bluuebunny/crossref_metadata_2025_split huggingface dataset:
MYC Functions as a Switch for Natural Killer Cell-Mediated Immune Surveillance of Lymphoid Malignancies
Authors: Swaminathan Srividya , Heftdal Line D. , Liefwalker Daniel F. , Dhanasekaran Renumathy , Deutzmann Anja , Horton Crista , Mosley Adriane , Liebersbach Mariola , Maecker Holden T. , Felsher Dean W. 
Published: December 2018

AbstractThe MYC oncogene drives T and B lymphoid malignancies, including Burkitt’s lymphoma (BL) and Acute Lymphoblastic Leukemia (ALL). Using CyTOF, we demonstrate a systemic reduction in natural killer (NK) cell-mediated surveillance inSRα-tTA/Tet-O-MYCONmice bearing MYC-driven T-lymphomas, due to an arrest in NK cell maturation. Inactivation of lymphoma-intrinsic MYC releases the brakes on NK maturation restoring NK homeostasis. Lymphoma-intrinsic MYC arrests NK maturation by transcriptionally repressing STAT1/2 and secretion of Type I Interferons (IFNs). Treating T-lymphoma-bearing mice with Type I IFN improves survival by rescuing NK cell maturation. In MYC-driven BL patients, low expression of both STAT1 and STAT2 correlates significantly with the absence of activated NK cells and predicts unfavorable clinical outcomes. Adoptive transfer of mature NK cells is sufficient to delay both T-lymphoma growth and recurrence post MYC inactivation. Our studies thus provide a rationale for developing NK cell-based therapies to effectively treat MYC-driven lymphomas in the future.Abbreviations:CyTOFCytometry Time of FlightBLBurkitt’s LymphomaALLAcute Lymphoblastic LeukemiaDLBCLDiffuse Large B Cell LymphomaNK CellNatural Killer CellSTAT1/2Signal Transducer and Activator of Transcription 1/2IFNInterferonDCDendritic CellMYCv-myc avian myelocytomatosis viral oncogene homologPRECOGPrediction of Clinical Outcomes from Genomic ProfilesBLIBioluminescence ImagingTSSTranscriptional Start SiteHTLVHuman T-lymphotropic VirushMYChuman MYC
Imputation of single-cell gene expression with an autoencoder neural network
Authors: Badsha Md. Bahadur , Li Rui , Liu Boxiang , Li Yang I. , Xian Min , Banovich Nicholas E. , Fu Audrey Qiuyan 
Published: December 2018

ABSTRACTBackgroundSingle-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses.MethodsTo overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates.ResultsOn both simulated and real data, LATE and TRANSLATE outperform existing scRNA-seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene-gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU.ConclusionsWe demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.
Chlamydiapan-genomic analysis reveals balance between host adaptation and selective pressure to genome reduction
Authors: Sigalova Olga , Chaplin Andrei V , Bochkareva Olga O , Shelyakin Pavel , Filaretov Vsevolod A , Akkuratov Evgeny , Burskaya Valentina , Gelfand Mikhail S 
Published: December 2018

AbstractChlamydiaare ancient intracellular pathogens with reduced, though strikingly conserved genome. Despite their parasitic lifestyle and isolated intracellular environment, these bacteria managed to avoid accumulation of deleterious mutations leading to subsequent genome degradation characteristic for many parasitic bacteria.We report pan-genomic analysis of eleven species from genusChlamydiaincluding identification and functional annotation of orthologous genes, and characterization of gene gains, losses, and rearrangements. We demonstrate the overall genome stability of these bacteria as indicated by a large fraction of common genes with conserved genomic locations. On the other hand, extreme evolvability is confined to several paralogous gene families such as polymorphic membrane proteins and phospholipase D and likely is caused by the pressure from the host immune system.This combination of a large, conserved core genome and a small, evolvable periphery likely reflect the balance between the selective pressure towards genome reduction and the need to adapt to changing host environment.
Synapse propensity of human memory CD8 T cells confers competitive advantage over naïve counterparts
Authors: Mayya Viveka , Judokusumo Edward , Abu-Shah Enas , Neiswanger Willie , Kam Lance C , Dustin Michael L 
Published: December 2018

AbstractMemory T cells are endowed with multiple functional features that enable them to be more protective than naïve T cells against infectious threats. It is not known if memory cells have a higher synapse propensity, i.e. increased probability to form immature immunological synapses that then provide an entry into different modes of durable interaction with antigen presenting cells. Here we show that only human memory CD8 T cells have remarkably high synapse propensity compared to naïve counterparts. Such a dichotomy between naïve and memory cells is not observed within the human CD4 or murine CD8 T cell population. Increased surface expression of LFA1 contributes to the higher synapse propensity in human memory CD8 T cells. Finally, we show that higher synapse propensity in human memory CD8 T cells allows them to compete out naïve CD8 T cells from getting recruited to the response. This observation has implications for original antigenic sin and aging of the immune system in humans.
Quantitative interactions driveBotrytis cinereadisease outcome across the plant kingdom
Authors: Caseys Celine , Shi Gongjun , Soltis Nicole , Gwinner Raoni , Corwin Jason , Atwell Susanna , Kliebenstein Daniel 
Published: December 2018

AbstractBotrytis cinereais a polyphagous fungal pathogen that causes necrotic disease on more than a thousand known hosts widely spread across the plant kingdom. While it is known that quantitative resistance in the host and quantitative virulence in the pathogen largely mediate this pathosystem, how this pathogen interacts with the extensive host diversity is unknown. Does this pathogen have quantitative virulence efficiency on all hosts or individual solutions for each host? To address this question, we generated an infectivity matrix of 98 strains ofBotrytis cinereaon 90 genotypes representing eight host plants. This experimental infectivity matrix showed that the predominant sources of quantitative variation are between host species and among pathogen strains. Furthermore, the eight eudicot hosts interacted individually withBotrytis cinereastrains independently of the evolutionary relatedness between hosts. An additive quantitative model can explain the complexity of these interactions in which Botrytis host specificity and general virulence have distinct polygenic architectures.
TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits
Authors: Nagpal Sini , Meng Xiaoran , Epstein Michael P. , Tsoi Lam C. , Patrick Matthew , Gibson Greg , De Jager Philip L. , Bennett David A. , Wingo Aliza P. , Wingo Thomas S. , Yang Jingjing 
Published: December 2018

AbstractThe transcriptome-wide association studies (TWAS) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, we propose an improved Bayesian method that assumes a data-driven nonparametric prior to impute gene expression. Our method is general and flexible and includes both the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian method fitted transcriptomic imputation models for 2X number of genes with 1.7X average regression R2 over PrediXcan, thus improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred for modeling the complex genetic architecture of transcriptomes and is expected to enhance transcriptome-integrated genetic association studies. We implement our Bayesian approach in a convenient software tool “TIGAR” (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWAS using individual-level or summary-level GWAS data.
Estrogen receptor alpha controls gene expression via translational offsetting
Authors: Lorent Julie , Rebello Richard J. , Hoef Vincent van , Lawrence Mitchell G. , Szkop Krzysztof J. , Kusnadi Eric , Samreen Baila , Balanathan Preetika , Scharmann Karin , Takizawa Itsuhiro , Leidel Sebastian A. , Risbridger Gail P. , Topisirovic Ivan , Larsson Ola , Furic Luc 
Published: December 2018

AbstractEstrogen receptor alpha (ERα) activity is associated with increased cancer cell proliferation. Studies aiming to understand the impact of ERα on cancer-associated phenotypes have largely been limited to its transcriptional activity. Herein, we demonstrate that ERα coordinates its transcriptional output with selective modulation of mRNA translation. Importantly, translational perturbations caused by depletion of ERα largely manifest as “translational offsetting” of the transcriptome, whereby amounts of translated mRNA and protein levels are maintained constant despite changes in mRNA abundance. Transcripts whose levels, but not polysome-association, are reduced following ERα depletion lack features which limit translational efficiency including structured 5’UTRs and miRNA target sites. In contrast, mRNAs induced upon ERα depletion whose polysome-association remains unaltered are enriched in codons requiring U34-modified tRNAs for efficient decoding. Consistently, ERα regulates levels of U34-modification enzymes, whereas altered expression of U34-modification enzymes disrupts ERα dependent translational offsetting. Altogether, we unravel a hitherto unprecedented mechanism of ERα-dependent orchestration of transcriptional and translational programs, and highlight that translational offsetting may be a pervasive mechanism of proteome maintenance in hormone-dependent cancers.
Plants emit informative airborne sounds under stress
Authors: Khait I. , Lewin-Epstein O. , Sharon R. , Saban K. , Perelman R. , Boonman A. , Yovel Y. , Hadany L. 
Published: December 2018

AbstractStressed plants show altered phenotypes, including changes in color, smell, and shape. Yet, the possibility that plants emitairborne soundswhen stressed – similarly to many animals – has not been investigated. Here we show, to our knowledge for the first time, that stressed plants emit airborne sounds that can be recorded remotely, both in acoustic chambers and in greenhouses. We recorded ∼65 dBSPL ultrasonic sounds 10 cm from tomato and tobacco plants, implying that these sounds could be detected by some organisms from up to several meters away. We developed machine learning models that were capable of distinguishing between plant sounds and general noises, and identifying the condition of the plants – dry, cut, or intact – based solely on the emitted sounds. Our results suggest that animals, humans, and possibly even other plants, could use sounds emitted by a plant to gain information about the plant’s condition. More investigation on plant bioacoustics in general and on sound emission in plants in particular may open new avenues for understanding plants and their interactions with the environment, and it may also have a significant impact on agriculture.
Novel Dual Labeled Fluorescence Probe Based Assay to Measure the Telomere Length
Authors: Sethi Itty , Bhat Gh. Rasool , Kumar Rakesh , Rai Ekta , Sharma Swarkar 
Published: December 2018

ABSTRACTTelomeres are highly repetitive regions capping the chromosomes and composed of multiple units of hexa-nucleotides, TTAGGG, making their quantification difficult. Most of the methods developed to estimate telomeres are extensively cumbersome and expensive. The quantitative polymerase chain reaction (qPCR) based assay is relatively easy and cheaper method that applies SyBr Green dye chemistry to measure telomere length. As SyBr Green dye fluoresces after intercalation into the dsDNA, lack of differentiation between specific PCR target products and unspecific products is a limitation and it affects accuracy in quantitation of telomeres. To overcome the limitations of SyBr Green, we developed a dual labeled fluorescence probe based quantitative polymerase chain reaction (qPCR) to measure the telomere length. This robust, accurate and highly reproducible (R2=0.96) proprietary method (patent pending), yet cost effective and easy, utilizes a probe that targets specifically the telomeric DNA.
Deciphering copper coordination in the animal prion protein amyloidogenic domain
Authors: Salzano Giulia , Brennich Martha , Mancini Giordano , Tran Thanh Hoa , Legname Giuseppe , Angelo Paola D’ , Giachin Gabriele 
Published: December 2018

ABSTRACTPrions are pathological isoforms of the cellular prion protein (PrPC) responsible for transmissible spongiform encephalopathies (TSE). PrPC interacts with copper through unique octarepeat and non-octarepeat (non-OR) binding sites. Previous works on human PrPC suggest that copper binding to the non-OR region may have a role during prion conversion. The molecular details of copper coordination within the non-OR region are not well characterized. By means of small angle X-ray scattering (SAXS) and extended X-ray absorption fine structure (EXAFS) spectroscopy, we have investigated the Cu(II) structural effects on the protein folding and its coordination geometries when bound to the non-OR region of recombinant PrPC (recPrP) from animal species considered high or less resistant to TSE. As TSE-resistant model, we used ovine PrPC carrying the protective polymorphism at residues A136, R154 and R171 (OvPrP ARR); while as highly TSE-susceptible PrPC models we employed OvPrP with polymorphism V136, R154 and Q171 (OvPrP VRQ) and Bank vole recPrP (BvPrP). Our results reveal that Cu(II) affects the structural plasticity of the non-OR region leading to a more compacted conformation of recPrP. We also identified two Cu(II) coordinations in the non-OR region of these animal species. In type-1 coordination present in OvPrP ARR, Cu(II) is coordinated by four residues (S95, Q98, M109 and H111). Conversely, the type-2 coordination is present in OvPrP VRQ and BvPrP, where Cu(II) is coordinated by three residues (Q98, M109 and H111) and by one water molecule, making the non-OR region more flexible and open to the solvent. These changes in copper coordination in prion resistant and susceptible species provide new insights into the molecular mechanisms governing the resistance or susceptibility of certain species to TSE.
High throughput genotyping of structural variations in a complex plant genome using an original Affymetrix® Axiom® array
Authors: Mabire Clément , Duarte Jorge , Darracq Aude , Pirani Ali , Rimbert Hélène , Madur Delphine , Combes Valérie , Vitte Clémentine , Praud Sébastien , Rivière Nathalie , Joets Johann , Pichon Jean-Philippe , Nicolas Stéphane D. 
Published: December 2018

AbstractBackgroundInsertions/deletions (InDels) and more specifically presence/absence variations (PAVs) are pervasive in several species and have strong functional and phenotypic effect by removing or drastically modifying genes. Genotyping of such variants on large panels remains poorly addressed, while necessary for approaches such as association mapping or genomic selection.ResultsWe have developed, as a proof of concept, a new high-throughput and affordable approach to genotype InDels. We first identified 141,000 InDels by aligning reads from the B73 line against the genome of three temperate maize inbred lines (F2, PH207, and C103) and reciprocally. Next, we designed an Affymetrix® Axiom® array to target these InDels, with a combination of probes selected at breakpoint sites (13%) or within the InDel sequence, either at polymorphic (25%) or non-polymorphic sites (63%) sites. The final array design is composed of 662,772 probes and targets 105,927 InDels, including PAVs ranging from 35bp to 129kbp. After Affymetrix® quality control, we successfully genotyped 86,648 polymorphic InDels (82% of all InDels interrogated by the array) on 445 maize DNA samples with 422,369 probes. Genotyping InDels using this approach produced a highly reliable dataset, with low genotyping error (~3%), high call rate (~98%), and high reproducibility (>95%). This reliability can be further increased by combining genotyping of several probes calling the same InDels (<0.1% error rate and >99.9% of call rate for 5 probes). This “proof of concept” tool was used to estimate the kinship matrix between 362 maize lines with 57,824 polymorphic InDels. This InDels kinship matrix was highly correlated with kinship estimated using SNPs from Illumina 50K SNP arrays.ConclusionsWe efficiently genotyped thousands of small to large InDels on a sizeable number of individuals using a new Affymetrix®Axiom®array. This powerful approach opens the way to studying the contribution of InDels to trait variation and heterosis in maize. The approach is easily extendable to other species and should contribute to decipher the biological impact of InDels at a larger scale.
A pairwise maximum entropy model uncovers the white matter scaffold underlying emergent dynamics in intracranial EEG
Authors: Ashourvan Arian , Shah Preya , Pines Adam , Gu Shi , Lynn Christopher W. , Bassett Danielle S. , Davis Kathryn A. , Litt Brian 
Published: December 2018

ABSTRACTA major challenge in systems neuroscience is to understand how the brain’s structural architecture gives rise to its complex functional dynamics. Here, we address this challenge by examining the inter-ictal activity of five patients with medically refractory epilepsy during ∼ 15 hours of multi-channel intracranial recording. By constructing a pairwise maximum entropy model (MEM) of the observed neural dynamics, we seek to uncover the fundamental relationship between functional activity and its underlying structural substrate. Despite only incorporating the pairwise correlations in the observed neural activity, we find that the pairwise MEM robustly fits large-scale patterns of inter-ictal power dynamics across a wide range of frequency bands, notably displaying time-invariance and cross-frequency similarity. Furthermore, across all frequency bands, we demonstrate that the pairwise MEM accurately identifies the structural white matter connections between brain regions, outperforming other common model-free measures of functional connectivity. Together, our findings show that a simple pairwise MEM, which is explicitly ignorant of higher-order correlations between three or more brain regions, not only captures complex spatiotemporal patterns of neural activity across the frequency spectrum, but also suggests that the network of structural connections in the human brain is a plausible scaffold capable of supporting observed wide-band neural dynamics.
Revisiting the dimensionality of biological diversity
Authors: Nakamura Gabriel , Gonçalves Larissa Oliveira , Duarte Leandro 
Published: December 2018

AbstractBiodiversity can be represented by a variety of different dimensions (e.g. functional diversity, phylogenetic diversity, genetic diversity and taxonomic diversity). While the many different representations of biodiversity can lead to a more complete description of communities, they also lead to a degree of ‘fragmentation’ of the biodiversity concept. Developing a unified measure that integrates all the dimensions of biodiversity is a theoretical solution for this problem, however, it remains an operationally utopian task. Alternatively, quantifying the dimensionality of biodiversity is a way to integrate the different dimensions of biological diversity. This concept is explored in the present study. We define the dimensionality of diversity as the number of different diversity metrics needed to effectively describe biodiversity. We provide a solution that joins the two components of dimensionality (correlation and the variation of each diversity metric) in the same framework, allowing us to quantify dimensionality. We show, through a simulation study, that considering the correlation among dimensions with the variation of each dimension offers important information that is neglected when only correlation information (the component used in the current methodology to measure dimensionality) is considered. We demonstrate how to apply the proposed framework by investigating the dimensionality of South American small mammal communities (cricetids and marsupials). Our example demonstrates that the taxonomic dimension of diversity captures most of the spatial variation among these communities. We conclude by highlighting the greater understanding of dimensionality that is obtained through this method, as well as the insights it provides into the importance of each diversity metric to the whole biodiversity space, insights which can not be obtained through the current common method.
Prefrontal pathways provide top down control of memory for sequences of events
Authors: Jayachandran Maanasa , Linley Stephanie , Schlecht Maximilian , Mahler Stephen V. , Vertes Robert P. , Allen Timothy A. 
Published: December 2018

SummaryWe remember our lives as sequences of events, but it is unclear how these memories are controlled during retrieval. In rats, prelimbic cortex (PL) is positioned to influence sequence memory through extensive top down inputs to the nucleus reuniens of the thalamus (RE) and perirhinal cortex (PER), regions heavily interconnected with the hippocampus. Here, we tested the hypothesis that specific PL➔RE and PL➔PER projections regulate sequence memory retrieval using an hM4Di synaptic-silencing approach. First, we show that the suppression of PL activity impairs sequence memory. Second, we show that inhibiting PL➔RE and PL➔PER pathways effectively eliminated sequence memory. Last, we performed a sequential lag analysis showing that the PL➔RE pathway contributes to a working memory retrieval strategy, and the PL➔PER pathway contributes to a temporal context memory retrieval strategy. These results demonstrate that the PL➔RE and PL➔PER pathways serve as top down mechanisms that control sequence memory retrieval strategies.