Thesis (Ph. D.)--Harvard-MIT Program in Health Sciences and Technology, 2012. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (p. 165-[181]). / Humans live in association with trillions of microbes and yet we know remarkably little about their symbiotic relationship. The role these microorganisms have in humans has been characterized only in the case of few bacteria and much less is understood about the dynamic of this relationship. Lately, the mass sequencing efforts accompanying the Human Microbiome Project have begun to uncover the composition of these different microbial niches, and shed light on some the effects they have on their host. The immune system largely determines the composition of bacterial populations living in association with humans. It lights off pathogens while allowing specific bacteria to colonize the body. However, immune system and microbiota appear even more intimately connected than previously imagined. Recent evidence shows that interaction with the associated microbiota is necessary for the proper development of the immune response throughout life. The interface with commensal microbes is notoriously difficult to probe experimentally, due to the diversity of its composition, which makes differentiating the individual ramifications of each associated microbe a much harder task. To understand the complex relationship between the human immune system and microbiome, we need methodologies that can simultaneously probe both in a high throughput fashion, as well as analysis tools to cope with the large amount of resulting data. Herein I present the development of immune mass screening tools capable of comprehensively profiling the antibody-mediated and cell-mediated immune response to microbes. I employ microfluidics techniques to describe the response of single immune cells at high-resolution and in a physiologically relevant environment. I also present the application of machine learning to gut microbiome data and demonstrate how it can be used to differentiate between diseased and healthy individuals in an IBD patient cohort and to allows to deal with the complexity of microbial community data. Moving forward, the goal is to combine these approaches to map how changes in the immune response affects microbiome composition and vice versa. In turn, characterizing this interplay will contribute to our understanding of how bacteria shape our homeostasis and health, facilitating the prediction of which imbalances may lead to disease. / by Eliseo Papa. / Ph.D.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/72653 |
| Date | January 2012 |
| Creators | Papa, Eliseo |
| Contributors | Eric J. Alm., Harvard--MIT Program in Health Sciences and Technology., Harvard--MIT Program in Health Sciences and Technology. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 165, [16] p., application/pdf |
| Rights | M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission., http://dspace.mit.edu/handle/1721.1/7582 |
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