June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Ocular Compliance in Mice
Author Affiliations & Notes
  • Stephen Andrew Schwaner
    Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
  • Joseph M Sherwood
    Bioengineering, Imperial College London, London, United Kingdom
  • Eric Snider
    Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA
    Biomedical Engineering, Emory University, Atlanta, GA
  • Eldon E Geisert
    Opthalmology, Emory University, Atlanta, GA
  • Darryl R Overby
    Bioengineering, Imperial College London, London, United Kingdom
  • C Ross Ethier
    Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA
    Biomedical Engineering, Emory University, Atlanta, GA
  • Footnotes
    Commercial Relationships Stephen Schwaner, None; Joseph Sherwood, None; Eric Snider, None; Eldon Geisert, None; Darryl Overby, None; C Ethier, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 6143. doi:https://doi.org/
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    • Get Citation

      Stephen Andrew Schwaner, Joseph M Sherwood, Eric Snider, Eldon E Geisert, Darryl R Overby, C Ross Ethier; Ocular Compliance in Mice. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):6143. doi: https://doi.org/.

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

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Abstract
 
Purpose
 

Scleral stiffness may be a risk factor for glaucoma and varies significantly between people. The genes controlling this variation are unknown. Scleral stiffness can be indirectly characterized by ocular compliance (OC), the rise in ocular volume per unit rise in intraocular pressure (IOP). We measured OC in BXD mouse substrains, a powerful tool for quantitative trait locus analysis (QTLA) (Geisert. Mol Vis. 2009), to provide baseline data for future studies identifying genes determining OC/scleral properties.

 
Methods
 

Method 1: OC was measured in C57BL/6J (B6), DBA/2J (D2), and 10 BXD substrains (68 mice; 11-14 weeks old; Male & Female). Freshly enucleated eyes were cannulated and a syringe pump delivered several 500 nL PBS boluses to individual eyes (200 nL/s x 2.5 seconds) at a starting IOP of 8 mmHg while the resulting change in IOP was measured. OC was calculated as φ = ΔVolume/ΔIOP. Eye diameter was measured from photographs taken with a microscope. QTLA was used to identify probable gene loci that influence OC. Method 2: The OC-IOP relationship in B6 mice (42 mice; 10-14 weeks old; Male) was measured. Freshly enucleated eyes were cannulated and submersed in PBS. IOP was controlled using an actuated open pressure reservoir while flow rate of PBS into the eye and IOP were measured. OC was calculated based on the step response of the hydraulic-electrical analogue circuit. The OC-IOP relationship was fit to φ(P)= φ0(P/P0)α, where φ0is the OC at a reference IOP, P0 = 8 mmHg.

 
Results
 

Method 1: OC differed between substrains (Fig 1; Kruskal-Wallis, p < 10-6) and was independent of eye diameter (ANCOVA, p = 0.707). Preliminary QTLA identified suggestive peaks associated with OC on chromosomes 1, 7, and 11. Method 2: The average φ0 and α for B6 mice were 106x/1.42 nL/mmHg and 1.37 ± 0.48 (Fig. 2). Lower apparent OC values by Method 1 vs Method 2 were due to IOP elevation inherent to Method 1.

 
Conclusions
 

OC differs between BXD mouse substrains and preliminary data suggests candidate genes controlling this variation, opening the possibility of identifying genes that influence scleral mechanics in glaucoma. Analysis of the relation between OC and IOP may allow identification of additional genes.  

 
Figure 1: Median, 25th and 75th percentile, maximum, and minimum OC values for each substrain (n ≥ 4 mice per substrain).
 
Figure 1: Median, 25th and 75th percentile, maximum, and minimum OC values for each substrain (n ≥ 4 mice per substrain).
 
 
Figure 2: OC vs. IOP for a B6 mouse eye with power law fit.
 
Figure 2: OC vs. IOP for a B6 mouse eye with power law fit.

 
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