Person:

Jordan, Daniel Michael

Bats account for one-fifth of mammalian species, are the only mammals with powered flight, and are among the few animals that echolocate. The insect-eating Brandt’s bat (Myotis brandtii) is the longest-lived bat species known to date (lifespan exceeds 40 years) and, at 4–8 g adult body weight, is the most extreme mammal with regard to disparity between body mass and longevity. Here we report sequencing and analysis of the Brandt’s bat genome and transcriptome, which suggest adaptations consistent with echolocation and hibernation, as well as altered metabolism, reproduction and visual function. Unique sequence changes in growth hormone and insulin-like growth factor 1 receptors are also observed. The data suggest that an altered growth hormone/insulin-like growth factor 1 axis, which may be common to other long-lived bat species, together with adaptations such as hibernation and low reproductive rate, contribute to the exceptional lifespan of the Brandt’s bat.

Estimating the effects of missense mutations is a problem with many important applications in a variety of fields, including medical genetics, evolutionary theory, population genetics, and protein structure and design. Many popular methods exist to solve this problem, the most widely used of which are PolyPhen-2 and SIFT. These methods, along with most other popular methods, rely on multiple sequence alignments of orthologous protein sequences. Based on the amino acids observed in each column of the alignment, they produce a profile describing how tolerated each amino acid is at each position. They then compare the wild-type and variant amino acids to this profile to produce a prediction.

In practice, these methods are fast, robust, and relatively reliable. However, from a theoretical perspective, they have at least three significant shortcomings:

1. They use effects on selection as a proxy for effects on phenotype and protein structure and function.
2. They treat each position as independent, ruling out most forms of interactions between sites.
3. They do not explicitly model the process of evolution, instead assuming that sequences we observe more or less represent an equilibrium state.

With the recent explosion of sequencing technology, as well as the steady increase of computational power, we are now beginning to have enough data to investigate these simplifications and see how much they really affect the performance of these methods.

In this dissertation, I present three such investigations. First, I describe a modified predictor designed to predict risk for a specific disease, hypertrophic cardiomyopathy (HCM), rather than general seletive effect. This method achieves significantly higher accuracy than methods without such specific domain knowledge. Next, I describe a model of pairwise interactions between sites, demonstrating both statistically and with in vivo evidence that approximately 7-12% of disease-causing variants may be mispredicted by these methods due to such interactions. Finally, I describe a hybrid method that uses an alignment-based estimator to inform a parametric model of evolution, resulting in a small but significant improvement in accuracy.

Patterns of amino acid conservation have served as a tool for understanding protein evolution. The same principles have also found broad application in human genomics, driven by the need to interpret the pathogenic potential of variants. We performed a systematic comparative genomics analysis of human disease-causing missense variants. We found that an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. We developed a model of genetic interactions that predicts most of these to be simple pairwise compensations. Functional testing of this model on two known human disease genes revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo. This approach was also applied to ab initio gene discovery to support the identification of a de novo disease driver in BTG2 that is subject to protective cis-modification in >50 species. Finally, we developed a computational tool to predict candidate residues subject to compensation. Taken together, our data highlight the importance of cis-genomic context as a contributor to protein evolution; they inform the complexity of allele effect on phenotype; and they are likely to assist methods for predicting allele pathogenicity.