Molecular genetics of the Drosophila eyes absent gene

The Drosophila compound eye has provided a genetic approach to understanding the specification of cell fates during differentiation. The eye is made up of some 750 repeated units or ommatidia, arranged in a lattice. The cellular composition of each ommatidium is identical. The arrangement of the lattice and the specification of cell fates in each ommatidium are thought to occur in development through cellular interactions with the local environment. Many mutations have been studied that disrupt the proper patterning and cell fating in the eye. The eyes absent (eya) mutation, the subject of this thesis, was chosen because of its eyeless phenotype. In eya mutants, eye progenitor cells undergo programmed cell death before the onset of patterning has occurred. The molecular genetic analysis of the gene is presented.

The eye arises from the larval eye-antennal imaginal disc. During the third larval instar, a wave of differentiation progresses across the disc, marked by a furrow. Anterior to the furrow, proliferating cells are found in apparent disarray. Posterior to the furrow, clusters of differentiating cells can be discerned, that correspond to the ommatidia of the adult eye. Analysis of an allelic series of eya mutants in comparison to wild type revealed the presence of a selection point: a wave of programmed cell death that normally precedes the furrow. In eya mutants, an excessive number of eye progenitor cells die at this selection point, suggesting the eya gene influences the distribution of cells between fates of death and differentiation.

In addition to its role in the eye, the eya gene has an embryonic function. The eye function is autonomous to the eye progenitor cells. Molecular maps of the eye and embryonic phenotypes are different. Therefore, the function of eya in the eye can be treated independently of the embryonic function. Cloning of the gene reveals two cDNA's that are identical except for the use of an alternatively-spliced 5' exon. The predicted protein products differ only at the N-termini. Sequence analysis shows these two proteins to be the first of their kind to be isolated. Trangenic studies using the two cDNA's show that either gene product is able to rescue the eye phenotype of eya mutants.

The eya gene exhibits interallelic complementation. This interaction is an example of an "allelic position effect": an interaction that depends on the relative position in the genome of the two alleles, which is thought to be mediated by chromosomal pairing. The interaction at eya is essentially identical to a phenomenon known as transvection, which is an allelic position effect that is sensitive to certain kinds of chromosomal rearrangements. A current model for the mechanism of transvection is the trans action of gene regulatory regions. The eya locus is particularly well suited for the study of transvection because the mutant phenotypes can be quantified by scoring the size of the eye.

The molecular genetic analysis of eya provides a system for uncovering mechanisms underlying differentiation, developmentally regulated programmed cell death, and gene regulation.

Robust Control of Evolutionary Dynamics

The application of principles from evolutionary biology has long been used to gain new insights into the progression and clinical control of both infectious diseases and neoplasms. This iterative evolutionary process consists of expansion, diversification and selection within an adaptive landscape - species are subject to random genetic or epigenetic alterations that result in variations; genetic information is inherited through asexual reproduction and strong selective pressures such as therapeutic intervention can lead to the adaptation and expansion of resistant variants. These principles lie at the center of modern evolutionary synthesis and constitute the primary reasons for the development of resistance and therapeutic failure, but also provide a framework that allows for more effective control.

A model system for studying the evolution of resistance and control of therapeutic failure is the treatment of chronic HIV-1 infection by broadly neutralizing antibody (bNAb) therapy. A relatively recent discovery is that a minority of HIV-infected individuals can produce broadly neutralizing antibodies, that is, antibodies that inhibit infection by many strains of HIV. Passive transfer of human antibodies for the prevention and treatment of HIV-1 infection is increasingly being considered as an alternative to a conventional vaccine. However, recent evolution studies have uncovered that antibody treatment can exert selective pressure on virus that results in the rapid evolution of resistance. In certain cases, complete resistance to an antibody is conferred with a single amino acid substitution on the viral envelope of HIV.

The challenges in uncovering resistance mechanisms and designing effective combination strategies to control evolutionary processes and prevent therapeutic failure apply more broadly. We are motivated by two questions: Can we predict the evolution to resistance by characterizing genetic alterations that contribute to modified phenotypic fitness? Given an evolutionary landscape and a set of candidate therapies, can we computationally synthesize treatment strategies that control evolution to resistance?

To address the first question, we propose a mathematical framework to reason about evolutionary dynamics of HIV from computationally derived Gibbs energy fitness landscapes -- expanding the theoretical concept of an evolutionary landscape originally conceived by Sewall Wright to a computable, quantifiable, multidimensional, structurally defined fitness surface upon which to study complex HIV evolutionary outcomes.

To design combination treatment strategies that control evolution to resistance, we propose a methodology that solves for optimal combinations and concentrations of candidate therapies, and allows for the ability to quantifiably explore tradeoffs in treatment design, such as limiting the number of candidate therapies in the combination, dosage constraints and robustness to error. Our algorithm is based on the application of recent results in optimal control to an HIV evolutionary dynamics model and is constructed from experimentally derived antibody resistant phenotypes and their single antibody pharmacodynamics. This method represents a first step towards integrating principled engineering techniques with an experimentally based mathematical model in the rational design of combination treatment strategies and offers predictive understanding of the effects of combination therapies of evolutionary dynamics and resistance of HIV. Preliminary in vitro studies suggest that the combination antibody therapies predicted by our algorithm can neutralize heterogeneous viral populations despite containing resistant mutations.