The Reconstruction and Analysis of Gene Regulatory Networks

Zheng, Guangyong; Huang, Tao

doi:10.1007/978-1-4939-7717-8_8

Cited by 13 publications

(8 citation statements)

References 67 publications

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“…To explore the cross talk between brain regions, we applied Random Walk with Restart (RWR) algorithm 35,[39][40][41][42] . To illustrate how RWR can reveal the cross talk, let us denote the STRING network as a graph comprised of a set of genes and a set of interactions .…”

Section: The Network Expansion Of Nac Mpfc and Pag Neuropathic Pain mentioning

confidence: 99%

The gene regulatory network in different brain regions of neuropathic pain mouse models

et al. 2019

Preprint

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Neuropathic pain is the direct result caused by lesions or somatosensory nervous system diseases that are associated with emotional regulation. The incidence of neuropathic pain in the general population is 7-10% and the mechanisms of neuropathic pain are largely unknown. It is often related to structural and functional abnormalities in multiple brain regions. The forebrain, including nucleus accumbens (NAc), medial prefrontal cortex (mPFC) and periaqueductal gray (PAG) have been shown to correspond with the regulation of neuropathic pain. To investigate the molecular mechanism of neuropathic pain across different brain regions, we identified the differentially expressed genes between the spared nerve injury model (SNI) mice of neuropathic pain and the control Sham mice in NAc, mPFC and PAG and mapped these genes onto comprehensive functional association network. With Random Walk with Restart (RWR) analysis, we identified more novel neuropathic pain genes in NAc, mPFC and PAG, such as Asic3, Cd200r1 and MT2, beside well known Capn11 and CYP2E1. What’s more, we discovered their interactions or cross talks. Our results provided novel insights of neuropathic pain and provided therapeutic targets for treating neuropathic pain.

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Section: The Network Expansion Of Nac Mpfc and Pag Neuropathic Pain mentioning

confidence: 99%

The gene regulatory network in different brain regions of neuropathic pain mouse models

et al. 2019

Preprint

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“…The gene regulatory network approach was then further utilized to infer causal relationships between genes within host and pathogen, and between host and pathogen (Guo et al, 2016;Banf and Rhee, 2017). This novel approach expands on the previous Pearson correlation analysis which studied the two organisms independently, and revealed a significant dependence between the interactions of the two organisms (Musungu et al, 2016;Zheng and Huang, 2018). In addition, genes were modeled into correlative interactions groups using linear correlation methods.…”

Section: Discussionmentioning

confidence: 99%

Use of Dual RNA-seq for Systems Biology Analysis of Zea mays and Aspergillus flavus Interaction

Musungu

Bhatnagar

Quiniou³

et al. 2020

Front. Microbiol.

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The interaction between Aspergillus flavus and Zea mays is complex, and the identification of plant genes and pathways conferring resistance to the fungus has been challenging. Therefore, the authors undertook a systems biology approach involving dual RNA-seq to determine the simultaneous response from the host and the pathogen. What was dramatically highlighted in the analysis is the uniformity in the development patterns of gene expression of the host and the pathogen during infection. This led to the development of a "stage of infection index" that was subsequently used to categorize the samples before downstream system biology analysis. Additionally, we were able to ascertain that key maize genes in pathways such as the jasmonate, ethylene and ROS pathways, were up-regulated in the study. The stage of infection index used for the transcriptomic analysis revealed that A. flavus produces a relatively limited number of transcripts during the early stages (0 to 12 h) of infection. At later stages, in A. flavus, transcripts and pathways involved in endosomal transport, aflatoxin production, and carbohydrate metabolism were up-regulated. Multiple WRKY genes targeting the activation of the resistance pathways (i.e., jasmonate, phenylpropanoid, and ethylene) were detected using causal inference analysis. This analysis also revealed, for the first time, the activation of Z. mays resistance genes influencing the expression of specific A. flavus genes. Our results show that A. flavus seems to be reacting to a hostile environment resulting from the activation of resistance pathways in Z. mays. This study revealed the dynamic nature of the interaction between the two organisms.

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“…All of these have several alternatives, and could be further incorporated in future analysis. Firstly, the PPI network can be replaced with other genetic interaction networks, for example a gene regulatory network that is derived from experimental data ( Zheng and Huang, 2018 ). Secondly, different types of experimental data can be used for ranking the genes and using their ranks as scores for the chosen propagation algorithm.…”

Section: Discussionmentioning

confidence: 99%

Network and Pathway Analysis of Toxicogenomics Data

Barel

Herwig

2018

Front. Genet.

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Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

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The Reconstruction and Analysis of Gene Regulatory Networks

Cited by 13 publications

References 67 publications

The gene regulatory network in different brain regions of neuropathic pain mouse models

The gene regulatory network in different brain regions of neuropathic pain mouse models

Use of Dual RNA-seq for Systems Biology Analysis of Zea mays and Aspergillus flavus Interaction

Network and Pathway Analysis of Toxicogenomics Data

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