bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modulation of gamma spectral amplitude and connectivity during reaching predicts peak velocity and movement duration
Authors: Tatti Elisa , Ferraioli Francesca , Cacciola Alberto , Chan Cameron , Quartarone Angelo , Ghilardi M. Felice 
Published: November 2021

AbstractVoluntary movements are accompanied by increased oscillatory activity or synchronization in the gamma range (> 25.5 Hz) within the sensorimotor system. Despite the extensive literature about movement-related gamma synchronization, the specific role of gamma oscillations for movement control is still debated. In this study, we characterized movement-related gamma oscillatory dynamics and its relationship with movement characteristics based on 256-channels EEG recordings in 64 healthy subjects while performing fast and uncorrected reaching movements to targets located at three distances. We found that movement-related gamma synchronization occurred during both movement planning and execution, albeit with different gamma peak frequencies and topographies. Also, the amplitude of gamma synchronization in both planning and execution increased with target distance. Additional analysis of phase coherence revealed a gamma-coordinated long-range network involving occipital, frontal and central regions during movement execution. Gamma synchronization amplitude and phase coherence pattern reliably predicted peak velocity amplitude and timing, thus suggesting that cortical gamma oscillations play a significant role in the selection of appropriate kinematic parameters during planning and in their implementation during movement execution.
FLASHIda: Intelligent data acquisition for top-down proteomics that doubles proteoform level identification count
Authors: Jeong Kyowon , Babović Maša , Gorshkov Vladimir , Kim Jihyung , Jensen Ole N. , Kohlbacher Oliver 
Published: November 2021

AbstractTop-down proteomics (TDP) has gained a lot of interest in biomedical application for detailed analysis and structural characterization of proteoforms. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complex signal of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. We present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In analysis in E. coli lysates, FLASHIda increased the number of unique proteoform level identifications from 800 to 1,500, or generated a near-identical number of identifications in ⅓ of instrument time when compared to standard DDA mode. Furthermore, FLASHIda enabled sensitive mapping of post translational modifications and detection of chemical adducts. As an extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.
Population genomics reveal distinct and diverging populations of An. minimus in Cambodia – a widespread malaria vector in Southeast Asia
Authors: St. Laurent Brandyce , Harding Nick , Deason Nick , Oy Kolthida , Loeun Chea Sok , Sary Men , Sunly Rous , Nhep Sen , Drury Eleanor , Rockett Kirk , Sovannaroth Siv , Goncalves Sonia , Kwiatkowski Dominic , Miles Alistair 
Published: November 2021

AbstractAnopheles minimus is an important malaria vector throughout its wide geographic range across Southeast Asia. Genome sequencing could provide important insights into the unique malaria transmission dynamics in this region, where many vector species feed and rest outdoors. We describe results from a study using Illumina deep whole-genome sequencing of 302 wild-caught An. minimus collected from three Cambodian provinces over several years (2010, 2014-2016) and seasons to examine the level of population structure and genetic diversity within this species. These specimens cluster into four distinct populations of An. minimus s.s., with two populations overlapping geographically. We describe the underlying genetic diversity and divergence of these populations and investigated the genetic variation in genes known to be involved in insecticide resistance. We found strong signals of selection within these An. minimus populations, most of which were present in the two Northeastern Cambodian populations and differ from those previously described in African malaria vectors. Cambodia is the focus of the emergence and spread of drug-resistant malaria parasites, so understanding the underlying genetic diversity and resilience of the vectors of these parasites is key to implementing effective malaria control and elimination strategies. These data are publicly available as part of the MalariaGEN Vector Observatory, an open access resource of genome sequence data.
Interactive Analysis of Biosurfactants in Fruit-Waste Fermentation Samples using BioSurfDB and MEGAN
Authors: da Silva Gabriela Fiori , Gautam Anupam , Silveira Duarte Iolanda Cristina , Delforno Tiago Palladino , de Oliveira Valéria Maia , Huson Daniel H. 
Published: November 2021

ABSTRACTMicrobial biosurfactants are of major interest due to their multifunctional properties, biodegradable nature and low toxicity. Agroindustrial waste, such as fruit waste, can be used as substrates for producing bacteria. In this study, six samples of fruit waste, from oranges, mangoes and mixed fruits, were self-fermented, and then subjected to short-read metagenomic sequencing, so as to allow assessment of the potential of the associated microbiota for biosurfactant production. Taxonomic analysis using the DIAMOND+MEGAN analysis pipeline shows that all six samples are dominated by Proteobacteria, in particular, a common core consisting of the genera Klebsiella, Enterobacter, Stenotrophomonas, Acinetobacter and Escherichia. To support the interactive visualization and exploration of the surfactant-related genes in such samples, we have integrated the BiosurfDB classification into MEGAN and make this available. Functional analysis indicates high similarity among samples and that a significant number of reads map to genes that are involved in the biosynthesis of lipopeptide-class biosurfactants. Gene-centric analysis reveals Klebsiella as the main assignment for genes related to putisolvins biosynthesis. This suggests that fruit waste is a promising substrate for fermentative processes because the associated microbiota may be able to produce biosurfactants that are potentially useful for the agricultural, chemical, food and pharmaceutical industries.
Genetic regulation of RNA splicing in human pancreatic islets
Authors: Atla Goutham , Bonas-Guarch Silvia , Beucher Anthony , Cuenca-Ardura Mirabai , Garcia-Hurtado Javier , Moran Ignasi ,   the T2DSystems consortium, Irimia Manuel , Prasad Rashmi B , Gloyn Anna L. , Marselli Lorella , Suleiman Mara , Berney Thierry , Koning Eelco J P de , Kerr-Conte Julie , Pattou Francois , Piemonti Lorenzo , Ferrer Jorge 
Published: November 2021

AbstractGenetic variants that influence transcriptional regulation in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D). For many susceptibility loci, however, the mechanisms are unknown. We examined splicing QTLs (sQTLs) in islets from 399 donors and observed that genetic variation has a widespread influence on splicing of genes with important functions in islet biology. In parallel, we profiled expression QTLs, and used transcriptome-wide association and co-localization studies to assign islet sQTLs or eQTLs to T2D susceptibility signals that lacked candidate effector genes. We found novel T2D associations, including an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effectors revealed overrepresented pathways, including regulators of G-protein-mediated cAMP production. This data exposes an underappreciated layer of genetic regulation in pancreatic islets, and nominates molecular mediators of T2D susceptibility.
PHGDH is required for germinal center formation and is a therapeutic target in MYC-driven lymphoma
Authors: D’Avola Annalisa , Legrave Nathalie , Tajan Mylène , Chakravarty Probir , Shearer Ryan L. , King Hamish W. , Cheung Eric C. , Clear Andrew J. , Gunawan Arief , Zhang Lingling , James Louisa K. , MacRae James I. , Gribben John G. , Calado Dinis P. , Vousden Karen H. , Riches John C. 
Published: November 2021

ABSTRACTThe synthesis of serine from glucose is a key metabolic pathway supporting cellular proliferation in healthy and malignant cells. Despite this, the role that this aspect of metabolism plays in germinal center biology and pathology is not known. Here, we performed a comprehensive characterization of the role of the serine synthesis pathway in germinal center B cells and lymphomas derived from these cells. We demonstrate that upregulation of a functional serine synthesis pathway is a metabolic hallmark of B-cell activation and the germinal center reaction. Inhibition of phosphoglycerate dehydrogenase (PHGDH), the first and rate limiting enzyme in this pathway, leads to defective germinal formation and impaired high-affinity antibody production. In addition, overexpression of enzymes involved in serine synthesis is a characteristic of germinal center B-cell derived lymphomas, with high levels of expression being predictive of reduced overall survival in diffuse large B cell lymphoma. Inhibition of PHGDH induces apoptosis in lymphoma cells reducing disease progression. These findings establish PHGDH as a critical player in humoral immunity and a clinically relevant target in lymphoma.
A novel method to identify cell-type specific regulatory variants and their role in cancer risk
Authors: Kalita Cynthia A. , Gusev Alexander 
Published: November 2021

BackgroundExpression quantitative trait loci (eQTLs) have been crucial in providing an understanding of how genetic variants influence gene expression. However, eQTLs are known to exert cell type specific effects, and existing methods to identify cell type specific QTLs in bulk data require large sample sizes.ResultsHere, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identified 3,664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63x increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell type specific eQTL data and were more enriched for cancer risk than conventional eQTLs. The intersection of tumorspecific QTL effects (tsQTLs) with GWAS loci identified rs4765621 and SCARB1, which has been previously linked to renal cell carcinoma (RCC) progression and experimentally validated in tumors.ConclusionsOur new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies. Our study contributes to a better understanding of germline mechanisms underlying the anticancer immune response as well as cfQTLs contributing to cancer risk.
A Multigraph model of the 3D genome
Authors: Makai Diána , Cseh András , Sepsi Adél , Makai Szabolcs 
Published: November 2021

ABSTRACTSpatial organisation of the genome has a fundamental effect on its biological functions. Chromatin-chromatin interactions and 3D spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements function within the larger genomic context, and how the genome reacts as a whole to environmental stimuli, the linear sequence information needs to be interpreted in 3-dimensional space. While recent advances in chromatin conformation capture technologies including Hi-C, considerably advanced our understanding of the genomes, defining the DNA, as it is organized in the cell nucleus is still a challenging task. 3D genome modelling needs to reflect the DNA as a flexible polymer, which can wind up to the fraction of its total length and greatly unwind and stretch to implement a multitude of functions. Here we propose a novel approach to model genomes as a multigraph based on Hi-C contact data. Multigraph-based 3D genome modelling of barley and rice revealed the well-known Rabl and Rosetta chromatin organizations, respectively, as well as other higher order structures. Our results shows that the well-established toolset of Graph theory is highly valuable in modelling large genomes in 3D.
Modeling chromatin state from sequence across angiosperms using recurrent convolutional neural networks
Authors: Wrightsman Travis , Marand Alexandre P. , Crisp Peter A. , Springer Nathan M. , Buckler Edward S. 
Published: November 2021

AbstractAccessible chromatin regions are critical components of gene regulation but modeling them directly from sequence remains challenging, especially within plants, whose mechanisms of chromatin remodeling are less understood than in animals. We trained an existing deep learning architecture, DanQ, on leaf ATAC-seq data from 12 angiosperm species to predict the chromatin accessibility of sequence windows within and across species. We also trained DanQ on DNA methylation data from 10 angiosperms, because unmethylated regions have been shown to overlap significantly with accessible chromatin regions in some plants. The across-species models have comparable or even superior performance to a model trained within species, suggesting strong conservation of chromatin mechanisms across angiosperms. Testing a maize held out model on a multi-tissue scATAC panel revealed our models are best at predicting constitutively-accessible chromatin regions, with diminishing performance as cell-type specificity increases. Using a combination of interpretation methods, we ranked JASPAR motifs by their importance to each model and saw that the TCP and AP2/ ERF transcription factor families consistently ranked highly. We embedded the top three JASPAR motifs for each model at all possible positions on both strands in our sequence window and observed position- and strand-specific patterns in their importance to the model. With our cross-species “a2z” model it is now feasible to predict the chromatin accessibility and methylation landscape of any angiosperm genome.
A modular and reusable model of epithelial transport in the proximal convoluted tubule
Authors: Noroozbabaee Leyla , Blanco Pablo J. , Safaei Soroush , Nickerson David P. 
Published: November 2021

AbstractWe review a collection of published renal epithelial transport models, from which we build a consistent and reusable mathematical model able to reproduce many observations and predictions from the literature. The flexible modular model we present here can be adapted to specific configurations of epithelial transport, and in this work we focus on transport in the proximal convoluted tubule of the renal nephron.Our mathematical model of the epithelial proximal convoluted tubule describes the cellular and subcellular mechanisms of the transporters, intracellular buffering, solute fluxes, and other processes. We provide free and open access to the Python implementation to ensure our multiscale proximal tubule model is accessible; enabling the reader to explore the model through setting their own simulations, reproducibility tests, and sensitivity analyses.
Shine: A novel strategy to extract specific, sensitive and well-conserved biomarkers from massive microbial genomic datasets
Authors: Ji Cong , Shao Junbin (Jack) 
Published: November 2021

AbstractBackgroundConcentrations of the pathogenic microorganisms’ DNA in biological samples are typically low. Therefore, DNA diagnostics of common infections are costly, rarely accurate, and challenging. Limited by failing to cover updated epidemic testing samples, computational services are difficult to implement in clinical applications without complex customized settings. Furthermore, the combined biomarkers used to maintain high conservation may not be cost effective and could cause several experimental errors in many clinical settings. Given the limitations of recent developed technology, 16S rRNA is too conserved to distinguish closely related species, and mosaic plasmids are not effective as well because of their uneven distribution across prokaryotic taxa.ResultsHere, we provide a computational strategy, Shine, that allows extraction of specific, sensitive and well-conserved biomarkers from massive microbial genomic datasets. Distinguished with simple concatenations with blast-based filtering, our method involves ade novogenome alignment-based pipeline to explore the original and specific repetitive biomarkers in the defined population. It can cover all members to detect newly discovered multicopy conserved species-specific or even subspecies-specific target probes and primer sets. The method has been successfully applied to a number of clinical projects and has the overwhelming advantages of automated detection of all pathogenic microorganisms without the limitations of genome annotation and incompletely assembled motifs. Using on our pipeline, users may select different configuration parameters depending on the purpose of the project for routine clinical detection practices on the websitehttps://bioinfo.liferiver.com.cnwith easy registration.ConclusionsThe proposed strategy is suitable for identifying shared phylogenetic markers while featuring low rates of false positive or false negative. This technology is suitable for the automatic design of minimal and efficient PCR primers and other types of detection probes.
Comprehensive analysis of GBA using a novel algorithm for Illumina whole-genome sequence data or targeted Nanopore sequencing
Authors: Toffoli Marco , Chen Xiao , Sedlazeck Fritz J , Lee Chiao-Yin , Mullin Stephen , Higgins Abigail , Koletsi Sofia , Garcia-Segura Monica Emili , Sammler Esther , Scholz Sonja W. , Schapira Anthony HV , Eberle Michael A. , Proukakis Christos 
Published: November 2021

AbstractGBA variants cause the autosomal recessive Gaucher disease, and carriers are at increased risk of Parkinson’s disease (PD) and Lewy body dementia (LBD). The presence of a highly homologous nearby pseudogene (GBAP1) predisposes to a range of structural variants arising from either gene conversion or reciprocal recombination, the latter resulting in copy number gains or losses, complicating genetic testing and analysis. To date, short-read sequencing has not been able to fully resolve these or other variants in the key homology region, and targeted long-read sequencing has not previously resolved reciprocal recombinants. We present and validate two independent methods to resolve recombinant alleles and other variants in GBA: Gauchian, a novel bioinformatics tool for short-read, whole-genome sequencing data analysis, and Oxford Nanopore long-read sequencing after enrichment with appropriate PCR. The methods were concordant for 42 samples including 30 with a range of recombinants and GBAP1-related mutations, and Gauchian outperforms the GATK Best Practices pipeline. Applying Gauchian to Illumina sequencing of over 10,000 individuals from publicly available cohorts shows that copy number variants (CNVs) spanning GBAP1 are relatively common in Africans. CNV frequencies in PD and LBD are similar to controls, but gains may coexist with other mutations in patients, and a modifying effect cannot be excluded. Gauchian detects a higher frequency of GBA variants in LBD than PD, especially severe ones. These findings highlight the importance of accurate GBA mutation detection in these patients, which is possible by either Gauchian analysis of short-read whole genome sequencing, or targeted long-read sequencing.
An efficient and adaptable workflow for editing disease-relevant single nucleotide variants using CRISPR/Cas9
Authors: Usher Inga , Ligammari Lorena , Ahrabi Sara , Hepburn Emily , Connolly Calum , Bond Gareth L. , Flanagan Adrienne M. , Cottone Lucia 
Published: November 2021

AbstractSingle nucleotide variants are the commonest genetic alterations in the human genome. At least 60,000 have been reported to be associated with disease. The CRISPR/Cas9 system has transformed genetic research, making it possible to edit single nucleotides and study the function of genetic variants in vitro. While significant advances have improved the efficiency of CRISPR/Cas9, the editing of single nucleotides remains challenging. There are two major obstacles: low efficiency of accurate editing and the isolation of these cells from a pool of cells with other editing outcomes. We present data from 85 transfections of induced pluripotent stem cells and an immortalised cell line, comparing the effects of altering CRISPR/Cas9 design and experimental conditions on rates of single nucleotide substitution. We targeted variants in TP53, which predispose to several cancers, and in TBXT which is implicated in the pathogenesis of the bone cancer, chordoma. We describe a scalable and adaptable workflow for single nucleotide editing that incorporates contemporary techniques including Illumina MiSeq™ sequencing, TaqMan™ qPCR and digital droplet PCR for screening transfected cells as well as quality control steps to mitigate against common pitfalls. This workflow can be applied to CRISPR/Cas9 and other genome editing systems to maximise experimental efficiency.Simple SummaryCRISPR/Cas9 has revolutionised genetic research. Cas9 generates a double strand break with high efficiency which is repaired by a cell’s pathways. If a genetic template is provided, the damage can be accurately repaired to introduce a desired genetic alteration. However, accurate repair occurs at a low efficiency and in a small proportion of edited cells, representing the main obstacles in harnessing CRISPR’s full potential. Using data from 85 CRISPR experiments for single nucleotide editing, targeting three locations in the human genome that are implicated in predisposition to cancer, we report the effect of different experimental conditions on editing efficiency. We describe current technologies that can be used to streamline the identification of accurately edited cells and synthesise these into an adaptable workflow that can be applied to CRISPR/Cas9 experiments to achieve single nucleotide editing in disease-relevant cell models.
The BCM rule allows a spinal cord model to learn rhythmic movements
Authors: Kohler Matthias , Stratmann Philipp , Röhrbein Florian , Knoll Alois , Albu-Schäffer Alin , Jörntell Henrik 
Published: November 2021

AbstractAnimal locomotion is hypothesized to be controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current CPG models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the BCM learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to CPG models, because rhythm generating neurons and genetically defined connectivity are not required in our model.Author summaryThe central pattern generator is the leading hypothesis of locomotor control in animals. There, rhythm generating neurons and genetically defined neural connectivity would form a circuit generating activity patterns suitable for locomotion. We provide a new hypothesis of locomotor control, where rhythmic patterns are learned by a Hebbian learning rule from a mechanical system that has an intrinsic tendency to oscillate.