bluuebunny/crossref_metadata_2025_split huggingface dataset:
Happy you, happy me: expressive changes on a stranger’s voice recruit faster implicit processes than self-produced expressions
Authors: Rachman Laura , Dubal Stéphanie , Aucouturier Jean-Julien 
Published: January 2019

AbstractIn social interactions, people have to pay attention both to thewhatandwho. In particular, expressive changes heard on speech signals have to be integrated with speaker identity, differentiating e.g. self- and other-produced signals. While previous research has shown that self-related visual information processing is facilitated compared to non-self stimuli, evidence in the auditory modality remains mixed. Here, we compared electroencephalography (EEG) responses to expressive changes in sequence of self- or other-produced speech sounds, using a mismatch negativity (MMN) passive oddball paradigm. Critically, to control for speaker differences, we used programmable acoustic transformations to create voice deviants which differed from standards in exactly the same manner, making EEG responses to such deviations comparable between sequences. Our results indicate that expressive changes on a stranger’s voice are highly prioritized in auditory processing compared to identical changes on the self-voice. Other-voice deviants generate earlier MMN onset responses and involve stronger cortical activations in a left motor and somatosensory network suggestive of an increased recruitment of resources for less internally predictable, and therefore perhaps more socially relevant, signals.
Topoisomerase 2β-dependent nuclear DNA damage shapes extracellular growth factor responses through AKT phosphorylation dynamics to control virus latency
Authors: Hu Hui-Lan , Shiflett Lora A. , Kobayashi Mariko , Chao Moses V. , Wilson Angus C. , Mohr Ian , Huang Tony T. 
Published: January 2019

SUMMARYThe mTOR pathway integrates both extracellular and intracellular signals and serves as a central regulator of cell metabolism, growth, survival and stress responses. Neurotropic viruses, such as herpes simplex virus-1 (HSV-1), also rely on cellular AKT-mTORC1 signaling to achieve viral latency. Here, we define a novel genotoxic response whereby spatially separated signals initiated by extracellular neurotrophic factors and nuclear DNA damage are integrated by the AKT-mTORC1 pathway. We demonstrate that endogenous DNA double-strand breaks (DSBs) mediated by Topoisomerase 2β-DNA cleavage complex (TOP2βcc) intermediates are required to achieve AKT-mTORC1 signaling and maintain HSV-1 latency in neurons. Suppression of host DNA repair pathways that remove TOP2βcc trigger HSV-1 reactivation. Moreover, perturbation of AKT phosphorylation dynamics by downregulating the PHLPP1 phosphatase led to AKT mis-localization and disruption of DSB-induced HSV-1 reactivation. Thus, the cellular genome integrity and environmental inputs are consolidated and co-opted by a latent virus to balance lifelong infection with transmission.
The CUL5 ubiquitin ligase complex mediates resistance to CDK9 and MCL1 inhibitors in lung cancer cells
Authors: Kabir Shaheen , Cidado Justin , Andersen Courtney , Dick Cortni , Lin Pei-Chun , Mitros Therese , Ma Hong , Baik Ron , Belmonte Matthew , Drew Lisa , Corn Jacob 
Published: January 2019

ABSTRACTOverexpression of anti-apoptotic proteins MCL1 and Bcl-xL is a frequent event in blood and solid cancers. Inhibitors targeting MCL1 are in clinical development, however many cancer models are intrinsically resistant to this approach. To discover mechanisms underlying resistance to MCL1 inhibition, we performed multiple flow-cytometry based genome-wide CRISPR screens that interrogate two drugs directly or indirectly targeting MCL1. Remarkably, both screens identified three components (CUL5, RNF7 and UBE2F) of a cullin-RING ubiquitin ligase complex (CRL5) that resensitized cells to MCL1 inhibition. We find that levels of the BH3-only pro-apoptotic proteins Bim and Noxa are proteasomally regulated by the CRL5 complex. Accumulation of Noxa caused by depletion of CRL5 components particularly skewed the balance in favor of apoptosis when cells were challenged with an MCL1 inhibitor. Discovery of a novel role of CRL5 in apoptosis and resistance to MCL1 inhibitors exposes new drug targets and the potential to improve combination treatments.
Molecular evolution across developmental time reveals rapid divergence in early embryogenesis
Authors: Cutter Asher D. , Garrett Rose H. , Mark Stephanie , Wang Wei , Sun Lei 
Published: January 2019

AbstractOntogenetic development hinges on the changes in gene expression in time and space within an organism, suggesting that the demands of ontogenetic growth can impose or reveal predictable pattern in the molecular evolution of genes expressed dynamically across development. Here we characterize co-expression modules of the C. elegans transcriptome, using a time series of 30 points from early-embryo to adult. By capturing the functional form of expression profiles with quantitative metrics, we find fastest evolution in the distinctive set of genes with transcript abundance that declines through development from a peak in young embryos. These genes are highly enriched for oogenic function (maternal provisioning), are non-randomly distributed in the genome, and correspond to a life stage especially prone to inviability in inter-species hybrids. These observations conflict with the “early conservation model” for the evolution of development, though expression-weighted sequence divergence analysis provides some support for the “hourglass model.” Genes in co-expression modules that peak toward adulthood also evolve fast, being hyper-enriched for roles in spermatogenesis, implicating a history of sexual selection and relaxation of selection on sperm as key factors driving rapid change to ontogenetically distinguishable co-expression modules of genes. We propose that these predictable trends of molecular evolution for dynamically-expressed genes across ontogeny predispose particular life stages, early embryogenesis in particular, to hybrid dysfunction in the speciation process.Impact SummaryThe development of an organism from a single-celled embryo to a reproductive adult depends on dynamic gene expression over developmental time, with natural selection capable of shaping the molecular evolution of those differentially-expressed genes in distinct ways. We quantitatively analyzed the dynamic transcriptome profiles across 30 timepoints in development for the nematode C. elegans. In addition to rapid evolution of adult-expressed genes with functional roles in sperm, we uncovered the unexpected result that the distinctive set of genes that evolve fastest are those with peak expression in young embryos, conflicting with some models of the evolution of development. The rapid molecular evolution of genes in early embryogenesis contrasts with the exceptional conservation of embryonic cell lineages between species, and corresponds to a developmental period that is especially sensitive to inviability in inter-species hybrid embryos. We propose that these predictable trends of molecular evolution for dynamically-expressed genes across development predispose particular life stages, early embryogenesis in particular, to hybrid dysfunction in the speciation process.
Post-hoc modification of linear models: combining machine learning with domain information to make solid inferences from noisy data
Authors: van Vliet Marijn , Salmelin Riitta 
Published: January 2019

AbstractLinear machine learning models “learn” a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the desired transformation is limited by the quality and size of the example dataset, which in neuroimaging studies is often notoriously noisy and small. In these cases, it is desirable to fine-tune the learned linear model using domain information beyond the example dataset. To this end, we present a framework that decomposes the weight matrix of a fitted linear model into three subcomponents: the data covariance, the identified signal of interest, and a normalizer. Inspecting these subcomponents in isolation provides an intuitive way to inspect the inner workings of the model and assess its strengths and weaknesses. Furthermore, the three subcomponents may be altered, which provides a straightforward way to inject prior information and impose additional constraints. We refer to this process as “post-hoc modification” of a model and demonstrate how it can be used to achieve precise control over which aspects of the model are fitted to the data through machine learning and which are determined through domain information. As an example use case, we decode the associative strength between words from electroencephalography (EEG) reading data. Our results show how the decoding accuracy of two example linear models (ridge regression and logistic regression) can be boosted by incorporating information about the spatio-temporal nature of the data, domain information about the N400 evoked potential and data from other participants.HighlightsWe present a framework to decompose any linear model into three subcomponents that are straightforward to interpret.By modifying the subcomponents before re-assembling them into a linear model, prior information and further constraints may be injected into the model.As an example, we boost the performance of a linear regressor and classifier by injecting knowledge about the spatio-temporal nature of the data, the N400 evoked potential and data from other participants.
zGrad: A nanobody-based degron system to inactivate proteins in zebrafish
Authors: Yamaguchi Naoya , Colak-Champollion Tugba , Knaut Holger 
Published: January 2019

AbstractThe analysis of protein function is essential to modern biology. While protein function has mostly been studied through gene or RNA interference, more recent approaches to degrade proteins directly have been developed. Here, we adapted the anti-GFP nanobody-based system deGradFP from flies to zebrafish. We named this system zGrad and show that zGrad efficiently degrades transmembrane, cytosolic and nuclear GFP-tagged proteins in zebrafish in an inducible and reversible manner. Using tissue-specific and inducible promoters in combination with functional GFP-fusion proteins, we demonstrate that zGrad can inactivate transmembrane, cytosolic and nuclear proteins globally, locally and temporally with different consequences. Global protein depletion results in phenotypes similar to loss of gene activity while local and temporal protein inactivation yields more restricted and novel phenotypes. Thus, zGrad is a versatile tool to study the spatial and temporal requirement of proteins in zebrafish.
Oxytocin reduces top-down control of attention by increasing bottom-up attention allocation to social but not non-social stimuli
Authors: Xu Xiaolei , Li Jialin , Chen Zhuo , Kendrick Keith M. , Becker Benjamin 
Published: January 2019

AbstractThe neuropeptide oxytocin (OXT) may facilitate attention to social stimuli by influencing early stage bottom-up processing although findings in relation to different emotional expressions are inconsistent and its influence on top-down cognitive processing mechanisms unclear. In the current double-blind, placebo (PLC) controlled, between subject design study we therefore recruited 71 male subjects to investigate the effects of intranasal OXT (24IU) on both bottom-up attention allocation and top-down attention inhibition using a visual antisaccade paradigm with concurrent eye movement acquisition. Our results show that OXT increased antisaccade errors for social stimuli (all types of emotional faces), but not shapes. This effect of OXT was modulated by trait behavioral inhibition and there was also evidence for reduced state anxiety after OXT treatment. Antisaccades are under volitional control and therefore this indicates that OXT treatment produced reduced top-down inhibition. However, the overall findings are consistent with OXT acting to reduce top-down control of attention as a result of increasing bottom-up early attentional processing of social, but not non-social, stimuli in situations where the two systems are in potential conflict. This effect of OXT is also modulated by individual levels of trait behavioral inhibition possibly as a result of an anxiolytic action.
Killer whale genomes reveal a complex history of recurrent admixture and vicariance
Authors: Foote Andrew D. , Martin Michael D. , Louis Marie , Pacheco George , Robertson Kelly M. , Sinding Mikkel-Holger S. , Amaral Ana R. , Baird Robin W. , Baker C. Scott , Ballance Lisa , Barlow Jay , Brownlow Andrew , Collins Tim , Constantine Rochelle , Dabin Willy , Dalla Rosa Luciano , Davison Nicholas J. , Durban John W. , Esteban Ruth , Ferguson Steven H. , Gerrodette Tim , Guinet Christophe , Bradley Hanson M. , Hoggard Wayne , Matthews Cory J. D. , Samarra Filipa I. P. , de Stephanis Renaud , Tavares Sara B. , Tixier Paul , Totterdell John A. , Wade Paul , Gilbert M. Thomas P. , Wolf Jochen B.W. , Morin Phillip A. 
Published: January 2019

AbstractReconstruction of the demographic and evolutionary history of populations assuming a consensus tree-like relationship can mask more complex scenarios, which are prevalent in nature. An emerging genomic toolset, which has been most comprehensively harnessed in the reconstruction of human evolutionary history, enables molecular ecologists to elucidate complex population histories. Killer whales have limited extrinsic barriers to dispersal and have radiated globally, and are therefore a good candidate model for the application of such tools. Here, we analyse a global dataset of killer whale genomes in a rare attempt to elucidate global population structure in a non-human species. We identify a pattern of genetic homogenisation at lower latitudes and the greatest differentiation at high latitudes, even between currently sympatric lineages. The processes underlying the major axis of structure include high drift at the edge of species’ range, likely associated with founder effects and allelic surfing during post-glacial range expansion. Divergence between Antarctic and non-Antarctic lineages is further driven by ancestry segments with up to four-fold older coalescence time than the genome-wide average; relicts of a previous vicariance during an earlier glacial cycle. Our study further underpins that episodic gene flow is ubiquitous in natural populations, and can occur across great distances and after substantial periods of isolation between populations. Thus, understanding the evolutionary history of a species requires comprehensive geographic sampling and genome-wide data to sample the variation in ancestry within individuals.
NMDAR-activated PP1 dephosphorylates GluN2B to modulate NMDAR synaptic content
Authors: Chiu Andrew M. , Wang Jiejie , Fiske Michael P. , Hubalkova Pavla , Barse Levi , Gray John A. , Sanz-Clemente Antonio 
Published: January 2019

SUMMARYIn mature neurons, postsynaptic NMDARs are segregated into two populations, synaptic and extrasynaptic, which differ in localization, function, and associated intracellular cascades. These two pools are connected via lateral diffusion, and receptor exchange between them modulates synaptic NMDAR content. Here, we identify the phosphorylation of the PDZ-ligand of the GluN2B subunit of NMDARs (at S1480) as a critical determinant in dynamically controlling NMDAR synaptic content. We find that phosphorylation of GluN2B at S1480 maintains NMDARs at extrasynaptic membranes as part of a protein complex containing Protein Phosphatase 1 (PP1). Global activation of NMDARs leads to the activation of PP1, which mediates dephosphorylation of GluN2B at S1480 to promote an increase in synaptic NMDAR content. Thus, PP1-mediated dephosphorylation of the GluN2B PDZ-ligand modulates the synaptic expression of NMDARs in mature neurons in an activity-dependent manner, a process with profound consequences for synaptic and structural plasticity, metaplasticity, and synaptic neurotransmission.HIGHLIGHTSPhosphorylation of the PDZ-ligand of the GluN2B subunit of NMDARs (GluN2B-pS1480) maintains NMDARs at extrasynaptic sites.Extrasynaptic NMDARs form a stable protein complex containing PP1.Global NMDAR activation increases NMDAR synaptic content by promoting PP1-mediated dephosphorylation of GluN2B-pS1480.GluN2B-pS1480 dephosphorylation is mediated by a PP1 subpopulation not involved in LTD and is enhanced by age.
High-throughput allelic expression imbalance analyses identify candidate breast cancer risk genes
Authors: Marjaneh Mahdi Moradi , Sivakumaran Haran , Hillman Kristine M , Kaufmann Susanne , Hussein Nehal , Lima Luize G , Ham Sunyoung , Kar Siddhartha , Beesley Jonathan , Fachal Laura , Easton Douglas F , Dunning Alison M , Möller Andreas , Chenevix-Trench Georgia , Edwards Stacey L , French Juliet D 
Published: January 2019

ABSTRACTFine-mapping of breast cancer GWAS regions has identified 195 high confidence signals containing more than 5,000 credible causal variants (CCVs). The CCVs are predominantly noncoding and enriched in regulatory elements and thus may confer the risk by altering gene expression in cis. We analyzed allelic expression imbalance (AEI) of genes surrounding known breast cancer signals, using normal breast and breast tumor transcriptome data and imputed genotypes. Fourteen genes, including NTN4, were identified whose expression was associated with CCV genotype. We showed that CCVs at this signal were located within an enhancer that physically interacts with the NTN4 promoter. Furthermore, knockdown of NTN4 in breast cells increased cell proliferation in vitro and tumor growth in vivo. Here, we present the most comprehensive AEI analysis of breast cancer CCVs resulting in identification of new candidate risk genes.
GuiltyTargets: Prioritization of Novel Therapeutic Targets with Deep Network Representation Learning
Authors: Muslu Özlem , Hoyt Charles Tapley , Hofmann-Apitius Martin , Fröhlich Holger 
Published: January 2019

AbstractThe majority of clinical trial failures are caused by low efficacy of investigated drugs, often due to a poor choice of target protein. Computational prioritization approaches aim to support target selection by ranking candidate targets in the context of a given disease. We propose a novel target prioritization approach, GuiltyTargets, which relies on deep network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled machine learning for candidate ranking. We evaluated our approach on six diseases of different types (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.94, significantly outperforming a previous approach, which relies on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. Applying GuiltyTargets to Alzheimer’s disease resulted into a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson’s disease, highlighting the potential for target repositioning of our method.AvailabilityThe GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets.Author summaryMany drug candidates fail in clinical trials due to low efficacy. One of the reasons is the choice of the wrong target protein, i.e. perturbation of the protein does not effectively modulate the disease phenotype on a molecular level. In consequence many patients do not demonstrate a clear response to the drug candidate. Traditionally, targets are selected based on evidence from the literature and follow-up experiments. However, this process is very labor intensive and often biased by subjective choices. Computational tools could help a more rational and unbiased choice of target proteins and thus increase the chance of drug discovery programs. In this work we propose a novel machine learning based method for target candidate ranking. The method (GuiltyTargets) captures properties of known targets to learn a ranking of candidates. GuiltyTargets compares favorably against existing machine learning based target prioritization methods and allowed us to propose novel targets for Alzheimer’s disease.
Highly efficient homology-directed repair using transient CRISPR/Cpf1-geminiviral replicon in tomato
Authors: Vu Tien Van , Sivankalyani Velu , Kim Eun-Jung , Doan Duong Thi Hai , Tran Mil Thi , Kim Jihae , Sung Yeon Woo , Park Minwoo , Kang Yang Jae , Kim Jae-Yean 
Published: January 2019

ABSTRACTGenome editing via the homology-directed repair (HDR) pathway in somatic plant cells is very inefficient compared to error-prone repair by nonhomologous end joining (NHEJ). Here, we increased HDR-based genome editing efficiency approximately 3-fold compared to a Cas9-based single-replicon system via the use of de novo multi-replicon systems equipped with CRISPR/LbCpf1 in tomato and obtained replicon-free but stable HDR alleles. The efficiency of CRISPR/LbCpf1-based HDR was significantly modulated by physical culture conditions such as temperature and light. Ten days of incubation at 31°C under a light/dark cycle after Agrobacterium-mediated transformation resulted in the best performance among the tested conditions. Furthermore, we developed our single-replicon system into a multi-replicon system that effectively increased HDR efficiency. Although this approach is still challenging, we showed the feasibility of HDR-based genome editing of a salt-tolerant SlHKT1;2 allele without genomic integration of antibiotic markers or any phenotypic selection. Self-pollinated offspring plants carrying the HKT1;2 HDR allele showed stable inheritance and germination tolerance in the presence of 100 mM NaCl. Our work may pave the way for transgene-free editing of alleles of interest in asexually as well as sexually reproducing plants.
Molecular recognition of M1-linked ubiquitin chains by native and phosphorylated UBAN domains
Authors: Herhaus Lina , Bedem Henry van den , Tang Sean , Wakatsuki Soichi , Dikic Ivan , Rahighi Simin 
Published: January 2019

AbstractAlthough the Ub-binding domain in ABIN proteins and NEMO (UBAN) is highly conserved, UBAN-containing proteins exhibit different Ub-binding properties, resulting in their diverse biological roles. Post-translational modifications further control UBAN domain specificity for poly-Ub chains. However, precisely, how the UBAN domain structurally confers such functional diversity remains poorly understood. Here we report crystal structures of ABIN-1 alone and in complex with one or two M1-linked di-Ub chains. ABIN-1 UBAN forms a homo-dimer that provides two symmetrical Ub-binding sites on either side of the coiled-coil structure. Moreover, crystal structures of ABIN1 UBAN in complex with di-Ub chains reveal a concentration-dependency of UBAN/di-Ub binding stoichiometry. Analysis of UBAN/M1-linked di-Ub binding characteristics indicates that phosphorylated S473 in OPTN and its corresponding phospho-mimetic residue in ABIN-1 (E484) are essential for high affinity interactions with M1-linked Ub chains. Also, a phospho-mimetic mutation of A303 in NEMO, corresponding to S473 of OPTN, increases binding affinity for M1-linked Ub chains. These findings are in line with the diverse physiological roles of UBAN domains, as phosphorylation of OPTN UBAN is required to enhance its binding to Ub during mitophagy.