Segmentation of the vertebrate anterior-posterior axis is dependent on oscillations of her/he's genes. In humans, mutation of HES7 results in spondylocostal dysostosis, a congenital disorder characterized by fusion or malformation of the vertebrae and ribs. Within the zebrafish segmentation clock, six members of the her/hes family form both hetero- and homodimers and negatively regulate their own transcription. Even within this simple network, there are 36 possible dimer-DNA interactions and, given 3 levels of repression, 1017 possible network topologies. Previous models of gene networks based on transcription factor (TF) binding included data on binding affinities, cell-specific protein concentrations and chromatin accessibility. However, TF binding may not always lead to significant changes in transcription and in many cases biochemical data may be incomplete. Here, a method for determining transcription factor networks that does not need extensive biochemical datasets will be developed. Mapping the network will rely on gene knockdown and quantitative measurement of transcription combined with mathematical modeling and global optimization algorithms. Predictions derived from the network model will be tested experimentally via compound gene knockdown, qPCR and in situ hybridization. Network-based explanations for several incongruous phenotypes will be attained. Lastly, modeling these interactions will be used to shed light on how networks can evolve between species or within an organism to complete a different task with minimal modifications. Understanding gene networks, such as the segmentation clock studied here, enables modeling of biological processes. These models can lead to identification of nodes of cellular pathways, the role of multiple genes in disease phenotypes, and insight into drug interactions.

Public Health Relevance

Understanding gene networks, such as the segmentation clock studied here, can lead to identification of nodes of cellular pathways, the role of multiple genes in disease phenotypes, and insight into drug interactions. Here, a method for determining transcription factor networks that does not need extensive biochemical datasets will be developed. Mapping the network will rely on gene knockdown and quantitative measurement of transcription combined with mathematical modeling and global optimization algorithms.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21HD076173-01A1
Application #
8636711
Study Section
Development - 2 Study Section (DEV2)
Program Officer
Javois, Lorette Claire
Project Start
2013-09-21
Project End
2015-08-31
Budget Start
2013-09-21
Budget End
2014-08-31
Support Year
1
Fiscal Year
2013
Total Cost
$249,750
Indirect Cost
$99,750
Name
Yale University
Department
Biochemistry
Type
Schools of Arts and Sciences
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
McMillen, Patrick; Holley, Scott A (2015) The tissue mechanics of vertebrate body elongation and segmentation. Curr Opin Genet Dev 32:106-11
McMillen, Patrick; Holley, Scott A (2015) Integration of cell-cell and cell-ECM adhesion in vertebrate morphogenesis. Curr Opin Cell Biol 36:48-53
Schwendinger-Schreck, Jamie; Kang, Yuan; Holley, Scott A (2014) Modeling the zebrafish segmentation clock's gene regulatory network constrained by expression data suggests evolutionary transitions between oscillating and nonoscillating transcription. Genetics 197:725-38