Hierarchical Modularity in ERα Transcriptional Network Is Associated with Distinct Functions and Implicates Clinical Outcomes

Tang, Binhua; Hsu, Hang Kai; Hsu, Pei Yin; Bonneville, Russell; Chen, Su Shing; Huang, Tim H.M.; Jin, Victor X.

doi:10.1038/srep00875

Cited by 27 publications

(14 citation statements)

References 48 publications

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“…New advances in network modeling allow for networks to be tested, quantified, and corrected (Sayyed-Ahmad et al, 2007). Time-course expression data in particular can capture dynamic properties of transcriptional networks that steady-state transcript measurements cannot (Nelson et al, 2004; Opper and Sanguinetti, 2010), and even time-course ChIP-seq data has been introduced into network models (Tang et al, 2012). Arabidopsis and Zinnia transdifferentiation systems are potentially useful models for generating time-course transcript data relating to SCW regulation, but existing time-course data (e.g., Kubo et al, 2005; Yamaguchi et al, 2011) lacks the temporal resolution to test and model the dynamic behavior of the SCW transcriptional network.…”

Section: The Scw Transcriptional Network: Structure Evolution and Dmentioning

confidence: 99%

Navigating the transcriptional roadmap regulating plant secondary cell wall deposition

Hussey¹,

Mizrachi²,

Creux³

et al. 2013

Front. Plant Sci.

140

133

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The current status of lignocellulosic biomass as an invaluable resource in industry, agriculture, and health has spurred increased interest in understanding the transcriptional regulation of secondary cell wall (SCW) biosynthesis. The last decade of research has revealed an extensive network of NAC, MYB and other families of transcription factors regulating Arabidopsis SCW biosynthesis, and numerous studies have explored SCW-related transcription factors in other dicots and monocots. Whilst the general structure of the Arabidopsis network has been a topic of several reviews, they have not comprehensively represented the detailed protein-DNA and protein-protein interactions described in the literature, and an understanding of network dynamics and functionality has not yet been achieved for SCW formation. Furthermore the methodologies employed in studies of SCW transcriptional regulation have not received much attention, especially in the case of non-model organisms. In this review, we have reconstructed the most exhaustive literature-based network representations to date of SCW transcriptional regulation in Arabidopsis. We include a manipulable Cytoscape representation of the Arabidopsis SCW transcriptional network to aid in future studies, along with a list of supporting literature for each documented interaction. Amongst other topics, we discuss the various components of the network, its evolutionary conservation in plants, putative modules and dynamic mechanisms that may influence network function, and the approaches that have been employed in network inference. Future research should aim to better understand network function and its response to dynamic perturbations, whilst the development and application of genome-wide approaches such as ChIP-seq and systems genetics are in progress for the study of SCW transcriptional regulation in non-model organisms.

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Section: The Scw Transcriptional Network: Structure Evolution and Dmentioning

confidence: 99%

Navigating the transcriptional roadmap regulating plant secondary cell wall deposition

Hussey¹,

Mizrachi²,

Creux³

et al. 2013

Front. Plant Sci.

140

133

View full text Add to dashboard Cite

show abstract

“…Therefore, identification of regulatory modules and gene networks is critical for understanding molecular mechanisms of transcriptional regulation in complex biological systems. Various mathematical algorithms or computational methods have been developed for integrative analysis of microarray gene expression and TF binding data to predict target genes of TFs, such as Bayesian hierarchical network [1], Bayesian multivariate modeling [2], matrix decomposition [3] and regression model [4]. Based on predicted target genes of multiple TFs, we can unravel transcriptional regulatory modules and reconstruct gene networks.…”

Section: Introductionmentioning

confidence: 99%

Unraveling Regulatory Programs for NF-kappaB, p53 and MicroRNAs in Head and Neck Squamous Cell Carcinoma

Yan

Yang

et al. 2013

PLoS ONE

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In head and neck squamous cell carcinoma (HNSCC), mutations of p53 usually coexist with aberrant activation of NF-kappaB (NF-κB), other transcription factors and microRNAs, which promote tumor pathogenesis. However, how these factors and microRNAs interact to globally modulate gene expression and mediate oncogenesis is not fully understood. We devised a novel bioinformatics method to uncover interactive relationships between transcription factors or microRNAs and genes. This approach is based on matrix decomposition modeling under the joint constraints of sparseness and regulator-target connectivity, and able to integrate gene expression profiling and binding data of regulators. We employed this method to infer the gene regulatory networks in HNSCC. We found that the majority of the predicted p53 targets overlapped with those for NF-κB, suggesting that the two transcription factors exert a concerted modulation on regulatory programs in tumor cells. We further investigated the interrelationships of p53 and NF-κB with five additional transcription factors, AP1, CEBPB, EGR1, SP1 and STAT3, and microRNAs mir21 and mir34ac. The resulting gene networks indicate that interactions among NF-κB, p53, and the two miRNAs likely regulate progression of HNSCC. We experimentally validated our findings by determining expression of the predicted NF-κB and p53 target genes by siRNA knock down, and by examining p53 binding activity on promoters of predicted target genes in the tumor cell lines. Our results elucidating the cross-regulations among NF-κB, p53, and microRNAs provide insights into the complex regulatory mechanisms underlying HNSCC, and shows an efficient approach to inferring gene regulatory programs in biological complex systems.

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“…However, there is considerable evidence that estrogens are also mammary carcinogens in the breast, involving the metabolism of estrogen to genotoxic, mutagenic metabolites and the stimulation of tissue growth [50]. For instance, ERα, an estrogen-inducible transcription factor, is member of the nuclear receptor super family, the dysfunction of which accounts for 70% breast tumors [71]. Indeed, clinical and experimental studies have suggested an important role for Estrogen signaling pathway in the treatment of breast cancer and the reduction for estrogen deprivation of mammary tumors [70].…”

Section: Resultsmentioning

confidence: 99%

A Methodology for Cancer Therapeutics by Systems Pharmacology-Based Analysis: A Case Study on Breast Cancer-Related Traditional Chinese Medicines

Li¹,

Wang²,

Feng³

et al. 2017

PLoS ONE

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Breast cancer is the most common carcinoma in women. Comprehensive therapy on breast cancer including surgical operation, chemotherapy, radiotherapy, endocrinotherapy, etc. could help, but still has serious side effect and resistance against anticancer drugs. Complementary and alternative medicine (CAM) may avoid these problems, in which traditional Chinese medicine (TCM) has been highlighted. In this section, to analyze the mechanism through which TCM act on breast cancer, we have built a virtual model consisting of the construction of database, oral bioavailability prediction, drug-likeness evaluation, target prediction, network construction. The 20 commonly employed herbs for the treatment of breast cancer were used as a database to carry out research. As a result, 150 ingredient compounds were screened out as active molecules for the herbs, with 33 target proteins predicted. Our analysis indicates that these herbs 1) takes a ‘Jun-Chen-Zuo-Shi” as rule of prescription, 2) which function mainly through perturbing three pathways involving the epidermal growth factor receptor, estrogen receptor, and inflammatory pathways, to 3) display the breast cancer-related anti-estrogen, anti-inflammatory, regulation of cell metabolism and proliferation activities. To sum it up, by providing a novel in silico strategy for investigation of the botanical drugs, this work may be of some help for understanding the action mechanisms of herbal medicines and for discovery of new drugs from plants.

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Hierarchical Modularity in ERα Transcriptional Network Is Associated with Distinct Functions and Implicates Clinical Outcomes

Cited by 27 publications

References 48 publications

Navigating the transcriptional roadmap regulating plant secondary cell wall deposition

Navigating the transcriptional roadmap regulating plant secondary cell wall deposition

Unraveling Regulatory Programs for NF-kappaB, p53 and MicroRNAs in Head and Neck Squamous Cell Carcinoma

A Methodology for Cancer Therapeutics by Systems Pharmacology-Based Analysis: A Case Study on Breast Cancer-Related Traditional Chinese Medicines

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