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Locusts are generalist herbivores meaning that they are able to consume a variety of plants. Because of their broad diet, and ability to respond rapidly to a favorable environment with giant swarms of voracious insects, they are dangerous pests. Their potential impacts on humans increase dramatically when individuals switch from

Locusts are generalist herbivores meaning that they are able to consume a variety of plants. Because of their broad diet, and ability to respond rapidly to a favorable environment with giant swarms of voracious insects, they are dangerous pests. Their potential impacts on humans increase dramatically when individuals switch from their solitarious phase to their gregarious phase where they congregate and begin marching and eventually swarming together. These swarms, often billions strong, can consume the vegetation of enormous swaths of land and can travel hundreds of kilometers in a single day producing a complex threat to food security. To better understand the biology of these important pests we explored the gut microbiome of the South American locust (Schistocerca cancellata). We hypothesized generally that the gut microbiome in this species would be critically important as has been shown in many other species. We extracted and homogenized entire guts from male S. cancellata, and then extracted gut microbiome genomic DNA. Genomic DNA was then confirmed on a gel. The initial extractions were of poor quality for sequencing, but subsequent extractions performed by collaborators during troubleshooting at Southern Illinois University Edwardsville proved more useful and were used for PCR. This resulted in the detections of the following bacterial genera in the gut of S. cancellata: Enterobacter, Enterococcus, Serratia, Pseudomonas, Actinobacter, and Weisella. With this data, we are able to speculate about the physiological roles that they hold within the locust gut generating hypotheses for further testing. Understanding the microbial composition of this species’ gut may help us better understand the locust in general in an effort to more sustainably manage them.

ContributorsGrief, Dustin (Author) / Overson, Rick (Thesis director) / Cease, Arianne (Committee member) / Peterson, Brittany (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The greatest barrier to understanding how life interacts with its environment is the complexity in which biology operates. In this work, I present experimental designs, analysis methods, and visualization techniques to overcome the challenges of deciphering complex biological datasets. First, I examine an iron limitation transcriptome of Synechocystis sp. PCC

The greatest barrier to understanding how life interacts with its environment is the complexity in which biology operates. In this work, I present experimental designs, analysis methods, and visualization techniques to overcome the challenges of deciphering complex biological datasets. First, I examine an iron limitation transcriptome of Synechocystis sp. PCC 6803 using a new methodology. Until now, iron limitation in experiments of Synechocystis sp. PCC 6803 gene expression has been achieved through media chelation. Notably, chelation also reduces the bioavailability of other metals, whereas naturally occurring low iron settings likely result from a lack of iron influx and not as a result of chelation. The overall metabolic trends of previous studies are well-characterized but within those trends is significant variability in single gene expression responses. I compare previous transcriptomics analyses with our protocol that limits the addition of bioavailable iron to growth media to identify consistent gene expression signals resulting from iron limitation. Second, I describe a novel method of improving the reliability of centroid-linkage clustering results. The size and complexity of modern sequencing datasets often prohibit constructing distance matrices, which prevents the use of many common clustering algorithms. Centroid-linkage circumvents the need for a distance matrix, but has the adverse effect of producing input-order dependent results. In this chapter, I describe a method of cluster edge counting across iterated centroid-linkage results and reconstructing aggregate clusters from a ranked edge list without a distance matrix and input-order dependence. Finally, I introduce dendritic heat maps, a new figure type that visualizes heat map responses through expanding and contracting sequence clustering specificities. Heat maps are useful for comparing data across a range of possible states. However, data binning is sensitive to clustering cutoffs which are often arbitrarily introduced by researchers and can substantially change the heat map response of any single data point. With an understanding of how the architectural elements of dendrograms and heat maps affect data visualization, I have integrated their salient features to create a figure type aimed at viewing multiple levels of clustering cutoffs, allowing researchers to better understand the effects of environment on metabolism or phylogenetic lineages.
ContributorsKellom, Matthew (Author) / Raymond, Jason (Thesis advisor) / Anbar, Ariel (Committee member) / Elser, James (Committee member) / Shock, Everett (Committee member) / Walker, Sarah (Committee member) / Arizona State University (Publisher)
Created2017