Microbial adaptation, differentiation, and community structure

Microbes play a central role in diverse processes ranging from global elemental cycles to human digestion. Understanding these complex processes requires a rm under- standing of the interplay between microbes and their environment. In this thesis, we utilize sequencing data to study how individual species adapt to different niches, and how species assemble to form communities. First, we study the potential temperature and salinity range of 16 marine Vibrio strains. We nd that salinity tolerance is at odds with the strains' natural habitats, and provide evidence that this incongruence may be explained by a molecular coupling between salinity and temperature tolerance. Next, we investigate the genetic basis of bacterial ecological differentiation by analyzing the genomes of two closely related, yet ecologically distinct populations of Vibrio splendidus. We nd that most loci recombine freely across habitats, and that ecological differentiation is likely driven by a small number of habitat-specic alle-les. We further present a model for bacterial sympatric speciation. Our simulations demonstrate that a small number of adaptive loci facilitates speciation, due to the op- posing roles horizontal gene transfer (HGT) plays throughout the speciation process: HGT initially promotes speciation by bringing together multiple adaptive alleles, but later hinders it by mixing alleles across habitats. Finally, we introduce two tools for analyzing genomic survey data: SparCC, which infers correlations between taxa from relative abundance data; and StrainFinder, which extracts strain-level information from metagenomic data. Employing these tools, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body, and show that 16S-defined groups are rarely composed of a single dominant strain.

Department dc:contributor.department

Massachusetts Institute of Technology. Computational and Systems Biology Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2013

Author dc:creator

• Friedman, Jonathan, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Eric J. Alm and Daniel H. Rothman.

• Computational and Systems Biology Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/81751

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/81751