bluuebunny/crossref_metadata_2025_split huggingface dataset:
GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learning
Authors: Soto Larisa M. , Olayo-Alarcón Roberto , Velázquez-Ramírez David Alberto , Munguía-Reyes Adrián , Balderas-Martínez Yalbi Itzel , Méndez-Cruz Carlos-Francisco , Collado-Vides Julio 
Published: November 2019

AbstractMotivationThe genetic mechanisms involved in human diseases are fundamental in biomedical research. Several databases with curated associations between genes and diseases have emerged in the last decades. Although, due to the demanding and time consuming nature of manual curation of literature, they still lack large amounts of information. Current automatic approaches extract associations by considering each abstract or sentence independently. This approach could potentially lead to contradictions between individual cases. Therefore, there is a current need for automatic strategies that can provide a literature consensus of gene-disease associations, and are not prone to making contradictory predictions.ResultsHere, we present GeDex, an effective and freely available automatic approach to extract consensus gene-disease associations from biomedical literature based on a predictive model trained with four simple features. As far as we know, it is the only system that reports a single consensus prediction from multiple sentences supporting the same association. We tested our approach on the curated fraction of DisGeNet (f-score 0.77) and validated it on a manually curated dataset, obtaining a competitive performance when compared to pre-existing methods (f-score 0.74). In addition, we effectively recovered associations from an article collection of chronic pulmonary diseases, and discovered that a large proportion is not reported in current databases. Our results demonstrate that GeDex, despite its simplicity, is a competitive tool that can successfully assist the curation of existing databases.AvailabilityGeDex is available athttps://bitbucket.org/laigen/gedex/src/master/and can be used as a docker imagehttps://hub.docker.com/r/laigen/gedexContactcmendezc@ccg.unam.mxSupplementary informationSupplementary material are available atbioRxivonline.
annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions
Authors: Gaonkar Krutika S. , Marini Federico , Rathi Komal S. , Jain Payal , Zhu Yuankun , Chimicles Nicholas A. , Brown Miguel A. , Naqvi Ammar S. , Zhang Bo , Storm Phillip B. , Maris John M. , Raman Pichai , Resnick Adam C. , Strauch Konstantin , Taroni Jaclyn N. , Rokita Jo Lynne 
Published: November 2019

AbstractBackgroundGene fusion events are a significant source of somatic variation across adult and pediatric cancers and are some of the most clinically-effective therapeutic targets, yet low consensus of RNA-Seq fusion prediction algorithms makes therapeutic prioritization difficult. In addition, events such as polymerase read-throughs, mis-mapping due to gene homology, and fusions occurring in healthy normal tissue require informed filtering, making it difficult for researchers and clinicians to rapidly discern gene fusions that might be true underlying oncogenic drivers of a tumor and in some cases, appropriate targets for therapy.ResultsWe developed annoFuse, an R package, and shinyFuse, a companion web application, to annotate, prioritize, and explore biologically-relevant expressed gene fusions, downstream of fusion calling. We validated annoFuse using a random cohort of TCGA RNA-Seq samples (N = 160) and achieved a 96% sensitivity for retention of high-confidence fusions (N = 603). annoFuse uses FusionAnnotator annotations to filter non-oncogenic and/or artifactual fusions. Then, fusions are prioritized if previously reported in TCGA and/or fusions containing gene partners that are known oncogenes, tumor suppressor genes, COSMIC genes, and/or transcription factors. We applied annoFuse to fusion calls from pediatric brain tumor RNA-Seq samples (N = 1,028) provided as part of the Open Pediatric Brain Tumor Atlas (OpenPBTA) Project to determine recurrent fusions and recurrently-fused genes within different brain tumor histologies. annoFuse annotates protein domains using the PFAM database, assesses reciprocality, and annotates gene partners for kinase domain retention. As a standard function, reportFuse enables generation of a reproducible R Markdown report to summarize filtered fusions, visualize breakpoints and protein domains by transcript, and plot recurrent fusions within cohorts. Finally, we created shinyFuse for algorithm-agnostic interactive exploration and plotting of gene fusions.ConclusionsannoFuse provides standardized filtering and annotation for gene fusion calls from STARFusion and Arriba by merging, filtering, and prioritizing putative oncogenic fusions across large cancer datasets, as demonstrated here with data from the OpenPBTA project. We are expanding the package to be widely-applicable to other fusion algorithms and expect annoFuse to provide researchers a method for rapidly evaluating, prioritizing, and translating fusion findings in patient tumors.
The astrocyte-enriched geneCG11000plays a crucial role in the development, locomotion and lifespan ofD. melanogaster
Authors: Najafi Hadi , Wong Kyle , Salkini Ammar , Miao Hongyu , Kim Woo Jae 
Published: November 2019

AbstractThe brain ofDrosophila melanogasteris a complex organ with various cell types, orchestrating the physiology and behaviors of the fly. While each cell type in theDrosophilabrain is known to express a unique set of genes, their complete genetic profile is still unknown. Advances in the RNA-sequencing techniques at single cell resolution facilitate identifying novel cell type-specific markers and/or examining the specificity of the available markers.In this study, exploiting a single cell RNA sequencing data ofDrosophilaoptic lobe (which comprises two thirds of the brain with extensive cell type diversity), we first categorized cell types based on their known molecular markers, then the genes with enriched expression in astrocytes were identified. Consistent with previous findings, the known glial markersCG34335,Inx2andnrv2as well as the astrocytic genesCG9394,Eaat1,Gat,Gs2andCG1552exhibited enriched expression in the identified astrocyte cluster. Moreover, we identifiedCG11000as a gene with positive expression correlation with the astrocytic markerEaat1. The positive expression correlation betweenCG11000andEaat1genes was also observed in the single-cell RNA-sequencing data ofDrosophilamid-brain as well as in the bulk RNA-sequencing data ofDrosophilawhole brain during development.Immunostaining of the brains dissected from adult flies showed overlapping fluorescence signals ofCG11000andEaat1expression, supporting co-expression of these genes in a set of single cells inDrosophilaoptic lobe. At the physiological level, RNAi-mediated suppression ofCG11000impeded th normal development of male flies without any effects on females. In adult flies,CG11000suppression affected the locomotion activity and lifespan ofD. melanogasterin an astrocyte-specific manner, suggesting pivotal role ofCG11000gene in astrocytes.
Cell cycle S-phase arrest drives cell extrusion
Authors: Dwivedi Vivek K. , Pardo-Pastor Carlos , Droste Rita , Denning Daniel P. , Rosenblatt Jody , Horvitz H. Robert 
Published: November 2019

SUMMARYCell extrusion is a process of cell elimination in which a cell is squeezed out from its tissue of origin. Extrusion occurs in organisms as diverse as sponges, nematodes, insects, fish and mammals. Defective extrusion is linked to many epithelial disorders, including cancer. Despite broad occurrence, cell-intrinsic triggers of extrusion conserved across phyla are generally unknown. We combined genome-wide genetic screens with live-imaging studies ofC. elegansembryos and mammalian epithelial cultures and found that S-phase arrest induced extrusion in both. Cells extruded fromC. elegansembryos exhibited S-phase arrest, and RNAi treatments that specifically prevent S-phase entry or arrest blocked cell extrusion. Pharmacological induction of S-phase arrest was sufficient to promote cell extrusion from a canine epithelial monolayer. Thus, we have discovered an evolutionarily conserved cell-cycle-dependent trigger of cell extrusion. We suggest that S-phase-arrest induced cell extrusion plays a key role in physiology and disease.
Blue/green light-responsive cyanobacteriochromes are cell shade sensors in red-light replete niches
Authors: Enomoto Gen , Ikeuchi Masahiko 
Published: November 2019

SummaryPhotoautotrophic cyanobacteria have developed sophisticated light response systems to capture and utilize the energy and information of incident light [1]. Cyanobacteria-specific photoreceptors cyanobacteriochromes (CBCRs) are distantly related to more widespread phytochromes. CBCRs show the most diverse spectral properties among the naturally occurring photoreceptors, typified by a unique and prevailing blue/green light-absorbing variant [2–6]. However, where the CBCR-mediated ‘colorful’ signaling systems function in nature has been elusive. We previously reported that the three CBCRs SesA/B/C synthesize/degrade a bacterial second messenger cyclic diguanylate (c-di-GMP) in response to blue/green light [6–8]. The cooperative action of SesA/B/C enables blue light-ON and green light-OFF regulation of the c-di-GMP-dependent cell aggregation of the thermophilic cyanobacteriumThermosynechococcus vulcanus[8, 9]. Here, we report that SesA/B/C can serve as a physiological sensor of cell density. Because cyanobacterial cells show lower transmittance of blue light than green light, higher cell density gives more green light-enriched irradiance to cells. The cell density-dependent suppression of cell aggregation under blue/green-mixed light and white light conditions support this idea. Such a sensing mechanism may provide information about the cell position in cyanobacterial mats in hot springs, the natural habitat ofThermosynechococcus. This cell position-dependent SesA/B/C-mediated regulation of cellular sessility (aggregation) might be ecophysiologically essential for the reorganization and growth of phototrophic mats. We also report that the green light-induced dispersion of cell aggregates requires red light-driven photosynthesis. Blue/green CBCRs might work as shade detectors in a different niche than red/far-red phytochromes, which may be why CBCRs have evolved in cyanobacteria.
Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches
Authors: Pedersen Brent S , Bhetariya Preeti J , Brown Joe , Marth Gabor , Jensen Randy L , Bronner Mary P , Underhill Hunter R , Quinlan Aaron R 
Published: November 2019

AbstractWhen interpreting sequencing data from multiple spatial or longitudinal biopsies, detecting sample mix-ups is essential yet more difficult than in studies of germline variation. In most genomic studies of tumors, genetic variation is frequently detected through pairwise comparisons of the tumor and a matched normal tissue from the sample donor, and in many cases, only somatic variants are reported. The disjoint genotype information that results hinders the use of existing tools that detect sample swaps solely based on genotypes of germline variants. To address this problem, we have developedsomalier, which can operate directly on the alignments, so as not to require jointly-called germline variants. Instead,somalierextracts a smallsketchof informative genetic variation for each sample. Sketches from hundreds of biopsies and normal tissues can then be compared in under a second. This speed also makes it useful for checking relatedness in large cohorts of germline samples. Somalier produces both text output and an interactive visual report that facilitates the detection and correction of sample swaps using multiple relatedness metrics. We introduce the tool and demonstrate its utility on a cohort of five glioma samples each with a normal, tumor, and cell-free DNA sample. Applyingsomalierto high-coverage sequence data from the 1000 Genomes Project also identifies several related samples.Somaliercan be applied to diverse sequencing data types and genome builds, and is freely available for academic use atgithub.com/brentp/somalier.
Patterns of socio-cognitive stratification and perinatal risk in the child brain
Authors: Alnæs Dag , Kaufmann Tobias , Marquand Andre F. , Smith Stephen M. , Westlye Lars T. 
Published: November 2019

AbstractThe expanding behavioral repertoire of the developing brain during childhood and adolescence is shaped by complex brain-environment interactions and flavored by unique life experiences. The transition into young adulthood offer opportunities for adaptation and growth, but also increased susceptibility to environmental perturbations, such as the characteristics of social relationships, family environment, quality of schools and activities, financial security, urbanization and pollution, drugs, cultural practices, and values, that all act in concert with our genetic architecture and biology. Our multivariate brain-behavior mapping in 7,577 children aged 9-11 years across 585 brain imaging phenotypes, and 617 cognitive, behavioral, psychosocial and socioeconomic measures revealed three population modes of brain co-variation, which were robust as assessed by cross-validation and permutation testing, taking into account siblings and twins, identified using genetic data. The first mode revealed traces of perinatal complications, including pre-term and twin-birth, eclampsia and toxemia, shorter period of breast feeding and lower cognitive scores, with higher cortical thickness and lower cortical areas and volumes. The second mode reflected a pattern of socio-cognitive stratification, linking lower cognitive ability and socioeconomic status to lower cortical thickness, area and volumes. The third mote captured a pattern related to urbanicity, with particulate matter pollution (PM25) inversely related to home value, walkability and population density, associated with diffusion properties of white matter tracts. These results underscore the importance of a multidimensional and interdisciplinary understanding, integrating social, psychological and biological sciences, to map the constituents of healthy development and to identify factors that may precede maladjustment and mental illness.
Computational Model Predicts Paracrine and Intracellular Drivers of Fibroblast Phenotype After Myocardial Infarction
Authors: Zeigler Angela C. , Nelson Anders R. , Chandrabhatla Anirudha S. , Brazhkina Olga , Holmes Jeffrey W. , Saucerman Jeffrey J. 
Published: November 2019

AbstractThe fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking of the proliferative phase required paired stimuli (e.g. TGFβ and angiotensin-II). Virtual overexpression screens with static cytokine pairs and after myocardial infarction predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the proliferative phase. NADPH oxidase overexpression sustained collagen expression from proliferative to maturation phases, driven by TGFβ and endothelin positive feedback loops. Interleukin-1 overexpression suppressed collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feedforward loops, but it then later sustained collagen expression due to the TGFβ positive feedback loop. These model-based predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction.
Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins
Authors: López-Cortés Andrés , Cabrera-Andrade Alejandro , Vázquez-Naya José M. , Pazos Alejandro , Gonzáles-Díaz Humberto , Paz-y-Miño César , Guerrero Santiago , Pérez-Castillo Yunierkis , Tejera Eduardo , Munteanu Cristian R. 
Published: November 2019

ABSTRACTBackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design.MethodsThis work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features.ResultsThe performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 ± 0.0037 and accuracy of 0.936 ± 0.0056 (3-fold cross-validation). Regarding the prediction of 4504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1.ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository athttps://github.com/muntisa/neural-networks-for-breast-cancer-proteins.
SUMOylation targets shugoshin to stabilize sister kinetochore biorientation
Authors: Su Xue Bessie , Wang Menglu , Schaffner Claudia , Clift Dean , Nerusheva Olga O. , Tammasalu Triin , Wallek Andreas , Wu Yehui , Kelly David A. , Jeyaprakash A. Arockia , Storchova Zuzana , Hay Ronald , Marston Adèle L. 
Published: November 2019

AbstractThe accurate segregation of chromosomes during mitosis relies on the attachment of sister chromatids to microtubules from opposite poles, called biorientation. Sister chromatid cohesion resists microtubule forces, generating tension which provides the signal that biorientation has occurred. How tension silences the surveillance pathways that prevent cell cycle progression and correct erroneous kinetochore-microtubule remains unclear. Here we identify SUMOylation as a mechanism that promotes anaphase onset upon biorientation. SUMO ligases modify the tension-sensing pericentromere-localized chromatin protein, shugoshin, to stabilize bioriented sister kinetochore-microtubule attachments. In the absence of SUMOylation, Aurora B kinase removal from kinetochores is delayed. Shugoshin SUMOylation prevents its binding to protein phosphatase 2A (PP2A) and release of this interaction is important for stabilizing sister kinetochore biorientation. We propose that SUMOylation modulates the kinase-phosphatase balance within pericentromeres to inactivate the error correction machinery, thereby allowing anaphase entry in response to biorientation.
Graph Convolutional Networks for Epigenetic State Prediction Using Both Sequence and 3D Genome Data
Authors: Lanchantin Jack , Qi Yanjun 
Published: November 2019

AbstractPredictive models of DNA epigenetic state such as transcription factor binding are essential for understanding regulatory processes and developing gene therapies. It is known that the 3D genome, or spatial structure of DNA, is highly influential in the epigenetic state. Deep neural networks have achieved state of the art performance on epigenetic state prediction by using short windows of DNA sequences independently. These methods, however, ignore the long-range dependencies when predicting the epigenetic states because modeling the 3D genome is challenging. In this work, we introduce ChromeGCN, a graph convolutional network for epigenetic state prediction by fusing both local sequence and long-range 3D genome information. By incorporating the 3D genome, we relax the i.i.d. assumption of local windows for a better representation of DNA. ChromeGCN explicitly incorporates known long-range interactions into the modeling, allowing us to identify and interpret those important long-range dependencies in influencing epigenetic states. We show experimentally that by fusing sequential and 3D genome data using ChromeGCN, we get a significant improvement over the state-of-the-art deep learning methods as indicated by three metrics. Importantly, we show that ChromeGCN is particularly useful for identifying epigenetic effects in those DNA windows that have a high degree of interactions with other DNA windows.
Disrupting pathogenic interactions between α-synuclein, c-Abl, and redox stress
Authors: Ghosh Soumitra , Won Seok Joon , Fong Rebecca , Butler Nicholas J. M. , Moss Arianna , Wong Candance , Pan June , Sanchez Jennifer , Wu Long , Wang Jiejie , Manfredsson Fredric P. , Swanson Raymond A. 
Published: November 2019

ABSTRACTObjectiveRedox stress, c-Abl activation, and α-synuclein aggregates each independently contribute to neurodegeneration in Parkinson’s disease. Interactions between these factors may underlie convergent and feed-forward mechanisms of disease progression.Methodsα-synuclein aggregate formation was induced in neuronal cultures and mouse substantia nigra by exposure to pre-formed human α-synuclein fibrils or by AAV-mediated over-expression of α-synuclein. Aggregate formation, c-Abl activation, redox stress, and neurodegeneration were evaluated by immunohistochemistry and Western blots, and mouse motor function was evaluated using the rota-rod and pole tests. To suppress redox stress, cultures and mice were treated with N-acetyl cysteine, a thiol repletion agent that supports neuronal glutathione metabolism.ResultsIn primary neuron cultures, the formation of α-synuclein aggregates led to redox stress and c-Abl activation. Redox stress alone, in the absence of α-synuclein aggregates, was also sufficient to induced c-Abl activation. N-acetyl cysteine suppressed redox stress, and likewise suppressed both c-Abl activation and α-synuclein aggregation. A similar pattern was observed in the two mouse models of Parkinson’s disease. In both models, α-synuclein aggregates in the substantia nigra were accompanied by redox stress, c-Abl activation, dopaminergic neurodegeneration and motor impairment, all of which were attenuated in mice treated with oral N-acetyl cysteine.InterpretationThese results indicate that α-synuclein aggregates induce c-Abl activation by a redox stress mechanism. c-Abl in turn promotes α-synuclein aggregation, and this potentially feed-forward process can be blocked by N-acetyl cysteine. The findings thus add mechanistic support for N-acetyl cysteine as a therapeutic for Parkinson’s disease.