Analysis of fecal biomarkers to impact clinical care and public health
Author(s)Matus García, Mariana Guadalupe
Massachusetts Institute of Technology. Computational and Systems Biology Program.
Eric J. Alm.
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DNA sequencing and metabolomics technologies have accelerated the discovery of novel biomarkers in clinical samples. In this thesis, I explore the potential of fecal biomarkers to impact clinical and public health practice through non-invasive assessments. First, I highlight the potential of the gut microbiome to provide novel diagnostic and therapeutic targets. By analyzing the gut microbiome and metabolome of mice exposed to a high salt diet, we identified Lactobacillus as a potential probiotic to counteract salt-sensitive conditions such as high blood pressure. Next, I present preliminary validation of wipe samples as a patient-friendly alternative to standard stool collection methods, in particular for the clinical management of Inflammatory Bowel Disease patients. By comparing paired stool and wipe samples, I show that wipe samples capture the same gut microbiome profiles as standard stool samples, and can also be used to quantify fecal calprotectin. Finally, I present the first ever analysis of the microbiome and metabolome of wastewater collected from a residential neighborhood. By testing samples collected hourly over one day, we identified thousands of bacteria and metabolites derived from human activity. Glucuronide compounds that directly reflect consumption of pharmaceutical products and drugs were identified for the first time in a wastewater epidemiology study. Our results highlight the potential of testing wastewater in geo-localized residential areas to produce high-quality data to inform public health practice. Together, these results show the potential of leveraging high-throughput technologies to create seamless readouts of human and population health.
Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2018.Cataloged from PDF version of thesis.Includes bibliographical references.
DepartmentMassachusetts Institute of Technology. Computational and Systems Biology Program.
Massachusetts Institute of Technology
Computational and Systems Biology Program.