Systems Genetics Approach to the Study of Brain Iron Regulation Byron C. Jones Professor of Biobehavioral Health & Pharmacology The Pennsylvania State University

Systems Genetics Iron management phenotypes are complex traits, i.e. –Influenced by multiple genes –Influenced by environment Includes epigenetic influences –Environment –Gene X Environment –Gene X gene

Our Animal Model BXD/Ty Recombinant Inbred Mice –30 strains –Genotyped and SNP-mapped –Genetic correlations –Extant database Genenetwork.org Phenotypes Gene expression QTL analysis is analogous to marker- association studies in humans

Data Strain means as index Data are cumulative Gene expression for BXD –Affy –Illumina Genetic correlations –Phenotype-phenotype –Marker-phenotype (QTL) –Gene expression (just another phenotype)

Iron Concentration in Ventral Midbrain BXD/Ty 120 days of age

Brain: Iron Tissue Concentrations on MRI R2* images in a 70 year old RLS patient and a 71 year old control subject. Much lower R2* relaxation rates are apparent in the RLS case in both red nucleus and substantia nigra. R2* (sec -1 ) 30 0 RLS Normal

Iron Concentrations in the Ventral Midbrain across BXD/Ty Recombinant Inbred Strains of Mice

QTL Analysis of VMB Iron – the blue line is the LRS statistic (indicates reliability of strength of association. The yellow bars are bootstrap statistics and the red line shows additive effect of allele. In this case, strains carrying C57BL/6 alleles on chromosome 7, 11, and 13 have more iron in the VMB than those carrying the DBA/2 allele

Genes, Brain and Behavior (2008) Commentary Of mice and men, periodic limb movements and iron: how the human genome informs the mouse genome L. C. Jones, C. J. Earley, R. P. Allen and B. C. Jones In this commentary, we report that the marker that we identified on Chr 17 points to a gene that was shown by association in two articles to be related to familial PLM/Restless Legs Syndrome and Iron regulation (1). The gene reported is BTBD9

Association between serum ferritin and BTBD9 allele (Stefansson etal NEJM 2007)

The QTL marker on Chr. 17 is also associated with Zn and Cu concentrations in various parts of the brain, and especially iron in the VMB. The other areas are also dopamine-related structures

Strain Distribution Pattern for Btbd9 Gene Expression

Strain Distribution Pattern for Glo1 gene Expression

QTL Analysis of Btbd9 Gene Expression. Note that the gene is cis-regulated -- blue triangle is its coding region

Conclusions QTL for Fe Zn and Cu may point to a divalent metal regulatory gene (protein) on mouse chromosome 17 Are there other candidates in the area? –Glo1? What is the function?

Iron Deficiency Project: Hypothesis: strains differ in their susceptibility to ID diet. Design: 29 strains, 120 d. on ID (3ppm) or CN (245 ppm) Teklad pellet diet. Endpoint measures: –Ventral Midbrain (VMB) Fe –Caudate Putamen (CP) Fe –Hemoglobin/Hematocrit –Plasma Fe –Liver Fe –Spleen Fe

Susceptibility to iron loss in the VMB shows wide genetic variability

Susceptibility to anemia shows wide genetic variability.

Hematocrit and VMB iron concentration across BXD stains of mice under normal and ID dietary conditions

Preliminary QTL Analysis We have only strains completed, which means low power and low LOD scores. Still, we have preliminary results for QTL analysis: –Conditional QTLs: QTLs for VMB Fe on control diet QTLs for VMB Fe on iron deficient diet –Susceptibility QTLs: QTLs for difference between control and iron deficient VMB Fe QTLs for difference between control and iron deficient hemoglobin

Conditional QTL Maps for VMB Fe Control Iron Deficient

We can investigate correlations with gene expression: Strains with high baseline TfR expression lose more iron in the VMB. Difference (ug/g) in VMB Fe Transferrin Receptor Expression (Whole Brain)

Summary: Iron Deficiency Project Preliminary Data We have found strong support for our hypothesis: –Strains differ widely in their susceptibility to iron losses on the iron deficient diet. We have contributed to the increasing evidence that iron regulation is highly complex and tissue-specific. –Susceptibility to iron losses in one tissue/variable is independent of susceptibility in another tissue/variable. We have replicated previous QTL and identified new QTL. –This will require the addition of more strains for validation, expected by Nov, 2008.

What Next? Investigate the genomics of iron homeostasis – i.e. expression of known iron regulatory genes –During iron deficiency –During iron overload Embark on gene discovery – microarrays Conduct gene network analyses Follow-up on candidate genes identified in humans by association studies

Dramatis Personae John L. Beard Leslie C. Jones Erica L. Unger Christopher J. Earley Richard P. Allen James R. Connor