I am a PhD student in the Bioengineering Department working under Dr. Justin Sonnenburg. I am currently studying the microbiome and how it relates to the immune system in human studies through machine learning and statistical methods. I am also committed to creating opportunities and providing mentorship to students of diverse backgrounds.
Honors & Awards
National Science Foundation Graduate Fellowship, National Science Foundation (March 2017)
Enhancing Diversity in Graduate Education Doctoral Fellowship Program, Stanford University (March 2017)
Professional Affiliations and Activities
Program Leader, Stanford Summer Research Program (2019 - 2019)
Advocacy Chair, Biomedical Association for the Interest of Minority Students (2018 - 2019)
Peer Leader, ADVANCE Summer Institute (2018 - 2018)
Education & Certifications
Bachelors of Science, University of Delaware, Biochemistry (2017)
High-Throughput Stool Metaproteomics: Method and Application to Human Specimens.
2020; 5 (3)
Stool-based proteomics is capable of significantly augmenting our understanding of host-gut microbe interactions. However, compared to competing technologies, such as metagenomics and 16S rRNA sequencing, it is underutilized due to its low throughput and the negative impact sample contaminants can have on highly sensitive mass spectrometry equipment. Here, we present a new stool proteomic processing pipeline that addresses these shortcomings in a highly reproducible and quantitative manner. Using this method, 290 samples from a dietary intervention study were processed in approximately 1.5weeks, largely done by a single researcher. These data indicated a subtle but distinct monotonic increase in the number of significantly altered proteins between study participants on fiber- or fermented food-enriched diets. Lastly, we were able to classify study participants based on their diet-altered proteomic profiles and demonstrated that classification accuracies of up to 89% could be achieved by increasing the number of subjects considered. Taken together, this study represents the first high-throughput proteomic method for processing stool samples in a technically reproducible manner and has the potential to elevate stool-based proteomics as an essential tool for profiling host-gut microbiome interactions in a clinical setting.IMPORTANCE Widely available technologies based on DNA sequencing have been used to describe the kinds of microbes that might correlate with health and disease. However, mechanistic insights might be best achieved through careful study of the dynamic proteins at the interface between the foods we eat, our microbes, and ourselves. Mass spectrometry-based proteomics has the potential to revolutionize our understanding of this complex system, but its application to clinical studies has been hampered by low-throughput and laborious experimentation pipelines. In response, we developed SHT-Pro, the first high-throughput pipeline designed to rapidly handle large stool sample sets. With it, a single researcher can process over one hundred stool samples per week for mass spectrometry analysis, conservatively approximately 10* to 100* faster than previous methods, depending on whether isobaric labeling is used or not. Since SHT-Pro is fairly simple to implement using commercially available reagents, it should be easily adaptable to large-scale clinical studies.
View details for DOI 10.1128/mSystems.00200-20
View details for PubMedID 32606025
Long-term dietary intervention reveals resilience of the gut microbiota despite changes in diet and weight.
The American journal of clinical nutrition
BACKGROUND: With the rising rates of obesity and associated metabolic disorders, there is a growing need for effective long-term weight-loss strategies, coupled with an understanding of how they interface with human physiology. Interest is growing in the potential role of gut microbes as they pertain to responses to different weight-loss diets; however, the ways that diet, the gut microbiota, and long-term weight loss influence one another is not well understood.OBJECTIVES: Our primary objective was to determine if baseline microbiota composition or diversity was associated with weight-loss success. A secondary objective was to track the longitudinal associations of changes to lower-carbohydrate or lower-fat diets and concomitant weight loss with the composition and diversity of the gut microbiota.METHODS: We used 16S ribosomal RNA gene amplicon sequencing to profile microbiota composition over a 12-mo period in 49 participants as part of a larger randomized dietary intervention study of participants consuming either a healthy low-carbohydrate or a healthy low-fat diet.RESULTS: While baseline microbiota composition was not predictive of weight loss, each diet resulted in substantial changes in the microbiota 3-mo after the start of the intervention; some of these changes were diet specific (14 taxonomic changes specific to the healthy low-carbohydrate diet, 12 taxonomic changes specific to the healthy low-fat diet) and others tracked with weight loss (7 taxonomic changes in both diets). After these initial shifts, the microbiota returned near its original baseline state for the remainder of the intervention, despite participants maintaining their diet and weight loss for the entire study.CONCLUSIONS: These results suggest a resilience to perturbation of the microbiota's starting profile. When considering the established contribution of obesity-associated microbiotas to weight gain in animal models, microbiota resilience may need to be overcome for long-term alterations to human physiology. This trial was registered at clinicaltrials.gov as NCT01826591.
View details for DOI 10.1093/ajcn/nqaa046
View details for PubMedID 32186326