The Experts below are selected from a list of 5328 Experts worldwide ranked by ideXlab platform
Deqiang Zhang - One of the best experts on this subject based on the ideXlab platform.
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transcriptome analysis and association mapping revealed the Genetic Regulatory Network response to cadmium stress in populus tomentosa
Journal of Experimental Botany, 2020Co-Authors: Mingyang Quan, Yuepeng Song, Deqiang Zhang, Xin Liu, Liang Xiao, Panfei Chen, Fangyuan SongAbstract:Long non-coding RNAs (lncRNAs) play essential roles in plant abiotic stress responses, however, the lncRNA-mediated Genetic Networks response to cadmium (Cd) treatment remain elusive in trees, the promising phytoremediation candidates for Cd contamination. Here, we identified 172 Cd-responsive lncRNAs and 295 differentially expressed target genes in the leaves of Populus tomentosa under Cd treatment. Functional annotation revealed that these lncRNAs involved in various processes, including photosynthesis, hormone regulation, and phenylalanine metabolism. Association studies identified 78 significant associations, representing 14 Cd-responsive lncRNAs and 28 target genes for photosynthetic and leaf physiological traits. Epistasis uncovered 83 pairwise interactions among these traits, unveiling Cd-responsive lncRNA-mediated Genetic Networks for photosynthesis and leaf physiology in P. tomentosa. We then focused on the roles of two Cd-responsive lncRNA-gene pairs, MSTRG.22608.1-PtoMYB73 and MSTRG.5634.1-PtoMYB27, in Cd tolerance of Populus, and detected insertions/deletions within lncRNAs as polymorphisms driving target gene expression. LncRNAs genotype analysis and heterologous overexpressing PtoMYB73 and PtoMYB27 in Arabidopsis indicated their positive effects on enhancing Cd tolerance, photosynthetic rate, and leaf growth, and the potential interaction mechanisms of PtoMYB73 with abiotic stresses. Our study provides the Genetic basis for Populus response to Cd treatment, facilitating Genetic improvement of enhancing Cd tolerance in trees.
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the Genetic Regulatory Network centered on pto wuschela and its targets involved in wood formation revealed by association studies
Scientific Reports, 2015Co-Authors: Xiaohui Yang, Zunzheng Wei, Jinhui Chen, Qingshi Wang, Mingyang Quan, Yuepeng Song, Jianbo Xie, Deqiang ZhangAbstract:Transcription factors (TFs) regulate gene expression and can strongly affect phenotypes. However, few studies have examined TF variants and TF interactions with their targets in plants. Here, we used Genetic association in 435 unrelated individuals of Populus tomentosa to explore the variants in Pto-Wuschela and its targets to decipher the Genetic Regulatory Network of Pto-Wuschela. Our bioinformatics and co-expression analysis identified 53 genes with the motif TCACGTGA as putative targets of Pto-Wuschela. Single-marker association analysis showed that Pto-Wuschela was associated with wood properties, which is in agreement with the observation that it has higher expression in stem vascular tissues in Populus. Also, SNPs in the 53 targets were associated with growth or wood properties under additive or dominance effects, suggesting these genes and Pto-Wuschela may act in the same Genetic pathways that affect variation in these quantitative traits. Epistasis analysis indicated that 75.5% of these genes directly or indirectly interacted Pto-Wuschela, revealing the coordinated Genetic Regulatory Network formed by Pto-Wuschela and its targets. Thus, our study provides an alternative method for dissection of the interactions between a TF and its targets, which will strength our understanding of the Regulatory roles of TFs in complex traits in plants.
Ranadip Pal - One of the best experts on this subject based on the ideXlab platform.
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robust approaches for Genetic Regulatory Network modeling and intervention a review of recent advances
IEEE Signal Processing Magazine, 2012Co-Authors: Ranadip Pal, Sukalyan Bhattacharya, Mehmet Umut CaglarAbstract:In this article, we will review approaches to study the robustness of GRN modeling and control strategies with emphasis on the steps of model selection, model inference, and Network intervention.
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generation of intervention strategy for a Genetic Regulatory Network represented by a family of markov chains
International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Noah Berlow, Ranadip PalAbstract:Genetic Regulatory Networks (GRNs) are frequently modeled as Markov Chains providing the transition probabilities of moving from one state of the Network to another. The inverse problem of inference of the Markov Chain from noisy and limited experimental data is an ill posed problem and often generates multiple model possibilities instead of a unique one. In this article, we address the issue of intervention in a Genetic Regulatory Network represented by a family of Markov Chains. The purpose of intervention is to alter the steady state probability distribution of the GRN as the steady states are considered to be representative of the phenotypes. We consider robust stationary control policies with best expected behavior. The extreme computational complexity involved in search of robust stationary control policies is mitigated by using a sequential approach to control policy generation and utilizing computationally efficient techniques for updating the stationary probability distribution of a Markov chain following a rank one perturbation.
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inference of a Genetic Regulatory Network model from limited time series data
International Conference on Bioinformatics, 2011Co-Authors: Saad Haider, Ranadip PalAbstract:Numerous approaches exist for modeling of Genetic Regulatory Networks (GRNs) but the low sampling rates often employed in biological studies prevents the inference of detailed models from experimental data. In this paper, we analyze the issues involved in estimating a model of a GRN from single cell line time series data with limited time points. We present an inference approach for a Boolean Network (BN) model of a GRN from limited transcriptomic or proteomic time series data based on prior biological knowledge of connectivity, constraints on attractor structure and robust design. Through theoretical analysis and simulations, we showed the rarity of arriving at a BN from limited time series data with plausible biological structure using random connectivity and absence of structure in data. We applied our inference approach to 6 time point transcriptomic data on HMEC cell lines after application of EGF and generated a BN with a plausible biological structure satisfying the data.
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steady state preserving reduction for Genetic Regulatory Network models
Computer-Based Medical Systems, 2009Co-Authors: Ranadip Pal, Sonal BhattacharyaAbstract:Fine-scale models based on stochastic differential equations can provide the most detailed description of the dynamics of gene expression and imbed, in principle, all the information about the biochemical reactions involved in gene interactions. However, the computational complexity involved in the design of optimal intervention strategies to favorably effect system dynamics for such detailed models is enormous. Hence, there is a need to design mappings from fine-scale models to coarse-scale models while maintaining sufficient structure for the problem at hand. In this paper, we propose a mapping from a fine-scale model represented by a Chemical Master Equation to a coarse-scale model represented by a Probabilistic Boolean Network that maintains the collapsed steady state distribution of the detailed model. We also evaluate the performance of the intervention strategy designed using the coarse scale model when applied to the fine-scale model.
Ying Chen - One of the best experts on this subject based on the ideXlab platform.
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Genetic Regulatory Network analysis reveals that low density lipoprotein receptor related protein 11 is involved in stress responses in mice
Psychiatry Research-neuroimaging, 2014Co-Authors: Rixin Cai, Xiaodong Wang, Chengwei Duan, Xuelei Tao, Dongjian Chen, Yonghua Liu, Maohong Cao, Ying ChenAbstract:To study whether Lrp11 is involved in stress response and find its expression Regulatory Network, the model of stress has been built using C57BL/6J (B6) and DBA/2 (D2) mice. Western blotting, qPCR and immunohistochemistry were used to investigate the expression variation of Lrp11 in amygdala tissue after exposure to stress. We found the quantity of Lrp11 was more obvious in stress models than that in normal mice (P<0.05) which suggests Lrp11 might participate in the process of stress response. The expression of Lrp11 is controlled by a cis-acting quantitative trait locus (cis-eQTL). We identified four genes that are regulated by Lrp11 and the expression of 66 genes highly correlated with Lrp11, seven of which have previously been implicated in stress pathways. To evaluate the relationship between Lrp11 and its downstream genes or Network members, we transfected HEK 293T cells and SH-SY5Y cells with Lrp11 siRNA leading to down-regulation of Lrp11mRNA and were able to confirm a significant influence of Lrp11 depletion on the expression of Xpnpep1, Maneal, Pgap1 and Uprt. These validated downstream targets and members of Lrp11 gene Network provide new insight into the biological role of Lrp11 and may be an important risk factor in the development of stress.
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Genetic Regulatory Network analysis for app based on Genetical genomics approach
Experimental Aging Research, 2009Co-Authors: Xusheng Wang, Ying Chen, Xiaodong WangAbstract:A number of studies have shown that amyloid precursor protein (App) plays a critical role in Alzheimer's disease (AD); however, little is known about the Genetic Regulatory Network. In this study, the authors combined array analysis and quantitative trait loci (QTL) mapping to characterize the Genetic variation and Genetic Regulatory Network for App using hippocampus of BXD recombinant inbred (RI) mice. The variation in expression level of App is conspicuous across the 78 BXD RI strains. Moreover, the expression level of App is significantly higher in DBA/2J than the level in C57BL/6J (p < .001). Quantitative reverse transcriptase–polymerase chain reaction (qRT-PCR) analysis has further confirmed the significant difference between the two parental strains C57BL/6J and DBA/2J. The authors performed an interval mapping for App gene expression and found that it is cis regulated with highly significant likelihood-ratio statistic (LRS) score (LRS = 19; p < .05). Four SNPs and two InDels (insertions or deletion...
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expression quantitative trait loci and Genetic Regulatory Network analysis reveals that gabra2 is involved in stress responses in the mouse
Stress, 2009Co-Authors: Jiajuan Dai, Xusheng Wang, Ying Chen, Xiaodong Wang, Jun ZhuAbstract:Previous studies have revealed that the subunit α 2 (Gabra2) of the γ-aminobutyric acid receptor plays a critical role in the stress response. However, little is known about the gentetic Regulatory Network for Gabra2 and the stress response. We combined gene expression microarray analysis and quantitative trait loci (QTL) mapping to characterize the Genetic Regulatory Network for Gabra2 expression in the hippocampus of BXD recombinant inbred (RI) mice. Our analysis found that the expression level of Gabra2 exhibited much variation in the hippocampus across the BXD RI strains and between the parental strains, C57BL/6J, and DBA/2J. Expression QTL (eQTL) mapping showed three microarray probe sets of Gabra2 to have highly significant linkage likelihood ratio statistic (LRS) scores. Gene co-Regulatory Network analysis showed that 10 genes, including Gria3, Chka, Drd3, Homer1, Grik2, Odz4, Prkag2, Grm5, Gabrb1, and Nlgn1 are directly or indirectly associated with stress responses. Eleven genes were implicated a...
Mingyang Quan - One of the best experts on this subject based on the ideXlab platform.
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transcriptome analysis and association mapping revealed the Genetic Regulatory Network response to cadmium stress in populus tomentosa
Journal of Experimental Botany, 2020Co-Authors: Mingyang Quan, Yuepeng Song, Deqiang Zhang, Xin Liu, Liang Xiao, Panfei Chen, Fangyuan SongAbstract:Long non-coding RNAs (lncRNAs) play essential roles in plant abiotic stress responses, however, the lncRNA-mediated Genetic Networks response to cadmium (Cd) treatment remain elusive in trees, the promising phytoremediation candidates for Cd contamination. Here, we identified 172 Cd-responsive lncRNAs and 295 differentially expressed target genes in the leaves of Populus tomentosa under Cd treatment. Functional annotation revealed that these lncRNAs involved in various processes, including photosynthesis, hormone regulation, and phenylalanine metabolism. Association studies identified 78 significant associations, representing 14 Cd-responsive lncRNAs and 28 target genes for photosynthetic and leaf physiological traits. Epistasis uncovered 83 pairwise interactions among these traits, unveiling Cd-responsive lncRNA-mediated Genetic Networks for photosynthesis and leaf physiology in P. tomentosa. We then focused on the roles of two Cd-responsive lncRNA-gene pairs, MSTRG.22608.1-PtoMYB73 and MSTRG.5634.1-PtoMYB27, in Cd tolerance of Populus, and detected insertions/deletions within lncRNAs as polymorphisms driving target gene expression. LncRNAs genotype analysis and heterologous overexpressing PtoMYB73 and PtoMYB27 in Arabidopsis indicated their positive effects on enhancing Cd tolerance, photosynthetic rate, and leaf growth, and the potential interaction mechanisms of PtoMYB73 with abiotic stresses. Our study provides the Genetic basis for Populus response to Cd treatment, facilitating Genetic improvement of enhancing Cd tolerance in trees.
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the Genetic Regulatory Network centered on pto wuschela and its targets involved in wood formation revealed by association studies
Scientific Reports, 2015Co-Authors: Xiaohui Yang, Zunzheng Wei, Jinhui Chen, Qingshi Wang, Mingyang Quan, Yuepeng Song, Jianbo Xie, Deqiang ZhangAbstract:Transcription factors (TFs) regulate gene expression and can strongly affect phenotypes. However, few studies have examined TF variants and TF interactions with their targets in plants. Here, we used Genetic association in 435 unrelated individuals of Populus tomentosa to explore the variants in Pto-Wuschela and its targets to decipher the Genetic Regulatory Network of Pto-Wuschela. Our bioinformatics and co-expression analysis identified 53 genes with the motif TCACGTGA as putative targets of Pto-Wuschela. Single-marker association analysis showed that Pto-Wuschela was associated with wood properties, which is in agreement with the observation that it has higher expression in stem vascular tissues in Populus. Also, SNPs in the 53 targets were associated with growth or wood properties under additive or dominance effects, suggesting these genes and Pto-Wuschela may act in the same Genetic pathways that affect variation in these quantitative traits. Epistasis analysis indicated that 75.5% of these genes directly or indirectly interacted Pto-Wuschela, revealing the coordinated Genetic Regulatory Network formed by Pto-Wuschela and its targets. Thus, our study provides an alternative method for dissection of the interactions between a TF and its targets, which will strength our understanding of the Regulatory roles of TFs in complex traits in plants.
Sanjay Jain - One of the best experts on this subject based on the ideXlab platform.
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the large scale Genetic Regulatory Network of e coli understanding functional robustness at the system level
Biophysical Journal, 2011Co-Authors: Sanjay JainAbstract:This talk will discuss some aspects of the large scale Regulatory Network that controls the Genetic expression of an E. coli cell as a whole. The expression of E. coli's 4-5 thousand genes is controlled by 150-200 transcription factors (TFs) and a few sigma factors. The activity of a TF or sigma factor is itself governed partly by external conditions such as temperature, pH and the chemical composition of the extracellular environment, and partly by the internal environment. The latter includes other TFs and sigma factors controlling the expression of the gene coding for the TF in question, molecules involved in post-transcriptional regulation, as well as metabolites that bind to the TF resulting in its regulation. The architecture of the global transcriptional Regulatory Network will be discussed, including feedbacks. A simple mathematical model that attempts to capture the dynamics of the switching on and off of the genes will be presented. This “global view” is “coarse grained”; it glosses over several details. However, this perspective reveals that the Network as a whole has a rather special or particular character, which could be the basis of the cell's robustness and adaptability.