Decoding sex

Genes in the sex regulatory hierarchy up- and down regulate genes sex specifically, ultimately resulting in sexual dimorphism between adult males and females. However, much of the downstream portions of this pathway are uncharacterized. The evolutionary implications of sex dimorphism and sex-dependent allelic effects are not fully understood. Current projects address the following questions:

1) How are quantitative differences in gene expression between the sexes encoded in regulatory regions? The sex regulatory hierarchy controls differences between males and females in Drosophila. Many, if not most, of the genes acting at the top of the pathway have been identified and three major branches of this network (dosage compensation, somatic differences, and male behavior) are well characterized. While some of the regulatory motifs recognized by these genes are known, how male or female specific modification of expression levels is encoded within the regulatory regions of downstream genes is an open question. We are using transgenic tools to address this question, taking a comparative approach to identifying downstream members of the sex regulatory pathway and corresponding regulatory regions.

2) How does sexually dimorphic gene expression evolve? Sex dimorphism is thought to evolve as a consequence of different fitness optima in males and females. We are working on a number of experiments aimed at understanding how sex dimorphism arises in gene regulatory networks with roles in regulatory responses to biotic and abiotic factors.

Genetic correlation of homeostatic behaviors

How well is pleiotropy predicted from existing knowledge of network topology and regulatory relationships? How much do these relationships explain genetic correlation between complex traits?

The insulin signaling pathway controls energy homeostasis and is a candidate network for natural variation in feeding and sleep, and for genetic variation in other complex traits (e.g., mating behavior, immune response). We are using artificial manipulation of gene expression to understand how perturbations of different strength affect multiple traits, testing predictions based on known regulatory networks. 

In addition to identifying genes and gene networks which explain genetic correlation between these traits we are addressing the more general question of how the behavior of gene networks is related to the phenotypic effects and genetic properties of natural variants, artificial mutations and transgenic constructs.