A framework for integrating the songbird brain.

Dr. Jarvis' laboratory studies the neurobiology of vocal communication. Emphasis is placed on the molecular pathways involved in the perception and production of learned vocalizations. They use an integrative approach that combines behavioral, anatomical, electrophysiological and molecular biological techniques. The main animal model used is songbirds, one of the few vertebrate groups that evolved the ability to learn vocalizations. The generality of the discoveries is tested in other vocal learning orders, such as parrots and hummingbirds, as well as non-vocal learners, such as pigeons and non-human primates. Some of the questions require performing behavior/molecular biology experiments in freely ranging animals, such as hummingbirds in tropical forest of Brazil. Recent results show that in songbirds, parrots and hummingbirds, perception and production of song are accompanied by anatomically distinct patterns of gene expression. All three groups were found to exhibit vocally-activated gene expression in exactly 7 forebrain nuclei that are very similar to each other. These structures for vocal learning and production are thought to have evolved independently within the past 70 million years, since they are absent from interrelated non-vocal learning orders. One structure, Area X of the basal ganglia's striatum in songbirds, shows large differential gene activation depending on the social context in which the bird sings. These differences may reflect a semantic content of song, perhaps similar to human language.

The overall goal of the research is to advance knowledge of the neural mechanisms for vocal learning and basic mechanisms of brain function. These goals are further achieved by combined collaborative efforts with the laboratories of Drs. Mooney and Nowicki at Duke University, who study respectively behavior and electrophysiological aspects of songbird vocal communication.

My laboratory is interested in fungal genomics.

In particular we use genomic sequencing of fungal strains and species in comparative analysis. Starting with the sequencing of Saccharomyces cerevisiae strain S288C, I have been involved in the genome sequencing and annotation of Ashbya gossypii, Cryptococcus neoformans var. grubii and ~100 additional S. cerevisiae strains. We currently use Illumina paired end and mate paired sequencing, as this is at presently the most cost effective widely used technology capable of generating high accuracy, zero gap whole genome sequences. The 100-genomes S. cerevisiae data as well as the fully updated fully annotated A. gossypii sequence (Genbank numbers AE016814-AE016820), which spans all seven chromosomes from telomere to telomere, were generated using Illumina data. In my laboratory we strive to utilize comparative genomics data to understand aspects of basic fungal biology. Some of our specific areas of interest are filamentous growth, mapping of complex traits, horizontal gene transfer, and identification of RNA coding genes. This work involves a combination of experimental work and bioinformatics analysis. Research in S. cerevisiae has greatly benefitted from an accurate, annotated S. cerevisiae reference genome, and that research into the tremendous diversity in this organism will similarly benefit from the availability of a large number of accurate, fully annotated genome sequences. The use of genomic information to better understand the biology of these organisms, and this is what students in my laboratory generally work on.

What is the set of genes found in a pathogenic fungus such as Cryptococcus?

Our interest in this human pathogen is to expand beyond looking at one isolate and to investigate the diversity in the population. Are there genes found in some Cryptococcus neoformans isolates but not in others? Are there regions of the genome or individual genes which are highly diverged between Cryptococcus isolates? Efforts are now underway at Stanford University to sequence the genome of the JEC21 strain of Cryptococcus. This is a strain that has been agreed upon by the community of Cryptococcus researchers as a reference strain. Obtaining the DNA sequence of this strain is only the start however. From that sequence identifying the complete set of genes will be a considerable challenge requiring both bioinformatic as well as experimental tools. While this work on gene identification is going on we plan on addressing the question of how much do other Cryptococcus isolates differ from JEC21.

What is the set of genes in humans?

The complete DNA sequence of human and mouse will become available soon. This does not mean that we will know the complete set of human or mouse genes. Our current state of knowledge does not allow us to accurately predict human genes directly from DNA sequence. We are interested in applying to the human genome some of the experimental and bioinformatic tools we are developing and utilizing in fungal systems.

Computational biology, machine learning, Bayesian statistics, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems biology, computational neurobiology, classification, feature selection