ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Endocrine Abstracts (2011) 25 P205

Identification of turner syndrome specific mRNA expression profiles that correlate with clinical response to growth hormone

Adam Stevens, Shahin Tajbakhsh, Andrew Whatmore, Melissa Westwood & Peter Clayton

University of Manchester, Manchester, UK.

Girls with Turner syndrome (TS) are treated with recombinant human growth hormone (rhGH) to improve their adult height but the gain is variable (0–20 cm). Current prediction models can account for only ~46% of the variability in the first year response to rhGH, thus genetic profiling has been suggested as a possible means of improving this prediction. The aim of this study was to explore mRNA expression profiles in an ex-vivo fibroblast model to characterise response to rhGH and to associate mRNA expression profiles with biological pathways.

Three fibroblast cell lines were used, a control line (C10)(4 replicates) and two TS lines (4 replicates each); T1, from a TS patient responding well to rhGH (Height SDS at start=−4.9 with a change in height SDS at 4 years of +1.8) and T5, from a poor responder to rhGH (Height SDS at start=−2.5 with a change in height SDS at 4 years of – 0.3). Gene expression was measured using Affymetrix HG-U133 plus 2.0 arrays, differential gene expression was analysed using ANOVA and Igenuity Pathway Analysis software (IPA) was used to assess biological function of genes.

Comparison of control fibroblasts to the combined TS lines showed that variability in gene expression clustered by cell line and genes in pathways associated with ‘skeletal and muscular function’ showed the greatest differential expression (35 out of 301 genes, P<0.0001) with insulin-like growth factor binding protein 5 (IGFBP5) upregulated in TS (670-fold). When the T1 and T5 cell lines were compared, 8 out of 25 differentially expressed genes were associated with ‘cellular growth and development’ (P<0.0001) and a Wnt pathway protein, Secreted frizzled-related protein 4 (SFRP4), was upregulated in T1 52 fold.

Identification of GH-dependent growth pathway genes is an important step towards the identification of biomarkers that may determine treatment decisions.

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