Methanotrophs are prokaryotes that naturally consume the potent greenhouse gas methane for energy. Through metabolic engineering at an industrial scale, these microorganisms hold potential to mitigate the contribution of methane emissions to global warming. In particular, Methylotuvimicrobium buryatense can sustain robust growth both in nature and experimental settings; it is a promising engineering candidate. To develop a robust metabolic engineering platform using M. buryatense, biologists require a deeper understanding of the genetic mechanisms by which it functions. Here, I present an open-source software tool designed to interactively explore the transcriptome of M. buryatense. By integrating bulk RNA-seq datasets collected from experiments over the past decade and applying an array of unsupervised machine learning clustering algorithms, we cluster genes by their expression profiles in differing growth conditions. These gene clusters are annotated with gene ontology (GO) terms using statistical enrichment analysis to assist in functional interpretation of the clusters and the genes that comprise them. To enhance domain-expert researchers’ ability to explore and drill-down into specific queries, I unify these cluster-specific analyses in a web-hosted tool using interactive data visualization techniques centered on a ReactJS frontend and Azure Cloud backend. With both exploratory and query-focused use cases, this software tool can support M. buryatense biologist workflows for predicting functions of hypothetical proteins, showcase new or confirming putative regulatory processes, and generate new experimental hypotheses from the presented transcriptomic trends.

With rising greenhouse gas emissions, both emissions reductions and greenhouse gas capture and conversion are necessary to mitigate the impacts of climate change. Though carbon dioxide comprises the largest proportion of global greenhouse gas emissions, methane causes over 80 times more global warming per unit than carbon dioxide. While methane can be converted catalytically at high temperatures and pressures, bacteria called methanotrophs transform methane into biomass at ambient temperatures and pressures. Engineering these organisms to consume methane globally can help slow climate change. At the Lidstrom Lab, I study the relationship between the methanotroph genome and metabolic regulation. Through this work, I have focused on finding genes that facilitate responses to environmental conditions and am now looking at genes that impact overall growth rate. I design and construct mutant strains, eliminating genes that appear to use the cell’s energy unnecessarily based on transcriptomics data. We expect that eliminating such genes will allow the cells to devote more energy to methane consumption and growth, improving growth rates under low methane conditions. This work is being extended to experiments in a bioreactor to observe how selected mutations and growth conditions impact growth rate at low methane. For this work, I am setting up the bioreactor to maintain the intended reaction conditions and utilizing a gas chromatogram to monitor methane consumption over time. These experiments reveal how the methanotrophs grow under specific conditions. I am also analyzing the results of the experiments using Python, MATLAB, and Excel. Long-term, this research will help prepare methanotrophs for deployment in the field to consume methane in areas of atmospheric methane release including landfills and agricultural sites.

Duchenne Muscular Dystrophy (DMD) is a severe muscle wasting disease caused by deficiency of the protein dystrophy and affects approximately 1/3500 boys. Patients have shortened life expectancy due to cardio-respiratory problems caused by the disease alongside impaired ambulatory function. Previous studies have described the “waddling” gait in patients with muscular dystrophy but not in the animal model of DMD. Here we present the characterization of exercise ability and quantified gait metrics in a novel DMDmdx model that better represents DMD in humans. We used the Noldus Catwalk XT motion capture system to identify different gait parameters between DMDmdx and wild-type rats at 14-15 weeks of age. Compared to wild-type rats, DMDmdx has a reduced stride length and swing time in both front paws and hind paws. Time to max contact in DMDmdx rats is 5% faster than wild-type, but max intensity at time of max paw contact is 15% lower in DMDmdx. The “waddling” gait is indicated by 13% higher uses of 3 and 4 paws supported by DMDmdx during a run compared to wild-type. Subsequently, this led to a higher abnormal step pattern as similarly observed in patients with muscular dystrophy due to hip muscle weakness, thus resulting in the “waddling” gait. Gait pattern of the novel DMDmdx rat model reflects the impaired ambulatory function commonly seen in patients with DMD, thus making this a potentially useful outcome for understanding disease progression, therapies, and development of exercise guidelines.