bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accurate and Sensitive Quantitation of the Dynamic Heat Shock Proteome using Tandem Mass Tags
Authors: Storey Aaron J. , Hardman Rebecca E. , Byrum Stephanie D. , Mackintosh Samuel G. , Edmondson Rick D. , Wahls Wayne P. , Tackett Alan J. , Lewis Jeffrey A. 
Published: July 2019

AbstractCells respond to environmental perturbations and insults through modulating protein abundance and function. However, the majority of studies have focused on changes in RNA abundance because quantitative transcriptomics has historically been more facile than quantitative proteomics. Modern Orbitrap mass spectrometers now provide sensitive and deep proteome coverage, allowing direct, global quantification of not only protein abundance, but also post-translational modifications (PTMs) that regulate protein activity. We implemented, and validated using the well-characterized heat shock response of budding yeast, a tandem mass tagging (TMT), triple-stage mass spectrometry (MS3) strategy to measure global changes in the proteome during the yeast heat shock response over nine timepoints. We report that basic pH, ultra-high performance liquid chromatography (UPLC) fractionation of tryptic peptides yields superfractions of minimal redundancy, a crucial requirement for deep coverage and quantification by subsequent LC-MS3. We quantified 2,275 proteins across 3 biological replicates, and found that differential expression peaked near 90 minutes following heat shock (with 868 differentially expressed proteins at 5% FDR). The sensitivity of the approach also allowed us to detect changes in the relative abundance of ubiquitination and phosphorylation PTMs over time. Remarkably, relative quantification of post-translationally modified peptides revealed striking evidence of regulation of the heat shock response by protein PTMs. These data demonstrate that the high precision of TMT-MS3enables peptide-level quantification of samples, which can reveal important regulation of protein abundance and regulatory PTMs under various experimental conditions.
An optimized competitive-aging method reveals gene-drug interactions underlying the chronological lifespan of Saccharomyces cerevisiae
Authors: Avelar-Rivas J. Abraham , Munguía-Figueroa Michelle , Juárez-Reyes Alejandro , Garay Erika , Campos Sergio E. , Shoresh Noam , DeLuna Alexander 
Published: July 2019

ABSTRACTThe chronological lifespan of budding yeast is a model of aging and age-related diseases. This paradigm has recently allowed genome-wide screening of genetic factors underlying post-mitotic viability in a simple unicellular system, which underscores its potential to provide a comprehensive view of the aging process. However, results from different large-scale studies show little overlap and typically lack quantitative resolution to derive interactions among different aging factors. We previously introduced a sensitive, parallelizable approach to measure the chronological-lifespan effects of gene deletions based on the competitive aging of fluorescence-labeled strains. Here, we present a thorough description of the method, including an improved multiple-regression model to estimate the association between death rates and fluorescent signals, which accounts for possible differences in growth rate and experimental batch effects. We illustrate the experimental procedure—from data acquisition to calculation of relative survivorship—for ten deletion strains with known lifespan phenotypes, which is achieved with high technical replicability. We apply our method to screen for gene-drug interactions in an array of yeast deletion strains, which reveals a functional link between protein glycosylation and lifespan extension by metformin. Competitive-aging screening coupled to multiple-regression modeling provides a powerful, straight-forward way to identify aging factors in yeast and their interactions with pharmacological interventions.
Decoding brain functional connectivity implicated in AD and MCI
Authors: Gupta Sukrit , Chan Yi Hao , Rajapakse Jagath C. ,   the Alzheimers Disease Neuroimaging Initiative
Published: July 2019

AbstractDeep neural networks have been demonstrated to extract high level features from neuroimaging data when classifying brain states. Identifying salient features characterizing brain states further refines the focus of clinicians and allows design of better diagnostic systems. We demonstrate this while performing classification of resting-state functional magnetic resonance imaging (fMRI) scans of patients suffering from Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We use a 5-layer feed-forward deep neural network (DNN) to derive relevance scores of input features and show that an empirically selected subset of features improves accuracy scores for patient classification. The common distinctive salient brain regions were in the uncus and medial temporal lobe which closely correspond with previous studies. The proposed methods have cross-modal applications with several neuropsychiatric disorders.
Temporal changes in genetic admixture are linked to heterozygosity and health diagnoses in humans
Authors: Mautz Brian S. , Hellwege Jacklyn N. , Li Chun , Xu Yaomin , Zhang Siwei , Denny Joshua C. , Roden Dan M. , McGregor Tracy L. , Velez Edwards Digna R. , Edwards Todd L. 
Published: July 2019

AbstractReproduction between individuals from different ancestral populations creates genetically admixed offspring. Admixture can have positive and negative impacts on individual health, feeding back to population health. Historical and forced migrations, and recent mobility, have brought formerly disparate populations of humans together. Here we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and health outcomes. We evaluated variation in ancestry over 100 birth years in 35,842 individuals from a genetic database linked to health records in a population in the Southeastern United States. Analysis of 2,678 ancestrally informative markers revealed increased admixture and heterozygosity for all clinically-defined race groups since 1990. Most groups also exhibited increasing long-range linkage disequilibrium over time. A phenome-wide association study of clinical outcomes detected protective associations with female reproductive disorders and increased risk for diseases with links to autoimmunity dysfunction. These mixed effects have important ramifications for human health.
HIV-1 Nef interacts with LMP7 to attenuate immunoproteasome formation and MHC-I antigen presentation
Authors: Yang Yang , Liu Weiyong , Hu Dan , Su Rui , Ji Man , Huang Yuqing , Shereen Muhammad Adnan , Xu Xiaodi , Luo Zhen , Zhang Qi , Liu Fang , Wu Kailang , Liu Yingle , Wu Jianguo 
Published: July 2019

AbstractProteasome is major protein degradation machinery and plays essential roles in diverse biological functions. Upon cytokine inductions, proteasome subunits β1, β2, and β5 are replaced by β1i/LMP2, β2i/MECL-1, and β5i/LMP7, leading to the formation of immunoproteasome. Immunoproteasome-degraded products are loaded onto the major histocompatibility complex class I (MHC-I) to regulate immune responses and induce cytotoxic-T-lymphocytes (CTLs). Human immunodeficiency virus type 1 (HIV-1) is the causal agent of acquired immunodeficiency syndrome (AIDS). HIV-1-specific CTLs represent critical immune responses to limit viral replication. HIV-1 negative regulatory factor (Nef) counteracts host immunity, especially the MHC-I/CTL. This study reveals a distinct mechanism by which Nef facilitates immune evasion through attenuating the functions of immunoproteasome and MHC-I. Nef interacts with LMP7 on the endoplasmic reticulum (ER) to down-regulate the incorporation of LMP7 into immunoproteasome, and thereby attenuating immunoproteasome formation. Moreover, Nef represses immunoproteasome protein degradation function, MHC-I trafficking, and antigen presentation activity.ImportanceUbiquitin-proteasome system (UPS) is essential for degradation of damaged proteins, which takes place in proteasome. Upon cytokine inductions, proteasome catalytic activities are replaced by distinct isoforms resulting in formation of immunoproteasome. Immunoproteasome generates peptides for MHC-I antigen presentation and plays important roles in immune responses. HIV-1 is the agent of AIDS, and HIV-1-specific CTLs represent immune responses to limit viral replication. This study reveals a distinct mechanism by which HIV-1 promotes immune evasion. Viral protein Nef interacts with immunoproteasome component LMP7 to attenuate immunoproteasome formation and protein degradation function, and repress MHC-I antigen presentation activity. Therefore, HIV-1 targets LMP7 to inhibit immunoproteasome activation and LMP7 may be used as a target for the development of anti-HIV-1/AIDS therapy.
VolcanoFinder: genomic scans for adaptive introgression
Authors: Setter Derek , Mousset Sylvain , Cheng Xiaoheng , Nielsen Rasmus , DeGiorgio Michael , Hermisson Joachim 
Published: July 2019

AbstractRecent research shows that introgression between closely-related species is an important source of adaptive alleles for a wide range of taxa. Typically, detection of adaptive introgression from genomic data relies on comparative analyses that require sequence data from both the recipient and the donor species. However, in many cases, the donor is unknown or the data is not currently available. Here, we introduce a genome-scan method—VolcanoFinder—to detect recent events of adaptive introgression using polymorphism data from the recipient species only.VolcanoFinderdetects adaptive introgression sweeps from the pattern of excess intermediate-frequency polymorphism they produce in the flanking region of the genome, a pattern which appears as a volcano-shape in pairwise genetic diversity.Using coalescent theory, we derive analytical predictions for these patterns. Based on these results, we develop a composite-likelihood test to detect signatures of adaptive introgression relative to the genomic background. Simulation results show thatVolcanoFinderhas high statistical power to detect these signatures, even for older sweeps and for soft sweeps initiated by multiple migrant haplotypes. Finally, we implementVolcanoFinderto detect archaic introgression in European and sub-Saharan African human populations, and uncovered interesting candidates in both populations, such asTSHRin Europeans andTCHH-RPTNin Africans. We discuss their biological implications and provide guidelines for identifying and circumventing artifactual signals during empirical applications ofVolcanoFinder.Author summaryThe process by which beneficial alleles are introduced into a species from a closely-related species is termed adaptive introgression. We present an analytically-tractable model for the effects of adaptive introgression on non-adaptive genetic variation in the genomic region surrounding the beneficial allele. The result we describe is a characteristic volcano-shaped pattern of increased variability that arises around the positively-selected site, and we introduce an open-source methodVolcanoFinderto detect this signal in genomic data. Importantly,VolcanoFinderis a population-genetic likelihood-based approach, rather than a comparative-genomic approach, and can therefore probe genomic variation data from a single population for footprints of adaptive introgression, even froma prioriunknown and possibly extinct donor species.
Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software
Authors: Decamps Clementine , Privé Florian , Bacher Raphael , Jost Daniel , Waguet Arthur ,   HADACA consortium, Houseman Eugene Andres , Lurie Eugene , Lutsik Pavlo , Milosavljevic Aleksandar , Scherer Michael , Blum Michael G.B. , Richard Magali 
Published: July 2019

AbstractCell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking.Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell’s rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-treatment steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms’ performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R packagemedepir.
The place-cell representation of volumetric space in rats
Authors: Grieves Roddy M. , Jedidi-Ayoub Selim , Mishchanchuk Karyna , Liu Anyi , Renaudineau Sophie , Jeffery Kate J. 
Published: July 2019

AbstractPlace cells are spatially modulated neurons found in the hippocampus that underlie spatial memory and navigation: how these neurons represent 3D space is crucial for a full understanding of spatial cognition. We wirelessly recorded place cells in rats as they explored a cubic lattice climbing frame which could be aligned or tilted with respect to gravity. Place cells represented the entire volume of the mazes: their activity tended to be aligned with the maze axes, and when it was more difficult for the animals to move vertically the cells represented space less accurately and less stably. These results demonstrate that even surface-dwelling animals represent 3D space and suggests there is a fundamental relationship between environment structure, gravity, movement and spatial memory.
PDAC-ANN: an artificial neural network to predict Pancreatic Ductal Adenocarcinoma based on gene expression
Authors: Almeida Palloma Porto , Cardoso Cristina Padre , de Freitas Leandro Martins 
Published: July 2019

AbstractBackgroundAlthough the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment.MethodsWe performed a gene expression microarray meta-analysis of the tumor against healthy tissues in order to identify differentially expressed genes shared among all datasets, named core-genes (CG). We confirmed the pancreatic expressed proteins of the CG through The Human Protein Atlas. The five most expressed proteins in the tumor group were selected to train an artificial neural network to classify samples.ResultsThis microarray included 110 tumor and 77 healthy samples. We identified a CG composed of 60 genes, 58 upregulated and two downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that thirteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our artificial neural network, we used the five most expressed genes (KRT19, LAMC2, MELK, MET, TOP2A). The artificial neural network model (PDAC-ANN) classified the train samples with sensitivity of 0.95, specificity of 0.9, and f1-score of 0.93. The PDAC-ANN could classify the test samples with a sensitivity of 0.97, specificity of 0.88, and f1-score 0.94.ConclusionThe gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on mRNA expression. This software can be useful in the PDAC diagnosis.
Cell growth kinetics and accumulation of secondary metabolite of Bletilla striata Rchb.f. using cell suspension culture
Authors: Pan Yinchi , Xu Delin , Xiao Shiji , Chen Zhongjie , Sarsaiya Surendra , Zhang Shebo , ShangGuan Yanni , Liu Houbo , Li Lin , Chen Jishuang 
Published: July 2019

AbstractBletilla striata (Orchidaceae) is a well-recognized endangered medicinal plant due to inadequate natural reproduction with high market worth. To evaluate the cell growth kinetics and accumulation of secondary metabolites (SMs), the cell suspension culture is proved to be a valuable approach for acquiring the high yield of medicinal parts. An effective cell suspension culture for obtaining B. striata cell growth and its SMs was in vitro induction of callus from B. striata seeds. The cell growth kinetics and accumulation of SMs were analyzed using the mathematical model. Results cell growth kinetic model revealed that the growth curve of B. striata suspension cells was curved as sigmoid shape, indicating the changes of the growth curve of suspension cells. Improved Murashige and Skoog cell growth medium was the utmost favorable medium for B. striata callus formation with the highest cell growth during the stationary phase of cultivation period, the cell growth acceleration was started after 7 days and thereafter gradually decrease at 24 day and then reached to highest at 36 day of cultivation period in both dry weight and fresh weight. The coelonin concentration was peak during exponential growth stage and decreased afterward at the stationary phase in the cell suspension culture. The maximum content of coelonin (about 0.3323 mg/g cell dry weight) was observed on the 18th day of the cultivation cycle while the dactylorhin A and militarine reached highest at 24 day, and p-hydroxybenzyl alcohol at 39 day. This investigation also laid a foundation for multi-mathematical model to better describe the accumulation variation of SMs. The production of SMs had shown great specificity during cells growth and development. This research provided a well-organized way to more accumulation and production of SMs, on scale-up biosynthesis in B. striata cell suspension culture.
TMT-Opsins differentially modulate medaka brain function in a context-dependent manner
Authors: Fontinha Bruno M. , Zekoll Theresa , Al-Rawi Mariam , Gallach Miguel , Reithofer Florian , Barker Alison J. , Hofbauer Maximilian , Fischer Ruth M. , von Haeseler Arndt , Baier Herwig , Tessmar-Raible Kristin 
Published: July 2019

AbstractVertebrate behavior is strongly influenced by light. Light receptors, encoded by functional Opsin proteins, are present inside the vertebrate brain and peripheral tissues. This expression feature is present from fishes to human and appears to be particularly prominent in diurnal vertebrates. Despite their conserved widespread occurrence, the non-visual functions of Opsins are still largely enigmatic. This is even more apparent when considering the high number of Opsins. Teleosts possess around 40 Opsin genes, present from young developmental stages to adulthood. Many of these Opsins have been shown to function as light receptors. This raises the question, if this large number might mainly reflect functional redundancy or rather maximally enables teleosts to optimally use the complex light information present under water. We focus ontmt-opsin1bandtmt-opsin2, c-Opsins with ancestral-type sequence features, conserved across several vertebrate phyla, expressed with partly similar expression in non-rod, non-cone, non-RGCs brain tissues and a similar spectral sensitivity. The characterization of the single mutants revealed age- and light-dependent behavioral changes, as well as an impact on the levels of the preprohormonesst1band the voltage-gated sodium channel subunitscn12aa.The amount of day-time rest is affected independently of eyes, pineal and the circadian clock intmt-opsin1bmutants. We further focused on day-time behavior and the molecular changes intmt-opsin1b/2double mutants, and revealed that – despite their similar expression and spectral features– these Opsins interact in part non-additively. Specifically, double mutants complement molecular and (age-dependently) behavioral phenotypes observed in single mutants.Our work provides a starting point to disentangle the highly complex interactions of vertebrate non-visual Opsins, suggesting thattmt-opsin-expressing cells together with other visual and non-visual Opsins provide detailed light information to the organism for behavioral fine-tuning. This work also provides a stepping stone to unravel how vertebrate species with conserved Opsins, but in different ecological niches respond to similar light cues and how human generated artificial light might impact on behavioral processes in natural environments.
From Single Neurons to Behavior in the JellyfishAurelia aurita
Authors: Pallasdies Fabian , Goedeke Sven , Braun Wilhelm , Memmesheimer Raoul-Martin 
Published: July 2019

AbstractJellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here we develop the first neuronal network model for the nerve nets of jellyfish. Specifically, we focus on the moon jellyAurelia auritaand the control of its energy-eZcient swimming motion. The proposed single neuron model disentangles the contributions of different currents to a spike. The network model identifies factors ensuring non-pathological activity and suggests an optimization for the transmission of signals. After modeling the jellyfish’s muscle system and its bell in a hydrodynamic environment, we explore the swimming elicited by neural activity. We find that different delays between nerve net activations lead to well-controlled, differently directed movements. Our model bridges the scales from single neurons to behavior, allowing for a comprehensive understanding of jellyfish neural control.