I'm a scientist with extensive experience in the analysis of large-scale sequencing data. I'm interested in leveraging bioinformatics and data science to understand physiological processes driving disease. I previously worked in Dr. Jonathan Jacobs's lab at UCLA, where I investigated whether genetic variants alter the microbiome and influence disease susceptibility. While in Dr. Jacobs's lab trying to figure out how to design my experiments, I also did a meta-analysis of gut biogeographic patterns in both specific-pathogen free mice and germ-free mice colonized with mouse or human feces. Outside of the lab, I spend my time serving two feline overlords (who have a Youtube channel @twofurryspirits), pretending to be a good cook, watching comedy (Silicon Valley is a personal favorite), and learning how to do new things like make this website, even if I did cheat by downloading a template.
For more information, have a look at my curriculum vitae .
Through microbiomic and predictive metagenomic analysis of samples collected from mouse models, we found that biotin deficiency, whether mediated through diet or inhibited host absorption, leads to growth of opportunistic microbes which are capable of biotin synthesis / uptake. This dysbiosis precedes the onset of intestinal inflammation.
DemoFecal microbiota transplantation has been the standard in the field for establishing whether host phenotypes can be conferred through the microbiome. However, whether the existing distribution of the microbiota and its functions along the regions of the mouse gastrointestinal tract can be recapitulated in germ-free mice colonized with mice or human stool remains unknown. We first identified region-specific microbes and their predicted functions in three cohorts of specific pathogen-free mice spanning two facilities. Of these region-specific microbes, the health-linked genus Akkermansia was consistently enriched in the lumen of the SI compared to the colon. Predictive gut-metabolic modeling revealed increased microbial metabolism in the SI, including carbohydrate degradation, lipolytic fermentation, and cross-feeding, while butyrate synthesis was colon-enriched. We also found evidence of biogeographical distribution of gut-brain modules, with increased degradation of the neurotransmitters nitric oxide and gamma-aminobutyric acid in the SI compared to the colon. Specifically, the jejunum and ileum stood out as sites with high predicted metabolic and gut-brain activity. Differences between luminal and mucosal microbiomes within each site of the GI tract were largely facility-specific, though within the colon, butyrate synthesis and polysaccharide degradation were mucosa-enriched. Importantly, in germ-free mice colonized with mice or human stool, region specificity of genera, in addition to predicted gut-metabolic and gut-brain pathways, was lost or perturbed. These results underscore the importance of investigating the spatial variation of the gut microbiome to better understand its impact on host health and disease, especially as the field continues to investigate candidate microbes involved in disease pathophysiology.
DemoWalks through installation and usage of FASTQC, MultiQC, Trimmomatic, and Salmon for transcriptomic data preprocessing. Includes Grid Engine shell scripts that can be looped over many files in a directory.
GithubJacobs Lab UCLA : Preprocess shotgun metagenomics data on the UCLA supercomputer
GithubFeel free to reach out via email or connect with me on LinkedIn! If you like this theme and want to use it for yourself, checkout the source code and the documentation at Github .