bluuebunny/crossref_metadata_2025_split huggingface dataset:
Eps8 is a convergence point integrating EGFR and integrin trafficking and crosstalk
Authors: Paul Nikki R , Thomas Joanna R , Maldonado Horacio , Wolanska Katarzyna I , Koper Ewa J , Humphries Jonathan D , Byron Adam , George Adams , Allen Nathan , Prior Ian A , Streuli Charles H , Humphries Martin J , Morgan Mark R 
Published: September 2018

AbstractCrosstalk between adhesion and growth factor receptors plays a critical role in tissue morphogenesis and repair, and aberrations contribute substantially to neoplastic disease. However, the mechanisms by which adhesion and growth factor receptor signalling are integrated, spatially and temporally, are unclear.We used adhesion complex enrichment coupled with quantitative proteomic analysis to identify rapid changes to adhesion complex composition and signalling following growth factor stimulation. Bioinformatic network and ontological analyses revealed a substantial decrease in the abundance of adhesion regulatory proteins and co-ordinators of endocytosis within 5 minutes of EGF stimulation. Together these data suggested a mechanism of EGF-induced receptor endocytosis and adhesion complex turnover.Combinatorial interrogation of the networks allowed a global and dynamic view of adhesion and growth factor receptor crosstalk to be assembled. By interrogating network topology we identified Eps8 as a putative node integrating α5β1 integrin and EGFR functions. Importantly, EGF stimulation promoted internalisation of both α5β1 and EGFR. However, perturbation of Eps8 increased constitutive internalisation of α5β1 and EGFR; suggesting that Eps8 constrains α5β1 and EGFR endocytosis in the absence of EGF stimulation. Consistent with this, Eps8 regulated Rab5 activity and was required for maintenance of adhesion complex organisation and for EGF-dependent adhesion complex disassembly. Thus, by co-ordinating α5β1 and EGFR trafficking mechanisms, Eps8 is able to control adhesion receptor and growth factor receptor bioavailability and cellular contractility.We propose that during tissue morphogenesis and repair, Eps8 functions to spatially and temporally constrain endocytosis, and engagement, of α5β1 and EGFR in order to precisely co-ordinate adhesion disassembly, cytoskeletal dynamics and cell migration.
More partners, more ranges: generalist legumes spread more easily around the globe
Authors: Harrison Tia L. , Simonsen Anna K. , Stinchcombe John R. , Frederickson Megan E. 
Published: September 2018

AbstractHow does mutualism affect range expansion? On one hand, mutualists might thrive in new habitats thanks to the resources, stress tolerance, or defense provided by their partners. On the other, specialized mutualists might fail to find compatible partners beyond their range margins, limiting further spread. A recent global analysis of legume ranges found that non-symbiotic legumes have been successfully introduced to more ranges than legumes that form symbioses with rhizobia, but there is still abundant unexplained variation in introduction success within symbiotic legumes. Here, we test the hypothesis that generalist legumes have spread to more introduced ranges than specialist legumes. We used published data and rhizobial 16S rRNA sequences from GenBank to quantify the number of rhizobia partners that associate with each of 159 legume species, spanning the legume phylogeny and the globe. We found that generalist legumes occur in more introduced ranges than specialist legumes, suggesting that among mutualists, specialization hinders range expansions.
mTADA: a framework for analyzing de novo mutations in multiple traits
Authors: Nguyen Hoang T. , Dobbyn Amanda , Buxbaum Joseph , Pinto Dalila , Purcell Shaun M , Sullivan Patrick F , He Xin , Stahl Eli A. 
Published: September 2018

AbstractJoint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. In addition, approaches that consider multiple traits can aid in the characterization of shared genetic etiology among those traits. In recent years, parent-offspring trio studies have reported an enrichment ofde novomutations (DNMs) in neuropsychiatric disorders. The analysis of DNM data in the context of neuropsychiatric disorders has implicated multiple putatively causal genes, and a number of reported genes are shared across disorders. However, a joint analysis method designed to integrate de novo mutation data from multiple studies has yet to be implemented. We here introduce multi pi e-trait TAD A (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. mTADA uses two single-trait analysis data sets to estimate the proportion of overlapping risk genes, and reports genes shared between and specific to the relevant disorders. We applied mTADA to >13,000 trios for six disorders: schizophrenia (SCZ), autism spectrum disorder (ASD), developmental disorders (DD), intellectual disability (ID), epilepsy (EPI), and congenital heart disease (CHD). We report the proportion of overlapping risk genes and the specific risk genes shared for each pair of disorders. A total of 153 genes were found to be shared in at least one pair of disorders. The largest percentages of shared risk genes were observed for pairs of DD, ID, ASD, and CHD (>20%) whereas SCZ, CHD, and EPI did not show strong overlaps In risk gene set between them. Furthermore, mTADA identified additional SCZ, EPI and CHD risk genes through integration with DDde novomutation data. For CHD, using DD information, 31 risk genes with posterior probabilities > 0.8 were identified, and 20 of these 31 genes were not in the list of known CHD genes. We find evidence that most significant CHD risk genes are strongly expressed in prenatal stages of the human genes. Finally, we validated our findings for CHD and EPI in independent cohorts comprising 1241 CHD trios, 226 CHD singletons and 197 EPI trios. Multiple novel risk genes identified by mTADA also had de novo mutations in these independent data sets. The joint analysis method introduced here, mTADA, is able to identify risk genes shared by two traits as well as additional risk genes not found through single-trait analysis only. A number of risk genes reported by mTADA are identified only through joint analysis, specifically when ASD, DD, or ID are one of the two traits examined. This suggests that novel genes for the trait or a new trait might converge to a core gene list of the three traits.
Comprehensive Analysis of Indels in Whole-genome Microsatellite Regions and Microsatellite Instability across 21 Cancer Types
Authors: Fujimoto Akihiro , Fujita Masashi , Hasegawa Takanori , Wong Jing Hao , Maejima Kazuhiro , Oku-Sasaki Aya , Nakano Kaoru , Shiraishi Yuichi , Miyano Satoru , Imoto Seiya , Stratton Michael R , Rosen Steven G , Nakagawa Hidewaki ,   ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network
Published: September 2018

AbstractMicrosatellites are repeats of 1-6bp units and ∼10 million microsatellites have been identified across the human genome. Microsatellites are vulnerable to DNA mismatch errors, and have thus been used to detect cancers with mismatch repair deficiency. To reveal the mutational landscape of the microsatellite repeat regions at the genome level, we analyzed approximately 20.1 billion microsatellites in 2,717 whole genomes of pan-cancer samples across 21 tissue types. Firstly, we developed a new insertion and deletion caller (MIMcall) that takes into consideration the error patterns of different types of microsatellites. Among the 2,717 pan-cancer samples, our analysis identified 31 samples, including colorectal, uterus, and stomach cancers, with higher microsatellite mutation rate (≥ 0.03), which we defined as microsatellite instability (MSI) cancers in genome-wide level. Next, we found 20 highly-mutated microsatellites that can be used to detect MSI cancers with high sensitivity. Third, we found that replication timing and DNA shape were significantly associated with mutation rates of the microsatellites. Analysis of germline variation of the microsatellites suggested that the amount of germline variations and somatic mutation rates were correlated. Lastly, analysis of mutations in mismatch repair genes showed that somatic SNVs and short indels had larger functional impact than germline mutations and structural variations. Our analysis provides a comprehensive picture of mutations in the microsatellite regions, and reveals possible causes of mutations, as well as provides a useful marker set for MSI detection.
Diagnosis and Prognosis of Alzheimer’s Disease Using Brain Morphometry and White Matter Connectomes
Authors: Wang Yun , Xu Chenxiao , Park Ji-Hwan , Lee Seonjoo , Stern Yaakov , Yoo Shinjae , Kim Jong Hun , Kim Hyoung Seop , Cha Jiook ,   The Alzheimer’s Disease Neuroimaging Initiative
Published: September 2018

ABSTRACTAccurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and structural connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n=60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparison of classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers show the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning.HighlightsWe showed the utility of multimodal MRI, combining morphometry and white matter connectomes, to classify the diagnosis of AD and MCI using machine learning.In predicting the progression from MCI to AD, the morphometry model showed the best performance.Two independent clinical datasets were used in this study: one for model building, the other for generalizability testing.
DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images
Authors: Çimen Sibel , Çapar Abdulkerim , Ekinci Dursun Ali , Ayten Umut Engin , Kerman Bilal Ersen , Töreyin Behçet Uğur 
Published: September 2018

AbstractOligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors’ knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.
GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data
Authors: Noskova Ekaterina , Ulyantsev Vladimir , Koepfli Klaus-Peter , O’Brien Stephen J. , Dobrynin Pavel 
Published: September 2018

AbstractThe demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum or AFS, the distribution of allele frequencies in populations. The joint allele frequency spectrum is commonly used to reconstruct the demographic history of multiple populations and several methods based on diffusion approximation (e.g.,∂a∂i) and ordinary differential equations (e.g.,moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint allele frequency spectrum data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Analysis,https://github.com/ctlab/GADMA). We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer demographic models at different local optima close to the global one, making it is possible to detect more biology corrected model during further research.
Integrated computational and experimental identification of p53, KRAS and VHL mutant selection associated with CRISPR-Cas9 editing
Authors: Sinha Sanju , Guerra Karina Barbosa , Cheng Kuoyuan , Leiserson Mark DM , Wilson David M , Ryan Bríd M. , Ronai Ze’ev A. , Lee Joo Sang , Deshpande Aniruddha J. , Ruppin Eytan 
Published: September 2018

AbstractRecent studies have reported that CRISPR-Cas9 gene editing induces a p53-dependent DNA damage response in primary cells, which may select for cells with oncogenic p53 mutations11,12. It is unclear whether these CRISPR-induced changes are applicable to different cell types, and whether CRISPR gene editing may select for other oncogenic mutations. Addressing these questions, we analyzed genome-wide CRISPR and RNAi screens to systematically chart the mutation selection potential of CRISPR knockouts across the whole exome. Our analysis suggests that CRISPR gene editing can select for mutants of KRAS and VHL, at a level comparable to that reported for p53. These predictions were further validated in a genome-wide manner by analyzing independent CRISPR screens and patients’ tumor data. Finally, we performed a new set of pooled and arrayed CRISPR screens to evaluate the competition between CRISPR-edited isogenic p53 WT and mutant cell lines, which further validated our predictions. In summary, our study systematically charts and points to the potential selection of specific cancer driver mutations during CRISPR-Cas9 gene editing.
Chemical blueprints to identifying fire ants: overview on venom alkaloids
Authors: Paterson Fox Eduardo Gonçalves 
Published: September 2018

AbstractSolenopsisfire ants are remarkably difficult to identify using morphological characters, particularly from the most abundant minor workers. The present manuscript introduces a set of chemical tools to facilitate species diagnosis from field-collected fire ant samples, herein focusing on minor workers. Fire ants from different localities (native and invaded) were analysed using gas-chromatography. Samples were collected from the field into organic solvents; sampling effort included 14 species, and a suspected hybrid. A total of 32 piperidine alkaloids were spotted and tentatively identified and representative relative chemical proportions for minor workers are presented for the first time for a number of species. It is hoped that the provided info will prove useful to researchers working on fire ants in future studies. Further compounds are being analysed for additional auxiliary tools.
Musashi binding elements in Zika and related Flavivirus 3’UTRs: A comparative studyin silico
Authors: de Bernardi Schneider Adriano , Wolfinger Michael T. 
Published: September 2018

ABSTRACTZika virus (ZIKV) belongs to a class of neurotropic viruses that have the ability to cause congenital infection, which can result in microcephaly or fetal demise. Recently, the RNA-binding protein Musashi-1 (Msi1), which mediates the maintenance and self-renewal of stem cells and acts as a translational regulator, has been associated with promoting ZIKV replication, neurotropism, and pathology. Msi1 predominantly binds to single-stranded motifs in the 3’ untranslated region (UTR) of RNA that contain aUAGtrinucleotide in their core. We systematically analyzed the properties of Musashi binding elements (MBEs) in the 3’UTR of flaviviruses with a thermodynamic model for RNA folding. Our results indicate that MBEs in ZIKV 3’UTRs occur predominantly in unpaired, single-stranded structural context, thus corroborating experimental observations by a biophysical model of RNA structure formation. Statistical analysis and comparison with related viruses show that ZIKV MBEs are maximally accessible among mosquito-borne flaviviruses. Our study addresses the broader question of whether other emerging arboviruses can cause similar neurotropic effects through the same mechanism in the developing fetus by establishing a link between the biophysical properties of viral RNA and teratogenicity. Moreover, our thermodynamic model can explain recent experimental findings and predict the Msi1-related neurotropic potential of other viruses.
Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models
Authors: Whiteway Matthew R , Socha Karolina , Bonin Vincent , Butts Daniel A 
Published: September 2018

AbstractSensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the information contained in those responses. Such variability is often shared across many neurons, which in principle can allow a decoder to mitigate the effects of such noise, depending on the structure of the shared variability and its relationship to sensory encoding at the population level. Latent variable models offer an approach for characterizing the structure of this shared variability in neural population recordings, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we leverage recent advances in machine learning to introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron’s stimulus selectivity and a set of latent variables that modulate these stimulus responses both additively and multiplicatively. While these approaches can detect general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an “affine model”. In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.
The Aquilegia genome reveals a hybrid origin of core eudicots
Authors: Aköz Gökçe , Nordborg Magnus 
Published: September 2018

AbstractBackgroundWhole-genome duplications (WGD) have dominated the evolutionary history of plants. One consequence of WGD is a dramatic restructuring of the genome as it undergoes diploidization, a process under which deletions and rearrangements of various sizes scramble the genetic material, leading to a repacking of the genome and eventual return to diploidy. Here, we investigate the history of WGD in the columbine genus Aquilegia, a basal eudicot, and use it to illuminate the origins of the core eudicots.ResultsWithin-genome synteny confirms that columbines are ancient tetraploids, and comparison with the grape genome reveals that this tetraploidy appears to be shared with the core eudicots. Thus, the ancient gamma hexaploidy found in all core eudicots must have involved a two-step process: first tetraploidy in the ancestry of all eudicots, then hexaploidy in the ancestry of core eudicots. Furthermore, the precise pattern of synteny sharing suggests that the latter involved allopolyploidization, and that core eudicots thus have a hybrid origin.ConclusionsNovel analyses of synteny sharing together with the well-preserved structure of the columbine genome reveal that the gamma hexaploidy at the root of core eudicots is likely a result of hybridization between a tetraploid and a diploid species.
Cell-free gene regulatory network engineering with synthetic transcription factors
Authors: Swank Zoe , Laohakunakorn Nadanai , Maerkl Sebastian J. 
Published: September 2018

AbstractGene regulatory networks are ubiquitous in nature and critical for bottom-up engineering of synthetic networks. Transcriptional repression is a fundamental function that can be tuned at the level of DNA, protein, and cooperative protein – protein interactions, necessitating high-throughput experimental approaches for in-depth characterization. Here we used a cell-free system in combination with a high-throughput microfluidic device to comprehensively study the different tuning mechanisms of a synthetic zinc-finger repressor library, whose affinity and cooperativity can be rationally engineered. The device is integrated into a comprehensive workflow that includes determination of transcription factor binding energy landscapes and mechanistic modeling, enabling us to generate a library of well-characterized synthetic transcription factors and corresponding promoters, which we then used to build gene regulatory networks de novo. The well-characterized synthetic parts and insights gained should be useful for rationally engineering gene regulatory networks and for studying the biophysics of transcriptional regulation.