A common cause for a common phenotype: the gatekeeper hypothesis in fetal programming

Abstract

Sub-optimal nutrition during pregnancy has been shown to have long-term effects on the health of offspring in both humans and animals. The most common outcomes of such programming are hypertension, obesity, dyslipidaemia and insulin resistance. This spectrum of disorders, collectively known as metabolic syndrome, appears to be the consequence of nutritional insult during early development, irrespective of the nutritional stress experienced. For example, diets low in protein diet, high in fat, or deficient in iron are all associated with programming of cardiovascular and metabolic disorders when fed during rat pregnancy. In this paper, we hypothesise that the nutritional stresses act on genes or gene pathways common to all of the insults. We have termed these genes and/or gene pathways the "gatekeepers" and hence developed the "gatekeeper hypothesis". In this paper, we examine the background to the hypothesis and postulate some possible mechanisms or pathways that may constitute programming gatekeepers.

Fig. 1

The gatekeeper hypothesis. The figure shows a Venn diagram of the genes and pathways altered by different nutritional stresses. Within each pattern is an overlap outlined in the shaded area, and we hypothesise that these common genes are the ones that are responsible for generating the phenotype.

Fig. 2

Proposed experimental design to test the gatekeeper hypothesis. The gatekeeper hypothesis asserts that relatively few common mechanisms will operate to determine the common phenotypes that follow programming insults. Testing the hypothesis therefore relies on the parallel study of two maternal dietary insults which share a common phenotypic outcome in the offspring. Use of techniques such as microarray or proteomics allows an unbiased and systematic approach to identify the molecular targets of the maternal insults. Analysis of proteomic and gene array data will identify core gatekeeper processes and pathways and provide insight into the mechanisms which drive programming across different developmental stages.