Mutations in CUL7, OBSL1 and CCDC8, leading to disordered ubiquitination, cause one of the commonest primordial growth disorders, 3-M syndrome. This condition is associated with i) abnormal p53 function, ii) GH and/or IGF1 resistance, which may relate to failure to recycle signalling molecules, and iii) cellular IGF2 deficiency. However the exact molecular mechanisms that may link these abnormalities generating growth restriction remain undefined. In this study, we have used immunoprecipitation/mass spectrometry and transcriptomic studies to generate a 3-M ‘interactome’, to define key cellular pathways and biological functions associated with growth failure seen in 3-M. We identified 189 proteins which interacted with CUL7, OBSL1 and CCDC8, from which a network including 176 of these proteins was generated. To strengthen the association to 3-M syndrome, these proteins were compared with an inferred network generated from the genes that were differentially expressed in 3-M fibroblasts compared with controls. This resulted in a final 3-M network of 131 proteins, with the most significant biological pathway within the network being mRNA splicing/processing. We have shown using an exogenous insulin receptor (INSR) minigene system that alternative splicing of exon 11 is significantly changed in HEK293 cells with altered expression of CUL7, OBSL1 and CCDC8 and in 3-M fibroblasts. The net result is a reduction in the expression of the mitogenic INSR isoform in 3-M syndrome. From these preliminary data, we hypothesise that disordered ubiquitination could result in aberrant mRNA splicing in 3-M; however, further investigation is required to determine whether this contributes to growth failure.
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Dan Hanson, Adam Stevens, Philip G Murray, Graeme C M Black, and Peter E Clayton
T L Ott, Y Zhou, M A Mirando, C Stevens, J P Harney, T F Ogle, and F W Bazer
This study characterized changes in levels of mRNA and protein for endometrial oestrogen receptors (ERs) and progesterone receptors (PRs) during luteolysis and maternal recognition of pregnancy. For cyclic and pregnant ewes, endometrium was collected on days 10, 12, 14, or 16 post-oestrus (4 ewes/day for each status) for the measurement of ER and PR mRNA and protein. The amount of receptor mRNA is expressed in relative units above background, measured from radiographs of dot-blot hybridization of total endometrial RNA with ER and PR cDNAs. At hysterectomy, jugular vein blood samples were collected and assayed for progesterone, total corpus luteum weight was recorded and, in vitro, endometrial oxytocin-stimulated inositol phosphate formation was estimated. In pregnant ewes, plasma progesterone increased gradually between days 10 and 16 (P<0·01), corpus luteum weight was stable at approximately 08 g and oxytocin did not stimulate endometrial formation of inositol phosphates in vitro. In contrast, in cyclic ewes, plasma progesterone decreased from day 10 to day 16 (P<0·01), corpus luteum weight decreased after day 14 to approximately 0·48 g (P=0·05) and oxytocin stimulated an increase of approximately 1300% in the endometrial formation of inositol phosphates on day 16. cDNAs specifically hybridized with 1·6 and 31 kb transcripts for PR mRNA and a 6·5 kb transcript for ER mRNA. In cyclic ewes, the amount of PR mRNA increased from day 10 to maximum levels on days 14–16. The number of PRs decreased from day 10 (225 pmol/mg DNA) to day 12 (0·98 pmol/mg DNA) and then increased from day 14 to day 16 (2·8 pmol/mg DNA). In pregnant ewes, PR mRNA levels were greatest on days 10–12 and decreased by approximately 50% by day 16. In contrast, the number of PRs was relatively unchanged from day 10 to day 16 (1·53 to 103 pmol/mg DNA). In cyclic ewes, the amount of ER mRNA was lowest at day 10 and increased fivefold by day 16. The number of ERs remained relatively unchanged from day 10 to day 14 (607 pmol/mg DNA) and increased by day 16 (1612 pmol/mg DNA). In pregnant ewes, ER mRNA decreased by approximately 80% from day 12 to day 16. Similarly, the number of ERs decreased from day 10 to day 16 (5·41 to 205 pmol/mg DNA). Correlations between ER mRNA and PR mRNA (r=0·68), ERs and PRs (r = 0·50) and ER mRNA and ERs (r=0·50) were high (P<0·01). PR mRNA and PRs, PR mRNA and ERs, and ER mRNA and PRs were not correlated (P>0·1). Pregnancy had the apparent effect of stabilizing the number of endometrial PRs and inhibiting ER production by decreasing both the amount of ER mRNA and ER protein.
A Stevens, C De Leonibus, D Hanson, A W Dowsey, A Whatmore, S Meyer, R P Donn, P Chatelain, I Banerjee, K E Cosgrove, P E Clayton, and M J Dunne
Systems biology is the study of the interactions that occur between the components of individual cells – including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied ‘omic’ datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.