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Mammalian Genome

, 22:556 | Cite as

Genetic analysis of iron-deficiency effects on the mouse spleen

  • Jennifer N. Gibson
  • Leslie C. Jellen
  • Erica L. Unger
  • Grant Morahan
  • Munish Mehta
  • Christopher J. Earley
  • Richard P. Allen
  • Lu Lu
  • Byron C. Jones
Article

Abstract

Iron homeostasis is crucial to many biological functions in nearly all organisms, with roles ranging from oxygen transport to immune function. Disruption of iron homeostasis may result in iron overload or iron deficiency. Iron deficiency may have severe consequences, including anemia or changes in immune or neurotransmitter systems. Here we report on the variability of phenotypic iron tissue loss and splenomegaly and the associated quantitative trait loci (QTLs), polymorphic areas in the mouse genome that may contain one or more genes that play a role in spleen iron concentration or spleen weight under each dietary treatment. Mice from 26 BXD/Ty recombinant inbred strains, including the parent C57BL/6 and DBA/2 strains, were randomly assigned to adequate iron or iron-deficient diets at weaning. After 120 days, splenomegaly was measured by spleen weight, and spleen iron was assessed using a modified spectrophotometry technique. QTL analyses and gene expression comparisons were then conducted using the WebQTL GeneNetwork. We observed wide, genetic-based variability in splenomegaly and spleen iron loss in BXD/Ty recombinant inbred strains fed an iron-deficient diet. Moreover, we identified several suggestive QTLs. Matching our QTLs with gene expression data from the spleen revealed candidate genes. Our work shows that individual differences in splenomegaly response to iron deficiency are influenced at least partly by genetic constitution. We propose mechanistic hypotheses by which splenomegaly may result from iron deficiency.

Keywords

Quantitative Trait Locus Iron Deficiency Iron Overload Iron Homeostasis Recombinant Inbred 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was supported in part by USPHS Grants PO1AG 021190 to CJE, U01AA014425 to LL, and NRSA Fellowship F31 NS060303 to LCJ. Support to GM and MM was provided by NHMRC Program Grant 516700 and UWA Strategy Fund Grant.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Jennifer N. Gibson
    • 1
  • Leslie C. Jellen
    • 1
  • Erica L. Unger
    • 2
  • Grant Morahan
    • 3
  • Munish Mehta
    • 3
  • Christopher J. Earley
    • 4
  • Richard P. Allen
    • 4
  • Lu Lu
    • 5
  • Byron C. Jones
    • 1
  1. 1.Department of Biobehavioral HealthThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of Nutritional SciencesThe Pennsylvania State UniversityUniversity ParkUSA
  3. 3.Centre for Diabetes Research, The Western Australia Institute for Medical ResearchThe University of Western AustraliaWAAustralia
  4. 4.The Johns Hopkins Medical InstitutesBaltimoreUSA
  5. 5.Department of Anatomy and NeurobiologyUniversity of Tennessee Health Sciences CenterMemphisUSA

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