bluuebunny/crossref_metadata_2025_split huggingface dataset:
Functional brain age prediction suggests accelerated aging in preclinical familial Alzheimer’s disease, irrespective of fibrillar amyloid-beta pathology
Authors: Gonneaud Julie , Baria Alex T. , Binette Alexa Pichet , Gordon Brian A. , Chhatwal Jasmeer P. , Cruchaga Carlos , Jucker Mathias , Levin Johannes , Salloway Stephen , Farlow Martin , Gauthier Serge , Benzinger T Tammie L.S. , Morris John C. , Bateman Randall J. , Breitner John C.S. , Poirier Judes , Vachon-Presseau Etienne , Villeneuve Sylvia ,   for the Alzheimer’s Disease Neuroimaging Initiative,   the Dominantly Inherited Alzheimer Network (DIAN),   the PREVENT-AD Research Group
Published: May 2020

AbstractWe aimed at developing a model able to predict brain aging from resting state functional connectivity (rs-fMRI) and assessing whether genetic risk/determinants of Alzheimer’s disease (AD) and amyloid (Aβ) pathology contributes to accelerated brain aging. Using data collected in 1340 cognitively unimpaired participants from 18 to 94 years old selected across multi-site cohorts, we showed that chronological age can be predicted across the whole lifespan from topological properties of graphs constructed from rs-fMRI. We subsequently used the difference between the model-predicted age and the chronological age in pre-symptomatic autosomal dominant AD (ADAD) mutation carriers and asymptomatic individuals at risk of sporadic AD and assessed the influence of genetics and Aβ pathology on brain age. Applying our predictive model in the context of preclinical AD revealed that the pre-symptomatic phase of ADAD is characterized by accelerated functional brain aging. This phenomenon is independent from, and might precede, detectable fibrillar Aβ deposition.
Elevated blood creatinine -a biomarker of renal function-associates with multiple metabolic perturbations in dogs
Authors: Ottka Claudia , Vapalahti Katariina , Määttä Ann-Marie , Huuskonen Nanna , Sarpanen Sinikka , Jalkanen Liisa , Lohi Hannes 
Published: May 2020

BACKGROUNDRenal diseases, such as chronic kidney disease (CKD) are common in dogs. While the kidneys have multiple important metabolic functions, the occurrence of metabolic disturbances in canine renal diseases has not been extensively studied.OBJECTIVESTo identify metabolic changes in blood samples exhibiting elevated blood creatinine, indicating reduced renal filtration.ANIMALSSamples consisted of clinical samples analysed by a 1H NMR-based metabolomics platform. The case group included 23 samples with creatinine > 125 μmol/l, and the control group 873 samples with creatinine within the reference interval.METHODSBiomarker association with elevated creatinine was evaluated utilizing three statistical approaches: Wilcoxon rank-sum test and logistic regression analysis (FDR-corrected p-values), and classification using random forest. Means of the biomarkers were compared to reference intervals. A heatmap and histograms visualized the differences.RESULTSThe levels of citrate, tyrosine, branched-chain amino acids, valine, leucine, albumin, linoleic acid % and the ratio of phenylalanine to tyrosine differed significantly both in the Wilcoxon test and logistic regression, acetate levels only in Wilcoxon test and docosapentaenoic acid % only in logistic regression (p <. 05). The ten most significant markers in random forest corresponded to the Wilcoxon test, supplemented with alanine.CONCLUSIONS AND CLINICAL IMPORTANCEThis study identified multiple metabolic changes associated with elevated blood creatinine, including prospective diagnostic markers and therapeutic targets. The NMR metabolomics test is a promising tool for improving diagnostics and management of canine renal diseases. Further research is needed to verify the association of these changes to the canine patient’s clinical state.
The changes in the p53 protein across the animal kingdom pointing to its involvement in longevity
Authors: Bartas Martin , Brázda Václav , Volná Adriana , Červeň Jiří , Pečinka Petr , Zawacka-Pankau Joanna 
Published: May 2020

AbstractRecently, the quest for the mythical fountain of youth has turned into extensive research programs aiming to extend the healthy lifespan in humans. Despite advances in our understanding of the aging process, the surprisingly extended lifespan and cancer resistance of some animal species remains unexplained. The p53 protein plays a crucial role in tumor suppression and in tissue homeostasis and aging. Long-lived, cancer-free African elephants, have 20 copies of TP53 gene including 19 retrogenes (38 alleles) which are partially active, whereas humans possess only one copy of TP53 and have an estimated cancer mortality of 11-25%. The mechanism through which p53 contributes to the resolution of the Peto’s paradox in the Animalia remains vague. Thus, in this work, we took advantage of the available datasets and inspected the p53 amino acid sequence of phylogenetically related organisms that show variations in the lifespan. We discovered new correlations between specific amino acid deviations in p53 and the lifespans across different animal species. We found that species with extended lifespan have certain characteristic amino acid substitutions in the p53 DNA binding domain that alter its function as depicted from the Phenotypic Annotation of p53 Mutations, using PROVEAN tool or SWISS-MODEL workflow. Our findings show a direct association between specific amino acid residues in p53 protein, changes in p53 functionality and the extended animal lifespan, and further highlight the importance of p53 protein in aging.
Integrative Single-cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Haematopoiesis
Authors: Maria Ranzoni Anna , Tangherloni Andrea , Berest Ivan , Riva Simone Giovanni , Myers Brynelle , Strzelecka Paulina M. , Xu Jiarui , Panada Elisa , Mohorianu Irina , Zaugg Judith B. , Cvejic Ana 
Published: May 2020

AbstractRegulation of haematopoiesis during human development remains poorly defined. Here, we applied single-cell (sc)RNA-Seq and scATAC-Seq analysis to over 8,000 human immunophenotypic blood cells from foetal liver and bone marrow. We inferred their differentiation trajectory and identified three highly proliferative oligopotent progenitor populations downstream from haematopoietic stem cell/multipotent progenitors (HSC/MPPs). Along this trajectory, we observed opposing patterns of chromatin accessibility and differentiation that coincided with dynamic changes in the activity of distinct lineage-specific transcription factors. Integrative analysis of chromatin accessibility and gene expression revealed extensive epigenetic but not transcriptional priming of HSC/MPPs prior to their lineage commitment. Finally, we refined and functionally validated the sorting strategy for the HSC/MPPs and achieved around 90% enrichment. Our study provides a useful framework for future investigation of human developmental haematopoiesis in the context of blood pathologies and regenerative medicine.
A recombineering pipeline to clone large and complex genes in Chlamydomonas
Authors: Emrich-Mills Tom , Yates Gary , Barrett James , Grouneva Irina , Lau Chun Sing , Walker Charlotte E , Kwok Tsz Kam , Davey John W , Johnson Matthew P , Mackinder Luke CM 
Published: May 2020

AbstractThe ability to clone genes has driven fundamental advances in cell and molecular biology, enabling researchers to introduce precise mutations, generate fluorescent protein fusions for localization and to confirm genetic causation by mutant complementation. Most gene cloning is PCR or DNA synthesis dependent, which can become costly and technically challenging as genes increase in size and particularly if they contain complex regions. This has been a long-standing challenge for theChlamydomonas reinhardtiiresearch community, with a high percentage of genes containing complex sequence structures, an average genomic GC content of 64% and gene expression requiring regular introns for stable transcription. Here we overcome these challenges via the development of a recombineering pipeline that enables the rapid parallel cloning of genes from a Chlamydomonas BAC collection. We show the method can successfully retrieve large and complex genes that PCR-based methods have previously failed to clone, including genes as large as 23 kilobases, thus making previously technically challenging genes to study now amenable to cloning. We initially applied the pipeline to 12 targets with a 92% cloning success rate. We then developed a high-throughput approach and targeted 191 genes relating to the Chlamydomonas CO2concentrating mechanism (CCM) with an overall cloning success rate of 77% that is independent of gene size. Localization of a subset of CCM targets has confirmed previous mass spectrometry data and identified new pyrenoid components. To expand the functionality of our system, we developed a series of localization vectors that enable complementation of Chlamydomonas Library Project mutants and enable protein tagging with a range of fluorophores. Vectors and detailed protocols are available to facilitate the easy adoption of this method by the Chlamydomonas research community. We envision that this technology will open up new possibilities in algal and plant research and be complementary to the Chlamydomonas mutant library.
Coding and regulatory variants affect serum protein levels and common disease
Authors: Emilsson Valur , Gudmundsdottir Valborg , Gudjonsson Alexander , Karim Mohd A , Ilkov Marjan , Staley James R. , Gudmundsson Elias F. , Jonsson Brynjolfur G. , Launer Lenore J. , Lindeman Jan H. , Morton Nicholas M. , Aspelund Thor , Lamb John R. , Jennings Lori L. , Gudnason Vilmundur 
Published: May 2020

AbstractCirculating proteins are prognostic for human outcomes including cancer, heart failure, brain trauma and brain amyloid plaque burden. A deep serum proteome survey recently revealed close associations of serum protein networks and common diseases. The present study reveals unprecedented number of individual serum proteins that overlap genetic signatures of diseases emanating from different tissues of the body. Here, 54,469 low-frequency and common exome-array variants were compared with 4782 protein measurements in the serum of 5343 individuals of the deeply annotated AGES Reykjavik cohort. Using a study-wide significant threshold, 2019 independent exome array variants affecting levels of 2135 serum proteins were identified. These variants overlapped genetic loci for hundreds of complex disease traits, emphasizing the emerging role for serum proteins as biomarkers of and potential causative agents of multiple diseases.
Impaired chloroplast positioning affects photosynthetic capacity and regulation of the central carbohydrate metabolism during cold acclimation
Authors: Kitashova Anastasia , Schneider Katja , Fürtauer Lisa , Schröder Laura , Scheibenbogen Tim , Fürtauer Siegfried , Nägele Thomas 
Published: May 2020

AbstractPhotosynthesis and carbohydrate metabolism of higher plants need to be tightly regulated to prevent tissue damage during environmental changes. The intracellular position of chloroplasts changes due to a changing light regime. Chloroplast avoidance and accumulation response under high and low light, respectively, are well known phenomena, and deficiency of chloroplast movement has been shown to result in photodamage and reduced biomass accumulation. Yet, effects of chloroplast positioning on underlying metabolic regulation are less well understood. Here, we analysed photosynthesis together with metabolites and enzyme activities of the central carbohydrate metabolism during cold acclimation of thechloroplast unusual positioning 1(chup1) mutant ofArabidopsis thaliana. We compared cold acclimation under ambient and low light and found that maximum quantum yield of PSII was significantly lower inchup1than in Col-0 under both conditions. Our findings indicated that net CO2assimilation inchup1is rather limited by biochemistry than by photochemistry. Further, cold-induced dynamics of sucrose phosphate synthase differed significantly between both genotypes. Together with a reduced rate of sucrose cycling derived from kinetic model simulations our study provides evidence for a central role of chloroplast positioning for photosynthetic and metabolic acclimation to low temperature.
Detailed temporal dissection of an enhancer cluster reveals two distinct roles for individual elements
Authors: Thomas Henry , Kotova Elena , Pilz Axel , Romeike Merrit , Lackner Andreas , Bock Christoph , Leeb Martin , Wysocka Joanna , Buecker Christa 
Published: May 2020

AbstractMany genes are regulated by multiple enhancers that often simultaneously activate their target gene. Yet, how individual enhancers collaborate to activate transcription is not well understood. Here, we dissect the functions and interdependencies of five enhancer elements that form a previously identified enhancer cluster and activate theFgf5locus during exit from naïve murine pluripotency. Four elements are located downstream of theFgf5gene and form a super-enhancer. Each of these elements contributes toFgf5induction at a distinct time point of differentiation. The fifth element is located in the first intron of theFgf5gene and contributes toFgf5expression at every time point by amplifying overallFgf5expression levels. This amplifier element strongly accumulates paused RNA Polymerase II but does not give rise to a matureFgf5mRNA. By transplanting the amplifier to a different genomic position, we demonstrate that it enriches for high levels of paused RNA Polymerase II autonomously. Based on our data, we propose a model for a mechanism by which RNA Polymerase II accumulation at a novel type of enhancer element, the amplifier, contributes to enhancer collaboration.
In Silico Trial to test COVID-19 candidate vaccines: a case study with UISS platform
Authors: Russo Giulia , Pennisi Marzio , Viceconti Marco , Pappalardo Francesco 
Published: May 2020

AbstractSARS-CoV-2 is a severe respiratory infection that infects humans. Its outburst entitled it as a pandemic emergence. To get a grip on this outbreak, specific preventive and therapeutic interventions are urgently needed. It must be said that, until now, there are no existing vaccines for coronaviruses. To promptly and rapidly respond to pandemic events, the application of in silico trials can be used for designing and testing medicines against SARS-CoV-2 and speed-up the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects. Here, we present an in silico platform that showed to be in very good agreement with the latest literature in predicting SARS-CoV-2 dynamics and related immune system host response. Moreover, it has been used to predict the outcome of one of the latest suggested approach to design an effective vaccine, based on monoclonal antibody. Universal Immune System Simulator (UISS) in silico platform is potentially ready to be used as an in silico trial platform to predict the outcome of vaccination strategy against SARS-CoV-2.
Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders
Authors: Malik Muhammad Ammar , Michoel Tom 
Published: May 2020

AbstractRandom effect models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data. In applications where some confounding factors are known, estimating simultaneously the contribution of known and latent variance components in random effect models is a challenge that has so far relied on numerical gradient-based optimizers to maximize the likelihood function. This is unsatisfactory because the resulting solution is poorly characterized and the efficiency of the method may be suboptimal. Here we prove analytically that maximum-likelihood latent variables can always be chosen orthogonal to the known confounding factors, in other words, that maximum-likelihood latent variables explain sample covariances not already explained by known factors. Based on this result we propose a restricted maximum-likelihood method which estimates the latent variables by maximizing the likelihood on the restricted subspace orthogonal to the known confounding factors, and show that this reduces to probabilistic PCA on that subspace. The method then estimates the variance-covariance parameters by maximizing the remaining terms in the likelihood function given the latent variables, using a newly derived analytic solution for this problem. Compared to gradient-based optimizers, our method attains greater or equal likelihood values, can be computed using standard matrix operations, results in latent factors that don’t overlap with any known factors, and has a runtime reduced by several orders of magnitude. Hence the restricted maximum-likelihood method facilitates the application of random effect modelling strategies for learning latent variance components to much larger gene expression datasets than possible with current methods.
Spatial heterogeneity of cell-matrix adhesive forces predicts human glioblastoma migration
Authors: Rezk Rasha , Jia Bill Zong , Wendler Astrid , Dimov Ivan , Watts Colin , Markaki Athina E , Franze Kristian , Kabla Alexandre J 
Published: May 2020

AbstractBackgroundGlioblastoma (GBM) is a highly aggressive incurable brain tumor. The main cause of mortality in GBM patients is the invasive rim of cells migrating away from the main tumor mass and invading healthy parts of the brain. Although motion is driven by forces, our current understanding of the physical factors involved in glioma infiltration remains limited. This study aims to investigate the adhesion properties within and between patients’ tumors on a cellular level and test whether these properties correlate with cell migration.MethodsNine tissue samples were taken from spatially separated sections during 5-aminolevulinic acid (5-ALA) fluorescence guided surgery. Navigated biopsy samples were collected from strongly fluorescent tumor cores, a weak fluorescent tumor rim, and non-fluorescent tumor margins. A microfluidics device was built to induce controlled shear forces to detach cells from monolayer cultures. Cells were cultured on low modulus polydimethylsiloxane representative of the stiffness of brain tissue. Cell migration and morphology were then obtained using time lapse microscopy.ResultsGBM cell populations from different tumor fractions of the same patient exhibited different migratory and adhesive behaviors. These differences were associated with sampling location and amount of 5-ALA fluorescence. Cells derived from weak- and non-fluorescent tumor tissue were smaller, adhered less well, and migrated quicker than cells derived from strongly fluorescent tumor mass.ConclusionGBM tumors are biomechanically heterogeneous. Selecting multiple populations and broad location sampling are therefore important to consider for drug testing.Key pointsGBM tumors are biomechanically heterogeneousGBM cell migration is inversely correlated with cell-matrix adhesion strength5-ALA fluorescence intensity during surgery correlates with the motility properties of GBM cellsImportance of the studyThis is the first study to compare single cell migration and cell-matrix adhesion strength of GBM, using cell lines derived from different tumors and from different regions within the same tumor. Not accounting for internal sampling location within each tumor obscures differences in cell morphology, motility and adhesion properties between patients. Peripherical and marginal tumor cells have different adhesion profiles and are highly migratory compared to those found in the core of the tumor. Aggressive regions of the tumor (highly motile) are linked to the spatial distribution of adhesion strength and are strongly associated with 5-ALA fluorescence intensity. Preclinical tests aimed at developing a treatment for GBM using anti-invasive drugs or adhesion inhibitors, would benefit from using cell lines derived from the tumor periphery (with low 5-ALA intensity) rather than cell lines derived from the tumor core.
Optimal forgetting: Semantic compression of episodic memories
Authors: Nagy David G. , Török Balázs , Orbán Gergő 
Published: May 2020

AbstractIt has extensively been documented that human memory exhibits a wide range of systematic distortions, which have been associated with resource constraints. Resource constraints on memory can be formalised in the normative framework of lossy compression, however traditional lossy compression algorithms result in qualitatively different distortions to those found in experiments with humans. We argue that the form of distortions is characteristic of relying on a generative model adapted to the environment for compression. We show that this semantic compression framework can provide a unifying explanation of a wide variety of memory phenomena. We harness recent advances in learning deep generative models, that yield powerful tools to approximate generative models of complex data. We use three datasets, chess games, natural text, and hand-drawn sketches, to demonstrate the effects of semantic compression on memory performance. Our model accounts for memory distortions related to domain expertise, gist-based distortions, contextual effects, and delayed recall.Author summaryHuman memory performs surprisingly poorly in many everyday tasks, which have been richly documented in laboratory experiments. While constraints on memory resources necessarily imply a loss of information, it is possible to do well or badlyin relationto available memory resources. In this paper we recruit information theory, which establishes how to optimally lose information based on prior and complete knowledge of environmental statistics. For this, we address two challenges. 1, The environmental statistics is not known for the brain, rather these have to be learned over time from limited observations. 2, Information theory does not specify how different distortions of original experiences should be penalised. In this paper we tackle these challenges by assuming that a latent variable generative model of the environment is maintained in semantic memory. We show that compression of experiences through a generative model gives rise to systematic distortions that qualitatively correspond to a diverse range of observations in the experimental literature.
Power and limits of selection genome scans on temporal data from a selfing population
Authors: Navascués Miguel , Becheler Arnaud , Gay Laurène , Ronfort Joëlle , Loridon Karine , Vitalis Renaud 
Published: May 2020

AbstractTracking genetic changes of populations through time allows a more direct study of the evolutionary processes acting on the population than a single contemporary sample. Several statistical methods have been developed to characterize the demography and selection from temporal population genetic data. However, these methods are usually developed under the assumption of outcrossing reproduction and might not be applicable when there is substantial selfing in the population. Here, we focus on a method to detect loci under selection based on a genome scan of temporal differentiation, adapting it to the particularities of selfing populations. Selfing reduces the effective recombination rate and can extend hitch-hiking effects to the whole genome, erasing any local signal of selection on a genome scan. Therefore, selfing is expected to reduce the power of the test. By means of simulations, we evaluate the performance of the method under scenarios of adaptation from new mutations or standing variation at different rates of selfing. We find that the detection of loci under selection in predominantly selfing populations remains challenging even with the adapted method. Still, selective sweeps from standing variation on predominantly selfing populations can leave some signal of selection around the selected site thanks to historical recombination before the sweep. Under this scenario, ancestral advantageous alleles at low frequency leave the strongest local signal, while new advantageous mutations leave no local footprint of the sweep.