Sulfur-Cycling in Methane-Rich Ecosystems: Uncovering Microbial Processes and Novel Niches

Microbial sulfur cycling communities were investigated in two methane-rich ecosystems, terrestrial mud volcanoes (TMVs) and marine methane seeps, in order to investigate niches and processes that would likely be central to the functioning of these crucial ecosystems. Terrestrial mud volcanoes represent geochemically diverse habitats with varying sulfur sources and yet sulfur-cycling in these environments remains largely unexplored. Here we characterized the sulfur-metabolizing microorganisms and activity in 4 TMVs in Azerbaijan, supporting the presence of active sulfur-oxidizing and sulfate-reducing guilds in all 4 TMVs across a range of physiochemical conditions, with diversity of these guilds being unique to each TMV. We also found evidence for the anaerobic oxidation of methane coupled to sulfate reduction, a process which we explored further in the more tractable marine methane seeps. Diverse associations between methanotrophic archaea (ANME) and sulfate-reducing bacterial groups (SRB) often co-occur in marine methane seeps, however the ecophysiology of these different symbiotic associations has not been examined. Using a combination of molecular, geochemical and fluorescence in situ hybridization coupled to nano-scale secondary ion mass spectrometry (FISH-NanoSIMS) analyses of in situ seep sediments and methane-amended sediment incubations from diverse locations, we show that the unexplained diversity in SRB associated with ANME cells can be at least partially explained by preferential nitrate utilization by one particular partner, the seepDBB. This discovery reveals that nitrate is likely an important factor in community structuring and diversity in marine methane seep ecosystems. The thesis concludes with a study of the dynamics between ANME and their associated SRB partners. We inhibited sulfate reduction and followed the metabolic processes of the community as well as the effect of ANME/SRB aggregate composition and growth on a cellular level by tracking 15N substrate incorporation into biomass using FISH-NanoSIMS. We revealed that while sulfate-reducing bacteria gradually disappeared over time in incubations with an SRB inhibitor, the ANME archaea persisted in the form of ANME-only aggregates, which are capable of little to no growth when sulfate reduction is inhibited. These data suggest ANME are not able to synthesize new proteins when sulfate reduction is inhibited.

The logic of receptor-ligand interactions in the Notch signaling pathway

The Notch signaling pathway enables neighboring cells to coordinate developmental fates in diverse processes such as angiogenesis, neuronal differentiation, and immune system development. Although key components and interactions in the Notch pathway are known, it remains unclear how they work together to determine a cell's signaling state, defined as its quantitative ability to send and receive signals using particular Notch receptors and ligands. Recent work suggests that several aspects of the system can lead to complex signaling behaviors: First, receptors and ligands interact in two distinct ways, inhibiting each other in the same cell (in cis) while productively interacting between cells (in trans) to signal. The ability of a cell to send or receive signals depends strongly on both types of interactions. Second, mammals have multiple types of receptors and ligands, which interact with different strengths, and are frequently co-expressed in natural systems. Third, the three mammalian Fringe proteins can modify receptor-ligand interaction strengths in distinct and ligand-specific ways. Consequently, cells can exhibit non-intuitive signaling states even with relatively few components.

In order to understand what signaling states occur in natural processes, and what types of signaling behaviors they enable, this thesis puts forward a quantitative and predictive model of how the Notch signaling state is determined by the expression levels of receptors, ligands, and Fringe proteins. To specify the parameters of the model, we constructed a set of cell lines that allow control of ligand and Fringe expression level, and readout of the resulting Notch activity. We subjected these cell lines to an assay to quantitatively assess the levels of Notch ligands and receptors on the surface of individual cells. We further analyzed the dependence of these interactions on the level and type of Fringe expression. We developed a mathematical modeling framework that uses these data to predict the signaling states of individual cells from component expression levels. These methods allow us to reconstitute and analyze a diverse set of Notch signaling configurations from the bottom up, and provide a comprehensive view of the signaling repertoire of this major signaling pathway.