Biology subjects

Cheng, Q.

Publications and source records attributed to Cheng, Q..

6 recordsLinked to original sources

bioRxiv2018

Dynamic alterations of retinal EphA5 expression in retinocollicular map plasticity

The topographically ordered retinocollicular projection is an excellent system for studying the mechanism of axon guidance. Gradients of EphA receptors in the retina and ephrin-As in the superior colliculus (SC) pattern the anteroposterior axis of the retinocollicular map, but whether they are involved in map plasticity after injury is unknown. Partial damage to the caudal SC at birth creates a compressed, complete retinotopic map in the remaining SC without affecting visual response properties. Previously, we found that the gradient of ephrinA expression in compressed maps is steeper than normal, suggesting an instructive role in compression (Tadesse et al., 2013). Here we measured EphA5 mRNA and protein levels after caudal SC damage in order to test the hypothesis that changes in retinal EphA5 expression occur that are complementary to the changes in collicular ephrin-A expression. We find that the nasotemporal gradient of EphA5 receptor expression steepens in the retina and overall expression levels change dynamically, especially in temporal retina, supporting the hypothesis. This change in receptor expression occurs after the change in ephrin-A ligand expression. We propose that changes in the retinal EphA5 gradient guide recovery of the retinocollicular projection from early injury. This could occur directly through the change in EphA5 expression instructing retino-SC map compression, or through ephrinA ligand signaling instructing a change in EphA5 receptor expression that in turn signals the retinocollicular map to compress. Understanding what molecular signals direct compensation for injury is essential to developing rehabilitative strategies and maximizing the potential for recovery.

neuroscience↗

bioRxiv2018

System-wide Identification of Enzyme Substrates by Thermal Analysis (SIESTA)

Despite the immense importance of enzyme-substrate reactions, there is a lack of generic and unbiased tools for identifying and prioritizing substrate proteins which are modulated in the structural and functional levels through modification. Here we describe a high-throughput unbiased proteomic method called System-wide Identification and prioritization of Enzyme Substrates by Thermal Analysis (SIESTA). The approach assumes that enzymatic post-translational modification of substrate proteins might change their thermal stability. SIESTA successfully identifies several known and novel substrate candidates for selenoprotein thioredoxin reductase 1, protein kinase B (AKT1) and poly-(ADP-ribose) polymerase-10 systems in up to a depth of 7179 proteins. Wider application of SIESTA can enhance our understanding of the role of enzymes in homeostasis and disease, open new opportunities in investigating the effect of PTMs on signal transduction, and facilitate drug discovery.

biochemistry↗

bioRxiv2018

Integrative data analysis predicts YY1 as a Cis-regulator in the 3D Cell Culture Models of MCF10A at the Stiffness Level of High Mammographic Density

Previous studies have shown that in 3D cell culture models of human mammary cells (HMEC) (i) colony organizations are heterogeneous, and (ii) ERBB2 is overexpressed in MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density (MD). The goal of the current study is to identify transcription factors that regulate processes associated with the increased stiffness of the microenvironment. Two HMEC premalignant lines of MCF7 and 184A1 are cultured in 3D, colonies are imaged using confocal microscopy, and colony organizations and heterogeneity are quantified as a function of the stiffness of the microenvironment. In parallel and surrogate assays, colony organizations are profiled by transcriptomics. Transcriptome data are enriched by correlative analysis with the computed morphometric indices, from 3D culture, and a subset of transcriptome data is selected. This subset is then processed with Model-based Analysis of Regulation of Gene Expression (MARGE) and publicly available ChIP-seq data to predict regulatory transcription factors. The integrative analysis indicated that YY1 regulates ERBB2 in the 3D cell culture of MCF10A when the stiffness of the microenvironment is increased to that of high MD. Subsequent experimental validation confirmed that YY1 is only expressed at the high stiffness value of the microenvironment concomitant with the overexpression of ERBB2 in MCF10A. Furthermore, using ERBB2 positive SKBR3 cell line, co-expression of YY1 and ERBB2 is absent, which indicates that YY1 regulates tumorigenicity through multiple pathways.\n\nAuthors summaryMCF10A is a premalignant immortalized human mammary cell that has been isolated from a patient with fibrocystic and lost several barriers toward transformation. In an earlier study, we showed that ERBB2 is upregulated in 3D cultures of MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density. Here, we leverage publicly available ChIP-seq data to predict and validate the cis-regulator of ERBB2. Our integrated experimental and computation protocol provides a pathway for elucidating regulators that can potentially be targeted for intervention.

cancer biology↗

bioRxiv2018

Circadian gene Clock regulates mitochondrial morphology and functions by posttranscriptional way

Many daily activities are under the control of circadian clock, including nutrition metabolism and energy generation. Mitochondria, as the core factories of oxidizing substrates and producing ATP, undergo changes in quantity and morphology to adapt to the demand for energy. It has been demonstrated that mitochondrial gene expression, dynamics and functions are all affected by circadian clock. Here, we demonstrated that circadian gene Clock affects the number, architecture and function of mitochondria via posttranscriptional regulation of Drp1. Clock{Delta}19 leads to fragmented mitochondria accompanied with the loss of membrane potential, excessive ROS accumulation and decreased mitochondrial respiration and ATP generation. Clock{Delta}19 mice exhibit disordered lipid metabolism and evident nonalcoholic fatty liver disease (NAFLD), which are rescued by treatment with the mitochondrial fission inhibitor Mdivi-1. These results suggest a strong relationship between Clock, mitochondrial dynamics and metabolic diseases and provide a new perspective on disordered circadian clock and related diseases.

cell biology↗

bioRxiv2017

DeepGS: Predicting phenotypes from genotypes using Deep Learning

MotivationGenomic selection (GS) is a new breeding strategy by which the phenotypes of quantitative traits are usually predicted based on genome-wide markers of genotypes using conventional statistical models. However, the GS prediction models typically make strong assumptions and perform linear regression analysis, limiting their accuracies since they do not capture the complex, non-linear relationships within genotypes, and between genotypes and phenotypes.\n\nResultsWe present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypic markers when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional marker data. We used a large GS dataset to train DeepGS and compare its performance with other methods. In terms of mean normalized discounted cumulative gain value, DeepGS achieves an increase of 27.70%~246.34% over a conventional neural network in selecting top-ranked 1% individuals with high phenotypic values for the eight tested traits. Additionally, compared with the widely used method RR-BLUP, DeepGS still yields a relative improvement ranging from 1.44% to 65.24%. Through extensive simulation experiments, we also demonstrated the effectiveness and robustness of DeepGS for the absent of outlier individuals and subsets of genotypic markers. Finally, we illustrated the complementarity of DeepGS and RR-BLUP with an ensemble learning approach for further improving prediction performance.\n\nAvailabilityDeepGS is provided as an open source R package available at https://github.com/cma2015/DeepGS.

bioinformatics↗

bioRxiv2017

PEA: an integrated R toolkit for plant epitranscriptome analysis

MotivationThe 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.\n\nResultsHere, 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.\n\nAvailabilityPEA is implemented using R and available at https://github.com/cma2015/PEA.

bioinformatics↗