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Shen, X.

Publications and source records attributed to Shen, X..

247 records · Page 14Linked to original sources

Distinct RanBP1 nuclear export and cargo dissociation mechanisms between fungi and animals

Ran binding protein 1 (RanBP1), the primary effector of nuclear GTPase Ran, is a cytoplasmic-enriched and nuclear-cytoplasmic shuttling protein, playing important roles in nuclear transport through preventing RanGTP from being trapped with karyopherin proteins and dissociating cargoes from nuclear export factor CRM1. Much of what we know about RanBP1 is learned from fungi. Here we show that animal RanBP1 has distinct cargo dissociation and nuclear export mechanisms. In contrast to CRM1-RanGTP sequestration mechanism of cargo dissociation in fungi, animal RanBP1 solely sequesters RanGTP from nuclear export complexes. In fungi, RanBP1, CRM1 and RanGTP form a 1:1:1 nuclear export complex; in contrast, animal RanBP1, CRM1 and RanGTP form a 1:1:2 nuclear export complex. The key feature for the two mechanistic changes from fungi to animals is the loss of affinity between RanBP1-RanGTP and CRM1, since residues mediating their interaction in fungi are not conserved in animals. The biological significances of these different mechanisms in fungi and animals are also studied and discussed. Our study illustrates how orthologous proteins may play conserved functions through distinct routes, and may provide directions for design of antifungal medicines.

biochemistry↗

The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder

Autism Spectrum Disorder (ASD) is associated with multiple complex abnormalities in functional brain connectivity measured with functional magnetic resonance imaging (fMRI). Despite much research in this area, to date, neuroimaging-based models are not able to characterize individuals with ASD with sufficient sensitivity and specificity; this is likely due to the heterogeneity and complexity of this disorder. Here we apply a data-driven subject-level approach, connectome-based predictive modeling, to resting-state fMRI data from a set of individuals from the Autism Brain Imaging Data Exchange. Using leave-one-subject-out and split-half analyses, we define two functional connectivity networks that predict continuous scores on the Social Responsiveness Scale (SRS) and Autism Diagnostic Observation Schedule (ADOS) and confirm that these networks generalize to novel subjects. Notably, these networks were found to share minimal anatomical overlap. Further, our results generalize to individuals for whom SRS/ADOS scores are unavailable, predicting worse scores for ASD than typically developing individuals. In addition, predicted SRS scores for individuals with attention-deficit/hyperactivity disorder (ADHD) from the ADHD-200 Consortium are linked to ADHD symptoms, supporting the hypothesis that the functional brain organization changes relevant to ASD severity share a component associated with attention. Finally, we explore the membership of predictive connections within conventional (atlas-based) functional networks. In summary, our results suggest that an individuals functional connectivity profile contains information that supports dimensional, non-binary classification in ASD, aligning with the goals of precision medicine and individual-level diagnosis.

neuroscience↗

Association of whole-genome and NETRIN1 signaling pathway-derived polygenic risk scores for Major Depressive Disorder and thalamic radiation white matter microstructure in UK Biobank

BackgroundMajor Depressive Disorder (MDD) is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome, and growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide an important route for identification of disease mechanisms by focusing on a specific process excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether MDD polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRS) and the whole genome excluding NETRIN1 pathway genes (genomic-PRS) were associated with white matter integrity.\n\nMethodsWe used two diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), in the most up-to-date UK Biobank neuroimaging data release (FA: N = 6,401; MD: N = 6,390).\n\nResultsWe found significantly lower FA in the superior longitudinal fasciculus ({beta} = -0.035, pcorrected = 0.029) and significantly higher MD in a global measure of thalamic radiations ({beta} = 0.029, pcorrected = 0.021), as well as higher MD in the superior ({beta} = 0.034, pcorrected = 0.039) and inferior ({beta} = 0.029, pcorrected = 0.043) longitudinal fasciculus and in the anterior ({beta} = 0.025, pcorrected = 0.046) and superior ({beta} = 0.027, pcorrected = 0.043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts.\n\nConclusionsOur findings indicate that variation in the NETRIN1 signaling pathway may confer risk for MDD through effects on thalamic radiation white matter microstructure.

genetics↗

Individual connectomes are unique and stable in the developing brain from adolescence to young adulthood.

Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across three months to three years. Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that the edges demonstrating the most individualized features tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and is not an idiosyncratic aspect of a specific dataset, but rather reflects stable individual differences in the functional connectivity of the brain.\n\nResearch highlightsO_LIWhole-brain functional connectivity profiles obtained from four resting-state fMRI datasets are unique and stable across 3 months-3 years in adolescents, young adults, and older adults\nC_LIO_LIMedial frontal and frontoparietal networks tended to be both unique and stable\nC_LIO_LIIndividual edges in the frontal and parietal cortices tended to be most discriminative of individual subjects\nC_LI

neuroscience↗

Intestinal crypts recover rapidly from focal damage with coordinated motion of stem cells that is impaired by aging

Despite the continuous renewal and turnover of the small intestinal epithelium, the intestinal stem cell niche maintains a soccer ball-like, alternating pattern of stem and Paneth cells in the crypt. To study the robustness of the niche pattern, we used intravital two-photon microscopy in mice with fluorescently-labeled Lgr5+ intestinal stem cells and precisely perturbed the mosaic pattern with femtosecond laser ablation. Ablation of one to three cells initiated rapid motion of niche cells that restored the alternation in the crypt pattern within about two hours without any cell proliferation. Crypt cells then performed a coordinated dilation of the crypt lumen, which resulted in peristalsis-like motion that forced damaged cells out of the niche. Crypt cell motion was reduced with inhibition of the ROCK pathway and attenuated with old age, and both resulted in incomplete pattern recovery. This suggests that in addition to proliferation and self-renewal, motility of stem cells is critical for maintaining homeostasis. Reduction of this novel behavior of stem cells could contribute to disease and age-related changes.

physiology↗

Genetic load and mutational meltdown in cancer cell populations

ABSRACTLarge and non-recombining genomes are prone to accumulating deleterious mutations faster than natural selection can purge (Mullers ratchet). A possible consequence would then be the extinction of small populations. Relative to most single-cell organisms, cancer cells, with large and non-recombining genomes, could be particularly susceptible to such \"mutational meltdown\". Curiously, deleterious mutations in cancer cells are rarely noticed despite the strong signals in cancer genome sequences. Here, by monitoring single-cell clones from HeLa cell lines, we characterize deleterious mutations that retard cell proliferation. The main mutational events are copy number variations (CNVs), which happen at an extraordinarily high rate of 0.29 events per cell division. The average fitness reduction, estimated to be 18% per mutation, is also very high. HeLa cell populations therefore have very substantial genetic load and, at this level, natural population would likely experience mutational meltdown. We suspect that HeLa cell populations may avoid extinction only after the population size becomes large. Because CNVs are common in most cell lines and cancer tissues, the observations hint at cancer cells vulnerability, which could be exploited by therapeutic strategies.

evolutionary biology↗

Separating an allele associated with late flowering and slow maturation of Arabidopsis thaliana from population structure

Genome-wide association analysis is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of double-phenotype analysisto uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, acommon allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locusis co-selected with flowering-time-related genes. The discovered pleiotropic genotypephenotype map provides new insights into understanding the genetic correlation of complex traits.

genetics↗

Protective effect of estrogen against intervertebral disc degeneration is attenuated by miR-221 through targeting estrogen receptor

Intervertebral disc degeneration (IDD) is a multifactorial disease that associates apoptosis, senescence and calcification of cartilage cells, inflammatory response and alterations in the extracellular matrix. Previous documents imply that estrogen and miR-221 may be involved in IDD. This study further investigated their regulatory mechanisms underlying IDD. Normal and degenerated cartilaginous endplates (CEP) tissues were isolated surgically from juvenile patients with idiopathic scoliosis and adult patients with IDD, respectively. PCR and western blot assays showed decreased aggrecan, Col2A1, TGF-{beta} and estrogen receptor (ER) levels in CEP, but increased MMP-3, adamts-5, IL-1{beta}, TNF-, IL-6 and miR-221 levels. CEP cells were harvested from degenerated CEP tissues and treated with doses of 17{beta}-E2. 17{beta}-E2 increased expression of aggrecan and Col2A1 levels in endplate chondrocytes and secretion of TGF-{beta}, but decreased IL-6 secretion. Moreover, 17{beta}-E2 inhibited the apoptosis and cell-cycle arrest in G0/G1, improving the cell viability. These data indicated estrogen confers protective effect against IDD. However, we found that ER was a target of miR-221 via luciferase assay. miR-221 up-regulation via the mimics or ER knockdown attenuated these protective effects conferred by estrogen, while intervention of miR-221 via the inhibitors promoted the protective effects. This study provided novel evidence that estrogen confers protective effects of CEP against IDD, however, up-regulated miR-221 in degenerated CEP decreased the protective effects via targeting ER, thus it may be an important cause for IDD.

cell biology↗

Genome-wide association study of depression phenotypes in UK Biobank (n = 322,580) identifies the enrichment of variants in excitatory synaptic pathways

Depression is a polygenic trait that causes extensive periods of disability and increases the risk of suicide, a leading cause of death in young people. Previous genetic studies have identified a number of common risk variants which have increased in number in line with increasing sample sizes. We conducted a genome-wide association study (GWAS) in the largest single population-based cohort to date, UK Biobank. This allowed us to estimate the effects of {approx} 8 million genetic variants in 320,000 people for three depression phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. Each phenotype was found to be significantly genetically correlated with the results from a previous independent study of clinically defined MDD. We identified 14 independent loci that were significantly associated (P < 5 x 10-8) with broad depression, two independent variants for probable MDD, and one independent variant for ICD-coded MDD. Gene-based analysis of our GWAS results with MAGMA revealed 46 regions significantly associated (P < 2.77 x 10-6) with broad depression, two significant regions for probable MDD and one significant region for ICD-coded MDD. Gene region-based analysis of our GWAS results with MAGMA revealed 59 regions significantly associated (P < 6.02 x 10-6) with broad depression, of which 27 were also detected by gene-based analysis. Variants for broad depression were enriched in pathways for excitatory neurotransmission, mechanosensory behavior, postsynapse, neuron spine and dendrite. This study provides a number of novel genetic risk variants that can be leveraged to elucidate the mechanisms of MDD and low mood.

genetics↗

Resting-state connectivity and its association with cognitive performance, educational attainment, and household income in UK Biobank (N = 3,950)

Cognitive ability is an important predictor of lifelong physical and mental well-being and its impairments are associated with many psychiatric disorders. Higher cognitive ability is also associated with greater educational attainment and increased household income. Understanding neural mechanisms underlying cognitive ability is therefore of crucial importance for determining the nature of these associations. In the current study, we examined the spontaneous activity of the brain at rest to investigate its relationships with not only cognitive ability, but also educational attainment and household income. We used a large sample of resting-state neuroimaging data from UK Biobank (N=3,950). Firstly, analysis at the whole-brain level showed that connections involving the default mode network (DMN), fronto-parietal network (FPN) and cingulo-opercular network (CON) were significantly positively associated with levels of cognitive performance assessed by a verbal-numerical reasoning test (standardised {beta} ranged from 0.054 to 0.097). Connections associated with higher levels of cognitive performance were also significantly positively associated with educational attainment (r=0.48, N=4,160) and household income (r=0.38, N=3,793). Further, analysis on the coupling of functional networks showed that better cognitive performance was associated with more positive DMN-CON connections, decreased cross-hemisphere connections between homotopic network in CON and FPN, and stronger CON-FPN connections (absolute {beta} ranged from 0.034 to 0.063). The present study finds that variation in brain resting state functional connectivity associated with individual differences in cognitive ability, largely involving DMN and lateral prefrontal networks. Additionally, we provide further evidence of shared neural associations of cognitive ability, educational attainment, and household income.

neuroscience↗

Instantaneous Brain Dynamics Mapped to a Continuous State Space

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brains dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting and task data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. We also demonstrate that resting brain activity includes brain states that are very similar to those adopted during some tasks, as well as brain states that are distinct from experimentally-defined tasks. Back-projection of segmented brain states onto the brains surface reveals the patterns of brain activity that support each experimental state.

neuroscience↗

Sex Differences In The Adult Human Brain: Evidence From 5,216 UK Biobank Participants

Sex differences in the human brain are of interest, for example because of sex differences in the observed prevalence of psychiatric disorders and in some psychological traits. We report the largest single-sample study of structural and functional sex differences in the human brain (2,750 female, 2,466 male participants; 44-77 years). Males had higher volumes, surface areas, and white matter fractional anisotropy; females had thicker cortices and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume or height. There was generally greater male variance across structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.

neuroscience↗

A Novel And Efficient Algorithm For De Novo Discovery Of Mutated Driver Pathways In Cancer

Next-generation sequencing studies on cancer somatic mutations have discovered that driver mutations tend to appear in most tumor samples, but they barely overlap in any single tumor sample, presumably because a single driver mutation can perturb the whole pathway. Based on the corresponding new concepts of coverage and mutual exclusivity, new methods can be designed for de novo discovery of mutated driver pathways in cancer. Since the computational problem is a combinatorial optimization with an objective function involving a discontinuous indicator function in high dimension, many existing optimization algorithms, such as a brute force enumeration, gradient descent and Newton's methods, are practically infeasible or directly inapplicable. We develop a new algorithm based on a novel formulation of the problem as non-convex programming and nonconvex regularization. The method is computationally more efficient, effective and scalable than existing Monte Carlo searching and several other algorithms, which have been applied to The Cancer Genome Atlas (TCGA) project. We also extend the new method for integrative analysis of both mutation and gene expression data. We demonstrate the promising performance of the new methods with applications to three cancer datasets to discover de novo mutated driver pathways.

bioinformatics↗