Biology subjects

Ni, Y.

Publications and source records attributed to Ni, Y..

9 recordsLinked to original sources

bioRxiv2018

Decreased kinesin-1 mitigates NMDA-induced exicitotoxicity and ischemia-evoked neurodegeneration

N-methyl-D-aspartate receptor (NMDAR) is highly compartmentalized in neurons and the dysfunction has been implicated in various neuropsychiatric and neurodegenerative disorders. Recent failure to exploit NMDAR antagonization as a potential therapeutic target has driven the need to identify molecular mechanisms that regulate NMDAR compartmentalization. Here, we report that neural activity-dependent reduction of Kif5b, the heavy chain of kinesin-1, protected neurons against NMDA-induced excitotoxicity and ischemia-provoked neurodegeneration. Direct binding of Kinesin-1 to the GluN2B cytoplasmic tails regulated levels of NMDAR at extrasynaptic sites and the subsequent influx of calcium mediated by extrasynaptic NMDAR via regulating the insertion of NMDARs into neuronal surface. Transient increase of Kif5b restored the surface levels of NMDAR and the decreased neuronal susceptibility to NMDA-induced excitotoxicity. Our findings reveal that kinesin-1 regulates extrasynaptic NMDAR targeting and signaling, and the reduction of kinesin-1 could be regulated by neural activity and could be exploited to postpone or halt neurodegeneration.

neuroscience↗

bioRxiv2018

Polygenic Prediction via Bayesian Regression and Continuous Shrinkage Priors

Polygenic prediction has shown promise in identifying individuals at high risk for complex diseases, and may become clinically useful as the predictive performance of polygenic risk scores (PRS) improves. Here, we present PRS-CS, a novel polygenic prediction method that infers posterior SNP effect sizes using GWAS summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of effect size distributions, especially when the training sample size is large. We apply PRS-CS to predict six complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.

genetics↗

bioRxiv2018

Verification of the phenylpropanoid pinoresinol biosynthetic pathway and its glycosides in Phomopsis sp. XP-8 using 13C stable isotope labeling and liquid chromatography coupled with time-of-flight mass spectrometry

Phomopsis sp. XP-8, an endophytic fungus from the bark of Tu-Chung (EucommiaulmoidesOliv), revealed the pinoresinol diglucoside (PDG) biosynthetic pathway after precursor feeding measurements and genomic annotation. To verify the pathway more accurately, [13C6]-labeled glucose and [13C6]-labeled phenylalanine were separately fed to the strain as sole substrates and [13C6]-labeled products were detected by ultra-high performance liquid chromatography-quantitative time of flight mass spectrometry. As results, [13C6]-labeled phenylalanine was found as [13C6]-cinnamylic acid and p-coumaric acid, and [13C12]-labeled pinoresinol revealed that the pinoresinol benzene ring came from phenylalanine via the phenylpropane pathway. [13C6]-Labeled cinnamylic acid and p-coumaric acid, [13C12]-labeled pinoresinol, [13C18]-labeled pinoresinol monoglucoside (PMG), and [13C18]-labeled PDG products were found when [13C6]-labeled glucose was used, demonstrating that the benzene ring and glucoside of PDG originated from glucose. It was also determined that PMG was not the direct precursor of PDG in the biosynthetic pathway. The study verified the occurrence of the plant-like phenylalanine and lignan biosynthetic pathway in fungi.\n\nImportanceVerify the phenylpropanoid-pinoresinol biosynthetic pathway and its glycosides in an endophytic fungi.

microbiology↗

bioRxiv2017

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

MotivationLeft-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed.\n\nResultsWe have developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity.\n\nAvailability and implementationThe R code for GSimp, evaluation pipeline, vignette, real-world and simulated targeted metabolomics dataset are available on: https://github.com/WandeRum/GSimp.

bioinformatics↗

bioRxiv2017

Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

IntroductionMissing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection of methods can significantly affect following data analyses and interpretations. According to the definition, there are three types of missing values, missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).\n\nObjectivesThe aim of this study was to comprehensively compare common imputation methods for different types of missing values using two separate metabolomics data sets (977 and 198 serum samples respectively) to propose a strategy to deal with missing values in metabolomics studies.\n\nMethodsImputation methods included zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC). Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate the imputation accuracy for MCAR/MAR and MNAR correspondingly. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes sum of squared error were used to evaluate the overall sample distribution. Students t-test followed by Pearson correlation analysis was conducted to evaluate the effect of imputation on univariate statistical analysis.\n\nResultsOur findings demonstrated that RF imputation performed the best for MCAR/MAR and QRILC was the favored one for MNAR.\n\nConclusionCombining with \"modified 80% rule\", we proposed a comprehensive strategy and developed a public-accessible web-tool for missing value imputation in metabolomics data.

bioinformatics↗

bioRxiv2017

The Neural System of Metacognition Accompanying Decision-Making in The Prefrontal Cortex

Decision-making is usually accompanied by metacognition, through which a decision maker monitors the decision uncertainty and consequently revises the decision, even prior to feedback. However, the neural mechanisms of metacognition remain controversial: one theory proposes that metacognition coincides the decision-making process; and another addresses that it entails an independent neural system in the prefrontal cortex (PFC). Here we devised a novel paradigm of \"decision-redecision\" to investigate the metacognition process in redecision, in comparison with the decision process. We here found that the anterior PFC, including dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were exclusively activated after the initial decisions. dACC was involved in decision uncertainty monitoring, whereas lFPC was involved in decision adjustment controlling, subject to control demands of the tasks. Our findings support that the PFC is essentially involved in metacognition and further suggest that functions of the PFC in metacognition are dissociable.

neuroscience↗

bioRxiv2017

Early urinary candidate biomarker discovery in a rat thioacetamide-induced liver fibrosis model

Biomarker is the change associated with the disease. Blood is relatively stable because of the homeostatic mechanisms of the body. However, urine accumulates changes of the body, which makes it a better early biomarker source. Liver fibrosis, which results from the deposition of extracellular matrix (ECM) components, is a reversible pathological condition, whereas cirrhosis, the end-stage of liver fibrosis, is irreversible. Consequently, noninvasive early biomarkers for fibrosis are desperately needed. In this study, differential urinary proteins were identified in the thioacetamide (TAA) liver fibrosis rat model using tandem mass tagging and two-dimensional liquid chromatography tandem mass spectrometry (2DLC-MS/MS). A total of 766 urinary proteins were identified, 143 and 118 of which were significantly changed in the TAA 1-week and 3-week groups, respectively. Multiple reaction monitoring (MRM)-targeted proteomics was used to further validate the abundant differentially expressed proteins in the TAA 1-week, 3-week, 6-week and 8-week groups. A total of 40 urinary proteins were statistically significant (fold change >2 and p<0.05), 15 of which had been previously reported as biomarkers of liver fibrosis, cirrhosis or other related diseases and 10 of which had been reported to be associated with the pathology and mechanism of liver fibrosis. These differential proteins were detected in urine before the alanine aminotransferase (ALT) and aspartate transaminase (AST) changes in the serum and before fibrosis was observed upon hematoxylin and eosin (HE) and Massons staining.

molecular biology↗

bioRxiv2017

Early Candidate Biomarkers Found From Urine Of Astrocytoma Rat Before Changes In MRI

Astrocytoma is the most common aggressive glioma and its early diagnosis remains difficult. Biomarkers are changes associated with the disease. Urine, which is not regulated by homeostatic mechanisms, accumulates changes and therefore is a better source for biomarker discovery. In this study, C6 cells were injected into Wistar rats brain as astrocytoma model. Urine samples were collected at day 2, day 6, day 10 and day 13 after injection, and the urinary proteomes were analyzed. On the 10th day, lesions appeared in magnetic resonance imaging. On the 13th day, clinical symptoms started. But differential urinary proteins were changed with the development of the astrocytoma, and can provide clues even on the 2nd and 6th day. Twenty-seven differential proteins with human orthologs had been reported to associate with astrocytoma. Thirty-nine proteins were verified in four more rats as candidate biomarkers of astrocytoma using multiple-reaction monitoring. A panel of differential urinary proteins may provide early biomarkers for diagnose of astrocytoma.

biochemistry↗

bioRxiv2017

Estimation of Pairwise Genetic Distances Under Independent Sampling of Segregating Sites vs. Haplotype Sampling

Genetic distance is a standard measure of variation in populations. When sequencing genomes individually, genetic distances are computed over all pairs of multilocus haplotypes in a sample. However, when next-generation sequencing methods obtain reads from heterogeneous assemblages of genomes (e.g. for microbial samples in a biofilm or cells from a tumor), individual reads are often drawn from different genomes. This means that pairwise genetic distances are calculated across independently sampled sites rather than across haplotype pairs. In this paper, we show that while the expected pairwise distance under whole haplotype sampling (WHS) is the same as with independent locus sampling (ILS), the sample variances of pairwise distance differ and depend on the direction and magnitude of linkage disequilibrium (LD) among polymorphic sites. We derive a weighted LD value that, when positive, predicts higher sample variance in estimated genetic distance for WHS. Weighted LD is positive when on average, the most common alleles at two loci are in positive LD. Using individual-based simulations of an infinite sites model under Fisher-Wright genetic drift, variances of estimated genetic distance are found to be almost always higher under WHS than under ILS, suggesting a reduction in estimation error when sites are sampled independently. We apply these results to haplotype frequencies from a lung cancer tumor to compute weighted LD and the variances in estimated genetic distance under ILS vs. WHS, and find that the the relative magnitudes of variances under WHS vs. ILS are sensitive to sampled allele frequencies.

genetics↗