch Olivier , Thompson Julie D. 
Published: May 2020

AbstractThe Covid19 infection is caused by the SARS-CoV-2 virus, a novel member of the coronavirus (CoV) family. CoV genomes code for a ORF1a / ORF1ab polyprotein and four structural proteins widely studied as major drug targets. The genomes also contain a variable number of open reading frames (ORFs) coding for accessory proteins that are not essential for virus replication, but appear to have a role in pathogenesis. The accessory proteins have been less well characterized and are difficult to predict by classical bioinformatics methods. We propose a computational tool GOFIX to characterize potential ORFs in virus genomes. In particular, ORF coding potential is estimated by searching for enrichment in motifs of theXcircular code, that is known to be over-represented in the reading frames of viral genes. We applied GOFIX to study the SARS-CoV-2 and related genomes including SARS-CoV and SARS-like viruses from bat, civet and pangolin hosts, focusing on the accessory proteins. Our analysis provides evidence supporting the presence of overlapping ORFs 7b, 9b and 9c in all the genomes and thus helps to resolve some differences in current genome annotations. In contrast, we predict that ORF3b is not functional in all genomes. Novel putative ORFs were also predicted, including a truncated form of the ORF10 previously identified in SARS-CoV-2 and a little known ORF overlapping the Spike protein in Civet-CoV and SARS-CoV. Our findings contribute to characterizing sequence properties of accessory genes of SARS coronaviruses, and especially the newly acquired genes making use of overlapping reading frames.
Nested species distribution models ofChlamydialesin tick hostIxodes ricinusin Switzerland
Authors: Rochat Estelle , Vuilleumier Séverine , Aeby Sebastien , Greub Gilbert , Joost Stéphane 
Published: May 2020

AbstractThe tickIxodes ricinusis the vector of various pathogens, includingChlamydialesbacteria, potentially causing respiratory infections. In this study, we modelled the spatial distribution ofI. ricinusand associatedChlamydialesover Switzerland from 2009 to 2019. We used a total of 2293 ticks and 186Chlamydialesoccurrences provided by a Swiss Army field campaign, a collaborative smartphone application and a prospective campaign. For each tick location, we retrieved from Swiss federal datasets the environmental factors reflecting the topography, climate and land cover. We then used the Maxent modelling technique to estimate the suitability forI. ricinusand to subsequently build the nested niche ofChlamydialesbacteria. Results indicate thatI. ricinushigh habitat suitability is determined by higher temperature and vegetation index (NDVI) values, lower temperature during driest months and a higher percentage of artificial and forests areas. The performance of the model was increased when extracting the environmental variables for a 100 m-radius buffer around the sampling points and when considering the data over the two years previous sampling date. ForChlamydialesbacteria, the suitability was favoured by lower percentage of artificial surfaces, driest conditions, high precipitation during coldest months and short distances to wetlands. From 2009 to 2018, we observed an extension of tick andChlamydialessuitable areas, associated with a shift towards higher altitude. The importance to consider spatio-temporal variations of the environmental conditions for obtaining better prediction was also demonstrated.ImportanceIxodes ricinusis the vector of pathogens, including the agent of Lyme disease, the tick borne encephalitis virus and the less knownChlamydialesbacteria at the origin of some respiratory infections. In this study, we identified the environmental factors influencing the presence ofI. ricinusandChlamydialesin Switzerland and generated maps of their distribution from 2009 to 2018. We found an important expansion of suitable areas for both the tick and the bacteria during the last decade. Results provided also the environmental factors that determine the presence ofChlamydialeswithin ticks. Distribution maps as generated here are expected to bring valuable informations for decision-makers to control tick-borne diseases in Switzerland and establish prevention campaigns. The methodological framework presented could be used to predict the distribution and spread of other host-pathogen couples, to identify environmental factors driving their distribution and to develop control or prevention strategies accordingly.
Comparative co-occurrence network analyses of the cichlid fish gut microbiota: community conservation and diet-associated shifts
Authors: Lluís Riera Joan , Baldo Laura 
Published: May 2020

AbstractCo-occurrence networks of bacteria associations are a powerful approach to explore ecologically relevant aspects of the gut microbiota structure, beyond community composition alone. Here we exploit the remarkable diversity of cichlid fishes and their multiple lake assemblages to investigate a) network features and patterns of microbial associations that were robust to major phylogeographical variables, and b) community structure changes along cichlid dietary shifts. We tackled these objectives using the large gut microbiota sequencing dataset available (nine lakes from Africa and America), building geographical and diet-specific networks and performing comparative analyses. Major findings indicated that lake and continental networks were highly resembling in global topology and node taxonomic composition, suggesting important constraints in the cichlid gut community assembling. A small fraction of the observed co-occurrence pairwises was conserved across all lake assemblages; while the origin and ecological relevance of these core associations remains unclear, their persistence suggests a potential functional role in the cichlid gut. Comparison of carnivores and herbivores-specific networks as well as mapping of diet-specific values on the African Lake Tanganyika network revealed a clear community shift as a function of diet, with an increase in complexity and node taxonomic diversity from carnivores-omnivores-plantkivores to herbivores. More importantly, diet-associated nodes in herbivores formed complex modules of positive interactions. By intersecting results from association patterns and experimental trials, future studies will be directed to test the strength of these microbial associations and predict the outcome of community alterations driven by diet.ImportanceThe gut microbiota is a complex community of interacting bacteria. Predicting patterns of co-occurrence among microbes can help understanding key ecological aspects driving community structure, maintenance and dynamics. Here we showed a powerful application of co-occurrence networks to explore gut bacteria interactions in a primary model system to study animal diversification, the cichlid fishes. Taking advantage of the large scale of phylogeographical and ecological diversity of this fish family, we built gut microbiota networks from distinct lake and continental fish assemblages and performed extensive comparative analyses to retrieve conserved and trait-specific patterns of bacteria associations. Our results identified network features that were independent from the fish biogeography and that indicated an important host selection effect on gut community assembling. Focusing on a single lake assemblage, and therefore excluding the major geographical effect, we observed that the gut microbiota structure dramatically shifted from carnivore to herbivore fishes, with a substantial increase in the number and complexity of microbial interactions.
ExtensiveIn SilicoAnalysis of the Functional and Structural Consequences of SNPs in HumanARXGene
Authors: Mustafa Mujahed I. , Murshed Naseem S. , Abdelmoneim Abdelrahman H. , Makhawi Abdelrafie M. 
Published: May 2020

AbstractEarly infantile epileptic encephalopathy 1 (EIEE1) is a rare but devastating neurologic disorder that displays concomitant cognitive and motor impairment, and is often presented in the first months of life with severe intellectual disability. The objective of this study is to classify the most deleterious nsSNPs inARXgene that may cause EIEE1 disease. Despite the reported association ofARXgene mutations with vulnerability to several neurologic condition, there is lack of in silico analysis on the functional and structural impacts of single nucleotide polymorphisms (SNPs) of theARXat protein level. Therefore, the pathogenic nsSNPs in the humanARXobtained from NCBI were analyzed for their functional and structural impact using bioinformatics tools like SIFT, Polyphen, PROVEAN, I-Mutant, and MUPro. The effects of the mutations on tertiary structure of the human ARX protein were predicted using RaptorX and visualized by UCSF Chimera while STRING was used to investigate its protein–protein interaction. Our extensivein silicoanalysis revealed 11 mutations that will significantly alter the structure of human ARX protein; that may disturb the domain which will affect the function of the protein. Extensivein silicoanalysis of the functional and structural consequences of SNPs in human ARX gene revealed 11 mutations (L535Q, R528S, R380L, V374D, L343Q, T333N, T333S, R332H, R330H, G34R and L33P) that may cause EIEE1.Therefore, can be used as diagnostic markers for EIEE1.