bluuebunny/crossref_metadata_2025_split huggingface dataset:
Is a large eye size a risk factor for myopia? A Mendelian randomization study
Authors:   The UK Biobank Eye and Vision Consortium.
Published: December 2017

AbstractMyopia (nearsightedness) is an increasingly common cause of irreversible visual impairment. The ocular structures with greatest impact on refractive error are corneal curvature and axial length. Emmetropic eyes range in size within and across species, yet possess a balance between corneal curvature and axial length that is under genetic control. This scaling goes awry in myopia: 1 mm axial elongation is associated with ~3 Dioptres (D) myopia. Evidence that eye size prior to onset is a risk factor for myopia is conflicting. We applied Mendelian randomisation to test for a causal effect of eye size on refractive error. Genetic variants associated with corneal curvature identified in emmetropic eyes (22,180 individuals) were used as instrumental variables and tested for association with refractive error (139,697 individuals). A genetic risk score for the variants was tested for association with corneal curvature and axial length in an independent sample (315 emmetropes). The genetic risk score explained 2.3% (P=0.007) and 2.7% (P=0.002) of the variance in corneal curvature and axial length, respectively, in the independent sample, confirming these variants are predictive of eye size in emmetropes. The estimated causal effect of eye size on refractive error was + 1.41 D (95% CI. 0.65 to 2.16) less myopic refractive error per mm flatter cornea (P<0.001), corresponding to +0.48 D (95% CI. 0.22 to 0.73) more hypermetropic refractive error for an eye with a 1mm longer axial length. These results do not support the hypothesis that a larger eye size is a risk factor for myopia. We conclude the genetic determinants of normal eye size are not shared with those influencing susceptibility to myopia.
Bias toward long gene misregulation in synaptic disorders can be an artefact of amplification-based methods
Authors: Raman Ayush T. , Pohodich Amy E. , Wan Ying-Wooi , Yalamanchili Hari Krishna , Lowry Bill , Zoghbi Huda Y. , Liu Zhandong 
Published: December 2017

SUMMARYSeveral recent studies have suggested that genes that are longer than 100 kilobases are more likely to be misregulated in neurological diseases associated with synaptic dysfunction, such as autism and Rett syndrome. These length-dependent transcriptional changes are modest in Mecp2-mutant samples, but, given the low sensitivity of high-throughput transcriptome profiling technology, the statistical significance of these results needs to be re-evaluated. Here, we show that the apparent length-dependent trends previously observed in MeCP2 microarray and RNA-Sequencing datasets, particularly in genes with low fold-changes, disappeared after accounting for baseline variability estimated from randomized control samples. As we found no similar bias with NanoString technology, this long-gene bias seems to be particular to PCR amplification-based platforms. In contrast, authentic long gene effects, such as those caused by topoisomerase inhibition, can be detected even after adjustment for baseline variability. Accurate detection of length-dependent trends requires establishing a baseline from randomized control samples.HIGHLIGHTSLength-dependent gene misregulation is not intrinsic to Mecp2 disruption.Topoisomerase inhibition produces an authentic long gene bias.PCR amplification-based high-throughput datasets are biased toward long genes.
Extending Tests of Hardy-Weinberg Equilibrium to Structured Populations
Authors: Hao Wei , Storey John D. 
Published: December 2017

AbstractTesting for Hardy-Weinberg equilibrium (HWE) is an important component in almost all analyses of population genetic data. Genetic markers that violate HWE are often treated as special cases; for example, they may be flagged as possible genotyping errors or they may be investigated more closely for evolutionary signatures of interest. The presence of population structure is one reason why genetic markers may fail a test of HWE. This is problematic because almost all natural populations studied in the modern setting show some degree of structure. Therefore, it is important to be able to detect deviations from HWE for reasons other than structure. To this end, we extend statistical tests of HWE to allow for population structure, which we call a test of “structural HWE” (sHWE). Additionally, our new test allows one to automatically choose tuning parameters and identify accurate models of structure. We demonstrate our approach on several important studies, provide theoretical justification for the test, and present empirical evidence for its utility. We anticipate the proposed test will be useful in a broad range of analyses of genome-wide population genetic data.
A likelihood-free estimator of population structure bridging admixture models and principal components analysis
Authors: Cabreros Irineo , Storey John D. 
Published: December 2017

AbstractWe introduce a simple and computationally efficient method for fitting the admixture model of genetic population structure, calledALStructure. The strategy ofALStructureis to first estimate the low-dimensional linear subspace of the population admixture components and then search for a model within this subspace that is consistent with the admixture model’s natural probabilistic constraints. Central to this strategy is the observation that all models belonging to this constrained space of solutions are risk-minimizing and have equal likelihood, rendering any additional optimization unnecessary. The low-dimensional linear subspace is estimated through a recently introduced principal components analysis method that is appropriate for genotype data, thereby providing a solution that has both principal components and probabilistic admixture interpretations. Our approach differs fundamentally from other existing methods for estimating admixture, which aim to fit the admixture model directly by searching for parameters that maximize the likelihood function or the posterior probability. We observe thatALStructuretypically outperforms existing methods both in accuracy and computational speed under a wide array of simulated and real human genotype datasets. Throughout this work we emphasize that the admixture model is a special case of a much broader class of models for which algorithms similar toALStructuremay be successfully employed.
PEA: an integrated R toolkit for plant epitranscriptome analysis
Authors: Zhai Jingjing , Song Jie , Cheng Qian , Tang Yunjia , Ma Chuang 
Published: December 2017

AbstractMotivationThe epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety.ResultsHere, we introduce PEA, an integrated R toolkit to facilitate the analysis of plant epitranscriptome data. The PEA toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery, and gene functional enrichment analysis. PEA also takes advantage of machine learning technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction, and annotation, and we describe its application to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity, which is superior to existing m6A predictors. PEA is potentially broadly applicable to the in-depth study of epitranscriptomics.AvailabilityPEA is implemented using R and available at https://github.com/cma2015/PEA.
Visualization of class A GPCR oligomerization by image-based fluorescence fluctuation spectroscopy
Authors: Isbilir Ali , Möller Jan , Bock Andreas , Zabel Ulrike , Annibale Paolo , Lohse Martin J. 
Published: December 2017

AbstractG protein-coupled receptors (GPCRs) represent the largest class of cell surface receptors conveying extracellular information into intracellular signals. Many GPCRs have been shown to be able to oligomerize and it is firmly established that Class C GPCRs (e.g. metabotropic glutamate receptors) function as obligate dimers. However, the oligomerization capability of the larger Class A GPCRs (e.g. comprising the β-adrenergic receptors (β-ARs)) is still, despite decades of research, highly debated.Here we assess the oligomerization behavior of three prototypical Class A GPCRs, the β1-ARs, β2-ARs, and muscarinic M2Rs in single, intact cells. We combine two image correlation spectroscopy methods based on molecular brightness, i.e. the analysis of fluorescence fluctuations over space and over time, and thereby provide an assay able to robustly and precisely quantify the degree of oligomerization of GPCRs. In addition, we provide a comparison between two labelling strategies, namely C-terminally-attached fluorescent proteins and N-terminally-attached SNAP-tags, in order to rule out effects arising from potential fluorescent protein-driven oligomerization. The degree of GPCR oligomerization is expressed with respect to a set of previously reported as well as newly established monomeric or dimeric control constructs. Our data reveal that all three prototypical GPRCs studied display, under unstimulated conditions, a prevalently monomeric fingerprint. Only the β2-AR shows a slight degree of oligomerization.From a methodological point of view, our study suggests three key aspects. First, the combination of two image correlation spectroscopy methods allows addressing cells transiently expressing high concentrations of membrane receptors, far from the single molecule regime, at a density where the kinetic equilibrium should favor dimers and higher-order oligomers. Second, our methodological approach, allows to selectively target cell membrane regions devoid of artificial oligomerization hot-spots (such as vesicles). Third, our data suggest that the β1-AR appears to be a superior monomeric control than the widely used membrane protein CD86.Taken together, we suggest that our combined image correlation spectroscopy method is a powerful approach to assess the oligomerization behavior of GPCRs in intact cells at high expression levels.
Improvements to Bayesian Gene Activity State Estimation from Genome-Wide Transcriptomics Data
Authors: Disselkoen Craig , Hekman Nathan , Gilbert Brian , Benson Sydney , Anderson Matthew , DeJongh Matt , Best Aaron , Tintle Nathan 
Published: December 2017

AbstractAn important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with certainty. Second, MultiMM assumes that genes that change states in the dataset can be distinguished with certainty from those that remain in one state. Third, the model can be sensitive to extreme measures (outliers) of gene expression. In this manuscript, we propose a modified Bayesian approach, which addresses these three limitations by improving outlier handling and by explicitly modeling network and other uncertainty yielding improved gene activity state estimates when compared to MultiMM.
3.7 Å cryo-EM structure of the core Centromere Binding Factor 3 complex
Authors: Zhang Wenjuan , Lukoynova Natalya , Miah Shomon , Vaughan Cara K 
Published: December 2017

SummaryThe Centromere Binding Factor 3 (CBF3) complex binds the third Centromere DNA Element in organisms with point centromeres, such asS. cerevisiae. It is the only essential centromere binding complex as it facilitates genetic specification of point centromeres. It is therefore the most fundamental complex of the kinetochore in these organisms and its association with centromere DNA allows association of all other kinetochore components. We have determined the atomic structure of the core complex of CBF3, comprising 3 of its 4 components, using cryo-EM. The architecture of the complex is 'U'-shaped, with a deep, strongly basic channel that is narrow at one end and wide at the other. Combining our structure and in vitro assays, we present a model for its association with centromere DNA.
Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance
Authors: Maltas Jeff , Wood Kevin B. 
Published: December 2017

ABSTRACTEvolved resistance to one antibiotic may be associated with “collateral” sensitivity to other drugs. Here we provide an extensive quantitative characterization of collateral effects in Enterococcus faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict, as independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity as well as markedly different fitness costs. Using whole-genome sequencing of evolved populations, we identified mutations in a number of known resistance determinants, including mutations in several genes previously linked with collateral sensitivity in other species. While phenotypic drug sensitivity profiles show significant diversity, they cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the statistical structure in these resistance profiles, we develop a simple mathematical model based on a stochastic control process and use it to design optimal drug policies that assign a unique drug to every possible resistance profile. Stochastic simulations reveal that these optimal drug policies outperform intuitive cycling protocols by maintaining long-term sensitivity at the expense of short-term periods of high resistance. The approach reveals a new conceptual strategy for mitigating resistance by balancing short-term inhibition of pathogen growth with infrequent use of drugs intended to steer pathogen populations to a more vulnerable future state. Experiments in laboratory populations confirm that model-inspired sequences of four drugs reduce growth and slow adaptation relative to naive protocols involving the drugs alone, in pairwise cycles, or in four-drug uniform cycles.
Co-translational protein targeting facilitates centrosomal recruitment of PCNT during centrosome maturation
Authors: Sepulveda Guadalupe , Antkowiak Mark , Brust-Mascher Ingrid , Mahe Karan , Ou Tingyoung , Castro Noemi , Christensen Lana N. , Cheung Lee , Yoon Daniel , Huang Bo , Jao Li-En 
Published: December 2017

AbstractAs microtubule-organizing centers of animal cells, centrosomes guide the formation of the bipolar spindle that segregates chromosomes during mitosis. At mitosis onset, centrosomes maximize microtubule-organizing activity by rapidly expanding the pericentriolar material (PCM). This process is in part driven by the large PCM protein pericentrin (PCNT), as its level increases at the PCM and helps recruit additional PCM components. However, the mechanism underlying the timely centrosomal enrichment of PCNT remains unclear. Here we show that PCNT is delivered co-translationally to centrosomes during early mitosis by cytoplasmic dynein, as evidenced by centrosomal enrichment ofPCNTmRNA, its translation near the centrosome, and requirement of intact polysomes forPCNTmRNA localization. Additionally, the microtubule minus-end regulator, ASPM, is also targeted co-translationally to mitotic spindle poles. Together, these findings suggest that co-translational targeting of cytoplasmic proteins to specific subcellular destinations may be a generalized protein targeting mechanism.
Disrupted brain structural connectivity in Pediatric Bipolar Disorder with psychosis
Authors: Fernandes Henrique M. , Cabral Joana , Van Hartevelt Tim J. , Lord Louis-David , Gleesborg Carsten , Moller Arne , Deco Gustavo , Whybrow Peter C. , Petrovic Predrag , James Anthony C. , Kringelbach Morten L. 
Published: December 2017

AbstractBipolar disorder (BD) has been linked to disrupted structural and functional connectivity between prefrontal networks and limbic brain regions. Studies of patients with pediatric bipolar disorder (PBD) can help elucidate the developmental origins of altered structural connectivity underlying BD and provide novel insights into the aetiology of BD. Here we compare the network properties of whole-brain structural connectomes of PBD patients with psychosis and euthymic matched healthy controls. Our results show widespread changes in the structural connectivity of PBD patients in both cortical and subcortical networks, notably affecting the orbitofrontal cortex, frontal gyrus, amygdala, hippocampus and basal ganglia. Graph theoretical analysis revealed that PBD connectomes have fewer hubs, weaker rich club organization, different modular fingerprint and inter-modular communication, compared to healthy participants. The relationship between network features and neurocognitive and psychotic scores was also assessed. Patients’ IQ and psychotic symptoms significantly correlated with the local efficiency of the orbitofrontal cortex. Our findings reveal that PBD is associated with significant widespread changes in structural network topology, thus strengthening the hypothesis of a reduced capacity for integrative processing of information across brain regions. Localised network changes involve core regions for emotional processing and regulation, as well as memory and executive function, some of which correlate with neurocognitive faculties and symptoms. Together, our findings provide the first comprehensive characterisation of the alterations in local and global structural brain connectivity and network topology, which may contribute to the deficits in cognition and emotion processing and regulation found in PBD.
Analysis of ipsilateral corticocortical connectivity in the mouse brain
Authors: Watakabe Akiya , Hirokawa Junya 
Published: December 2017

AbstractIn primates, proximal cortical areas are interconnected via within-cortex “intrinsic” pathway, whereas distant areas are connected via “extrinsic” white matter pathway. It is not well known how cortical areas are interconnected in small-brained mammals like rodents. In this study, we systematically analyzed the data of Allen Mouse Brain Connectivity Atlas to answer this question and found that the ipsilateral cortical connections in mice are almost exclusively contained within the grey matter with the exception of the retrosplenial area. We analyzed the layer-specific distribution of axonal projections within the grey matter using Cortical Box method and obtained the following results. First, widespread axonal projections were observed in both upper and lower layers in the vicinity of injections, whereas highly specific “point-to-point” projections were observed toward remote areas. Second, such long-range projections were predominantly aligned in the anteromedial-posterolateral direction. Third, in majority of these projections, the connecting axons traveled through layer 6. Finally, the projections from the primary and higher order areas to distant targets preferentially terminated in the middle and superficial layers, respectively, suggesting hierarchical connections similar to those of primates. Overall, our study suggests the conserved nature of neocortical organization across species despite conspicuous differences in wiring strategy.
The Functional False Discovery Rate with Applications to Genomics
Authors: Chen Xiongzhi , Robinson David G. , Storey John D. 
Published: December 2017

AbstractThe false discovery rate measures the proportion of false discoveries among a set of hypothesis tests called significant. This quantity is typically estimated based on p-values or test statistics. In some scenarios, there is additional information available that may be used to more accurately estimate the false discovery rate. We develop a new framework for formulating and estimating false discovery rates and q-values when an additional piece of information, which we call an “informative variable”, is available. For a given test, the informative variable provides information about the prior probability a null hypothesis is true or the power of that particular test. The false discovery rate is then treated as a function of this informative variable. We consider two applications in genomics. Our first is a genetics of gene expression (eQTL) experiment in yeast where every genetic marker and gene expression trait pair are tested for associations. The informative variable in this case is the distance between each genetic marker and gene. Our second application is to detect differentially expressed genes in an RNA-seq study carried out in mice. The informative variable in this study is the per-gene read depth. The framework we develop is quite general, and it should be useful in a broad range of scientific applications.
MicroED Structures of HIV-1 Gag CTD-SP1 Reveal Binding Interactions with the Maturation Inhibitor Bevirimat
Authors: Purdy Michael D. , Shi Dan , Chrustowicz Jakub , Hattne Johan , Gonen Tamir , Yeager Mark 
Published: December 2017

AbstractHIV-1 protease (PR) cleavage of the Gag polyprotein triggers the assembly of mature, infectious particles. Final cleavage of Gag occurs at the junction helix between the capsid protein CA and the SP1 spacer peptide. Here we used MicroED to delineate the binding interactions of the maturation inhibitor bevirimat (BVM) using very thin frozen-hydrated, three-dimensional microcrystals of a CTD-SP1 Gag construct with and without bound BVM. The 2.9-Å MicroED structure revealed that a single BVM molecule stabilizes the 6-helix bundle via both electrostatic interactions with the dimethysuccinyl moiety and hydrophobic interactions with the pentacyclic triterpenoid ring. These results provide insight into the mechanism of action of BVM and related maturation inhibitors that will inform further drug discovery efforts. This study also demonstrates the capabilities of MicroED for structure-based drug design.One-sentence summaryThe MicroED structure of HIV-1 Gag CTD-SP1 at 2.9 Å resolution reveals the molecular basis for binding of the maturation inhibitor bevirimat that will inform further drug discovery efforts.