Biology subjects

Cui, F.

Publications and source records attributed to Cui, F..

5 recordsLinked to original sources

bioRxiv2018

Methyl viologen can affect mitochondrial function in Arabidopsis.

Reactive oxygen species (ROS) are key signalling intermediates in plant metabolism, defence, and stress adaptation. The chloroplast and mitochondria are centres of metabolic control and ROS production, which coordinate stress responses in other cell compartments. The herbicide and experimental tool, methyl viologen (MV) induces ROS generation in the chloroplast under illumination, but is also toxic in non-photosynthetic organisms. We used MV to probe plant ROS signalling in compartments other than the chloroplast. Taking a genetic approach in Arabidopsis thaliana, we used natural variation, QTL mapping, and mutant studies with MV in the light, but also under dark conditions, when the chloroplast electron transport is inactive. These studies revealed a light-independent MV-induced ROS-signalling pathway, suggesting mitochondrial involvement. Mitochondrial Mn SUPEROXIDE DISMUTASE was required for ROS-tolerance and the effect of MV was enhanced by exogenous sugar, providing further evidence for the role of mitochondria. Mutant and hormone feeding assays revealed roles for stress hormones in organellar ROS-responses. The radical-induced cell death1 mutant, which is tolerant to MV-induced ROS and exhibits altered mitochondrial signalling, was used to probe interactions between organelles. Our studies implicate mitochondria in the response to ROS induced by MV.

plant biology↗

bioRxiv2018

BioVR: a platform for virtual reality assisted biological data integration and visualization

Background: The effective visualization and presentation of biological data is of critical importance to research scientists. The increasing rate at which experiments generate data has exacerbated the visualization problem. While biological datasets continue to increase in size and complexity, the shift to adopt new user interface (UI) paradigms has historically lagged. Consequently, a major bottleneck for analysis of next-generation sequencing data is the continued use of the UIs primarily inspired from single-type and small-scale data in 1990s.\n\nResults: We have developed BioVR, an easy-to-use interactive, virtual reality (VR)-assisted platform for visualizing DNA/RNA sequences and protein structures using Unity3D and the C# programming language. It utilizes the cutting-edge Oculus Rift, and Leap Motion hand detection, resulting in intuitive navigation and exploration of various types of biological data. Using Gria2 and its associated gene product as an example, we present this proof-of-concept software to integrate protein sequence and structure information. For any residue of interest in the Gria2 sequence, it can be quickly visualized in its protein structure within VR.\n\nConclusions: Using innovative 3D techniques, we provide a VR-based platform for visualization of DNA/RNA sequences and protein structures in aggregate, which can be extended to view omics data.

bioinformatics↗

bioRxiv2018

GNE: A deep learning framework for gene network inference by aggregating biological information

The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here. We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low-dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries. GNE is freely available under the GNU General Public License and can be downloaded from Github (https://github.com/kckishan/GNE)

systems biology↗

bioRxiv2017

Nucleosome rotational positioning is influenced by cis- and trans-acting factors

Nucleosomal DNA sequences generally follow a well-known pattern with [~]10-bp periodic WW (where W is A or T) dinucleotides that oscillate in phase with each other and out of phase with SS (where S is G or C) dinucleotides. However, nucleosomes having a DNA sequence pattern inverse to the conventional one have not been systematically analyzed in eukaryotes. Here, we show that anti-WW/SS nucleosomes are widespread and exhibit a species- and context-specific distribution in the genomes. The enrichment of anti-WW/SS nucleosomes in mammalian genomes is positively correlated with RNA Pol II transcriptional levels, but negatively correlated with the presence of the periodic WW or SS sequence patterns. In addition, chromatin remodeling complexes have an impact on the abundance of anti-WW/SS nucleosomes. The data reveal distinct roles of cis- and trans-acting factors in the rotational positioning of nucleosomes between non-mammals and mammals. Implications of the anti-WW/SS sequence pattern for RNA Pol II transcription are discussed.

bioinformatics↗

bioRxiv2017

p53 binding sites in normal and cancer cells are characterized by distinct chromatin context

The tumor suppressor protein p53 interacts with DNA in a sequence-dependent manner. Thousands of p53 binding sites have been mapped genome-wide in normal and cancer cells. However, the way p53 selectively binds its cognate sites in different types of cells is not fully understood. Here, we performed a comprehensive analysis of 25 published p53 cistromes and identified 3,551 and 6,039 high-confidence binding sites in normal and cancer cells, respectively. Our analysis revealed two distinct epigenetic features underlying p53-DNA interactions in vivo. First, p53 binding sites are associated with transcriptionally active histone marks (H3K4me3 and H3K36me3) in normal-cell chromatin, but with repressive histone marks (H3K27me3) in cancer-cell chromatin. Second, p53 binding sites in cancer cells are characterized by a lower level of DNA methylation than their counterparts in normal cells, probably related to global hypomethylation in cancers. Intriguingly, regardless of the cell type, p53 sites are highly enriched in the endogenous retroviral elements of the ERV1 family, highlighting the importance of this repeat family in shaping the transcriptional network of p53. Moreover, the p53 sites exhibit an unusual combination of chromatin patterns: high nucleosome occupancy and, at the same time, high sensitivity to DNase I. Our results suggest that p53 can access its target sites in a chromatin environment that is non-permissive to most DNA-binding transcription factors, which may allow p53 to act as a pioneer transcription factor in the context of chromatin.

genomics↗