Improving Our Comprehension of Microbial Communities

This study details the evaluation of three commonly used methodologies for characterizing microbial communities. The use of universal genes, such as those for small subunit ribosomal ribonucleic acid (SSU rRNA), or randomly sequenced genome fragments form the foundation upon which a majority of microbiologists evaluate community structure, metabolic capabilities and community dynamics. SSU rRNA sequence-based studies, which are the most common methods for providing a microbial census, rely upon the faithful amplification of the corresponding genes from the original DNA sample. Chapter 2 provides a comprehensive reevaluation of the most commonly used amplification primers and presents a pair of formulations corresponding to the common 27f and 1492r sites that better maintain original SSU rRNA gene ratios. Other SSU rRNA-based studies, such as denaturing gradient gel electrophoresis (DGGE), have been used as an alternative to SSU rRNA gene sequencing because they can provide many community profiles for a fraction of the time, effort and money required for sequencing. Chapter 3 provides a comparison of DGGE community profiles and sequence-based analyses of the same samples demonstrating the limitations of DGGE in evaluating community structure. During the last decade, community analyses have expanded from single genes to microbial community genomics in which the gene content of an environment can provide not only a census, but also direct information on metabolic capabilities of community members. Chapter 4 describes computational tools developed for processing and analyzing multiple forms of sequence data.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Microbiology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Frank, Jeremy

Contributors dc:contributor

• Gary Olsen

• Biology, Microbiology

Language dc:language

eng

Identifier

(MiAaPQ)AAI3347383

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/86708