Dataset Information

Characterizing dynamic changes in the human blood transcriptional network.

ABSTRACT: Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the changes of gene expression patterns in response to time and food intake. We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day. The simulation result shows that combining many short time series together is as powerful to infer Granger causality as using a single long time series. Using the Granger causality test, we identified genes that were supported as the most likely causal candidates for the coordinated temporal changes in the network. These results show that PER1 is a key regulator of the blood transcriptional network, in which multiple biological processes are under circadian rhythm regulation. The fasted and fed dynamic Bayesian networks showed that over 72% of dynamic connections are self links. Finally, we show that different processes such as inflammation and lipid metabolism, which are disconnected in the static network, become dynamically linked in response to food intake, which would suggest that increasing nutritional load leads to coordinate regulation of these biological processes. In conclusion, our results suggest that food intake has a profound impact on the dynamic co-regulation of multiple biological processes, such as metabolism, immune response, apoptosis and circadian rhythm. The results could have broader implications for the design of studies of disease association and drug response in clinical trials.

SUBMITTER: Zhu J

PROVIDER: S-EPMC2820517 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characterizing dynamic changes in the human blood transcriptional network.

Zhu Jun J Chen Yanqing Y Leonardson Amy S AS Wang Kai K Lamb John R JR Emilsson Valur V Schadt Eric E EE

PLoS computational biology 20100212 2

Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the ...[more]

PMID: 20168994

Similar Datasets

Project description:BACKGROUND: To understand transcriptional regulatory networks (TRNs), especially the coordinated dynamic regulation between transcription factors (TFs) and their corresponding target genes during development, computational approaches would represent significant advances in the genome-wide expression analysis. The major challenges for the experiments include monitoring the time-specific TFs' activities and identifying the dynamic regulatory relationships between TFs and their target genes, both of which are currently not yet available at the large scale. However, various methods have been proposed to computationally estimate those activities and regulations. During the past decade, significant progresses have been made towards understanding pollen development at each development stage under the molecular level, yet the regulatory mechanisms that control the dynamic pollen development processes remain largely unknown. Here, we adopt Networks Component Analysis (NCA) to identify TF activities over time course, and infer their regulatory relationships based on the coexpression of TFs and their target genes during pollen development. RESULTS: We carried out meta-analysis by integrating several sets of gene expression data related to Arabidopsis thaliana pollen development (stages range from UNM, BCP, TCP, HP to 0.5 hr pollen tube and 4 hr pollen tube). We constructed a regulatory network, including 19 TFs, 101 target genes and 319 regulatory interactions. The computationally estimated TF activities were well correlated to their coordinated genes' expressions during the development process. We clustered the expression of their target genes in the context of regulatory influences, and inferred new regulatory relationships between those TFs and their target genes, such as transcription factor WRKY34, which was identified that specifically expressed in pollen, and regulated several new target genes. Our finding facilitates the interpretation of the expression patterns with more biological relevancy, since the clusters corresponding to the activity of specific TF or the combination of TFs suggest the coordinated regulation of TFs to their target genes. CONCLUSIONS: Through integrating different resources, we constructed a dynamic regulatory network of Arabidopsis thaliana during pollen development with gene coexpression and NCA. The network illustrated the relationships between the TFs' activities and their target genes' expression, as well as the interactions between TFs, which provide new insight into the molecular mechanisms that control the pollen development.

Project description:Background and aimsFollowing liver injury, a fraction of hepatocytes adopt features of biliary epithelial cells (BECs) in a process known as biliary reprogramming. The aim of this study was to elucidate the molecular events accompanying this dramatic shift in cellular identity.Approach and resultsWe applied the techniques of bulk RNA-sequencing (RNA-seq), single-cell RNA-seq, and assay for transposase-accessible chromatin with high-throughput sequencing to define the epigenetic and transcriptional changes associated with biliary reprogramming. In addition, we examined the role of TGF-β signaling by profiling cells undergoing reprogramming in mice with hepatocyte-specific deletion in the downstream TGF-β signaling component mothers against decapentaplegic homolog 4 (Smad4). Biliary reprogramming followed a stereotyped pattern of altered gene expression consisting of robust induction of biliary genes and weaker repression of hepatocyte genes. These changes in gene expression were accompanied by corresponding modifications at the chromatin level. Although some reprogrammed cells had molecular features of "fully differentiated" BECs, most lacked some biliary characteristics and retained some hepatocyte characteristics. Surprisingly, single-cell analysis of Smad4 mutant mice revealed a dramatic increase in reprogramming.ConclusionHepatocytes undergo widespread chromatin and transcriptional changes during biliary reprogramming, resulting in epigenetic and gene expression profiles that are similar to, but distinct from, native BECs. Reprogramming involves a progressive accumulation of biliary molecular features without discrete intermediates. Paradoxically, canonical TGF-β signaling through Smad4 appears to constrain biliary reprogramming, indicating that TGF-β can either promote or inhibit biliary differentiation depending on which downstream components of the pathway are engaged. This work has implications for the formation of BECs and bile ducts in the adult liver.

Dataset Information

Characterizing dynamic changes in the human blood transcriptional network.

Publications

Characterizing dynamic changes in the human blood transcriptional network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets