Gene expression signature in adipose tissue of acromegaly patients
BMC Bioinformatics volume 15, Article number: P28 (2014)
Acromegaly is a rare endocrine disorder with excess growth hormone (GH) production. This disorder has important metabolic effects in insulin resistance and lipolysis. The objective of this study was to explore transcriptional changes induced by GH in adipose tissue.
Materials and methods
Patients with acromegaly (n=9) or non-functioning pituitary adenoma (n=11) were prospectively observed from March 2011 to June 2012. The patients underwent clinical and metabolic profiling including assessment of HOMA-IR. Explants of adipose tissue were assayed ex-vivo for lipolysis and ceramide levels. Adipose tissue was analyzed by RNA sequencing (RNAseq).
There was evidence of reduced insulin sensitivity based on the increase in fasting glucose, insulin and HOMA-IR score. We observed several previously reported transcriptional changes (IGF1, IGFBP3) as well as several novel transcriptional changes, some of which may be important for GH signal regulation (PTPN3 and PTPN4) and the effect of GH on growth and proliferation. Several transcripts could potentially be important in GH-induced metabolic changes. Specifically, induction of LPL, ABHD5, ACVR1C could contribute to enhanced lipolysis and may explain the suggestive enhancement of adipose tissue lipolysis in acromegaly patients as reflected by glycerol release from the explants of the two groups of patients (p=0.09). Higher expression of SCD and TCF7L2 could contribute to insulin resistance. Expression of HSD11B1 was reduced and GR was increased, predicting modified glucocorticoid activity in acromegaly.
We identified the acromegaly gene expression signature in human adipose tissue. The significance of altered expression of specific transcripts will enhance our understanding of the metabolic and proliferative changes associated with acromegaly.
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Hochberg, I., Tran, Q.T., Barkan, A.R. et al. Gene expression signature in adipose tissue of acromegaly patients. BMC Bioinformatics 15 (Suppl 10), P28 (2014). https://doi.org/10.1186/1471-2105-15-S10-P28