bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conductivity of the phloem inMangifera indicaL.
Authors: Miguel Barceló-Anguiano , Hormaza José I , Losada Juan M 
Published: January 2021

ABSTRACTMangifera indicais the fifth most consumed fruit worldwide, and the most important in tropical regions, but its anatomy is quite unexplored. Previous studies examined the effect of chemicals on the xylem structure in the stems of mango, but the anatomy of the phloem has remained elusive, leaving the long distance transport of photo assimilates understudied.In this work, we used a combination of fluorescence and electron microscopy to evaluate in detail the structure of the sieve tube elements composing the phloem tissue in the tapering branches of mango trees. We then used this information to better understand the hydraulic conductivity of the sieve tubes following current models of fluid transport in trees.Our results revealed that the anatomy of the phloem in the stems changes from current year branches, where it was protected by pericyclic fibers, to older ones, where the lack of fibers was concomitant with laticiferous canals embedded in the phloem tissue. Callose was present in the sieve plates, but also in the walls of the phloem conduits, making them discernible from other phloem cells in fresh sections. A scaling geometry of the sieve tube elements, including the number of sieve areas and the pore size across tapering branches resulted in an exponential conductivity from current year branches to the base of the tree.Our measurements of the phloem in mango fit with measurements of the phloem architecture in the stems of forest woody species, and imply that, despite agronomic pruning practices, the sieve conduits of the phloem scale with the tapering branches. As a result, the pipe model theory applied to the continuous tubing system of the phloem appears as a good approach to understand the “long distance” hydraulic transport of photoassimilates in fruit trees.
Evolution of phenotypic variance provides insights into the genetic basis of adaptation
Authors: Lai Wei-Yun , Nolte Viola , Jakšić Ana Marija , Schlötterer Christian 
Published: January 2021

AbstractMost traits are polygenic and the contributing loci can be identified by GWAS. Their adaptive architecture is, however, difficult to characterize. Here, we propose to study the adaptive architecture of traits by monitoring the evolution of their phenotypic variance during adaptation to a new environment in well-defined laboratory conditions. Extensive computer simulations show that the evolution of phenotypic variance in a replicated experimental evolution setting can distinguish between oligogenic and polygenic adaptive architectures. We compared gene expression variance in maleDrosophila simulansbefore and after 100 generations of adaptation to a novel hot environment. The variance change in gene expression was indistinguishable for genes with and without a significant change in mean expression after 100 generations of evolution. We suggest that a majority of adaptive gene expression evolution can be explained by a polygenic architecture. We propose that tracking the evolution of phenotypic variance across generations can provide an approach to characterize the adaptive architecture.Significant StatementIt is widely accepted that most complex traits have a polygenic basis. Nevertheless, it is difficult to predict which of these loci are responding to selection when a population is exposed to a new selection regime. To address this situation, we propose to infer the adaptive architecture for traits by tracking the evolution of their phenotypic variance during adaptation to a new environment. As a case study, we analyze the evolution of gene expression variance in outbredDrosophila simulanspopulations adapting to a new temperature regime to infer the genetic architecture of adaptive gene expression evolution. We suggested that the adaptive gene expression evolution is better explained by a polygenic architecture.
H2A.B is a cancer/testis factor involved in activation of ribosome biogenesis in Hodgkin Lymphoma
Authors: Jiang Xuanzhao , Wen Jiayu , Paver Elizabeth , Yu-Huan Wu , Sun Gege , Bullman Amanda , Dahlstrom Jane E. , Tremethick David J. , Soboleva Tatiana A. 
Published: January 2021

ABSTRACTTestis-specific regulators of chromatin function are commonly ectopically expressed in human cancers, but their roles are poorly understood. Examination of 81 primary Hodgkin Lymphoma (HL) samples showed that the ectopic expression of the eutherian testis-specific histone variant H2A.B is an inherent feature of HL. In experiments using two HL-derived cell lines derived from different subtypes of HL, H2A.B knockdown inhibited cell proliferation. H2A.B was enriched in both the nucleoli of these HL cell lines and primary HL samples. We found that H2A.B enhanced ribosomal DNA (rDNA) transcription, was enriched at the rDNA promoter and transcribed regions, and interacted with RNA Pol I. Depletion of H2A.B caused the loss of RNA Pol I from rDNA chromatin. Remarkably, H2A.B was also required for high levels of ribosomal protein gene expression being located at the transcriptional start site and within the gene body. H2A.B knockdown reduced gene body chromatin accessibility of active RNA Pol II genes concurrent with a decrease in transcription. Taken together, our data show that in HL H2A.B has acquired a new function, the ability to increase ribosome biogenesis.
Significance of linkage disequilibrium and epistasis on the genetic variances and covariance between relatives in non-inbred and inbred populations
Authors: Viana José Marcelo Soriano , Garcia Antonio Augusto Franco 
Published: January 2021

AbstractBecause no feasible theoretical model can depict the complexity of phenotype development from a genotype, the joint significance of linkage disequilibrium (LD), epistasis, and inbreeding on the genetic variances remains unclear. The objective of this investigation was to assess the impact of LD and epistasis on the genetic variances and covariances between relatives in non-inbred and inbred populations using simulated data. We provided the theoretical background and simulated grain yield assuming 400 genes in 10 chromosomes of 200 and 50 cM. We generated five populations with low to high LD levels, assuming 10 generations of random cross and selfing. The analysis of the parametric LD in the populations shows that the LD level depends mainly on the gene density. The significance of the LD level is impressive on the magnitude of the genotypic and additive variances, which is the most important component of the genotypic variance, regardless of the LD level and the degree of inbreeding. Regardless of the type of epistasis, the ratio epistatic variance/genotypic variance is proportional to the percentage of the epistatic genes. For the epistatic variances, except for duplicate epistasis and dominant and recessive epistasis, with 100% of epistatic genes, their magnitudes are much lower than the magnitude of the additive variance. The additive x additive variance is the most important epistatic variance. Our results explain why LD for genes and relationship information are key factors affecting the genomic prediction accuracy of complex traits and the efficacy of association studies.
Recovering Single-cell Heterogeneity Through Information-based Dimensionality Reduction
Authors: DeMeo Benjamin , Berger Bonnie 
Published: January 2021

AbstractDimensionality reduction is crucial to summarizing the complex transcriptomic landscape of single cell datasets for downstream analyses. However, current dimensionality reduction approaches favor large cellular populations defined by many genes, at the expense of smaller and more subtly-defined populations. Here, we present surprisal component analysis (SCA), a technique that leverages the information-theoretic notion of surprisal for dimensionality reduction, and demonstrate its ability to improve the representation of clinically important populations that are indistinguishable using existing pipelines. For example, in cytotoxic T-cell data, SCA cleanly separates the gamma-delta and MAIT cell subpopulations, which are not detectable via PCA, ICA, scVI, or a wide array of specialized rare cell recovery tools. We also show that, when used instead of PCA, SCA improves downstream imputation to more accurately restore mRNA dropouts and recover important gene-gene relationships. SCA’s information-theoretic paradigm opens the door to more meaningful signal extraction, with broad applications to the study of complex biological tissues in health and disease.
A scalable pipeline for local ancestry inference using tens of thousands of reference haplotypes
Authors: Durand Eric Y. , Do Chuong B. , Wilton Peter R. , Mountain Joanna L. , Auton Adam , Poznik G. David , Macpherson J. Michael 
Published: January 2021

AbstractAncestry deconvolution is the task of identifying the ancestral origins of chromosomal segments of admixed individuals. It has important applications, from mapping disease genes to identifying loci potentially under natural selection. However, most existing methods are limited to a small number of ancestral populations and are unsuitable for large-scale applications.In this article, we describe Ancestry Composition, a modular pipeline for accurate and efficient ancestry deconvolution. In the first stage, a string-kernel support-vector-machines classifier assigns provisional ancestry labels to short statistically phased genomic segments. In the second stage, an autoregressive pair hidden Markov model corrects phasing errors, smooths local ancestry estimates, and computes confidence scores.Using publicly available datasets and more than 12,000 individuals from the customer database of the personal genetics company, 23andMe, Inc., we have constructed a reference panel containing more than 14,000 unrelated individuals of unadmixed ancestry. We used principal components analysis (PCA) and uniform manifold approximation and projection (UMAP) to identify genetic clusters and define 45 distinct reference populations upon which to train our method. In cross-validation experiments, Ancestry Composition achieves high precision and recall.
AutoSmarTrace: Automated Chain Tracing and Flexibility Analysis of Biological Filaments
Authors: Schneider Mathew , Al-Shaer Alaa , Forde Nancy R. 
Published: January 2021

AbstractSingle-molecule imaging is widely used to determine statistical distributions of molecular properties. One such characteristic is the bending flexibility of biological filaments, which can be parameterized via the persistence length. Quantitative extraction of persistence length from images of individual filaments requires both the ability to trace the backbone of the chains in the images and sufficient chain statistics to accurately assess the persistence length. Chain tracing can be a tedious task, performed manually or using algorithms that require user input and/or supervision. Such interventions have the potential to introduce user-dependent bias into the chain selection and tracing. Here, we introduce a fully automated algorithm for chain tracing and determination of persistence lengths. Dubbed “AutoSmarTrace”, the algorithm is built off a neural network, trained via machine learning to identify filaments within images recorded using atomic force microscopy (AFM). We validate the performance of AutoSmarTrace on simulated images with widely varying levels of noise, demonstrating its ability to return persistence lengths in agreement with the ground truth. Persistence lengths returned from analysis of experimental images of collagen and DNA agree with previous values obtained from these images with different chain-tracing approaches. While trained on AFM-like images, the algorithm also shows promise to identify chains in other single-molecule imaging approaches, such as rotary shadowing electron microscopy and fluorescence imaging.Statement of SignificanceMachine learning presents powerful capabilities to the analysis of large data sets. Here, we apply this approach to the determination of bending flexibility – described through persistence length – from single-molecule images of biological filaments. We present AutoSmarTrace, a tool for automated tracing and analysis of chain flexibility. Built on a neural network trained via machine learning, we show that AutoSmarTrace can determine persistence lengths from AFM images of a variety of biological macromolecules including collagen and DNA. While trained on AFM-like images, the algorithm works well to identify filaments in other types of images. This technique can free researchers from tedious tracing of chains in images, removing user bias and standardizing determination of chain mechanical parameters from single-molecule conformational images.
Methylome dynamic upon proteasome inhibition by thePseudomonas syringaevirulence factor Syringolin A
Authors: Bonnet DMV , Grob S , Tirot L , Jullien PE 
Published: January 2021

AbstractDNA methylation is an important epigenetic mark required for proper gene expression and silencing of transposable elements. DNA methylation patterns can be modified by environmental factors such as pathogen infection, where modification of DNA methylation can be associated with plant resistance. To counter the plant defense pathways, pathogens produce effectors molecule, several of which act as proteasome inhibitors. Here we investigated the effect of proteasome inhibition by the bacterial virulence factor Syringolin A on genome-wide DNA methylation. We show that Syringolin A treatment results in an increase of DNA methylation at centromeric and pericentromeric regions ofArabidopsischromosomes. We identify several CHH DMRs that are enriched in the proximity of transcriptional start sites. Syringolin A treatment does not result in significant changes in small RNA composition. However, significant changes in genome transcriptional activity can be observed, including a strong upregulation of resistance genes that are located on chromosomal arms. We hypothesize that DNA methylation changes could be linked to the upregulation of some atypical members of thede novoDNA methylation pathway:AGO3,AGO9andDRM1. Our data suggests that modification of genome-wide DNA methylation resulting from an inhibition of the proteasome by bacterial effectors could be part of a epi-genomic arms race against pathogens.
CometChip Enables Parallel Analysis of Multiple DNA Repair Activities
Authors: Ge Jing , Ngo Le P. , Kaushal Simran , Tay Ian J. , Thadhani Elina , Kay Jennifer E. , Mazzucato Patrizia , Chow Danielle N. , Fessler Jessica L. , Weingeist David M. , Sobol Robert W. , Samson Leona D. , Floyd Scott R. , Engelward Bevin P. 
Published: January 2021

ABSTRACTDNA damage can be cytotoxic and mutagenic and is directly linked to aging, cancer, and heritable diseases. To counteract the deleterious effects of DNA damage, cells have evolved highly conserved DNA repair pathways. Many commonly used DNA repair assays are relatively low throughput and are limited to analysis of one protein or one pathway. Here, we have explored the capacity of the CometChip platform for parallel analysis of multiple DNA repair activities. Taking advantage of the versatility of the traditional comet assay and leveraging micropatterning techniques, the CometChip platform offers increased throughput and sensitivity compared to the traditional comet assay. By exposing cells to DNA damaging agents that create substrates of Base Excision Repair, Nucleotide Excision Repair, and Non-Homologous End Joining, we show that the CometChip is an effective method for assessing repair deficiencies in all three pathways. With these advanced applications of the CometChip platform, we expand the efficacy of the comet assay for precise, high-throughput, parallel analysis of multiple DNA repair activities.
Close-kin methods to estimate census size and effective population size
Authors: Waples Robin S. , Feutry Pierre 
Published: January 2021

AbstractThe last two decades have witnessed rapid developments and increasing interest in use of (1) genetic methods to estimate effective population size (Ne) and (2) close-kin mark-recapture (CKMR) methods to estimate abundance based on the incidence of close relatives. WhereasNeestimation methods have been applied to a wide range of taxa, all CKMR applications to date have been for aquatic species. These two fields of inquiry have developed largely independently, and this is unfortunate because deeper insights can be gained by joint evaluation of eco-evolutionary processes. In this synthesis, we use simple analytical models and simulated pedigree data to illustrate how various factors (life-history traits; patterns of variation in individual reproductive success; experimental design; stochasticity; marker type) can affect performance of the estimators. We show that theNe/Nratio and the probability of a close-kin match both depend on a vector of parental weights that specify relative probabilities that different individuals will produce offspring. Although age-specific vital rates are central to both methodologies, for CKMR they can potentially bias abundance estimates unless properly accounted for, whereas they represent the signals of genetic drift thatNeestimation methods depend upon. CoordinatingNeand CKMR estimation methods using the same or overlapping datasets would facilitate joint evaluation of both the ecological and evolutionary consequences of abundance.
Impact of sterilization on a conjugated polymer based bioelectronic patch
Authors: Yan Yihan , Travaglini Lorenzo , Lau Kieran , Rnjak-Kovacina Jelena , Eslami Minoo , Yang Shaohua , Lauto Antonio , Officer David , Mawad Damia 
Published: January 2021

ABSTRACTTranslation into the clinic of organic bioelectronic devices having conjugated polymers as the active material will hinge on their long-term operationin vivo. This will require the device to be subject to clinically approved sterilization techniques without a deterioration in its physical and electronic properties. To date, there remains a gap in the literature addressing the impact of this critical pre-operative procedure on the properties of conjugated polymers. This study aims to address this gap by assessing the physical and electronic properties of a sterilized porous bioelectronic patch having polyaniline as the conjugated polymer. The patch was sterilized by autoclave, ethylene oxide and gamma (γ-) irradiation at 15, 25, and 50 kGy doses. Autoclaving resulted in cracking and macroscopic degradation of the patch, while patches sterilized by γ-irradiation at 50 kGy exhibited reduced mechanical and electronic properties, attributed to chain scission and non-uniform crosslinking caused by the high dose irradiation. Ethylene oxide and γ-irradiation at 15 and 25 kGy sterilization appeared to be the most effective at maintaining the mechanical and electronic properties of the patch, as well as inducing a minimal immune response as revealed by a receding fibrotic capsule after 4 weeks implantation. Our findings pave the way towards closing the gap for the translation of organic bioelectronic devices from acute to long-termin vivomodels.
Increased elastase sensitivity and decreased intramolecular interactions in the more transmissible SARS-CoV-2 variants’ spike protein: Analysis of the new UK and SA SARS-CoV-2 variants
Authors: Pokhrel S. , Lee L. , Kraemer B.R. , Samardzic K. , Mochly-Rosen D. 
Published: January 2021

AbstractTwo SARS-CoV-2 variants showing increased transmissibility relative to the Wuhan virus have recently been identified. Although neither variant causes more severe illness or increased risk of death, the faster spread of the virus is a major threat. Using computational tools, we found that the new SARS-CoV-2 variants may acquire an increased transmissibility by increasing the propensity of its spike protein to expose the receptor binding domain. This information leads to the identification of potential treatments to avert the imminent threat of these more transmittable SARS-CoV-2 variants.TeaserThe more infective SARS-CoV-2 variants may expose its Achilles Heel – an opportunity to reduce their spreading.
Thermodynamic and Kinetic Characterization of Protein Conformational Dynamics within a Riemannian Framework
Authors: Goolsby Curtis , Fakharzadeh Ashkan , Moradi Mahmoud 
Published: January 2021

AbstractWe have formulated a Riemannian framework for describing the geometry of collective variable spaces of biomolecules within the context of molecular dynamics (MD) simulations. The formalism provides a theoretical framework to develop enhanced sampling techniques, path-finding algorithms, and transition rate estimators consistent with a Riemannian treatment of the collective variable space, where the quantities of interest such as the potential of mean force (PMF) and minimum free energy path (MFEP) remain invariant under coordinate transformation. Specific algorithms within this framework are discussed such as the Riemannian umbrella sampling, the Riemannian string method, and a Riemannian-Bayesian estimator of free energy and diffusion constant, which can be used to estimate the transition rate along an MFEP.
A conserved machinery underlies the synthesis of a chitosan layer in theCandidachlamydospore cell wall
Authors: Bemena Leo D. , Min Kyunghun , Konopka James B. , Neiman Aaron M. 
Published: January 2021

AbstractThe polysaccharide chitosan is found in the cell wall of specific cell types in a variety of fungal species where it contributes to stress resistance, or in pathogenic fungi, virulence. Under certain growth conditions, the pathogenic yeastCandida dubliniensisforms a cell type termed a chlamydosospore, which has an additional internal layer in its cell wall as compared to hyphal or yeast cell types. We report that this internal layer of the chlamydospore wall is rich in chitosan. The ascospore wall ofSaccharomyces cerevisiaealso has a distinct chitosan layer. As inS. cerevisiae, formation of the chitosan layer in theC. dubliniensis wall requires the chitin synthaseCHS3and the chitin deacetylaseCDA2. In addition, three lipid droplet-localized proteins Rrt8, Srt1, and Mum3, identified inS. cerevisiaeas important for chitosan layer assembly in the ascospore wall, are required for the formation of the chitosan layer of the chlamydospore wall inC. dubliniensis. These results reveal that a conserved machinery is required for the synthesis of a distinct chitosan layer in the walls of these two yeasts and may be generally important for incorporation of chitosan into fungal walls.ImportanceThe cell wall is the interface between the fungal cell and its environment and disruption of cell wall assembly is an effective strategy for antifungal therapies. Therefore, a detailed understanding of how cell walls form is critical to identify potential drug targets and develop therapeutic strategies. This work shows that a set of genes required for assembly of a chitosan layer in the cell wall ofS. cerevisiaeis also necessary for chitosan formation in a different cell type in a different yeast,C. dubliniensis. Because chitosan incorporation into the cell wall can be important for virulence, the conservation of this pathway suggests possible new targets for antifungals aimed at disrupting cell wall function.