High-throughput experimental and computational tools for exploring immunity and the microbiome

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

Profiling B cell immune responses by microengraving

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (leaves 83-87).The ability to monitor an immune response in the course of vaccination or disease progression is highly desirable. Currently, no technique is able to generate a comprehensive profile of the individual cells involved and the antibodies they produce at a particular point during the immune response. The ability to obtain such detailed "snapshots" describing the immune response with a high level of resolution would have implications for diagnostics and biological discovery. Improvement in vaccination schemes, specific tailoring of anti-viral administrations, large-scale monitoring of complex latent infections in a population are all possibilities that would stem from a better understanding of the dynamics of immune responses. currently available methods for profiling of B cells that produce antigen-specific antibodies helped clarify humoral responses, but it remains a challenge to generate measurements capable of detailing the phenotypic changes and secretion patterns of individual lymphocytes. To address this need a soft lithographic approach termed microengraving ([mu]En) - previously used for the isolation and rapid selection of monoclonal antibodies[31] - was further developed and adapted to measure the affinity and isotype of secreted antibodies. The objective of this thesis was to employ microengraving in conjunction with bioinformatics analysis to obtain routinely state-based comprehensive profiles detailing cellular and humoral immune responses to antigens to the level of clonal B cells. Here I show how bioinformatics methods were employed to generate multidimensional datasets for large numbers of individual primary B cells (10² - 10⁴). These data include three characteristics of the antibodies secreted by each cell: antigenic specificity, isotype, and affinity.(cont.) These data are sufficient to classify individual cells into distinct groups of related cells using algorithms for data clustering. In a series of mice immunizations designed to mimic a multipart vaccination, I apply this method to profile the resulting B cell response with single cell resolution.by Eliseo Papa.S.M

Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs

For Artificial Intelligence to have a greater impact in biology and medicine, it is crucial that recommendations are both accurate and transparent. In other domains, a neurosymbolic approach of multi-hop reasoning on knowledge graphs has been shown to produce transparent explanations. However, there is a lack of research applying it to complex biomedical datasets and problems. In this paper, the approach is explored for drug discovery to draw solid conclusions on its applicability. For the first time, we systematically apply it to multiple biomedical datasets and recommendation tasks with fair benchmark comparisons. The approach is found to outperform the best baselines by 21.7% on average whilst producing novel, biologically relevant explanations

Characterisation of the Trichinella spiralis deubiquitinating enzyme, TsUCH37, an evolutionarily conserved proteasome interaction partner.

Trichinella spiralis is a parasitic nematode that infects mammals indiscriminately. Although the biggest impact of trichinellosis is observed in developing countries, the parasite is found on all continents except Antarctica. In humans, Trichinella infection contributes globally to helminth related morbidity and disability adjusted life years. In animals, infection is implicated as a serious agricultural problem and drug treatment is largely ineffective. During chronic infection, larvae invade skeletal muscle cells, forming a nurse cell complex in which they become encysted. The nurse cell is a product of the severe disruption of the host cell homeostasis. Proteins of the Ub/proteasome pathway are highly conserved throughout evolution, and considering their importance in the regulation of cell homeostasis, provide interesting and novel therapeutic targets for various diseases. In order to target this system in parasites, pathogen proteins that play a role in this pathway must be identified. We report the identification of the first T. spiralis deubiquitinating enzyme, and show evidence that the function of this protein as a proteasome interaction partner has been evolutionarily conserved. We show that members of this enzyme family are important for T. spiralis survival and that the use of inhibitor compounds may help elucidate their role in infection

Non-Invasive Mapping of the Gastrointestinal Microbiota Identifies Children with Inflammatory Bowel Disease

Background: Pediatric inflammatory bowel disease (IBD) is challenging to diagnose because of the non-specificity of symptoms; an unequivocal diagnosis can only be made using colonoscopy, which clinicians are reluctant to recommend for children. Diagnosis of pediatric IBD is therefore frequently delayed, leading to inappropriate treatment plans and poor outcomes. We investigated the use of 16S rRNA sequencing of fecal samples and new analytical methods to assess differences in the microbiota of children with IBD and other gastrointestinal disorders. Methodology/Principal Findings: We applied synthetic learning in microbial ecology (SLiME) analysis to 16S sequencing data obtained from i) published surveys of microbiota diversity in IBD and ii) fecal samples from 91 children and young adults who were treated in the gastroenterology program of Children’s Hospital (Boston, USA). The developed method accurately distinguished control samples from those of patients with IBD; the area under the receiver-operating-characteristic curve (AUC) value was 0.83 (corresponding to 80.3% sensitivity and 69.7% specificity at a set threshold). The accuracy was maintained among data sets collected by different sampling and sequencing methods. The method identified taxa associated with disease states and distinguished patients with Crohn’s disease from those with ulcerative colitis with reasonable accuracy. The findings were validated using samples from an additional group of 68 patients; the validation test identified patients with IBD with an AUC value of 0.84 (e.g. 92% sensitivity, 58.5% specificity). Conclusions/Significance: Microbiome-based diagnostics can distinguish pediatric patients with IBD from patients with similar symptoms. Although this test can not replace endoscopy and histological examination as diagnostic tools, classification based on microbial diversity is an effective complementary technique for IBD detection in pediatric patients.Natural Sciences and Engineering Research Council of Canada (Award NSERC PGS D)National Institutes of Health (U.S.) (1-R21-A1084032-01A1

The Tetraspanin CD82 Is Specifically Recruited to Fungal and Bacterial Phagosomes prior to Acidification▿ †

CD82 is a member of the tetraspanin superfamily, whose physiological role is best described in the context of cancer metastasis. However, CD82 also associates with components of the class II major histocompatibility complex (MHC) antigen presentation pathway, including class II MHC molecules and the peptide-loading machinery, as well as CD63, another tetraspanin, suggesting a role for CD82 in antigen presentation. Here, we observe the dynamic rearrangement of CD82 after pathogen uptake by imaging CD82-mRFP1 expressed in primary living dendritic cells. CD82 showed rapid and specific recruitment to Cryptococcus neoformans-containing phagosomes compared to polystyrene-containing phagosomes, similar to CD63. CD82 was also actively recruited to phagosomes containing other pathogenic fungi, including Candida albicans and Aspergillus fumigatus. Recruitment of CD82 to fungal phagosomes occurred independently of Toll-like receptor (TLR) signaling. Recruitment was not limited to fungi, as bacterial organisms, including Escherichia coli and Staphylococcus aureus, also induced CD82 recruitment to the phagosome. CD82 intersected the endocytic pathway used by lipopolysaccharide (LPS), implicating CD82 in trafficking of small, pathogen-associated molecules. Despite its partial overlap with lysosomal compartments, CD82 recruitment to C. neoformans-containing phagosomes occurred independently of phagosome acidification. Kinetic analysis of fluorescence imaging revealed that CD82 and class II MHC simultaneously appear in the phagosome, indicating that the two proteins may be associated. Together, these data show that the CD82 tetraspanin is specifically recruited to pathogen-containing phagosomes prior to fusion with lysosomes

Profiling antibody responses by multiparametric analysis of primary B cells

Determining the efficacy of a vaccine generally relies on measuring neutralizing antibodies in sera. This measure cannot elucidate the mechanisms responsible for the development of immunological memory at the cellular level, however. Quantitative profiles that detail the cellular origin, extent, and diversity of the humoral (antibody-based) immune response would improve both the assessment and development of vaccines. Here, we describe a novel approach to collect multiparametric datasets that describe the specificity, isotype, and apparent affinity of the antibodies secreted from large numbers of individual primary B cells (≈103-104). The antibody/antigen binding curves obtained by this approach can be used to classify closely related populations of cells using algorithms for data clustering, and the relationships among populations can be visualized graphically using affinity heatmaps. The technique described was used to evaluate the diversity of antigen-specific antibody-secreting cells generated during an in vivo humoral response to a series of immunizations designed to mimic a multipart vaccination. Profiles correlating primary antibody-producing cells with the molecular characteristics of their secreted antibodies should facilitate both the evaluation of candidate vaccines and, broadly, studies on the repertoires of antibodies generated in response to infectious or autoimmune diseases