June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Co-expression based regulatory network for lens development
Author Affiliations & Notes
  • Atul Kakrana
    Center for Bioinfo & Comp. Biology, University of Delaware, Newark, DE
  • Djordje Djordjevic
    Victor Chang Cardiac Institute, Sydney, NSW, Australia
  • Andrian Yang
    Victor Chang Cardiac Institute, Sydney, NSW, Australia
  • Deepti Anand
    Department of Biological Sciences, University of Delaware, Newark, DE
  • Abhyudai Singh
    Department of Electrical & Computer Engineering, University of Delaware, Newark, DE
  • Cathy Wu
    Center for Bioinfo & Comp. Biology, University of Delaware, Newark, DE
  • Blake Meyers
    Plant and Soil Sciences, University of Delaware, Newark, DE
  • Joshua Ho
    Victor Chang Cardiac Institute, Sydney, NSW, Australia
    Faculty of Medicine, University of South Wales, Sydney, NSW, Australia
  • Salil Anil Lachke
    Center for Bioinfo & Comp. Biology, University of Delaware, Newark, DE
    Department of Biological Sciences, University of Delaware, Newark, DE
  • Footnotes
    Commercial Relationships Atul Kakrana, None; Djordje Djordjevic, None; Andrian Yang, None; Deepti Anand, None; Abhyudai Singh, None; Cathy Wu, None; Blake Meyers, None; Joshua Ho, None; Salil Lachke, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 2638. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Atul Kakrana, Djordje Djordjevic, Andrian Yang, Deepti Anand, Abhyudai Singh, Cathy Wu, Blake Meyers, Joshua Ho, Salil Anil Lachke; Co-expression based regulatory network for lens development. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):2638.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose: Although there are >100 mouse lens microarray gene expression datasets representing various developmental stages and mutational states, these have not been integrated to comprehend lens biology on the systems level. We hypothesized that systematic processing and analyses of these datasets can yield novel insights into the regulatory circuitry underlying lens development and maintenance of transparency. Therefore we used a co-expression based approach to derive a condition-dependent network that predicts known- as well as novel-regulators and their interactions in mouse lens development.

Methods: Affymetrix Mouse Genome 430 and Illumina MouseWG-6 v2.0 lens microarray datasets were obtained from NCBI-GEO. Additionally, five new mouse lens microarray datasets were generated to extend representation of post-natal stages. We developed a statistical method to identify key players in lens development and cataract formation. Genes with the highest scores were used to identify stage specific regulators by self-organizing tree (SOTA) clustering, and to generate co-expression network using a weighted correlation approach.

Results: Our analysis revealed four major SOTA clusters containing genes that exhibited distinct co-regulatory patterns in the lens. These included known regulators with experimentally validated spatio-temporal expression patterns like Pax6, Six3, Foxe3 and Hmx1 in a TF-rich cluster representing early lens development. Expectedly, crystallins and other fiber proteins were enriched in a cluster representing late-development. Importantly, this analysis identified several uncharacterized candidates for lens development (Sall4, Dmrta2). Interestingly, Sall4 is implicated in formation of sensory placodes and its mutations in human are associated with lens defects. Finally, this analysis places Sall4 in the lens regulatory circuit with Pax6, Six3, Foxe3, Pitx3 and Maf, further supporting its candidacy in lens development.

Conclusions: In past we developed a strategy termed “whole embryo body in silico subtraction” to generate tissue-enriched gene expression profiles and demonstrated its utility to predict genes associated with cataract. Here, we build on identification of lens-enriched genes and derive a co-expression regulatory network that highlights interactions between known regulators in lens and successfully predicts new nodes (Sall4, Dmrta2) for further investigation in early lens development.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×