The Microbial and Comparative Genomics Initiative (MCG)
Team Leaders: Mitch Sogin (MBL) and Casey Dunn (Brown)
The goal of the Microbial and Comparative Genomics Initiative is to advance our understanding of the genomic biodiversity in the context of organismal and environmental change, and to build the phylogenetic and informational framework for using high throughput sequencing to probe organisms and environments in genomically enlightened ways.
The MCG Initiative will address two reciprocal themes: (1) the documentation of genomic diversity in nature; (2) the interpretation of biodiversity in a functional framework. The first theme will focus on massively parallel sequence analysis of microbial and metazoan diversity from defined environments. The second theme will focus on building phylogenetically informed genomic tools for sampling environments in functionally meaningful ways. This initiative will be lead by Dr. Mitch Sogin from the Bay Paul Center at MBL, and Dr. Casey Dunn in EEB at Brown. Dr. Sogin has pioneered the use of DNA sequence based surveys of microbial diversity in a wide array of environments. His recent studies unveiling the vast “rare biosphere” open up many exciting research questions in the basic area of biodiversity (Santelli et al. 2008). Dr. Dunn is an expert in phylogenomics and the evolution of animal body plans. His recent paper on the metazoan tree of life based on genome wide samples of orthologs has revolutionized how phylogenetic analysis is done, and simultaneously forced major revision of our understanding of the evolution of body plans (Dunn et al. 2008). Together these team leaders will organize graduate training in this area so that IGERT Fellows will be able to engage in phylogenomically informed approaches to environmental genomics and reverse ecology.
The Organismal Responses to Environmental Gradients Initiative (OREGS)
Team Leaders: Johanna Schmitt (Brown) and Zoe Cardon (MBL)
The goals of the OREGS Initiative are to examine how exposures to biological, and/or physical gradients, including stressors, affect organismal distributions, diversity, function, and evolution, and to explore how that diversity, distribution, function and evolution in turn affect ecosystem function.
The OREGS Initiative will tackle (1) spatial and temporal variation in ecologically relevant environmental conditions, including stressors; (2) the gene expression, organismal function and distribution, and community structure driven by that environmental variation; and (3) the contribution of the genomic, cellular, physiological, or morphological programs of organisms to environmental variation and ecosystem function. Drs Schmitt and Cardon are internationally recognized leaders in evolutionary ecology, plant ecology, and soil microbial ecology and biogeochemistry. Dr. Schmitt’s work on clinal variation and climatic adaptation in Arabidopsis is an extremely powerful model of ecological genomics where field experiments of defined genotypes are carried out in the wild (Wilczek et al. 2009). Dr. Cardon’s documentation of diversity and physiological novelty of green algae living in desert microbiotic crusts provides a parallel system focusing on adaption to extreme environments (Cardon et al. 2008), as does her exploration of soil microbial activity within more common physical and biogeochemical gradients around plant roots (in the rhizosphere, (Cardon and Whitbeck 2007)). Together Drs Schmitt and Cardon offer strength in organismal and ecosystem models of how genotypes and organismal responses map onto heterogeneous and stressful environments.
The Community Genome Assembly Initiative (CGA)
Team Leaders: Sorin Istrail (Brown) and David Mark Welch (MBL)
The CGA Initiative will address the primary goal of trying to assemble intact organisms from high throughput genomic surveys. While other groups have been able to achieve this in simple communities (Tyson et al. 2004), this remains a challenging goal for most natural environments. This effort will integrate state of the art tools in genome assembly tools with functional modeling of how communities work together as integrated metabolisms. The explosion of studies documenting microbial diversity has turned the problem of a biodiversity “crisis” on its head: there are so many unstudied, but observable, microbes that crisis is about how to understand diversity. Typical microbial diversity surveys, even those using “deep” high through put sequencing, focus on a diagnostic rDNA region that is highly informative. What is missing from this effort is a clear understanding of what genes, and hence metabolic functions, are linked to this “barcode”, so that we can move towards the goal of a genomic understanding of community function.
The CGA team will approach this problem from two angles 1) a computational approach where new assembly tools are developed to incorporate sequence diversity in the inference of assembled genomes, and 2) a metabolic, functional approach, were experiments and quantitative models help decipher microbial communities. The computational approach will develop new algorithms that focus on parallel shifts in orthologous sequences based on deep sequence data sets. Combined with single cell amplification and sequencing methods, research will focus on the goal of assembling individual genomes from metagenomic samples. This aspect will be spearheaded by Dr. Istrail who led the genome assembly team at Celera Genomics and is an expert on the computational aspects of genome assembly and transcriptional networks (Samanta et al. 2006). Dr. Istrail will work closely with Dr. Mark Welch at MBL who has pioneered the evolutionary genomics of bdelloid rotifers (Mark Welch et al. 2008). Together, these team leaders will help integrate the basic research questions and specific graduate training goals of the IGERT program. Drs. Chip Lawrence and Ben Raphael of the Center for Computational Molecular Biology have worked on these assembly problems (Bashir et al. 2008) and offer rigorous statistical and computational skills that can be applied to a variety of reverse ecology problems.