bluuebunny/crossref_metadata_2025_split huggingface dataset:
Embryonic Stem Cell–Derived mmu-miR-291a-3p Inhibits Cellular Senescence in Human Dermal Fibroblasts Through the TGF-β Receptor 2 Pathway
Authors: Bae Yun-Ui , Son Youlim , Kim Chang-Hyun , Kim Kwang Seok , Hyun Se Hee , Woo Hyun Goo , Jee Byul A , Choi Jun-Hyuk , Sung Hoon-Ki , Choi Hyung-Chul , Park So Young , Bae Ju-Hyun , Doh Kyung-Oh , Kim Jae-Ryong 
Published: September 2018

Abstract
Senescent cells accumulate in various tissues over time and contribute to tissue dysfunction and aging-associated phenotypes. Accumulating evidence suggests that cellular senescence can be inhibited through pharmacological intervention, as well as through treatment with soluble factors derived from embryonic stem cells (ESCs). In an attempt to investigate the anti-senescence factors secreted by ESCs, we analyzed mouse ESC-derived extracellular microRNAs in conditioned medium via microRNA array analysis. We selected mmu-miR-291a-3p as a putative anti-senescence factor via bioinformatics analysis. We validated its inhibitory effects on replicative, Adriamycin-induced, and ionizing radiation–induced senescence in human dermal fibroblasts. Treatment of senescent cells with mmu-miR-291a-3p decreased senescence-associated β-galactosidase activity, enhanced proliferative potential, and reduced mRNA and protein expression of TGF-β receptor 2, p53, and p21. mmu-miR-291a-3p in conditioned medium was enclosed in ESC-derived exosomes and exosomes purified from ESC conditioned medium inhibited cellular senescence. The inhibitory effects of mmu-miR-291a-3p were mediated through the TGF-β receptor 2 signaling pathway. Hsa-miR-371a-3p and hsa-miR-520e, the human homologs of mmu-miR-291a-3p, showed similar anti-senescence activity. Furthermore, mmu-miR-291a-3p accelerated the excisional skin wound healing process in aged mice. Our results indicate that the ESC-derived mmu-miR-291a-3p is a novel candidate agent that can be utilized for cell-free therapeutic intervention against aging and aging-related diseases.
Critique and Alternativity in International Relations
Authors: Visoka Gëzim 
Published: September 2018

AbstractThis article critically interrogates the episteme of alternativity in international relations (IR) to rethink the purpose of critical knowledge in global politics. It questions what critical knowledge is for and whose purpose it serves. While alternativity is the very condition that has given rise to critical approaches, there is a deep-rooted division among critical scholars regarding the relationship between criticality and alternativity. This article argues that alternativity provides an opportunity for critical scholars to remain relevant without being affiliated with positivist logics of inquiry. In examining the potential of alternativity, the article explores three modes of alternativity in peace and conflict studies: critique-without-alternative, critique-as-alternative, and critique-with-alternative. It probes the merits and limits of the episteme of alternativity in generating new possibilities for advancing emancipatory interests and saving critical theory from losing its original transformative impetus. In the final part, the article explores future directions for rejuvenating the purpose of critique by exploring the nexus between criticality and alternativity on postparadigmatic and practical grounds.
Spatial Chromatin Architecture Alteration by Structural Variations in Human Genomes at Population Scale
Authors: Sadowski Michal , Kraft Agnieszka , Szalaj Przemyslaw , Wlasnowolski Michal , Tang Zhonghui , Ruan Yijun , Plewczynski Dariusz 
Published: September 2018

AbstractThis genome-wide study is focused on the impact of structural variants identified in individuals from 26 human populations onto three-dimensional structures of their genomes. We assess the tendency of structural variants to accumulate in spatially interacting genomic segments and design a high-resolution computational algorithm to model the 3D conformational changes resulted by structural variations. We show that differential gene transcription is closely linked to variation in chromatin interaction networks mediated by RNA polymerase II. We also demonstrate that CTCF-mediated interactions are well conserved across population, but enriched with disease-associated SNPs. Altogether, this study assesses the critical impact of structural variants on the higher order organization of chromatin folding and provides unique insight into the mechanisms regulating gene transcription at the population scale, among which the local arrangement of chromatin loops seems to be the leading one. It is the first insight into the variability of the human 3D genome at the population scale.
Stage specific classification of DEGs via statistical profiling and network analysis reveals potential biomarker associated with various stages of TB
Authors: Ishrat Romana 
Published: September 2018

AbstractBackgroundTuberculosis (TB) is a deadly transmissible disease that can infect almost any body-part of the host but is mostly infect the lungs. It is one of the top 10 causes of death worldwide. In the 30 high TB burden countries, 87% of new TB cases occurred in 2016. Seven countries: India, Indonesia, China, Philippines, Pakistan, Nigeria, and South Africa accounted for 64% of the new TB cases. To stop the infection and progression of the disease, early detection of TB is important. In our study, we used microarray data set and compared the gene expression profiles obtained from blood samples of patients with different datasets of Healthy control, Latent infection, Active TB and performed network-based analysis of DEGs to identify potential biomarker.ObjectivesWe want to observe the transition of genes from normal condition to different stages of the TB and identify, annotate those genes/pathways/processes that play key role in the progression of TB disease during its cyclic interventions in human body.ResultsWe identified 319 genes that are differentially expressed in various stages of TB (Normal to LTTB, Normal to Active TB and LTTB to active TB) and allocated to pathways from multiple databases which comprised of curated class of associated genes. These pathway’s importance was then evaluated according to the no. of DEGs present in the pathway and these genes show the broad spectrum of processes that take part in every state. In addition, we studied the regulatory networks of these classified genes, network analysis does consider the interactions between genes (specific for TB) or proteins provide us new facts about TB disease, which in turn can be used for potential biomarkers identification. We identified total 29 biomarkers from various comparison groups of TB stages in which 14 genes are over expressed as host responses against pathogen, but 15 genes are down regulated that means these genes has allowed the process of host defense to cease and give time to pathogen for its progression.ConclusionsThis study revealed that gene-expression profiles can be used to identify and classified the genes on stage specific pattern among normal, LTTB and active TB and network modules associated with various stages of TB were elucidated, which in turn provided a basis for the identification of potential pathways and key regulatory genes that may be involved in progression of TB disease.
Yeast Models Of Phosphomannomutase 2 Deficiency, A Congenital Disorder Of Glycosylation
Authors: Lao Jessica P. , DiPrimio Nina , Prangley Madeleine , Sam Feba S. , Mast Joshua D. , Perlstein Ethan O. 
Published: September 2018

AbstractPhosphomannomutase 2 Deficiency (PMM2-CDG) is the most common monogenic congenital disorder of glycosylation (CDG) affecting at least 800 patients globally. PMM2 orthologs are present in model organisms, including the budding yeastSaccharomyces cerevisiaegene SEC53. Here we describe conserved genotype-phenotype relationships across yeast and human patients between five PMM2 loss-of-function missense mutations and their orthologous SEC53 mutations. These alleles range in severity from folding defective (hypomorph) to dimerization defective (severe hypomorph) to catalytic dead (null). We included the first and second most common missense mutations – R141H, F119L respectively– and the most common compound heterozygote genotype – PMM2R141H/F119L– observed in PMM2-CDG patients. Each mutation described is expressed in haploid as well as homozygous and heterozygous diploid yeast cells at varying protein expression levels as either SEC53 protein variants or PMM2 protein variants. We developed a 384-well-plate, growth-based assay for use in a screen of the 2,560-compound Microsource Spectrum library of approved drugs, experimental drugs, tool compounds and natural products. We identified three compounds that suppress growth defects of SEC53 variants, F126L and V238M, based on the biochemical defect of the allele, protein abundance or ploidy. The rare PMM2 E139K protein variant is fully functional in yeast cells, suggesting that its pathogenicity in humans is due to the underlying DNA mutation that results in skipping of exon 5 and a nonfunctional truncated protein. Together, these results demonstrate that yeast models can be used to characterize known and novel PMM2 patient alleles in quantitative growth and enzymatic activity assays, and used as patient avatars for PMM2-CDG drug screens yielding compounds that could be rapidly cross-validated in zebrafish, rodent and human organoid models.
A seed resource for screening functionally redundant genes and isolation of new mutants impaired in CO2 and ABA responses
Authors: Hausera Felix , Ceciliatoa Paulo H. O. , Lin Yi-Chen , Guo DanDan , Gregerson JD , Abbasi Nazia , Youhanna Diana , Park Jiyoung , Dubeaux Guillaume , Shani Eilon , Poomchongkho Nusra , Schroeder Julian I. 
Published: September 2018

AbstractThe identification of homologous genes with functional overlap in forward genetic screens is severely limited. Here we report the generation of over 14,000 amiRNA-expressing plants that enable screens of the functionally redundant gene space in Arabidopsis. A protocol is developed here for isolating robust and reproducible amiRNA-mutants. Examples of validation approaches and essential controls are presented for two new amiRNA mutants that exhibit genetically redundant phenotypes and circumvent double mutant lethality. In a forward genetic screen for abscisic acid (ABA)-mediated inhibition of seed germination, amiRNAs that target combinations of known redundant ABA receptor and SnRK2 kinase genes were rapidly isolated, providing a strong proof of principle for this approach. A new ABA insensitive amiRNA line is isolated, which targets three genes encoding avirulence-induced gene2-like (AIG2) genes. A thermal imaging screen for plants with impaired stomatal opening in response to low CO2 exposure led here to isolation of a new amiRNA targeting two essential proteasomal subunits, PAB1 and PAB2. The seed library of 14,000 T2 amiRNA lines generated here provides a new platform for forward genetic screens and is being made available to the Arabidopsis Biological Resource Center (ABRC) and optimized procedures for amiRNA screening and controls are described.HighlightThe generation of over 14,000 amiRNA-expressing plants is reported that are being made publicly available enabling screens of redundant genes in Arabidopsis. Identification of known and new genes is reported.
Foraging as an evidence accumulation process
Authors: Davidson Jacob D. , Hady Ahmed El 
Published: September 2018

AbstractA canonical foraging task is the patch-leaving problem, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. Evidence accumulation models belong to the class of drift diffusion processes and have been used to model decision making in different contexts especially in cognitive and systems neuroscience. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different “strategies”, for example an increment-decrement or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To account for sub-optimal decisions, we introduce an energy-dependent utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch leaving decisions.
SuperCT: A supervised-learning-framework to enhance the characterization of single-cell transcriptomic profiles
Authors: Xie Peng , Gao Mingxuan , Wang Chunming , Noel Pawan , Yang Chaoyong , Hoff Daniel Von , Han Haiyong , Zhang Michael Q. , Lin Wei 
Published: September 2018

AbstractCharacterization of individual cell types is fundamental to the study of multicellular samples such as tumor tissues. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task. Currently, most of the scRNA-seq data analyses are commenced with unsupervised clustering of cells followed by visualization of clusters in a low-dimensional space. Clusters are often assigned to different cell types based on canonical markers. However, the efficiency of characterizing the known cell types in this way is low and limited by the investigator[s] knowledge. In this study, we present a technical framework of training the expandable supervised-classifier in order to reveal the single-cell identities based on their RNA expression profiles. Using multiple scRNA-seq datasets we demonstrate the superior accuracy, robustness, compatibility and expandability of this new solution compared to the traditional methods. We use two examples of model upgrade to demonstrate how the projected evolution of the cell-type classifier is realized.
Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning Framework
Authors: Yang Jinyu , Hoppe Adam D. , Liu Bingqiang , Ma Qin 
Published: September 2018

ABSTRACTIdentification of transcription factor binding sites (TFBSs) and cis-regulatory motifs (motifs for short) from genomics datasets, provides a powerful view of the rules governing the interactions between TFs and DNA. Existing motif prediction methods however, are limited by high false positive rates in TFBSs identification, contributions from non-sequence-specific binding, and complex and indirect binding mechanisms. High throughput next-generation sequencing data provides unprecedented opportunities to overcome these difficulties, as it provides multiple whole-genome scale measurements of TF binding information. Uncovering this information brings new computational and modeling challenges in high-dimensional data mining and heterogeneous data integration. To improve TFBS identification and novel motifs prediction accuracy in the human genome, we developed an advanced computational technique based on deep learning (DL) and high-performance computing, named DESSO. DESSO utilizes deep neural network and binomial distribution to optimize the motif prediction. Our results showed that DESSO outperformed existing tools in predicting distinct motifs from the 690 in vivo ENCODE ChIP-Sequencing (ChIP-Seq) datasets for 161 human TFs in 91 cell lines. We also found that protein-protein interactions (PPIs) are prevalent among human TFs, and a total of 61 potential tethering binding were identified among the 100 TFs in the K562 cell line. To further expand DESSO’s deep-learning capabilities, we included DNA shape features and found that (i) shape information has a strong predictive power for TF-DNA binding specificity; and (ii) it aided in identification of the shape motifs recognized by human TFs which in turn contributed to the interpretation of TF-DNA binding in the absence of sequence recognition. DESSO and the analyses it enabled will continue to improve our understanding of how gene expression is controlled by TFs and the complexities of DNA binding. The source code and the predicted motifs and TFBSs from the 690 ENCODE TF ChIP-Seq datasets are freely available at the DESSO web server: http://bmbl.sdstate.edu/DESSO.
Ultra-high throughput multiplexing and sequencing of >500 bp amplicon regions on the Illumina HiSeq 2500 platform
Authors: Holm Johanna B. , Humphrys Michael S. , Robinson Courtney K. , Settles Matthew L. , Ott Sandra , Fu Li , Yang Hongqiu , Gajer Pawel , He Xin , McComb Elias , Gravitt Patti E , Ghanem Khalil G. , Brotman Rebecca M. , Ravel Jacques 
Published: September 2018

AbstractAmplification, sequencing and analysis of the 16S rRNA gene affords characterization of microbial community composition. As this tool has become more popular and amplicon-sequencing applications have grown in the total number of samples, growth in sample multiplexing is becoming necessary while maintaining high sequence quality and sequencing depth. Here, modifications to the Illumina HiSeq 2500 platform are described which produce greater multiplexing capabilities and 300 bp paired-end reads of higher quality than produced by the current Illumina MiSeq platform. To improve the feasibility and flexibility of this method, a 2-Step PCR amplification protocol is also described that allows for targeting of different amplicon regions, thus improving amplification success from low bacterial bioburden samples.ImportanceAmplicon sequencing has become a popular and widespread tool for surveying microbial communities. Lower overall costs associated with high throughput sequencing have made it a widely-adopted approach, especially for projects which necessitate sample multiplexing to eliminate batch effect and reduced time to acquire data. The method for amplicon sequencing on the Illumina HiSeq 2500 platform described here provides improved multiplexing capabilities while simultaneously producing greater quality sequence data and lower per sample cost relative to the Illumina MiSeq platform, without sacrificing amplicon length. To make this method more flexible to various amplicon targeted regions as well as improve amplification from low biomass samples, we also present and validate a 2-Step PCR library preparation method.
Two decades of suspect evidence for adaptive DNA-sequence evolution - Failure in consistent detection of positive selection
Authors: He Ziwen , Chen Qipian , Yang Hao , Chen Qingjian , Shi Suhua , Wu Chung-I 
Published: September 2018

AbstractA recent study suggests that the evidence of adaptive DNA sequence evolution accumulated in the last 20 years may be suspect1. The suspicion thus calls for a re-examination of the reported evidence. The two main lines of evidence are from the McDonald-Kreitman (MK) test, which compares divergence and polymorphism data, and the PAML test, which analyzes multi-species divergence data. Here, we apply these two tests concurrently on the genomic data ofDrosophilaandArabidopsis. To our surprise, the >100 genes identified by the two tests do not overlap beyond random expectations. The results could mean i) high false positives by either test or ii) high false-negatives by both tests due to low powers. To rule out the latter, we merge every 20 - 30 genes into a “supergene”. At the supergene level, the power of detection is high, with 8% - 56% yielding adaptive signals. Nevertheless, the calls still do not overlap. Since it is unlikely that one test is largely correct and the other is mostly wrong (see Discussion), the total evidence of adaptive DNA sequence evolution should be deemed unreliable. As suggested by Chenet al.1, the reported evidence for positive selection may in fact be signals of fluctuating negative selection, which are handled differently by the two tests. Possible paths forward on this central evolutionary issue are discussed.
Determination of host cell proteins constituting the molecular microenvironment of coronavirus replicase complexes by proximity-labeling
Authors: Philip V’kovski , Markus Gerber , Jenna Kelly , Stephanie Pfaender , Nadine Ebert , Sophie Braga Lagache , Cedric Simillion , Jasmine Portmann , Hanspeter Stalder , Véronique Gaschen , Remy Bruggmann , Michael Stoffel , Manfred Heller , Ronald Dijkman , Volker Thiel 
Published: September 2018

AbstractPositive-sense RNA viruses hijack intracellular membranes that provide niches for viral RNA synthesis and a platform for interactions with host proteins. However, little is known about host factors at the interface between replicase complexes and the host cytoplasm. We engineered a biotin ligase into a coronaviral replication/transcription complex (RTC) and identified >500 host proteins constituting the RTC microenvironment. siRNA-silencing of each RTC-proximal host factor demonstrated importance of vesicular trafficking pathways, ubiquitin-dependent and autophagy-related processes, and translation initiation factors. Notably, detection of translation initiation factors at the RTC was instrumental to visualize and demonstrate active translation proximal to replication complexes of several coronaviruses.Collectively, we establish a spatial link between viral RNA synthesis and diverse host factors of unprecedented breadth. Our data may serve as a paradigm for other positive-strand RNA viruses and provide a starting point for a comprehensive analysis of critical virus-host interactions that represent targets for therapeutic intervention.