Browsing by Subject "Gradient"

How do single cells—like neutrophils, amoebae, neurons, yeast, etc.—grow or move in a directed fashion in response to spatial chemical gradients? To address this question, we used the mating response in the budding yeast, Saccharomyces cerevisiae, as a biological model. To mate, pairs of yeast cells orient their cell fronts toward each other and fuse. Each cell relies on a pheromone gradient established by its partner to orient correctly. The ability for cells to resolve gradients is striking, because each cell is only ~5 μm wide and is thought to be operating in complex and noisy environments. Interestingly, mating pairs of cells often start out not facing each other. When this happens, the front of each cell—defined by a patch of cortical polarity proteins—undergoes a series of erratic and random movements along the cell cortex till it ‘finds’ the mating partner’s patch. We sought to understand how polarity patches in misaligned cells find each other. To this end, we first characterized patch movement in cells by the distribution of their step-lengths and turning angles and analyzed a bottom-up model of the polarity patch’s dynamics. The final version of our model combines 11 reaction-diffusion equations representing polarity protein dynamics with a stochastic module representing vesicle trafficking on a plane with periodic boundary conditions. We found that the model could not quantitatively reproduce step-length and turning angle distributions, which suggested that some mechanisms driving patch movement may not be present in the model. Incorpo-rating biologically inspired features into the model—such as focused vesicle delivery, sudden fluctuations in vesicle delivery rates, and the presence of polarity inhibitors on vesicles—allowed us to quantitatively match the in vivo polarity patch’s behavior. We then introduced a pathway, which connects pheromone sensing to polarity, to see how the model behaved when exposed to pheromone gradients. Concurrently, we analyzed the behavior of fluores-cently labeled polarity patches in mating pairs of cells. We discovered that the ~1 μm wide patch could (remarkably) sense and bias its movement up pheromone gradients, a result corroborated by our model. Further analysis of the model revealed that while the polarity patch tends to bias the location of a cluster of pheromone-sensing-receptors, the receptors can transform an external pheromone distribution into a peaked non-linear “polarity-activation” profile that “pulls” the patch. Stochastic perturbations cause the patch to “ping-pong” around the activation-profile. In a gradient of pheromone, this ping-ponging be-comes biased, leading to net patch movement up the gradient. We speculate that such a mechanism could be used by single cells with mobile fronts to track chemical gradients.

Microorganisms mediate many biogeochemical processes critical to the functioning of ecosystems, which places them as an intermediate between environmental change and the resulting ecosystem response. Yet, we have an incomplete understanding of these relationships, how to predict them, and when they are influential. Understanding these dynamics will inform ecological principles developed for macroorganisms and aid expectations for microbial responses to new gradients. To address this research goal, I used two studies of environmental gradients and a literature synthesis.

With the gradient studies, I assessed microbial community composition in stream biofilms across a gradient of alkaline mine drainage. I used multivariate approaches to examine changes in the non-eukaryote microbial community composition of taxa (chapter 2) and functional genes (chapter 3). I found that stream biofilms at sites receiving alkaline mine drainage had distinct community composition and also differed in the composition of functional gene groups compared with unmined reference sites. Compositional shifts were not dominated by groups that could benefit from mining associated increases of terminal electron acceptors; two-thirds of responsive taxa and functional gene groups were negatively associated with mining. The majority of subsidies and stressors (nitrate, sulfate, conductivity) had no consistent relationships with taxa or gene abundances. However, methane metabolism genes were less abundant at mined sites and there was a strong, positive correlation between selenate reductase gene abundance and mining-associated selenium. These results highlighted the potential for indirect factors to also play an important role in explaining compositional shifts.

In the fourth chapter, I synthesized studies that use environmental perturbations to explore microbial community structure and microbial process connections. I examined nine journals (2009–13) and found that many qualifying papers (112 of 148) documented structure and process responses, but few (38 of 112 papers) reported statistically testing for a link. Of these tested links, 75% were significant. No particular approach for characterizing structure or processes was more likely to produce significant links. Process responses were detected earlier on average than responses in structure. Together, the findings suggested that few publications report statistically testing structure-process links; but when tested, links often occurred yet shared few commonalities in linked processes or structures and the techniques used for measuring them.

Although the research community has made progress, much work remains to ensure that the vast and growing wealth of microbial informatics data is translated into useful ecological information. In part, this challenge can be approached through using hypotheses to guide analyses, but also by being open to opportunities for hypothesis generation. The results from my dissertation work advise that it is important to carefully interpret shifts in community composition in relation to abiotic characteristics and recommend considering ecological, thermodynamic, and kinetic principles to understand the properties governing community responses to environmental perturbation.

Previous papers have elaborated formal gradient analysis for spatial processes, focusing on the distribution theory for directional derivatives associated with a response variable assumed to follow a Gaussian process model. In the current work, these ideas are extended to additionally accommodate one or more continuous covariate(s) whose directional derivatives are of interest and to relate the behavior of the directional derivatives of the response surface to those of the covariate surface(s). It is of interest to assess whether, in some sense, the gradients of the response follow those of the explanatory variable(s), thereby gaining insight into the local relationships between the variables. The joint Gaussian structure of the spatial random effects and associated directional derivatives allows for explicit distribution theory and, hence, kriging across the spatial region using multivariate normal theory. The gradient analysis is illustrated for bivariate and multivariate spatial models, non-Gaussian responses such as presence-absence and point patterns, and outlined for several additional spatial modeling frameworks that commonly arise in the literature. Working within a hierarchical modeling framework, posterior samples enable all gradient analyses to occur as post model fitting procedures.