bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deciphering of Somatic Mutational Signatures of Cancer
Authors: Ji Xiangwen , Wang Edwin , Cui Qinghua 
Published: March 2022

AbstractSomatic mutational signatures (MSs) identified by genome sequencing play important roles in exploring the cause and development of cancer. Thus far, many such signatures have been identified, and some of them do imply causes of cancer. However, a major bottleneck is that we do not know the potential meanings (i.e., cancer causal or biological functions) and contributing genes for most of them. Here we presented a computational framework, Gene Somatic Genome Pattern (GSGP), which can decipher the molecular mechanisms of the MSs. More importantly, it is the first time, GSGP is able to process MSs from RNA sequencing, which greatly extended the applications of both MS analysis and RNA sequencing. As a result, GSGP analysis matches consistently with previous reports and identify the aetiologies for a number of novel signatures. Notably, we applied GSGP to RNA sequencing data and revealed an RNA-derived MS involved in deficient DNA mismatch repair (dMMR) and microsatellite instability (MSI) in colorectal cancer (CRC).
Sexual deprivation induces a CRF independent stress response and decreases resistance to stressors inDrosophilavia a subpopulation of Neuropeptide F receptor-expressing neurons
Authors: Ryvkin Julia , Shmueli Anat , Levi Mali , Jacob Avi , Shalit Tali , Bentzur Assa , Agranovich Bella , Abramovich Ifat , Gottlieb Eyal , Nässel Dick R. , Shohat-Ophir Galit 
Published: March 2022

AbstractLiving in a changing environment composed of other behaving animals entails both opportunities and challenges to obtain resources and mating partners. Actions that promote survival and reproduction are reinforced by the brain reward systems, whereas coping with the challenges associated with obtaining these rewards are mediated by stress response pathways. The activation of the latter can impair health and shorten lifespan. Although similar responses to social opportunity and challenge exist across the animal kingdom, little is known about the mechanisms that process reward and stress under different social conditions. Here, we studied the interplay between deprivation of sexual reward and stress response inDrosophila melanogaster anddiscovered that repeated failures to obtain sexual reward induces a frustration-like state that is characterized by increased arousal, persistent sexual motivation, and impaired ability to cope with starvation and oxidative stressors. We show that this increased arousal and sensitivity to starvation is mediated by disinhibition of neurons that express receptors for the fly homologue of neuropeptide Y (neuropeptide F, NPF). We furthermore demonstrate the existence of an anatomical overlap between stress and reward systems in the fly brain in the form of neurons that co-express receptors for NPF (NPFR) and the corticotropin-releasing factor (CRF)-like homologue Diuretic hormone 44 (Dh44), and that deprivation of sexual reward leads to translocation of forkhead box-subgroup O (FoxO) to the cytoplasm in these neurons. Nevertheless, the activity of Dh44 neurons alone does not mediate sensitivity to starvation and aroused behavior following sexual deprivation, instead, these responses are mediated by disinhibition of ~12-16 NPFR-expressing neurons via a dynamin-independent synaptic signaling mechanism, suggesting the existence of a NPFR mediated stress pathway which is Dh44-independent. This paves the path for using simple model organisms to dissect mechanisms behind anticipation of reward, and more specifically, to determine what happens when expectations to obtain natural and drug rewards are not met.
Macrosystem community change in lake phytoplankton and its implications for diversity and function
Authors: Weigel Benjamin , Kotamäki Niina , Malve Olli , Vuorio Kristiina , Ovaskainen Otso 
Published: March 2022

ABSTRACTThe combined effects of eutrophication, land-use and climate change are major threats to aquatic ecosystems, their biodiversity and integrity in sustaining ecosystem functions. Disentangling the mechanisms by which environmental change contributes to community assembly processes and species niches remains challenging, especially at macro-ecological scales. Here, we collated phytoplankton community data including 853 lakes along a 1200km latitudinal gradient, monitored over four decades, to quantify the spatio-temporal and scale-dependent environmental impacts on species niches and assembly processes while accounting for species traits and phylogenetic constraints. Our results demonstrate the emergence of novel and widespread community composition clusters in previously more uniform communities. While total species richness remained relatively stable, changes in community weighted mean traits of the clusters indicate functional differences. A robust phylogenetic signal of species responses to the environment indicates strong niche conservatism and low taxonomic dispersion. Our findings imply profound spatio-temporal structuring of species co-occurrence patterns and highlight emerging functional differences of lake phytoplankton communities to environmental change over space and time.
The network makeup artist (NORMA-2.0): Distinguishing annotated groups in a network using innovative layout strategies
Authors: Karatzas Evangelos , Koutrouli Mikaela , Baltoumas Fotis , Papanikolopoulou Katerina , Bouyioukos Costas , Pavlopoulos Georgios A. 
Published: March 2022

AbstractMotivationNetwork biology is a dominant player in today’s multi-omics era. Therefore, the need for visualization tools which can efficiently cope with intra-network heterogeneity emerges.ResultsNORMA-2.0 is a web application which uses efficient layouts to group together areas of interest in a network. In this version, NORMA-2.0 utilizes three different strategies to make such groupings as distinct as possible while it preserves all of the properties from its first version where one can handle multiple networks and annotation files simultaneously.AvailabilityThe web resource is available at http://norma.pavlopouloslab.info/The source code is freely available at https://github.com/PavlopoulosLab/NORMAContactpavlopoulos@fleming.gr
Robust Inference of Bi-Directional Causal Relationships in Presence of Correlated Pleiotropy with GWAS Summary Data
Authors: Xue Haoran , Pan Wei 
Published: March 2022

AbstractTo infer a causal relationship between two traits, several correlation-based causal direction (CD) methods have been proposed with the use of SNPs as instrumental variables (IVs) based on GWAS summary data for the two traits; however, none of the existing CD methods can deal with SNPs with correlated pleiotropy. Alternatively, reciprocal Mendelian randomization (MR) can be applied, which however may perform poorly in the presence of (unknown) invalid IVs, especially for bi-directional causal relationships. In this paper, first, we propose a CD method that performs better than existing methods regardless of the presence of correlated pleiotropy. Second, along with a simple but yet effective IV screening rule, we propose applying a closely related and state-of-the-art MR method in reciprocal MR, showing its almost identical performance to that of the new CD method when their model assumptions hold; however, if the modeling assumptions are violated, the new CD method is expected to better control type I errors. Notably bi-directional causal relationships impose some unique challenges beyond those for uni-directional ones, and thus requiring special treatments. For example, we point out for the first time several scenarios where a bi-directional relationship, but not a uni-directional one, can unexpectedly cause the violation of some weak modeling assumptions commonly required by many robust MR methods. Finally we applied the proposed methods to 12 risk factors and 4 common diseases, confirming mostly well-known uni-directional causal relationships, while identifying some novel and plausible bi-directional ones such as between BMI and T2D, and between BMI and CAD.
Bumblebees navigate using path integration while walking
Authors: Patel Rickesh N. , Kempenaers Julian , Heinze Stanley 
Published: March 2022

SUMMARYPath integration is a computational strategy that allows an animal to maintain an internal estimate of its position relative to a point of origin. Many species use path integration to navigate back to specific locations, typically their homes, after lengthy and convoluted excursions. Hymenopteran insects are impressive path integrators, directly returning to their hives after hundreds of meters of outward travel. Recent neurobiological insights have established hypotheses for how path integration may be mediated by the brains of bees, but clear ways to test these hypotheses in the laboratory are currently unavailable. Here we report that the bumblebee, Bombus terrestris, uses path integration while walking over short distances in an indoor arena. They estimate accurate vector distances after displacement and orient by artificial celestial cues. Walking bumblebees also exhibited systematic search patterns when home vectors failed to lead them accurately back to the nest, closely resembling searches performed by other species in natural conditions. We thus provide a robust experimental system to test navigation behavior in the laboratory that reflects most aspects of natural path integration. Importantly, we established this assay in an animal that is both readily available and resilient to invasive manipulations. In the future, our behavioral assay therefore can be combined with current electrophysiological techniques, opening a path towards directly probing the neural basis of the sophisticated vector navigation abilities of bees.
Cellular and clinical impact of Protein Phosphatase Enzyme epigenetic silencing in multiple cancer tissues
Authors: Joshi Ricky S. , Esteller Manel 
Published: March 2022

AbstractProtein Phosphatase Enzymes (PPE) and protein kinases simultaneously control phosphorylation mechanisms that tightly regulate intracellular signaling pathways and stimulate cellular responses. In human malignancies, PPE and protein kinases are frequently mutated resulting in uncontrolled kinase activity and PPE suppression, leading to cell proliferation, migration and resistance to anti-cancer therapies. Cancer associated DNA hypermethylation at PPE promoters gives rise to transcriptional silencing (epimutations) and is a hallmark of cancer. Despite recent advances in sequencing technologies, data availability and computational capabilities, only a fraction of PPE have been reported as transcriptionally inactive as a consequence of epimutations. Using the Infinium HumanMethylation450 BeadChip, we compared DNA methylation profiles from 705 cancer patients across 5 major tissues and 3 cancer cell models against a cohort of healthy controls. Here, we report epimutations in PPE (and their interacting proteins or PPEIP) are a frequent occurrence in the cancer genome and manifest independent of transcriptional activity. We observed that different tumors have varying susceptibility to epimutations and identify specific cellular signalling networks that are primarily affected by epimutations. Additionally, RNA-seq analysis showed the negative impact of epimutations on most (not all) Protein Tyrosine Phosphatase transcription. Finally, we detected novel clinical biomarkers that inform on patient mortality and anti-cancer treatment sensitivity. We propose that DNA hypermethylation marks at PPEIP frequently contribute to the pathogenesis of malignancies and within the precision medicine space, hold promise as biomarkers to inform on clinical features such as patient survival and therapeutic response.
Broadly neutralizing anti-S2 antibodies protect against all three human betacoronaviruses that cause severe disease
Authors: Zhou Panpan , Song Ge , He Wan-ting , Beutler Nathan , Tse Longping V. , Martinez David R. , Schäfer Alexandra , Anzanello Fabio , Yong Peter , Peng Linghang , Dueker Katharina , Musharrafieh Rami , Callaghan Sean , Capozzola Tazio , Yuan Meng , Liu Hejun , Limbo Oliver , Parren Mara , Garcia Elijah , Rawlings Stephen A. , Smith Davey M. , Nemazee David , Jardine Joseph G. , Wilson Ian A. , Safonova Yana , Rogers Thomas F. , Baric Ralph S. , Gralinski Lisa E. , Burton Dennis R. , Andrabi Raiees 
Published: March 2022

AbstractPan-betacoronavirus neutralizing antibodies may hold the key to developing broadly protective vaccines against coronaviruses that cause severe disease, for anticipating novel pandemic-causing viruses, and to respond more effectively to SARS-CoV-2 variants. The emergence of the Omicron variant of SARS-CoV-2 has illustrated the limitations of solely targeting the receptor binding domain (RBD) of the envelope Spike (S)-protein. Here, we isolated a large panel of broadly neutralizing antibodies (bnAbs) from SARS-CoV-2 recovered-vaccinated donors that target a conserved S2 region in the fusion machinery on betacoronavirus spikes. Select bnAbs show broadin vivoprotection against all three pathogenic betacoronaviruses, SARS-CoV-1, SARS-CoV-2 and MERS-CoV, that have spilled over into humans in the past 20 years to cause severe disease. The bnAbs provide new opportunities for antibody-based interventions and key insights for developing pan-betacoronavirus vaccines.
Improved mammalian retromer cryo-EM structures reveal a new assembly interface
Authors: Kendall Amy K. , Chandra Mintu , Xie Boyang , Wan William , Jackson Lauren P. 
Published: March 2022

AbstractRetromer (VPS26/VPS35/VPS29 subunits) assembles with multiple sorting nexin (SNX) proteins on membranes to mediate endosomal recycling of transmembrane protein cargoes. Retromer has been implicated in other cellular events, including mitochondrial homeostasis, nutrient sensing, autophagy, and fission events. Mechanisms for mammalian retromer assembly remain undefined, and retromer engages multiple sorting nexin proteins to sort cargoes to different destinations. Published structures demonstrate mammalian retromer forms oligomers in vitro, but several structures were poorly resolved. We report here improved retromer oligomer structures using single particle cryo-electron microscopy (cryo-EM) by combining data collected from tilted specimens with multiple improvements in data processing, including using a three-dimensional (3D) starting model for improved automated particle picking in RELION. A retromer mutant (3KE retromer) that breaks VPS35-mediated interfaces was used to determine a structure of a new assembly interface formed by the VPS26A and VPS35 N-termini. The interface reveals how an N-terminal VPS26A arrestin saddle can link retromer chains by engaging a neighboring VPS35 N-terminus, on the opposite side from the well-characterized C-VPS26/N-VPS35 interaction observed within heterotrimers. The new interaction interface exhibits substantial buried surface area (∼7,000 Å2) and further suggests metazoan retromer may serve as an adaptable scaffold.
Effect of repeated explicit instructions on visuomotor adaptation and intermanual transfer
Authors: Werner Susen , Strüder Heiko K. 
Published: March 2022

AbstractThe aim of the present study was to investigate the effect of repeated explicit instructions on visuomotor adaptation, awareness, and intermanual transfer. In a comprehensive study design, 48 participants performed center-out reaching movements before and during exposure to a 60° rotation of visual feedback. Awareness and intermanual transfer were then determined. Twelve participants each were assigned to one of the following adaptation conditions: gradual adaptation, sudden adaptation without instructions, sudden adaptation with a single instruction before adaptation, and sudden adaptation with multiple instructions before and during adaptation. The explicit instructions explained the nature of the visual feedback perturbation and were given using an illustration of a clock face. Analysis of adaptation indices revealed neither increased nor decreased adaptation after repeated instructions compared with a single instruction. Our data also show less adaptation with gradual than with sudden adaptation and less adaptation without than with instruction. These differences were pronounced at the beginning of adaptation; however, by the end of adaptation, all four groups reached similar levels of adaptation. In addition, we found significant group differences for the awareness index, with lower awareness after gradual adaptation than after sudden, instructed adaptation. Intermanual transfer did not differ between groups. However, we found a significant correlation between the awareness and intermanual transfer indices. We conclude that the magnitude of the explicit process cannot be further increased by repeated instruction and that intermanual transfer appears to be largely related to the explicit adaptation process.
A mass spectrometry-based atlas of extracellular matrix proteins across 25 mouse organs
Authors: McCabe Maxwell C. , Saviola Anthony J. , Hansen Kirk C. 
Published: March 2022

AbstractThe extracellular matrix is a critical non-cellular component of multi-cellular organisms containing a variety of proteins, glycoproteins, and proteoglycans which has been implicated in a wide variety of essential biological processes, including development, wound healing, and aging. Due to low solubility, many ECM proteins have been underrepresented in previous proteomics datasets. Using an optimized 3-step decellularization and ECM extraction method involving chaotrope extraction and digestion via hydroxylamine hydrochloride, we have generated coverage of the matrisome across 25 organs. We observe that the top 100 most abundant proteins from the ECM fractions of all tissues are generally present in all tissues, indicating that tissue matrices are principally composed of a shared set of ECM proteins. However, these proteins vary up to 4,000-fold between tissues, resulting in highly unique matrix profiles even with the same primary set of proteins. A data reduction approach was used to reveal related networks of expressed ECM proteins across varying tissues, including basement membrane and collagen subtypes.
Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data
Authors: Yassue Rafael Massahiro , Galli Giovanni , Fritsche-Neto Roberto , Morota Gota 
Published: March 2022

AbstractRecent technological advances in high-throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyper-spectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize lines with or without plant growth-promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1021 nm and 13,826 single-nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth-related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyper-spectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral-based phenotyping for management and phenomic prediction studies.Core ideasHyperspectral reflectance data can classify plant growth-promoting bacteria inoculation statusPhenomic prediction performs better than genomic prediction depending on the target phenotypeAutoML is a promising approach for automating hyperparameter tuning for classification and prediction
Protein-Ligand Interaction Graphs: Learning from Ligand-Shaped 3D Interaction Graphs to Improve Binding Affinity Prediction
Authors: Moesser Marc A. , Klein Dominik , Boyles Fergus , Deane Charlotte M. , Baxter Andrew , Morris Garrett M. 
Published: March 2022

AbstractGraph Neural Networks (GNNs) have recently gained in popularity, challenging molecular fingerprints or SMILES-based representations as the predominant way to represent molecules for binding affinity prediction. Although simple ligand-based graphs alone are already useful for affinity prediction, better performance on multi-target datasets has been achieved with models that incorporate 3D structural information. Most recent advances utilize complex GNN architectures to capture 3D protein-ligand information by incorporating ligand-interacting protein atoms as additional nodes in the graphs; or by building a second protein-based graph in parallel. This expands the graph considerably while obfuscating the shape of the underlying ligand, diminishing the advantage that GNNs have when encoding molecular structures. There is therefore a need for a simple and elegant molecular graph representation that retains the topology of the ligand while simultaneously encoding 3D protein-ligand interactions.We present Protein-Ligand Interaction Graphs (PLIGs): a simple way of representing atom-atom contacts of 3D protein-ligand complexes as node features for GNNs. PLIGs featurize an atom node in the molecular graph by describing each atom’s properties as well as all atom-atom contacts made with protein atoms within a distance threshold. The edges of the graph are therefore identical to ligand-based graphs, but the nodes encode the 3D protein-ligand contacts. Since PLIGs are applicable to any GNN architecture, we have benchmarked their performance with six different GNN architectures, and compared them to conventional ligand-based graphs and fingerprint-based multi-layer perceptron (MLP) models using the CASF-2016 benchmark set where we found PLIG-based Graph Attention Networks (GATNet) to be the best performing model (ρ=0.84, RMSE=1.22 pK). In summary, we created a novel graph-based representation that incorporates 3D structural information into the node features of ligand-shaped molecular graphs. The PLIG representation is simple, elegant, flexible and easily customizable, opening up many possibilities of incorporating other 2D and 3D properties into the graph.AccessThe code and implementation for PLIGs and all models can be found at github.com/MarcMoesser/Protein-Ligand-Interaction-Graphs.
Pliable synaptic fidelity and excitatory inter-synaptic crosstalk in the intact brain
Authors: Reynolds James P. , Jensen Thomas P. , Rama Sylvain , Zheng Kaiyu , Savtchenko Leonid P. , Rusakov Dmitri A. 
Published: March 2022

AbstractMemory formation in neural circuits may involve changes in synaptic efficacy and in cell intrinsic excitability, yet how this process unfolds in the living brain has remained elusive. Here, we employed multiplexed imaging of genetically encoded indicators of glutamate and Ca2+ in mouse barrel cortex to detect increased fidelity coupled with reduced excitation of thalamocortical connections that undergo whisker-stimulation induced LTP. High-resolution imaging revealed that whisker stimuli trigger excitatory synaptic activity that generates extrasynaptic glutamate transients reaching the bulk of neighbouring synapses in the target cortical area. Our findings pave the way to understanding basic plasticity features of the synaptic connectome while revealing a significant component of volume-transmitted glutamatergic signalling among cells in the intact brain.One Sentence SummarySensory-stimulation LTP increases fidelity while reducing excitation at individual thalamocortical connections which generate spatially intersecting glutamate discharges