Biology subjects

Cui, H.

Publications and source records attributed to Cui, H..

118 records · Page 7Linked to original sources

bioRxiv2019

NbCycB2 represses Nbwo activity via a negative feedback loop in the tobacco trichome developmemt

The wo protein and its downstream gene, SlCycB2 have been demonstrated to regulate the trichome development in tomato. It was shown that only gain-of-function mutant form of wo, Wov (wo woolly motif mutant allele) could induce the increase of trichome density. However, it is still unclear the relationships between wo, Wov and SlCycB2 in trichome regulation. In this study, we demonstrated Nbwo (NbWov) directly regulated the expressions NbCycB2 by binding to the promoter of NbCycB2 and its genomic sequences. As a feedback regulation, NbCycB2 negatively regulates the trichome formation by repressing Nbwo activity at protein level. We further found that the mutations of Nbwo woolly motif could prevent repression of NbWov by NbCycB2, which results in the significant increase of active Nbwo proteins, trichome density and branches. Our results revealed a novel reciprocal mechanism between NbCycB2 and Nbwo during the trichome formation in Nicotiana benthamiana.\n\nHighlightNbCycB2 is specifically expressed in trichomes of Nicotiana benthamiana and represses the Nbwo activity via a negative feedback loop in tobacco trichome developmemt.

plant biology↗

bioRxiv2019

Role of coiled-coil registry shifts in activation of human Bicaudal D2 for dynein recruitment upon cargo-binding

Dynein adaptors such as Bicaudal D2 (BicD2) recognize cargo for dynein-dependent transport. BicD2-dependent transport pathways are important for brain and muscle development. Cargo-bound adaptors are required to activate dynein for processive transport, but the mechanism of action is elusive. Here, we report the structure of the cargo-binding domain of human BicD2 that forms a dimeric coiled-coil with homotypic registry, in which both helices are aligned. To investigate if BicD2 can switch to an asymmetric registry, where a portion of one helix is vertically shifted, we performed molecular dynamics simulations. Both registry types are stabilized by distinct conformations of F743. For the F743I variant, which increases dynein recruitment in the Drosophila homolog, and for the human R747C variant, which causes spinal muscular atrophy, spontaneous coiled-coil registry shifts are observed, which may cause the BicD2-hyperactivation phenotype and disease. We propose that a registry shift upon cargo-binding activates auto-inhibited BicD2 for dynein recruitment.\n\nHighlightsO_LIStable, bona fide BicD2 coiled-coils with distinct registries can be formed.\nC_LIO_LIWe provide evidence that a human disease mutation causes a coiled-coil registry shift.\nC_LIO_LIA coiled-coil registry shift could relieve BicD2-autoinhibition upon cargo-binding.\nC_LIO_LIThe ability to undergo registry shifts may be an inherent property of coiled-coils.\nC_LI\n\nIn BriefOur results support that stable coiled-coils of BicD2 with distinct registries can be formed, and suggest a molecular mechanism for such registry switches. We provide evidence that disease-causing mutations in coiled-coils may alter the equilibrium between registry-shifted conformers, which we propose as a general mechanism of pathogenesis for coiled-coils.\n\nGraphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

biophysics↗

bioRxiv2019

VPAC: Variational projection for accurate clustering of single-cell transcriptomic data

BackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With the exponential growth of the number of cells profiled in individual scRNA-seq experiments, the demand for identifying putative cell types from the data has become a great challenge that appeals for novel computational methods. Although a variety of algorithms have recently been proposed for single-cell clustering, such limitations as low accuracy, inferior robustness, and inadequate stability greatly impede the scope of applications of these methods.\n\nResultsWe propose a novel model-based algorithm, named VPAC, for accurate clustering of single-cell transcriptomic data through variational projection, which assumes that single-cell samples follow a Gaussian mixture distribution in a latent space. Through comprehensive validation experiments, we demonstrate that VPAC can not only be applied to datasets of discrete counts and normalized continuous data, but also scale up well to various data dimensionality, different dataset size and different data sparsity. We further illustrate the ability of VPAC to detect genes with strong unique signatures of a specific cell type, which may shed light on the studies in system biology. We have released a user-friendly python package of VPAC in Github (https://github.com/ShengquanChen/VPAC). Users can directly import our VPAC class and conduct clustering without tedious installation of dependency packages.\n\nConclusionsVPAC enables highly accurate clustering of single-cell transcriptomic data via a statistical model. We expect to see wide applications of our method to not only transcriptome studies for fully understanding the cell identity and functionality, but also the clustering of more general data.

bioinformatics↗

bioRxiv2018

Lipid interactions enhance activation and potentiation of cystic fibrosis transmembrane conductance regulator (CFTR)

The recent cryo-electron microscopy structures of phosphorylated, ATP-bound CFTR in detergent micelles failed to reveal an open anion conduction pathway as expected on the basis of previous functional studies in biological membranes. We tested the hypothesis that interaction of CFTR with lipids is important for opening of its channel. Interestingly, molecular dynamics studies revealed that phospholipids associate with regions of CFTR proposed to contribute to its channel activity. More directly, we found that CFTR purified together with associated lipids using the amphipol: A8-35, exhibited higher rates of catalytic activity, channel activation and potentiation using ivacaftor, than did CFTR purified in detergent. Catalytic activity in CFTR detergent micelles was partially rescued by addition of phospholipids plus cholesterol, arguing that these lipids contribute directly to its modulation. In summary, these studies highlight the importance of lipids in regulated CFTR channel activation and potentiation.

biophysics↗

bioRxiv2018

Structural and biochemical studies define Nudt12 as a new class of RNA deNADding enzyme in mammalian cells

We recently demonstrated mammalian cells harbor NAD-capped mRNAs that are hydrolyzed by the DXO deNADding enzyme. Here we report the Nudix protein Nudt12 is a second mammalian deNADding enzyme structurally and mechanistically distinct from DXO and targets different RNAs. Crystal structure of mouse Nudt12 in complex with the deNADding product AMP and three Mg2+ ions at 1.6 [A] resolution provides exquisite insights into the molecular basis of the deNADding activity within the NAD pyrophosphate. Disruption of the Nudt12 gene stabilizes transfected NAD-capped RNA in cells and its endogenous NAD-capped mRNA targets are enriched in those encoding proteins involved in cellular energetics. Furthermore, exposure of cells to metabolic stress manifests changes in NAD-capped RNA levels indicating an association between NAD-capped RNAs and cellular metabolism. Lastly, we show that the bacterial RppH protein also possesses deNADding activity toward NAD-capped RNA but not free NAD, revealing a third class of deNADding enzymes.

molecular biology↗

bioRxiv2018

A familial case of Syndactyly type IV due to a novel duplication of ~222.23 kb covering exons 2-17 of the LMBR1 gene: a case report

Syndactyly is one of the most frequent hereditary limb malformations with clinical and genetical complexity. Autosomal dominant Syndactyly type IV (SD4) is a very rare form of syndactyly, occurring as a result of heterozygous mutation in an SHH regulatory element (ZRS) that resides in intron 5 of the LMBR1 gene on chromosome 7q36.3. The SD4 is characterized by complete cutaneous syndactyly of all fingers, cup-shaped hands due to flexion of the fingers and accompanied by polydactyly. Here, we firstly reported a big Chinese family, manifesting cup-shaped hands consistent with SD4 and intrafamilial heterogeneity in clinical phenotype of tibial and fibulal shortening, triphalangeal thumb-polysyndactyly syndrome (TPTPS). Genetically, we identified a novel duplication of [~]222.23 kb covering exons 2-17 of the LMBR1 gene in this family by next generation sequencing. This case expands our new clinical understanding of SD4 phenotype.

genetics↗

bioRxiv2018

Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems

Natural language descriptions of organismal phenotypes - a principal object of study in biology, are abundant in biological literature. Expressing these phenotypes as logical statements using formal ontologies would enable large-scale analysis on phenotypic information from diverse systems. However, considerable human effort is required to make the semantics of phenotype descriptions amenable to machine reasoning by (a) recognizing appropriate on-tological terms for entities in text and (b) stringing these terms into logical statements. Most existing Natural Language Processing tools stop at entity recognition, leaving a need for tools that can assist with both aspects of the task. The recently described Semantic CharaParser aims to meet this need. We describe the first expert-curated Gold Standard corpus for ontology-based annotation of phenotypes from the systematics literature. We use it to evaluate Semantic CharaParsers annotations and explore differences in performance between humans and machine. We use four annotation accuracy metrics that can account for both semantically identical and similar matches. We found that machine-human consistency was significantly lower than inter-curator (human-human) consistency. Surprisingly, allowing curators access to external information that was not available to Semantic CharaParser did not significantly increase the similarity of their annotations to the Gold Standard nor have a significant effect on inter-curator consistency. We found that the similarity of machine annotations to the Gold Standard increased after new ontology terms relevant to the input text had been added. Evaluation by the original authors of the character descriptions indicated that the Gold Standard annotations came closer to representing their intended meaning than did either the curator or machine annotations. These findings point toward ways to better design of software to augment human curators, and the Gold Standard corpus will allow training and assessment of new tools to improve phenotype annotation accuracy at scale.

evolutionary biology↗

bioRxiv2018

Multilayer view of pathogenic SNVs in human interactome through in-silico edgetic profiling

Non-synonymous mutations linked to the complex diseases often have a global impact on a biological system, affecting large biomolecular networks and pathways. However, the magnitude of the mutation-driven effects on the macromolecular network is yet to be fully explored. In this work, we present an systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring, \"edgetic\", effects on the human interactome. Our in-silico analysis highlights the intrinsic differences and important similarities between the pathogenic single nucleotide variants (SNVs) and frameshift mutations. We show that pathogenic SNVs are more likely to cause gene pleiotropy than pathogenic frameshift mutations and are enriched on the protein interaction interfaces. Functional profiling of SNVs indicates widespread disruption of the protein-protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction-disrupting mutations in type 2 diabetes mellitus, and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer. With the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the patient-specific genetic variation. The current and future in-silico edgotyping tools present a cheap and fast solution to deal with the rapidly growing datasets of discovered mutations.

systems biology↗

bioRxiv2017

Boosting Gene Expression Clustering with System-Wide Biological Information: A Robust Autoencoder Approach

Gene expression analysis provides genome-wide insights into the transcriptional activity of a cell. One of the first computational steps in exploration and analysis of the gene expression data is clustering. With a number of standard clustering methods routinely used, most of the methods do not take prior biological information into account. In this paper, we propose a new approach for gene expression clustering analysis. The approach benefits from a new deep learning architecture, Robust Autoencoder, which provides a more accurate high-level representation of the feature sets, and from incorporating prior biological information into the clustering process. We tested our approach on two distinct gene expression datasets and compared the performance with two widely used clustering methods, hierarchical clustering and k-means, as well as with a recent deep learning clustering approach. As a result, our approach outperformed all other clustering methods on the labeled yeast gene expression dataset. Furthermore we showed that it is better in identifying the functionally common clusters than k-means on the unlabeled human gene expression dataset. The results demonstrate that our new deep learning architecture could generalize well the specific properties of gene expression profiles. Furthermore, the results confirm our hypothesis that the prior biological network knowledge could be helpful in the gene expression clustering task.

bioinformatics↗

bioRxiv2017

Spike-Timing Patterns Conform to Gamma Distribution with Regional and Cell Type-Specific Characteristics

Spike-timing patterns - crucial for synaptic plasticity and neural computation - are often modeled as Poisson-like random processes, log-normal distribution or gamma-distribution patterns, each with different underlying assumptions that may or may not be biologically true. However, it is not entirely clear whether (and how well) these different models would or would not capture spike-timing statistical patterns across different neurons, regions, animal species and cognitive states. Here, we examine statistical patterns of spike-timing irregularity in 13 different cortical and subcortical regions from mouse, hamster, cat and monkey brains. In contrast to the widely-assumed Poisson or log-normal distribution patterns, we show that spike-timing patterns of various projection neurons-including cortical excitatory principal cells, hippocampal pyramidal cells, inhibitory striatal medium spiny neurons and dopaminergic neurons, as well as fast-spiking interneurons - all invariantly conform to the gamma-distribution model. While higher regularity in spike-timing patterns are observed in a few cases, such as mouse DA neurons and monkey motor cortical neurons, there is no clear tendency in increased firing regularity from the sensory and subcortical neurons to prefrontal or motor cortices, as previously entertained. Moreover, gamma shapes of spike-timing patterns remain robust over various natural cognitive states, such as sleep, awake periods, or during fearful episodic experiences. Interestingly, ketamine-induced general anesthesia or unconsciousness is associated with the breakdown of forebrain spike patterns from a singular gamma distribution into two distinct subtypes of gamma distributions, suggesting the importance of this spike-timing pattern in supporting natural cognitive states. These results suggest that gamma-distribution patterns of spike timing reflect not only a fundamental property conserved across different neurons, regions and animal species, but also an operation crucial for supporting natural cognitive states. Such gamma-distribution-based spike-timing patterns can also have important implications for real-time neural coding and realistic neuromorphic computing.

neuroscience↗