bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Coding And Small-Non-Coding Hippocampal Synaptic RNAome
Authors: Epple Robert , Krüger Dennis , Berulava Tea , Brehm Gerrit , Islam Rezaul , Köster Sarah , Fischer Andre 
Published: November 2020

AbstractNeurons are highly compartmentalized cells that depend on local protein synthesis. Thus, messenger RNAs (mRNAs) have been detected in neuronal dendrites and more recently also at the pre- and postsynaptic compartment. Other RNA species, such as microRNAs, have also been described at synapses where they are believed to control mRNA availability for local translation. Nevertheless, a combined dataset analyzing the synaptic coding and non-coding RNAome via next-generation sequencing approaches is missing. Here we isolate synaptosomes from the hippocampus of young wild type mice and provide the coding and non-coding synaptic RNAome. These data are complemented by a novel approach to analyze the synaptic RNAome from primary hippocampal neurons grown in microfluidic chambers. Our data show that synaptic microRNAs control almost the entire synaptic mRNAome and we identified several hub microRNAs. By combining the in vivo synaptosomal data with our novel microfluidic chamber system, we also provide evidence to support the hypothesis that part of the synaptic microRNAome may be supplied to neurons via astrocytes. Moreover, the microfluidic system is suitable to study the dynamics of the synaptic RNAome in response to stimulation. In conclusion, our data provide a valuable resource and hint to several important targets for future experiments.
Mutations associated with pyrethroid resistance in Varroa mites, a parasite of honey bees, are widespread across the USA
Authors: Millán-Leiva Anabel , Marín Óscar , Christmon Krisztina , vanEngelsdorp Dennis , González-Cabrera Joel 
Published: November 2020

ABSTRACTBACKGROUNDManaged honey bees are key pollinators of many crops and play an essential role in the United States food production. For more than 10 years, beekeepers in the US have been reporting high rate of colony losses. One of the drivers of this colony loss is the parasitic mite Varroa destructor. Preserving healthy honey bee colonies in the US is dependent on a successful control of this mite. The pyrethroid tau-fluvalinate (Apistan®) was among the first synthetic varroacide registered in the US. With over 20 years of use, population of mites resistant to Apistan® have emerged, and so it is unsurprising that treatment failures have been reported. Resistance in US mite populations to tau-fluvalinate is associated with point mutations at position 925 of the voltage-gated sodium channel, L925I and L925M.RESULTSHere, we have generated a distribution map of pyrethroid resistance alleles in Varroa samples collected from US apiaries in 2016 and 2017, using a high throughput allelic discrimination assay based on TaqMan®. Our results evidence that these kdr-type mutations are widely distributed in Varroa populations across the country showing high variability among apiaries.CONCLUSIONWe highlight the relevance of monitoring the resistance in mite populations to achieve an efficient control of this pest, and the benefit of implementing this methodology in pest management programs for varroosis.
Cryo-EM structure and kinetics reveal electron transfer by 2D diffusion of cytochromecin the yeast III-IV respiratory supercomplex
Authors: Moe Agnes , Di Trani Justin , Rubinstein John L. , Brzezinski Peter 
Published: November 2020

AbstractEnergy conversion in aerobic organisms involves an electron current from low-potential donors, such as NADH and succinate, to dioxygen through the membrane-bound respiratory chain. Electron transfer is coupled to transmembrane proton transport that maintains the electrochemical proton gradient used to produce ATP and drive other cellular processes. Electrons are transferred between respiratory complexes III and IV (CIII and CIV) by water-soluble cyt.c. InS. cerevisiaeand some other organisms, these complexes assemble into larger CIII2CIV1/2supercomplexes, the functional significance of which has remained enigmatic. In this work, we measured the kinetics of theS. cerevisiaesupercomplex’s cyt.c-mediated QH2:O2oxidoreductase activity under various conditions. The data indicate that the electronic link between CIII and CIV is confined to the surface of the supercomplex. Cryo-EM structures of the supercomplex with cyt.creveal distinct states where the positively-charged cyt.cis bound either to CIII or CIV, or resides at intermediate positions. Collectively, the structural and kinetic data indicate that cyt.ctravels along a negatively-charged surface patch of the supercomplex. Thus, rather than enhancing electron-transfer rates by decreasing the distance cyt.cmust diffuse in 3D, formation of the CIII2CIV1/2supercomplex facilitates electron transfer by 2D diffusion of cyt.c. This mechanism enables the CIII2CIV1/2supercomplex to increase QH2:O2oxidoreductase activity and suggests a possible regulatory role for supercomplex formation in the respiratory chain.Significance StatementIn the last steps of food oxidation in living organisms, electrons are transferred to oxygen through the membrane-bound respiratory chain. This electron transfer is mediated by mobile carriers such as membrane-bound quinone and water-soluble cyt.c. The latter transfers electrons from respiratory complex III to IV. In yeast these complexes assemble into III2IV1/2supercomplexes, but their role has remained enigmatic. This study establishes a functional role for this supramolecular assembly in the mitochondrial membrane. We used cryo-EM and kinetic studies to show that cyt.cshuttles electrons by sliding along the surface of III2IV1/2(2D diffusion). The structural arrangement into III2IV1/2supercomplexes suggests a mechanism to regulate cellular respiration.
Predicting Brain Regions Related to Alzheimer’s Disease Based on Global Feature
Authors: Wang Qi , Chen Siwei , Wang He , Chen Luzeng , Sun Yongan , Yan Guiying 
Published: November 2020

AbstractAlzheimer’s disease (AD) is a common neurodegenerative disease in the elderly, early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most of the brain regions related to AD were identified based on the imaging method, which can only identify some atrophic brain regions. In this work, we used mathematical models to find out the potential brain regions related to AD. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, we set a new local feature index 2hop-connectivity to measure the correlation among different areas. The 2hop-connectivity utilizes the higher-order information of the graph structure relative to the traditional graph theory metrics. And for this, we proposed a novel algorithm named 2hopRWR to measure 2hop-connectivity. At last, we proposed a new index GFS (Global Feature Score) based on a global feature by combing 5 local features: degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are likely related to Alzheimer’s Disease. As a result, all the top ten brain regions in the GFS scoring difference between the AD group and the non-AD group were related to AD by literature verification. Literature validation results comparing GFS with local features showed that GFS outperforms individual local features. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the mini-mental state examination (MMSE) scale and the Montreal cognitive assessment (MoCA) scale. So, we believe the GFS can also be used as a new index to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method in other areas of network analysis.
FiberAI: A Deep Learning model for automated analysis of nascent DNA Fibers
Authors: Mohsin Azam , Arnovitz Stephen , Khan Aly A , Gounari Fotini 
Published: November 2020

AbstractAll life forms undergo cell division and are dependent on faithful DNA replication to maintain the stability of their genomes. Both intrinsic and extrinsic factors can stress the replication process and multiple checkpoint mechanisms have evolved to ensure genome stability. Understanding these molecular mechanisms is crucial for preventing and treating genomic instability associated diseases including cancer. DNA replicating fiber fluorography is a powerful technique that directly visualizes the replication process and a cell’s response to replication stress. Analysis of DNA-fiber microscopy images provides quantitative information about replication fitness. However, a bottleneck for high throughput DNA-fiber studies is that quantitative measurements are laborious when performed manually. Here we introduce FiberAI, which uses state-of-the art deep learning frameworks to detect and quantify DNA-fibers in high throughput microscopy images. FiberAI efficiently detects DNA fibers, achieving a bounding box average precision score of 0.91 and a segmentation average precision score of 0.90. We then use FiberAI to measure the integrity of replication checkpoints. FiberAI is publicly available and allows users to view model predicted selections, add their own manual selections, and easily analyze multiple image sets. Thus, FiberAI can help elucidate DNA replication processes by streamlining DNA-fiber analyses.
Comparative analysis of Wnt signaling-related proteins in normal, benign, malignant and metastasised human liver tumors
Authors: Kanwal Bushra , Zen Yoh , Deheragoda Maesha , Millar Michael , Thrasivoulou Christopher , Rashid Sheikh Tamir , Ahmed Aamir 
Published: November 2020

AbstractBackground and AimsLiver cancer comprises of benign or malignant tumors including hepatocellular carcinoma (HCC), cholangiocarcinoma (CC), hepatoblastoma (HB), and other rarer tumor types. There is evidence of aberrant Wnt signaling during initiation and progression of HCC, CC and HB.MethodsWe investigated the expression of Wnt/β-catenin transcription related proteins, Cyclin D1, c-Myc, Fra-1 and Pygo-1, in human liver tumors by using an unbiased, quantitative immunohistochemical (qIHC) approach.ResultsSemi-automated, unbiased quantitation of individual proteins revealed reduced expression of Cyclin D1 and Pygo-1 in CC (P < 0.0001 and P < 0.01, respectively) and HB (P < 0.05 and P < 0.01, respectively) compared to normal liver (NL). Receiver operating characteristic curves showed Cyclin D1 as a putative marker for CC (AUC > 0.8) that discriminates CC from both NL and HCC (P ≤ 0.0001), and Pygo-1 (AUC > 0.7) as a marker for both CC and HCC (P < 0.01) compared to NL. Combining Cyclin D1/Pygo-1 and applying a logistic regression model further improved the diagnostic potential (classifying 84% of NL and CC cases, P < 0.0001). Quantitative co-localisation of tissue samples simultaneously labeled with the four biomarkers, indicated that co-localisation of both Pygo-1/Fra-1 and c-Myc/Fra-1 was also significantly changed in CC and HCC (P < 0.0001) vs NL. Additionally, co-localisation of Pygo-1/Fra-1, in particular, could also distinguish CC from HCC (P < 0.05).ConclusionOur results indicate that measurement of Wnt signaling markers could be used to stratify liver cancer.
Architecture and mechanism of metazoan retromer:SNX3 tubular coat assembly
Authors: Leneva Natalya , Kovtun Oleksiy , Morado Dustin R. , Briggs John A. G. , Owen David J. 
Published: November 2020

AbstractRetromer is a master regulator of cargo retrieval from endosomes, which is critical for many cellular processes including signalling, immunity, neuroprotection and virus infection. To function in different trafficking routes, retromer core (VPS26/VPS29/VPS35) assembles with a range of sorting nexins to generate tubular carriers and incorporate assorted cargoes. We elucidate the structural basis of membrane remodelling and coupled cargo recognition by assembling metazoan and fungal retromer core trimers on cargo-containing membranes with sorting nexin adaptor SNX3 and determining their structures using cryo-electron tomography. Assembly leads to formation of tubular carriers in the absence of canonical membrane curvature drivers. Interfaces in the retromer coat provide a structural explanation for Parkinson’s disease-linked mutations. We demonstrate that retromer core trimer forms an invariant, evolutionarily-conserved scaffold that can incorporate different auxiliary membrane adaptors by changing its mode of membrane recruitment, so modulating membrane bending and cargo incorporation and thereby allowing retromer to traffic assorted cargoes along different cellular transport routes.
Designing viral diagnostics with model-based optimization
Authors: Metsky Hayden C. , Welch Nicole L. , Pillai Priya P. , Haradhvala Nicholas J. , Rumker Laurie , Mantena Sreekar , Zhang Yibin B. , Yang David K. , Ackerman Cheri M. , Weller Juliane , Blainey Paul C. , Myhrvold Cameron , Mitzenmacher Michael , Sabeti Pardis C. 
Published: November 2020

AbstractDiagnostics, particularly for rapidly evolving viruses, stand to benefit from a principled, measurement-driven design that harnesses machine learning and vast genomic data—yet the capability for such design has not been previously built. Here, we develop and extensively validate an approach to designing viral diagnostics that applies a learned model within a combinatorial optimization framework. Concentrating on CRISPR-based diagnostics, we screen a library of 19,209 diagnostic–target pairs and train a deep neural network that predicts, from RNA sequence alone, diagnostic signal better than contemporary techniques. Our model then makes it possible to design assays that are maximally sensitive over the spectrum of a virus’s genomic variation. We introduce ADAPT (https://adapt.guide), a system for fully-automated design, and use ADAPT to design optimal diagnostics for the 1,933 vertebrate-infecting viral species within 2 hours for most species and 24 hours for all but 3. We experimentally show ADAPT’s designs are sensitive and specific down to the lineage level, including against viruses that pose challenges involving genomic variation and specificity. ADAPT’s designs exhibit significantly higher fluorescence and permit lower limits of detection, across a virus’s entire variation, than the outputs of standard design techniques. Our model-based optimization strategy has applications broadly to viral nucleic acid diagnostics and other sequence-based technologies, and, paired with clinical validation, could enable a critically-needed, proactive resource of assays for surveilling and responding to pathogens.
MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules
Authors: Cao Liu , Guler Mustafa , Tagirdzhanov Azat , Lee Yiyuan , Gurevich Alexey , Mohimani Hosein 
Published: November 2020

AbstractIdentification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. This is a challenging task as currently it is not clear how small molecules are fragmented in mass spectrometry. The existing approaches use the domain knowledge from chemistry to predict fragmentation of molecules. However, these rule-based methods fail to explain many of the peaks in mass spectra of small molecules. Recently, spectral libraries with tens of thousands of labelled mass spectra of small molecules have emerged, paving the path for learning more accurate fragmentation models for mass spectral database search. We present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by (i) utilizing an efficient algorithm to generate mass spectrometry fragmentations, and (ii) learning a probabilistic model to match small molecules with their mass spectra. We show our database search is an order of magnitude more efficient than the state-of-the-art methods, which enables searching against databases with millions of molecules. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that our probabilistic model can correctly identify nearly six times more unique small molecules than previous methods. Moreover, by applying molDiscovery on microbial datasets with both mass spectral and genomics data we successfully discovered the novel biosynthetic gene clusters of three families of small molecules.AvailabilityThe command-line version of molDiscovery and its online web service through the GNPS infrastructure are available athttps://github.com/mohimanilab/molDiscovery.
Kozak sequence acts as a negative regulator forde novotranscription initiation of newborn coding sequences in the plant genome
Authors: Hata Takayuki , Satoh Soichirou , Takada Naoto , Matsuo Mitsuhiro , Obokata Junichi 
Published: November 2020

ABSTRACTThe manner in which newborn coding sequences and their transcriptional competency emerge during the process of gene evolution remains unclear. Here, we experimentally simulated eukaryotic gene origination processes by mimicking horizontal gene transfer events in the plant genome. We mapped the precise position of the transcription start sites (TSSs) of hundreds of newly introduced promoterless firefly luciferase (LUC) coding sequences in the genome ofArabidopsis thalianacultured cells. The systematic characterization of theLUC-TSSs revealed that 80% of them occurred under the influence of endogenous promoters, while the remainder underwentde novoactivation in the intergenic regions, starting from pyrimidine-purine dinucleotides. Thesede novoTSSs obeyed unexpected rules; they predominantly occurred ~100 bp upstream of theLUCinserts and did not overlap with Kozak-containing putative open reading frames (ORFs). These features were the output of the immediate responses to the sequence insertions, rather than a bias in the screening of theLUCgene function. Regarding the wild-type genic TSSs, they appeared to have evolved to lack any ORFs in their vicinities. Therefore, the repulsion by thede novoTSSs of Kozak-containing ORFs described above might be the first selection gate for the occurrence and evolution of TSSs in the plant genome. Based on these results, we characterized thede novotype of TSS identified in the plant genome and discuss its significance in genome evolution.
Robust and efficient software for reference-free genomic diversity analysis of GBS data on diploid and polyploid species
Authors: Parra-Salazar Andrea , Gomez Jorge , Lozano-Arce Daniela , Reyes-Herrera Paula H. , Duitama Jorge 
Published: November 2020

AbstractGenotype-by-sequencing (GBS) is a widely used cost-effective technique to obtain large numbers of genetic markers from populations. Although a standard reference-based pipeline can be followed to analyze these reads, a reference genome is still not available for a large number of species. Hence, several research groups require reference-free approaches to generate the genetic variability information that can be obtained from a GBS experiment. Unfortunately, tools to perform de-novo analysis of GBS reads are scarce and some of the existing solutions are difficult to operate under different settings generated by the existing GBS protocols. In this manuscript we describe a novel algorithm to perform reference-free variants detection and genotyping from GBS reads. Non-exact searches on a dynamic hash table of consensus sequences allow to perform efficient read clustering and sorting. This algorithm was integrated in the Next Generation Sequencing Experience Platform (NGSEP) to integrate the state-of- the-art variants detector already implemented in this tool. We performed benchmark experiments with three different real populations of plants and animals with different structures and ploidies, and sequenced with different GBS protocols at different read depths. These experiments show that NGSEP has comparable and in some cases better accuracy and always better computational efficiency compared to existing solutions. We expect that this new development will be useful for several research groups conducting population genetic studies in a wide variety of species.
Predicting and validating protein degradation in proteomes using deep learning
Authors: Ozols Matiss , Eckersley Alexander , Platt Christopher I. , McGuinness Callum S. , Hibbert Sarah A. , Revote Jerico , Li Fuyi , Griffiths Christopher E.M. , Watson Rachel E.B. , Song Jiangning , Bell Mike , Sherratt Michael J. 
Published: November 2020

AbstractAge, disease, and exposure to environmental factors can induce tissue remodelling and alterations in protein structure and abundance. In the case of human skin, ultraviolet radiation (UVR)-induced photo-ageing has a profound effect on dermal extracellular matrix (ECM) proteins. We have previously shown that ECM proteins rich in UV-chromophore amino acids are differentially susceptible to UVR. However, this UVR-mediated mechanism alone does not explain the loss of UV-chromophore-poor assemblies such as collagen. Here, we aim to develop novel bioinformatics tools to predict the relative susceptibility of human skin proteins to not only UVR and photodynamically produced ROS but also to endogenous proteases. We test the validity of these protease cleavage site predictions against experimental datasets (both previously published and our own, derived by exposure of either purified ECM proteins or a complex cell-derived proteome, to matrix metalloproteinase [MMP]-9). Our deep Bidirectional Recurrent Neural Network (BRNN) models for cleavage site prediction in nine MMPs, four cathepsins, elastase-2, and granzyme-B perform better than existing models when validated against both simple and complex protein mixtures. We have combined our new BRNN protease cleavage prediction models with predictions of relative UVR/ROS susceptibility (based on amino acid composition) into the Manchester Proteome Susceptibility Calculator (MPSC) webapphttp://www.manchesterproteome.manchester.ac.uk/#/MPSC(orhttp://130.88.96.141/#/MPSC). Application of the MPSC to the dermal proteome suggests that fibrillar collagens and elastic fibres will be preferentially degraded by proteases alone and by UVR/ROS and protease in combination, respectively. We also identify novel targets of oxidative damage and protease activity including dermatopontin (DPT), fibulins (EFEMP-1,-2, FBLN-1,-2,-5), defensins (DEFB1, DEFA3, DEFA1B, DEFB4B), proteases and protease inhibitors themselves (CTSA, CTSB, CTSZ, CTSD, TIMPs-1,-2,-3, SPINK6, CST6, PI3, SERPINF1, SERPINA-1,-3,-12). The MPSC webapp has the potential to identify novel protein biomarkers of tissue damage and to aid the characterisation of protease degradomics leading to improved identification of novel therapeutic targets.
Design optimization of metamaterial units using a genetic algorithm based optimization methodology
Authors: Li Yiying , Yang Shiyou 
Published: November 2020

Purpose
The purpose of this paper is to develop a pertinent design optimization methodology for symmetric designs of a metamaterial (MM) unit.

Design/methodology/approach
A cell division mechanism is introduced and used to design a new selecting mechanism in the proposed algorithm, a non-dominated sorting cellular genetic algorithm (NSCGA).

Findings
The numerical results on solving standard multi-objective test functions and a prototype MM unit positively demonstrate the advantages of the proposed NSCGA.

Originality/value
A new NSGAII-based optimization algorithm, NSCGA, for multi-objective optimization designs of a MM unit is proposed.