ESPE Abstracts (2014) 82 FC4.3

Oscillations in Gene Expression Profiles Across Childhood Highlight the Relation of Growth and Specific Metabolic Functions in Both Sexes

Adam Stevensa, Christopher Knightb, Chiara De Leonibusa, Andrew Dowseya, Neil Swainstonc, Philip Murraya & Peter Claytona

aManchester Academic Health Sciences Centre, Royal Manchester Children’s Hospital, Manchester, UK; bFaculty of Life Sciences, The University of Manchester, Manchester, UK; cManchester Institute of Biotechnology, The University of Manchester, Manchester, UK

Background: The phases of human growth are associated with gene expression (GE) changes1, raising the possibility that rhythmic patterns of GE occur throughout childhood.

Objective: In this study, we have assessed time-series patterns of GE profiles associated with age to characterise oscillations.

Methods: GE analysis was conducted on cells of lymphoid origin from normal individuals through childhood (n=87, 43 males and 44 females, range 2 months–29 years)1. Clustered patterns of GE time-series were identified by STEM (short time–series expression miner)2 and confirmed using unsupervised multidimensional scaling (MDS) (Qlucore Omics Explorer 3.0). Statistically associated GE was assessed using ANOVA (P<1×10−4). Associated causal networks associated with GE were identified algorithmically (Ingenuity Pathway Analysis).

Results: Gender associated groups of gene probe (GP) set profiles were identified with oscillations of gene expression; 487 GP sets in males and 3302 in females with an overlap of 145. MDS and time-series analysis identified two underlying patterns of GE (i and ii), the first (i) with peaks at 5.0, 7.2 and 12.1 years (99 GP-sets) and the second (ii) with peaks at 5.6 and 10.2 years (388 GP-sets) in males; similar oscillations were seen in females with (i) peaks at 5.0, 7.0 and 11.3 years (1221 GP-sets) and (ii) 3.7 and 7.0 years (2081 GP-sets). Causal network analysis on these GE profiles in both sexes implicated metabolic processes (P<1.3×10−3) with specific differences in the second pattern of oscillation (ii) in GE between males and females in lipid and amino acid metabolism (both P<0.017). The overlap in GE between the sexes was associated with growth (P<1.3×10−5) causally linked to GH and IGF1 action.

Conclusion: This study has identified gender specific variations in age-related GE oscillations associated with human growth and development that highlight gender-specific differences in metabolism and emphasise the role of the GH/IGF1 axis.

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