bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ultrafast Sample Placement on Existing Trees (UShER) Empowers Real-Time Phylogenetics for the SARS-CoV-2 Pandemic
Authors: Turakhia Yatish , Thornlow Bryan , Hinrichs Angie S. , De Maio Nicola , Gozashti Landen , Lanfear Robert , Haussler David , Corbett-Detig Russell 
Published: September 2020

AbstractAs the SARS-CoV-2 virus spreads through human populations, the unprecedented accumulation of viral genome sequences is ushering a new era of “genomic contact tracing” – that is, using viral genome sequences to trace local transmission dynamics. However, because the viral phylogeny is already so large – and will undoubtedly grow many fold – placing new sequences onto the tree has emerged as a barrier to real-time genomic contact tracing. Here, we resolve this challenge by building an efficient, tree-based data structure encoding the inferred evolutionary history of the virus. We demonstrate that our approach improves the speed of phylogenetic placement of new samples and data visualization by orders of magnitude, making it possible to complete the placements under real-time constraints. Our method also provides the key ingredient for maintaining a fully-updated reference phylogeny. We make these tools available to the research community through the UCSC SARS-CoV-2 Genome Browser to enable rapid cross-referencing of information in new virus sequences with an ever-expanding array of molecular and structural biology data. The methods described here will empower research and genomic contact tracing for laboratories worldwide.Software AvailabilityUSHER is available to users through the UCSC Genome Browser athttps://genome.ucsc.edu/cgi-bin/hgPhyloPlace. The source code and detailed instructions on how to compile and run UShER are available fromhttps://github.com/yatisht/usher.
Maternal High Fiber Diet Protects Offspring Against Type 2 Diabetes
Authors: Toh Huishi , Thomson James A. , Jiang Peng 
Published: September 2020

AbstractPrevious studies have reported that maternal malnutrition is linked to increased risk of developing type 2 diabetes in adulthood. Although several diabetic risk factors associated with early life environment have been identified, protective factors remain elusive. Here, we modulate the early life environment using a maternal high fiber diet to investigate whether the offspring are protected from type 2 diabetes. We examined the interplay between early life environment (maternal diet) and later life environment (offspring diet) using a longitudinal study with 671 Nile rats. We found that exposure to a high fiber maternal diet prior to weaning significantly lowers the risk of diet-induced diabetes in the offspring. Interestingly, offspring consuming a high fiber diet after weaning do not get diet-induced diabetes, even when exposed to a diabetogenic maternal diet. Here, we provide the first evidence that the protective effect of high fiber can be transmitted to the offspring through a maternal diet, which has important implications in diabetes prevention.
Towards establishing Extracellular Vesicle-associated RNAs as biomarkers for HER2+ breast cancer
Authors: Hisey Colin L. , Tomek Petr , Nursalim Yohanes N.S. , Chamley Lawrence W. , Leung Euphemia 
Published: September 2020

AbstractExtracellular vesicles (EVs) are emerging as key players in breast cancer progression and hold immense promise as cancer biomarkers. However, difficulties in obtaining sufficient quantities of EVs for the identification of potential biomarkers hampers progress in this area. To circumvent this obstacle, we cultured BT-474 breast cancer cells in a two chambered bioreactor with CDM-HD serum replacement to significantly improve the yield of cancer cell-associated EVs and eliminate bovine EV contamination. Cancer-relevant mRNAsBIRC5(Survivin) andYBX1as well as long-noncoding RNAsHOTAIR, ZFAS1, andAGAP2-AS1were detected in BT-474 EVs by quantitative RT-PCR. Bioinformatics meta-analyses showed thatBIRC5andHOTAIRRNAs were substantially upregulated compared to non-tumour breast tissue, encouraging further studies to explore their usefulness as biomarkers in patient EV samples. We contend that this effective procedure for obtaining large amounts of cancer-specific EVs will accelerate discovery of EV-associated RNA biomarkers for detection of HER2+ breast cancer.
Skeletal muscle tissue secretes more extracellular vesicles than white adipose tissue and myofibers are a major source ex vivo but not in vivo
Authors: Estrada Andrea L. , Valenti Zackary , Hehn Gabriella , Allen Christopher P. , Kruh-Garcia Nicole A. , Lark Daniel S. 
Published: September 2020

AbstractCirculating extracellular vesicles (EVs) are biomarkers of and contributors to the etiology of disease. Skeletal muscle (SkM) and white adipose tissue (WAT) are the two largest organs by mass in humans and rodents but the relative contribution of EVs from these tissues is unknown. We hypothesized that SkM tissue secretes more EVs than WAT and that a dual fluorescent reporter mouse could be used to detect SkM myofiber-derived EVsin vivo. Human Protein Atlas data and directly measuring EV secretion in mouse SkM and WAT using anex vivotissue explant model confirmed that SkM tissue secretes more EVs than WAT. Differences in EV secretion between SkM and WAT were not due to SkM contraction but may be explained by differences in tissue metabolic capacity. A SkM myofiber-specific dual fluorescent reporter mouse was created. Spectral flow cytometry revealed that SkM myofibers are a major source of SkM tissue-derived EVsex vivobut few reach the circulationin vivo. Our findings demonstrate that SkM secretes more EVs than WAT and many come from SkM myofibers, but ourin vivodata indicate that EVs secreted by SkM myofibers may remain primarily in their local extracellular environment.
CircRNA may not be “circular”
Authors: Sun Handong , Wu Zijuan , Liu Ming , Yu Liang , Li Jianyong , Ding Xiangming , Jin Hui 
Published: September 2020

AbstractCircular RNA (circRNA) is a novel regulatory non-coding RNA and participates in diverse physiological and pathological processes. However, the structures and molecular mechanisms of circRNAs remain unclear. In this study, we took advantage of circRNA array and bioinformatics analysis and found lots of internal complementary base-pairing sequences (ICBPS) existed in plenty of circRNAs, especially in extremely long circRNAs (el-circRNAs, > 5,000 nt). The result indicated that circRNA may not be a simple circular structure. In addition, we put forward the hypothesis of “open-close effect “ in the transition for specific circRNA from normal state to morbid state. Taken together, our results not only expand the knowledge of circRNAs, but also highlight the potential molecular mechanism of circRNAs.
Residue-by-residue analysis of cotranslational membrane protein integrationin vivo
Authors: Nicolaus Felix , Metola Ane , Mermans Daphne , Liljenström Amanda , Krč Ajda , Abdullahi Salmo Mohammed , Zimmer Matthew , Miller Thomas F. , von Heijne Gunnar 
Published: September 2020

AbstractWe follow the cotranslational biosynthesis of three multi-spanningE. coliinner membrane proteinsin vivousing high-resolution Force Profile Analysis. The force profiles show that the nascent chain is subjected to rapidly varying pulling forces during translation, and reveal unexpected complexities in the membrane integration process. We find that an N-terminal cytoplasmic domains can fold in the ribosome exit tunnel before membrane integration starts, that charged residues and membrane-interacting segments such as re-entrant loops and surface helices flanking a transmembrane helix (TMH) can advance or delay membrane integration, and that point mutations in an upstream TMH can affect the pulling forces generated by downstream TMHs in a highly position-dependent manner, suggestive of residue-specific interactions between TMHs during the integration process.
Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks
Authors: Wang Juexin , Ma Anjun , Ma Qin , Xu Dong , Joshi Trupti 
Published: September 2020

AbstractDiscovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson’s correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available athttps://github.com/juexinwang/GRGNN.HighlightsWe present a novel formulation of graph classification in inferring gene regulatory relationships from gene expression and graph embedding.Our method leverages a powerful framework, gene regulatory graph neural network (GRGNN), which is flexible and powerful to ensemble statistical powers from a number of heuristic skeletons.Our results show GRGRNN outperforms previous supervised and unsupervised methods inductively on benchmarks.GRGNN can be interpreted and explained following the biological network motif hypothesis in gene regulatory networks.
Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: Effects on signal quality and sensitivity
Authors: Benitez-Andonegui A. , Lührs M. , Nagels-Coune L. , Ivanov D. , Goebel R. , Sorger B. 
Published: September 2020

AbstractDesigning optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Available resources often dictate the approach researchers use for optode-layout design. Here we compared four approaches that incrementally incorporated subject-specific magnetic resonance imaging (MRI) information while participants performed mental-calculation, mental-rotation and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB), employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional and vascular information of the same subject (fVASC). The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI and fVASC approaches resulted in a similar outcome. We conclude that additional individual MRI data leads to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.
Acis-regulatory atlas in maize at single-cell resolution
Authors: Marand Alexandre P. , Chen Zongliang , Gallavotti Andrea , Schmitz Robert J. 
Published: September 2020

SUMMARYCis-regulatory elements (CREs) encode the genomic blueprints of spatiotemporal gene expression programs enabling highly specialized cell functions. To identify CREs at cell-type resolution inZea mays, we implemented single-cell sequencing of Assay for Transposase Accessible Chromatin (scATAC-seq) in seedlings, embryonic roots, crown roots, axillary buds, and pistillate and staminate inflorescence. We describe 92 states of chromatin accessibility across 165,913 putative CREs and 52 known cell types. Patterns of transcription factor (TF) motif accessibility predicted cell identity with high accuracy, uncovered putative non-cell autonomous TFs, and revealed TF motifs underlying higher-order chromatin interactions. Comparison of maize andArabidopsis thalianadevelopmental trajectories identified TF motifs with conserved patterns of accessibility. Cell type-specific CREs were enriched with enhancer activity, phenotype-associated genetic variants, and signatures of breeding-era selection. These data, along with companion software,Socrates, afford a comprehensive framework for understanding cellular heterogeneity, evolution, andcis-regulatory grammar of cell-type specification in a major crop.
Metagenomic Noncoding RNA Profiling and Biomarker Discovery
Authors: Liu Ben , Thippabhotla Sirisha , Zhang Jun , Zhong Cuncong 
Published: September 2020

AbstractNoncoding RNA plays important regulatory and functional roles in microorganisms, such as gene expression regulation, signaling, protein synthesis, and RNA processing. Given its essential role in microbial physiology, it is natural to question whether we can use noncoding RNAs as biomarkers to distinguish among environments under different biological conditions, such as those between healthy versus disease status. The current metagenomic sequencing technology primarily generates short reads, which contain incomplete structural information that may complicate noncoding RNA homology detection. On the other hand, de novo assembly of the metagenomics sequencing data remains fragmentary and has a risk of missing low-abundant noncoding RNAs. To tackle these challenges, we have developed DRAGoM (Detection ofRNA usingAssemblyGraph fromMetagenomics data), a novel noncoding RNA homology search algorithm. DRAGoM operates on a metagenome assembly graph, rather than on unassembled reads or assembled contigs. Our benchmark experiments show DRAGoM’s improved performance and robustness over the traditional approaches. We have further demonstrated DRAGoM’s real-world applications in disease characterization via analyzing a real case-control gut microbiome dataset for Type-2 diabetes (T2D). DRAGoM revealed potential ncRNA biomarkers that can clearly separate the T2D gut microbiome from those of healthy controls. DRAGoM is freely available fromhttps://github.com/benliu5085/DRAGoM.
Improved yellow-green split fluorescent proteins for protein labeling and signal amplification
Authors: Zhou Shuqin , Feng Siyu , Brown David , Huang Bo 
Published: September 2020

AbstractThe flexibility and versatility of self-complementing split fluorescent proteins (FPs) have enabled a wide range of applications. In particular, the FP1-10/11split system contains a small fragment that facilitates efficient generation of endogenous-tagged cell lines and animals as well as signal amplification using tandem FP11tags. To improve the FP1-10/11toolbox we previously developed, here we used a combination of directed evolution and rational design approaches, resulting in two mNeonGreen (mNG)-based split FPs (mNG3A1-10/11and mNG3K1-10/11) and one mClover-based split FP (CloGFP1-10/11). mNG3A1-10/11and mNG3K1-10/11not only enhanced the complementation efficiency at low expression levels, but also allowed us to demonstrate signal amplification using tandem mNG211fragments in mammalian cells.
Quantifying the Brain Predictivity of Artificial Neural Networks with Nonlinear Response Mapping
Authors: Anand Aditi , Sen Sanchari , Roy Kaushik 
Published: September 2020

AbstractQuantifying the similarity between artificial neural networks (ANNs) and their biological counterparts is an important step towards building more brain-like artificial intelligence systems. Recent efforts in this direction useneural predictivity, or the ability to predict the responses of a biological brain given the information in an ANN (such as its internal activations), when both are presented with the same stimulus. We propose a new approach to quantifying neural predictivity by explicitly mapping the activations of an ANN to brain responses with a nonlinear function, and measuring the error between the predicted and actual brain responses. Further, we propose to use a neural network to approximate this mapping function by training it on a set of neural recordings. The proposed method was implemented within the Tensorflow framework and evaluated on a suite of 8 state-of-the-art image recognition ANNs. Our experiments suggest that the use of a non-linear mapping function leads to higher neural predictivity. Our findings also reaffirm the observation that the latest advances in classification performance of image recognition ANNs are not matched by improvements in their neural predictivity. Finally, we examine the impact of pruning, a widely used ANN optimization, on neural predictivity, and demonstrate that network sparsity leads to higher neural predictivity.
Self-organized pattern formation increases functional diversity
Authors: Hülsemann Janne , Klauschies Toni , Guill Christian 
Published: September 2020

AbstractSelf-organized formation of spatial patterns is known from a variety of different ecosystems, yet little is known how these patterns affect functional diversity of local and regional communities. Here we use a food chain model in which autotroph diversity is described by a continuous distribution of a trait that affects both growth rate and defense against a heterotroph. On a single patch, stabilizing selection always promotes the dominance of a single autotroph species. Two alternative community states, with either defended or undefended species, are possible. In a metacommunity context, dispersal can destabilize these states, and complex spatio-temporal patterns emerge. This creates varying selection pressures on the local autotroph communities, which feed back on the trait dynamics. Local functional diversity increases ten-fold compared to a situation without self-organized pattern formation, thereby maintaining the adaptive potential of communities in an environment threatened by fragmentation and global change.
Endosomal Sorting Drives the Formation of Mutant Prion Endoggresomes
Authors: Chassefeyre Romain , Chaiamarit Tai , Verhelle Adriaan , Novak Sammy Weiser , Andrade Leonardo R. , Manor Uri , Encalada Sandra E. 
Published: September 2020

AbstractIntra-axonal misfolded protein aggregates are a pathological feature of neurodegenerative diseases. How aggregates are formed and cleared is key to maintaining proteostasis. By systematically analyzing the trafficking itinerary of a misfolded GPI-anchored prion protein (PrP) mutant, we unveil endocytic pathways that drive its immediate degradation in the soma, versus its aggregation in axons inside endosomal structures we termed endoggresomes. Axonal sorting occurs post-Golgi, by association of mutant PrP vesicles with Arl8b/kinesin-1/HOPS, a complex that earmarks them for axonal entry, fusion, and aggregation via a mechanism ofaxonalrapidendosomalsorting andtransport-dependentaggregation (ARESTA). Endoggresomes persist in axons due to transport and lysosomal deficits, impairing calcium dynamics and accelerating neuronal death. Reducing ARESTA inhibits endoggresome formation and circumvents these defects. These data identify the endo-lysosomal system as critical for the sorting of misfolded PrP, and ARESTA as an actionable anti-aggregation target that can ameliorate axonal dysfunction in the prionopathies.
Acute noise causes down-regulation of ECM protein expression in guinea pig cochlea
Authors: Shi Min , Shi Lei , Ding Daxiong , Hu Yiyong , Qi Guowei , Zhan Li , Zhu Yuhua , Lv Ping , Yu Ning 
Published: September 2020

AbstractObjectiveTo study the differences in cochlea protein expression before and after noise exposure using proteomics to reveal the pathological mechanism of noise-induced hearing loss (NIHL).MethodsA guinea pig NIHL model was established to test the ABR thresholds before and after noise exposure. The proteomics technology was used to study the mechanism of differential protein expression in the cochlea by noise stimulation.ResultsThe average hearing threshold of guinea pigs on the first day after noise exposure was 57.00±6.78dB SPL, high-frequency hearing loss was more severe than low frequency; the average hearing threshold on the seventh day after noise exposure was 45.83±6.07dB SPL, and the 4k Hz hearing threshold has the best recovery. The proteomics technology identified 3122 different inner ear proteins, of which six proteins related to the hearing were down-regulation: TenascinC, Collagen type XI alpha two chains, Collagen type II alpha one chain, Thrombospondin 2, Collagen type XI alpha one chain and Ribosomal protein L38, and are enriched in protein absorption, focal adhesion, and extracellular matrix receptor pathways.ConclusionImpulse noise can affect the expression of differential proteins through focal adhesion pathways. This data can provide an experimental basis for the research on the prevention and treatment of NIHL.
THAP1 Modulates Oligodendrocyte Maturation by Regulating ECM Degradation in Lysosomes
Authors: Yellajoshyula Dhananjay , Pappas Samuel S. , Rogers Abigail , Choudhury Biswa , Reed Xylena , Ding Jinhui , Cookson Mark R. , Shakkottai Vikram , Giger Roman , Dauer William T. 
Published: September 2020

ABSTRACTMechanisms controlling myelination during CNS maturation play a pivotal role in the development and refinement of CNS circuits. The transcription factor THAP1 is essential for timing the inception of myelination during CNS maturation through a cell-autonomous role in the oligodendrocyte lineage. Here, we demonstrate that THAP1 modulates ECM composition by regulating glycosaminoglycan (GAG) catabolism within oligodendrocyte progenitor cells (OPCs).Thap1-/-OPCs accumulate and secrete excess GAGs, inhibiting their maturation through an auto-inhibitory mechanism. THAP1 controls GAG metabolism by binding to and regulating theGusBgene encoding β-glucuronidase, a GAG-catabolic lysosomal enzyme. Applying GAG-degrading enzymes or overexpressing β-glucuronidase rescuesThap1-/-OL maturation deficitsin vitroandin vivo.Our studies establish lysosomal GAG catabolism within OPCs as a critical mechanism regulating oligodendrocyte development.