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A model for how cells network
Researchers at Brigham and Women's Hospital have figured out how unique tissues arise from cells that look exactly alike and not from signals coming from their own regulators but from a core biological "machinery" shared across all tissues.
Published in Cell Reports, Kimberly Glass PhD, of the Channing Division of Network Medicine, and her team explain how they have used PANDA (Passing Attributes between Networks for Data Assimilation) to create a network model for each interaction between transcription factors and genes.PANDA Video
Kimberly Glass PhD and her team found different tissue functions are the result of subtle, tissue-specific shifts within a regulatory network.
For each of these tissue-specific functions, the network has the same core components, but combined in different ways, and with added genetic and environmental information. The team analyzed data from the Genotype-Tissue Expression (GTEx) consortium, among other regulatory information sources, to reconstruct and characterize regulatory networks for 38 tissues.
PANDA, a model created by Glass and her team in 2013, was uniquely qualified for this investigation because it can more accurately model interactions between transcription factors - which help control where, when and to what extent genes get activated - and their targets. Summarizing the complex interactions between transcription factors and genes is an important step in understanding patterns in the network that inform how gene regulation gives rise to a variety of specific tissue functions.
The authors also observed that the regulation of specific tissue function is largely independent of transcription factor expression. They note that there are approximately 30,000 genes in the human genome, but fewer than 2,000 of them encode transcription factors.
"A large number of processes must be carried out for any tissue to function properly. Rather than activating transcription factors to carry out these processes, we find that networks connecting regulators to their target genes are reconfigured more effectively, coordinating tissue activation."
The team notes that their work highlights the importance of considering the context of specific tissues when developing drug therapies. Given that shifted regulatory networks govern different functions, this will be important in order to understand the potential side effects of drugs outside of the target tissue.
Regulatory network more tissue specific than nodes (genes or transcription factors)
Tissue-specific function is not solely regulated by transcription factor expression
Tissue-specific genes assume bottleneck positions in their corresponding networks
Tissue specificity is driven by context-dependent, non-canonical regulatory paths
Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.
Authors: Abhijeet Rajendra Sonawane, John Platig, Maud Fagny, Cho-Yi Chen, Joseph Nathaniel Paulson, Camila Miranda Lopes-Ramos, Dawn Lisa DeMeo, John Quackenbush, Kimberly Glass, Kimberly Glass, Marieke Lydia Kuijjer
This work was supported by grants from the US National institutes of Health, including grants from the National Heart, Lung, and Blood Institute (5P01HL105339, 5R01HL111759, 5P01HL114501, K25HL133599), the National Cancer Institute (5P50CA127003, 1R35CA197449, 1U01CA190234, 5P30CA006516, P50CA165962), the National Institute of Allergy and Infectious Disease (5R01AI099204), and the Charles A. King Trust Postdoctoral Research Fellowship Program, Sara Elizabeth O'Brien Trust, Bank of America, N.A., Co-Trustees. Additional funding was provided through a grant from the NVIDIA foundation. This work was conducted under dbGaP approved protocol #9112 (accession phs000424.v6.p1).
Paper Cited: Sonawane AR et al. "Understanding Tissue-Specific Gene Regulation." Cell Reports DOI: 10.1016/j.celrep.2017.10.001
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PANDA (Passing Attributes between Networks for Data Assimilation)
creates a network of interactions between transcription factors and genes.