Catechol 2,3-dioxygenase-assisted cleavage of aromatics by “anaerobic” termite gut spirochetes and genomic evidence of a complete meta-pathway

The termite hindgut microbial ecosystem functions like a miniature lignocellulose-metabolizing natural bioreactor, has significant implications to nutrient cycling in the terrestrial environment, and represents an array of microbial metabolic diversity. Deciphering the intricacies of this microbial community to obtain as complete a picture as possible of how it functions as a whole, requires a combination of various traditional and cutting-edge bioinformatic, molecular, physiological, and culturing approaches. Isolates from this ecosystem, including Treponema primitia str. ZAS-1 and ZAS-2 as well as T. azotonutricium str. ZAS-9, have been significant resources for better understanding the termite system. While not all functions predicted by the genomes of these three isolates are demonstrated in vitro, these isolates do have the capacity for several metabolisms unique to spirochetes and critical to the termite system’s reliance upon lignocellulose. In this thesis, work culturing, enriching for, and isolating diverse microorganisms from the termite hindgut is discussed. Additionally, strategies of members of the termite hindgut microbial community to defend against O₂-stress and to generate acetate, the “biofuel” of the termite system, are proposed. In particular, catechol 2,3-dioxygenase and other meta-cleavage catabolic pathway genes are described in the “anaerobic” termite hindgut spirochetes T. primitia str. ZAS-1 and ZAS-2, and the first evidence for aromatic ring cleavage in the phylum (division) Spirochetes is also presented. These results suggest that the potential for O₂-dependent, yet nonrespiratory, metabolisms of plant-derived aromatics should be re-evaluated in termite hindgut communities. Potential future work is also illustrated.

The combinatorics of transcriptional regulation

The ability to regulate gene expression is of central importance for the adaptability of living organisms to changes in their internal and external environment. At the transcriptional level, binding of transcription factors (TFs) in the vicinity of promoters can modulate the rate at which transcripts are produced, and as such play an important role in gene regulation. TFs with regulatory action at multiple promoters is the rule rather than the exception, with examples ranging from TFs like the cAMP receptor protein (CRP) in E. coli that regulates hundreds of different genes, to situations involving multiple copies of the same gene, such as on plasmids, or viral DNA. When the number of TFs heavily exceeds the number of binding sites, TF binding to each promoter can be regarded as independent. However, when the number of TF molecules is comparable to the number of binding sites, TF titration will result in coupling ("entanglement") between transcription of different genes. The last few decades have seen rapid advances in our ability to quantitatively measure such effects, which calls for biophysical models to explain these data. Here we develop a statistical mechanical model which takes the TF titration effect into account and use it to predict both the level of gene expression and the resulting correlation in transcription rates for a general set of promoters. To test these predictions experimentally, we create genetic constructs with known TF copy number, binding site affinities, and gene copy number; hence avoiding the need to use free fit parameters. Our results clearly prove the TF titration effect and that the statistical mechanical model can accurately predict the fold change in gene expression for the studied cases. We also generalize these experimental efforts to cover systems with multiple different genes, using the method of mRNA fluorescence in situ hybridization (FISH). Interestingly, we can use the TF titration affect as a tool to measure the plasmid copy number at different points in the cell cycle, as well as the plasmid copy number variance. Finally, we investigate the strategies of transcriptional regulation used in a real organism by analyzing the thousands of known regulatory interactions in E. coli. We introduce a "random promoter architecture model" to identify overrepresented regulatory strategies, such as TF pairs which coregulate the same genes more frequently than would be expected by chance, indicating a related biological function. Furthermore, we investigate whether promoter architecture has a systematic effect on gene expression by linking the regulatory data of E. coli to genome-wide expression censuses.