bluuebunny/crossref_metadata_2025_split huggingface dataset:
Patient-derived models of brain metastases recapitulate the histopathology and biology of human metastatic cancers
Authors: Faria Claudia C. , Custódia Carlos , Cascão Rita , Paisana Eunice , Carvalho Tânia , Pereira Pedro , Roque Rafael , Pimentel José , Miguéns José , Barata João T. 
Published: November 2020

ABSTRACTPurposeDissemination of cancer cells from primary tumors to the brain is observed in the great majority of cancer patients, contributing to increased morbidity and being the main cause of death. Most mechanistic and preclinical studies have relied on aggressive cancer cell lines, which fail to represent tumor heterogeneity and are unsuitable to validate therapies due to fast cancer progressionin vivo.Experimental designWe established a unique library of subcutaneous and intracardiac patient-derived xenografts (PDXs) of brain metastases (BMs) from eight distinct primary tumor origins. Cancer progression in mice was compared to the matched patient clinical outcome, metastatic dissemination pattern and histopathological features. Preclinical studies with FDA approved drugs were performed.ResultsIn vivotumor formation of flank-implanted BMs correlated with patients’ poor survival and serial passaging increased tumor aggressiveness. Subcutaneous xenografts originated spontaneous metastases in 61% of the cases, including in the leptomeningeal space (21%). The intracardiac model increased the tropism to the brain and leptomeninges (46%). Strikingly, 62% of intracardiac PDXs shared metastatic sites with the donor patients, including the primary cancer organ and the central nervous system (CNS). Of therapeutic relevance, PDX-derived cultures and corresponding mouse xenografts can be effectively treated with targeted anticancer drugs.ConclusionsPatient-derived models of BMs recapitulate the biology of human metastatic disease and can be a valuable translational platform for precision medicine.TRANSLATIONAL RELEVANCESubcutaneous and intracardiac mouse xenografts of human brain metastases exhibit a spontaneous dissemination pattern that resembles patients’ metastatic disease. The preclinical testing of targeted anticancer drugs using patient-derived cultures and patient-derived xenografts of brain metastasis showed an effective therapeutic response. These translational models represent an outstanding tool to advance the understanding of the biology of brain metastases and to foster the rapid discovery of novel therapeutics.
RangeShifter 2.0: An extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes
Authors: Bocedi Greta , Palmer Stephen C. F. , Malchow Anne-Kathleen , Zurell Damaris , Watts Kevin , Travis Justin M. J. 
Published: November 2020

AbstractProcess-based models are becoming increasingly used tools for understanding how species are likely to respond to environmental changes and to potential management options. RangeShifter is one such modelling platform, which has been used to address a range of questions including identifying effective reintroduction strategies, understanding patterns of range expansion and assessing population viability of species across complex landscapes.Here we introduce a new version, RangeShifter 2.0, which incorporates important new functionality. It is now possible to simulate dynamics over user-specified, temporally changing landscapes. Additionally, the genetic and evolutionary capabilities have been strengthened, notably by introducing an explicit genetic modelling architecture, which allows for simulation of neutral and adaptive genetic processes. Furthermore, emigration, transfer and settlement rules can now all evolve, allowing for sophisticated simulation of the evolution of dispersal. We illustrate the potential application of RangeShifter 2.0’s new functionality by two examples. The first illustrates the range expansion of a virtual species across a dynamically changing UK landscape. The second demonstrates how the software can be used to explore the concept of evolving connectivity in response to land-use modification, by examining how movement rules come under selection over landscapes of different structure and composition.RangeShifter 2.0 is built using object-oriented C++ providing computationally efficient simulation of complex individual-based, eco-evolutionary models. The code has been redeveloped to enable use across operating systems, including on high performance computing clusters, and the Windows GUI has been enhanced. Furthermore, the recoding of the package has supported the development of a new version running under the R platform, RangeShiftR.RangeShifter 2.0 will facilitate the development of in-silico assessments of how species will respond to environmental changes and to potential management options for conserving or controlling them. By making the code available open source, we hope to inspire further collaborations and extensions by the ecological community.
Predicting the impact of sequence motifs on gene regulation using single-cell data
Authors: Hepkema Jacob , Lee Nicholas Keone , Stewart Benjamin J. , Ruangroengkulrith Siwat , Charoensawan Varodom , Clatworthy Menna R. , Hemberg Martin 
Published: November 2020

BackgroundBinding of transcription factors (TFs) at proximal promoters and distal enhancers is central to gene regulation. Yet, identification of TF binding sites, also known as regulatory motifs, and quantification of their impact on gene expression remains challenging.ResultsHere we infer putative regulatory motifs along with their cell type-specific importance using a convolutional neural network trained on single-cell data. Comparison of the importance score to expression levels across cells allows us to identify the TFs most likely to be binding at a given motif. Using multiple mouse tissues we obtain a model with cell type resolution which explains 29% of the variance in gene expression. Finally, by applying scover to distal enhancers identified using scATAC-seq from the mouse cerebral cortex we characterize changes in distal regulatory motifs during development.ConclusionsIt is possible to identify regulatory motifs as well as their importance from single-cell data using a neural network model where all of the parameters and outputs are easily interpretable to the user.
Naturalistic affective behaviors decoded from spectro-spatial features of multi-day human intracranial recordings
Authors: Bijanzadeh Maryam , Khambhati Ankit N. , Desai Maansi , Wallace Deanna L. , Shafi Alia , Dawes Heather E. , Sturm Virginia E. , Chang Edward F. 
Published: November 2020

AbstractTask-based studies have uncovered distributed neural networks that support emotions, but little is known about how these networks produce affective behaviors in non-laboratory, ecological settings. We obtained continuous intracranial electroencephalography (iEEG) recordings from the emotion network in 11 patients with epilepsy during multi-day hospitalizations. We coded naturalistic affective behaviors (spontaneous expressions of positive or negative affect) from 116 hours of time-locked video recordings obtained over multiple days from subjects’ hospital rooms and utilized data driven classifiers to determine whether we could decode naturalistic affective behaviors from the neural data. Results indicated that binary within-subject random forest models could decode positive and negative affective behaviors from affectless behaviors (behaviors lacking valence) with up to 93% accuracy. Across the emotion network, positive and negative affective behaviors were associated with increased high frequency activity and decreased lower frequency activity. The anterior insula, amygdala, hippocampus, and anterior cingulate cortex (ACC) made strong contributions to affective behaviors in general. In a subset of subjects, three-state decoders distinguished among the positive, negative, and affectless behaviors using the spectro-spatial features from the emotion network. This study demonstrates that multi-day, highly resolved iEEG recordings in cortical and deep brain structures can reveal the circuit-level physiology of affective behaviors. By measuring behavior in an ecologically valid setting, our findings provide novel insights into the spatially distributed dynamics of local neural populations underlying naturalistic affective behaviors.SignificancePrevious neuroimaging and neurophysiological studies have identified a distributed network that supports emotions, but much remains unknown about how this network produces affective behaviors in ecological settings. We used intracranial electroencephalography recordings from the emotion network to decode naturalistic affective behaviors—spontaneous expressions of positive and negative affect that occurred during multi-day hospital stays—from neural data in patients with epilepsy. Our results complement prior neuroscientific studies of emotion and offer novel insights into the spectral and spatial dynamics of the emotion network that characterize naturalistic affective behaviors. The present study suggests intracranial electroencephalography can uncover new details about emotion network physiology and help to expand current neuroanatomical frameworks of human emotions.
Revisiting the Neural Architecture of Adolescent Decision-Making: Univariate and Multivariate Evidence for System-Based Models
Authors: Moreira João F. Guassi , Méndez Leal Adriana S. , Waizman Yael H. , Saragosa-Harris Natalie , Ninova Emilia , Silvers Jennifer A. 
Published: November 2020

AbstractUnderstanding adolescent decision-making is significant for informing basic models of neurodevelopment as well as for the domains of public health and criminal justice. System-based theories posit that adolescent decision-making is guided by activity amongst reward and control processes. While successful at explaining behavior, system-based theories have received inconsistent support at the neural level, perhaps because of methodological limitations. Here, we used two complementary approaches to overcome said limitations and rigorously evaluate system-based models. Using decision-level modeling of fMRI data from a risk-taking task in a sample of 2000+ decisions across 51 human adolescents (25 females, mean age = 15.00 years), we find support for system-based theories of decision-making. Neural activity in lateral prefrontal cortex and a multivariate pattern of cognitive control both predicted a reduced likelihood of risk-taking, whereas increased activity in the nucleus accumbens predicted a greater likelihood of risk-taking. Interactions between decision-level brain activity and age were not observed. These results garner support for system-based accounts of adolescent decision-making behavior.Significance StatementAdolescent decision-making behavior is of great import for basic science, and carries equally consequential implications for public health and criminal justice. While dominant psychological theories seeking to explain adolescent decision-making have found empirical support, their neuroscientific implementations have received inconsistent support. This may be partly due to statistical approaches employed by prior neuroimaging studies of system-based theories. We used brain modeling—an approach that predicts behavior from brain activity—of univariate and multivariate neural activity metrics to better understand how neural components of psychological systems guide decision behavior in adolescents. We found broad support for system-based theories such that neural systems involved in cognitive control predicted a reduced likelihood to make risky decisions, whereas value-based systems predicted greater risk-taking propensity.
FrustratometeR: an R-package to compute local frustration in protein structures, point mutants and MD simulations
Authors: Rausch Atilio O. , Freiberger Maria I. , Leonetti Cesar O. , Luna Diego M. , Radusky Leandro G. , Wolynes Peter G. , Ferreiro Diego U. , Parra R. Gonzalo 
Published: November 2020

Once folded, natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i. e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and molecular dynamics (MD) trajectories, allowing straightforward integration of local frustration analysis into pipelines for protein structural analysis.Contactgonzalo.parra@embl.deAvailability and implementationhttps://github.com/proteinphysiologylab/frustratometeR
Study of Real-Valued Distance Prediction For Protein Structure Prediction with Deep Learning
Authors: Li Jin , Xu Jinbo 
Published: November 2020

AbstractInter-residue distance prediction by deep ResNet (convolutional residual neural network) has greatly advanced protein structure prediction. Currently the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet. Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then employ a novel method to fold a protein by the predicted mean and deviation. Our findings include: 1) tested on the CASP13 FM (free-modeling) targets, our real-valued distance prediction obtains 81% precision on top L/5 long-range contact prediction, much better than the best CASP13 results (70%); 2) our real-valued prediction can predict correct folds for the same number of CASP13 FM targets as the best CASP13 group, despite generating only 20 decoys for each target; 3) our method greatly outperforms a very new real-valued prediction method DeepDist in both contact prediction and 3D structure modeling; and 4) when the same deep ResNet is used, our real-valued distance prediction has 1-6% higher contact and distance accuracy than our own discrete-valued prediction, but less accurate 3D structure models.
New Structural Insights into the Function of the Catalytically Active Human Taspase1
Authors: Nagaratnam Nirupa , Delker Silvia L. , Jernigan Rebecca , Edwards Thomas E. , Snider Janey , Thifault Darren , Williams Dewight , Nannenga Brent L. , Stofega Mary , Sambucetti Lidia , Hsieh James J. , Flint Andrew J. , Fromme Petra , Martin-Garcia Jose M. 
Published: November 2020

AbstractProteases can play essential roles in severe human pathology, ranging from degenerative and inflammatory illnesses to infectious diseases, with some, such as Taspase1, involved in growth and progression of tumors at primary and metastatic sites. Taspase1 is a N-terminal nucleophile (Ntn)-hydrolase overexpressed in primary human cancers, coordinating cancer cell proliferation, invasion, and metastasis. Loss of Taspase1 activity disrupts proliferation of human cancer cellsin vitroand in mouse xenograft models of glioblastoma, thus this protein has the potential to become a novel anticancer drug target. It belongs to the family of Ntn-hydrolases, a unique family of proteins synthesized as enzymatically inactive proenzymes that become activated upon cleavage of the peptide bond on the N-terminal side of a threonine residue, which then becomes the catalytic site nucleophile. The activation process simultaneously changes the conformation of a long domain at the C-terminus of the alpha-subunit for which no full-length structural information exists and its function is poorly understood. Here we present a novel cloning strategy to generate a fully active, circularly permuted form of Taspase1 to determine the crystallographic structure of catalytically active human Taspase1 to 3.04Å. We discovered that this region forms a long helical domain and is indispensable for the catalytic activity of Taspase1. Together, our study highlights the importance of this element for the enzymatic activity of Ntn-hydrolases and suggests that this long domain could be a novel target for the design of inhibitors with the potential to be developed into anticancer therapeutics.
DANGO: Predicting higher-order genetic interactions
Authors: Zhang Ruochi , Bafna Mihir , Ma Jianzhu , Ma Jian 
Published: November 2020

AbstractHigher-order genetic interactions, which have profound implications for understanding the molecular mechanisms of phenotypic variation, remain poorly characterized. Most studies to date have focused on pairwise interactions, as designing high-throughput experimental screenings for the vast combinatorial search space of higher-order molecular interactions is dauntingly challenging. Here, we develop Dango, a computational method based on a self-attention hypergraph neural network, designed to effectively predict higher-order genetic interaction among groups of genes. As a proof-of-concept, we provide comprehensive predictions for over 400 million trigenic interactions in the yeastS. cerevisiae, significantly expanding the quantitative characterization of such interactions. Our results demonstrate that Dangoaccurately predicts trigenic interactions, uncovering both known and novel biological functions related to cell growth. We further incorporate protein embeddings and model uncertainty scoring to enhance the biological relevance and interpretability of the predicted interactions. Moreover, the predicted interactions can serve as powerful genetic markers for growth response under diverse conditions. Together, Dangoenables a more complete map of complex genetic interactions that impinge upon phenotypic diversity.
Linear integration of sensory evidence over space and time underlies face categorization
Authors: Okazawa Gouki , Sha Long , Kiani Roozbeh 
Published: November 2020

AbstractVisual object recognition relies on elaborate sensory processes that transform retinal inputs to object representations, but it also requires decision-making processes that read out object representations and function over prolonged time scales. The computational properties of these decision-making processes remain underexplored for object recognition. Here, we study these computations by developing a stochastic multi-feature face categorization task. Using quantitative models and tight control of spatiotemporal visual information, we demonstrate that humans categorize faces through an integration process that first linearly adds the evidence conferred by task-relevant features over space to create aggregated momentary evidence, and then linearly integrates it over time with minimum information loss. Discrimination of stimuli along different category boundaries (e.g., identity or expression of a face) is implemented by adjusting feature weights of spatial integration. This linear but flexible integration process overspaceandtimebridges past studies on simple perceptual decisions to complex object recognition behavior.
Hemoglobin drives inflammation and initiates antigen spread and nephritis in lupus
Authors: Sharma Hritika , Bose Anjali , Kumar Uma , Pal Rahul 
Published: November 2020

AbstractHemoglobin (Hb) has well-documented inflammatory effects and is normally efficiently scavenged; clearance mechanisms can be overwhelmed during conditions of erythrocyte lysis, a condition that may occur in systemic lupus erythematosus. Whether Hb is preferentially inflammatory in lupus and additionally induces autoreactivity against prominent autoantigens was assessed. Peripheral blood mononuclear cells derived from SLE patients secreted higher levels of lupus-associated inflammatory cytokines when incubated with Hb, effects negated by haptoglobin. Hb (more particularly, ferric Hb) triggered the preferential release of lupus-associated cytokines from splenocytes, B cells, CD4 T cells, CD8 T cells and plasmacytoid dendritic cells isolated from aging NZM2410 mice, and also had mitogenic effects on B cells. Ferric Hb activated multiple signaling pathways which were differentially responsible for the generation of specific cytokines; inflammatory signaling also appeared to be cell-context dependent. Pull-downs, followed by mass spectrometry, revealed interactions of Hb with several lupus-associated autoantigens; co-incubation of ferric Hb with apoptotic blebs (structures which contain packaged autoantigens, believed to trigger lupus autoreactivity) revealed synergies (in terms of cytokine release and autoantibody productionin vitro) that were also restricted to the lupus genotype. Infusion of ferric Hb into NZM2410 mice led to enhanced release of lupus-associated cytokines, the generation of a spectrum of autoantibodies, and enhanced-onset glomerulosclerosis. Given that the biased recognition of ferric Hb in a lupus milieu, in concert with lupus-associated autoantigens, elicits the generation of inflammatory cytokines from multiple immune cell types and stimulates the generation of potentially pathogenic autoantibodies, neutralization of Hb could have beneficial effects.
Investigations into modifications of neuromuscular physiology by axonal transport disruptions inDrosophilaSOD1 mutants
Authors: O’Harrow Tristan C. D. G. , Ueda Atsushi , Xing Xiaomin , Wu Chun-Fang 
Published: November 2020

ABSTRACTThe reactive oxygen species (ROS)-scavenging enzyme Cu/Zn superoxide dismutase (SOD1) is an evolutionarily conserved mechanism for the maintenance of oxidative homeostasis, and missense mutations in the human SOD1 gene are associated with the motor neuron degenerative disease amyotrophic lateral sclerosis (ALS). Mutations in theDrosophila melanogasterSOD1 gene (Sod) shorten fly lifespan, attenuate motor function, and induce developmental mortality. We have previously found morphological and physiological defects at the neuromuscular junctions (NMJs) of hypomorphicSodn108and ALS modelSodG85Rmutant larvae. Here, we report genetic interactions causing striking modifications ofSodmutant phenotypes by mutations in the genePrickle(Pk), which are linked to planar cell polarity, epilepsy, and axonal transport disruptions.Pkis expressed as two isoformsprickle-spiny-legs(sple) andprickle-prickle(pk), which are each suppressed by specific hypomorphic mutations (sple1andpk1). Interestingly,Sodphenotypes are distinctly modified depending on both thePkisoform suppressed, and whether said suppression is heterozygous or homozygous. Heterozygoussple1andpk1improved the developmental survival ofSodmutants, whereas homozygoussple1andpk1drastically increased mortality. Further, only heterozygoussple1andpk1clearly ameliorated morphological defects at the neuromuscular junctions ofSodmutant larvae. Pharmacological treatment ofSodmutants reveals allele-specific motor neuron terminal hyperexcitability, characterized by synaptic transmissions of extended duration and abnormal presynaptic Ca2+transients. Heterozygoussple1mutation suppresses thisSodmutant terminal hyperexcitability, butpk1does not. We reversed this suppressive effect ofsple1by pharmacological blockade of Ca2+-activated K+channelslowpoke. Altogether, this study builds on our prior knowledge ofSodmutant development and physiology to show that axonal transport-linked gene mutations strikingly modifySodmutant phenotypes, providing strong evidence for a role of intracellular transport in alterations of neuromuscular morphology and physiology by SOD1 mutations.