Development and analysis of individual-based gut microbiome metabolic models

The human gut microbiota plays a large role in the metabolism of our diet. These microorganisms can break down indigestible materials such as polysaccharides and convert them into metabolites that the human body can take up and utilize (e.g., vitamins, essential amino acids, and short-chain fatty acids). Disbalances in the gut microbiome have been associated with several diseases, including diabetes and obesity. However, little is known about the detailed metabolic crosstalk that occurs between individual organisms within the microbiome and between the microbiome and the human intestinal cells. Because of the complexity of the intestinal ecosystem, these interactions are difficult to determine using existing experimental methods. Constraint-based reconstruction and analysis (COBRA) can help identify the possible metabolic mechanisms at play in the human gut. By combining mathematical, computational, and experimental methods, we can generate hypotheses and design targeted experiments to elucidate the metabolic mechanisms in the gut microbiome. In this thesis, I first applied comparative genomics to analyze the biosynthesis pathways of eight B-vitamins in hundreds of human gut microbial species. The results suggested that many gut microbes do not synthesize any B-vitamins, that is, they depend on the host’s diet and neighboring bacteria for these essential nutrients. Second, I developed a semi-automatic reconstruction refinement pipeline that quickly generates biologically relevant genome-scale metabolic reconstructions (GENREs) of human gut microbes based on automatically generated metabolic reconstructions, comparative genomics data, and data extracted from biochemical experiments on the relevant organisms. The pipeline generated metabolically diverse reconstructions that maintain high accuracy with known biochemical data. Finally, the refined GENREs were combined with metagenomic data from individual stool samples to build personalized human gut microbiome metabolic reconstructions. The resulting large-scale microbiome models were both taxonomically and functionally diverse. The work presented in this thesis has enabled the generation of biologically relevant human gut microbiome metabolic reconstructions. Metabolic models resulting from such reconstructions can be applied to study metabolism within the human gut microbiome and between the gut microbiome and the human host. Additionally, they can be used to study the effects of different dietary components on the metabolic exchanges in the gut microbiome and the metabolic differences between healthy and diseased microbiomes

The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. To address this gap, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox. The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox

LT-K63 Enhances B Cell Activation and Survival Factors in Neonatal Mice That Translates Into Long-Lived Humoral Immunity

Funding text 1 AA was a recipient of a doctoral study grant from the University of Iceland Research Fund (2015–18). This study was financially supported by grants from the Icelandic Research Fund (130675051-53), The University of Iceland Research Fund (2014–17) and the Landspitali Science Fund A-2015-083, A2015-084, A-2016-067, A-2017-069. Funding text 2 Part of the work presented in this paper was presented as posters at the European Congress of Immunology, Vienna, Austria, 6?9. September 2015 (abstract no P.A.27.14 and P.A.27.15) and at V?sindi a? hausti, scientific conference at Landsp?tali - the National University Hospital of Iceland, Reykjav?k, 7. October 2020 (abstract number 8). Funding. AA was a recipient of a doctoral study grant from the University of Iceland Research Fund (2015?18). This study was financially supported by grants from the Icelandic Research Fund (130675051-53), The University of Iceland Research Fund (2014?17) and the Landspitali Science Fund A-2015-083, A2015-084, A-2016-067, A-2017-069. Publisher Copyright: © Copyright © 2020 Aradottir Pind, Molina Estupiñan, Magnusdottir, Del Giudice, Jonsdottir and Bjarnarson.Adjuvants enhance magnitude and duration of immune responses induced by vaccines. In this study we assessed in neonatal mice if and how the adjuvant LT-K63 given with a pneumococcal conjugate vaccine, Pnc1-TT, could affect the expression of tumor necrosis factor receptor (TNF-R) superfamily members, known to be involved in the initiation and maintenance of antibody responses; B cell activating factor receptor (BAFF-R) and B cell maturation antigen (BCMA) and their ligands, BAFF, and a proliferation inducing ligand (APRIL). Initially we assessed the maturation status of different B cell populations and their expression of BAFF-R and BCMA. Neonatal mice had dramatically fewer B cells than adult mice and the composition of different subsets within the B cell pool differed greatly. Proportionally newly formed B cells were most abundant, but they had diminished BAFF-R expression which could explain low proportions of marginal zone and follicular B cells observed. Limited BCMA expression was also detected in neonatal pre-plasmablasts/plasmablasts. LT-K63 enhanced vaccine-induced BAFF-R expression in splenic marginal zone, follicular and newly formed B cells, leading to increased plasmablast/plasma cells, and their enhanced expression of BCMA in spleen and bone marrow. Additionally, the induction of BAFF and APRIL expression occurred early in neonatal mice immunized with Pnc1-TT either with or without LT-K63. However, BAFF+ and APRIL+ cells in spleens were maintained at a higher level in mice that received the adjuvant. Furthermore, the early increase of APRIL+ cells in bone marrow was more profound in mice immunized with vaccine and adjuvant. Finally, we assessed, for the first time in neonatal mice, accessory cells of the plasma cell niche in bone marrow and their secretion of APRIL. We found that LT-K63 enhanced the frequency and APRIL expression of eosinophils, macrophages, and megakaryocytes, which likely contributed to plasma cell survival, even though APRIL+ cells showed a fast decline. All this was associated with enhanced, sustained vaccine-specific antibody-secreting cells in bone marrow and persisting vaccine-specific serum antibodies. Our study sheds light on the mechanisms behind the adjuvanticity of LT-K63 and identifies molecular pathways that should be triggered by vaccine adjuvants to induce sustained humoral immunity in early life.Peer reviewe

A comparative study of adjuvants effects on neonatal plasma cell survival niche in bone marrow and persistence of humoral immune responses

Funding Information: AP was a recipient of a doctoral study grant from the University of Iceland Research Fund (2015-18). This study was financially supported by grants from the Icelandic Research Fund (130675051-53), The University of Iceland Research Fund (2018-20) and the Landspitali Science Fund (A-2017-068, A-2017-069, A-2018-076, A-2018-077, A-2019-084). Publisher Copyright: Copyright © 2022 Aradottir Pind, Thorsdottir, Magnusdottir, Meinke, Del Giudice, Jonsdottir and Bjarnarson. Copyright © 2022 Aradottir Pind, Thorsdottir, Magnusdottir, Meinke, Del Giudice, Jonsdottir and Bjarnarson.The neonatal immune system is distinct from the immune system of older individuals rendering neonates vulnerable to infections and poor responders to vaccination. Adjuvants can be used as tools to enhance immune responses to co-administered antigens. Antibody (Ab) persistence is mediated by long-lived plasma cells that reside in specialized survival niches in the bone marrow, and transient Ab responses in early life have been associated with decreased survival of plasma cells, possibly due to lack of survival factors. Various cells can secrete these factors and which cells are the main producers is still up for debate, especially in early life where this has not been fully addressed. The receptor BCMA and its ligand APRIL have been shown to be important in the maintenance of plasma cells and Abs. Herein, we assessed age-dependent maturation of a broad range of bone marrow accessory cells and their expression of the survival factors APRIL and IL-6. Furthermore, we performed a comparative analysis of the potential of 5 different adjuvants; LT-K63, mmCT, MF59, IC31 and alum, to enhance expression of survival factors and BCMA following immunization of neonatal mice with tetanus toxoid (TT) vaccine. We found that APRIL expression was reduced in the bone marrow of young mice whereas IL-6 expression was higher. Eosinophils, macrophages, megakaryocytes, monocytes and lymphocytes were important secretors of survival factors in early life but undefined cells also constituted a large fraction of secretors. Immunization and adjuvants enhanced APRIL expression but decreased IL-6 expression in bone marrow cells early after immunization. Furthermore, neonatal immunization with adjuvants enhanced the proportion of plasmablasts and plasma cells that expressed BCMA both in spleen and bone marrow. Enhanced BCMA expression correlated with enhanced vaccine-specific humoral responses, even though the effect of alum on BCMA was less pronounced than those of the other adjuvants at later time points. We propose that low APRIL expression in bone marrow as well as low BCMA expression of plasmablasts/plasma cells in early life together cause transient Ab responses and could represent targets to be triggered by vaccine adjuvants to induce persistent humoral immune responses in this age group.Peer reviewe

Short Vi-polysaccharide abrogates T-independent immune response and hyporesponsiveness elicited by long Vi-CRM197 conjugate vaccine

Publisher Copyright: © 2020 National Academy of Sciences. All rights reserved.Polysaccharide-protein conjugates have been developed to overcome the T-independent response, hyporesponsiveness to repeated vaccination, and poor immunogenicity in infants of polysaccharides. To address the impact of polysaccharide length, typhoid conjugates made with short- and long-chain fractions of Vi polysaccharide with average sizes of 9.5, 22.8, 42.7, 82.0, and 165 kDa were compared. Long-chain-conjugated Vi (165 kDa) induced a response in both wild-type and T cell-deficient mice, suggesting that it maintains a T-independent response. In marked contrast, short-chain Vi (9.5 to 42.7 kDa) conjugates induced a response in wild-type mice but not in T cell-deficient mice, suggesting that the response is dependent on T cell help. Mechanistically, this was explained in neonatal mice, in which long-chain, but not short-chain, Vi conjugate induced late apoptosis of Vi-specific B cells in spleen and early depletion of Vi-specific B cells in bone marrow, resulting in hyporesponsiveness and lack of long-term persistence of Vi-specific IgG in serum and IgG+ antibody-secreting cells in bone marrow. We conclude that while conjugation of long-chain Vi generates T-dependent antigens, the conjugates also retain T-independent properties, leading to detrimental effects on immune responses. The data reported here may explain some inconsistencies observed in clinical trials and help guide the design of effective conjugate vaccines.Peer reviewe

The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community

Modeling metabolism of the human gut microbiome

The human gut microbiome plays an important part in human health. The complexity of the microbiome makes it difficult to determine the detailed metabolic functions and cross-talk occurs between the individual species. In silico systems biology studies of the microbiome can help to identify metabolite exchanges among gut microbes. Constraint-based reconstruction and analysis methods use biochemically accurate genome-scale metabolic networks of microorganisms to simulate metabolism between species in a given microbiome and help generate novel hypotheses on microbial interactions. Here, we review metabolic modeling studies that have investigated metabolic functions of the gut microbiome

Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes

The human gut microbiota supplies its host with essential nutrients, including B-vitamins. Using the PubSEED platform, we systematically assessed the genomes of 256 common human gut bacteria for the presence of biosynthesis pathways for eight B-vitamins: biotin, cobalamin, folate, niacin, pantothenate, pyridoxine, riboflavin, and thiamin. On the basis of the presence and absence of genome annotations, we predicted that each of the eight vitamins was produced by 40–65% of the 256 human gut microbes. The distribution of synthesis pathways was diverse; some genomes had all eight biosynthesis pathways, whereas others contained no de novo synthesis pathways. We compared our predictions to experimental data from 16 organisms and found 88% of our predictions to be in agreement with published data. In addition, we identified several pairs of organisms whose vitamin synthesis pathway pattern complemented those of other organisms. This analysis suggests that human gut bacteria actively exchange B-vitamins among each other, thereby enabling the survival of organisms that do not synthesize any of these essential cofactors. This result indicates the co-evolution of the gut microbes in the human gut environment. Our work presents the first comprehensive assessment of the B-vitamin synthesis capabilities of the human gut microbiota. We propose that in addition to diet, the gut microbiota is an important source of B-vitamins, and that changes in the gut microbiota composition can severely affect our dietary B-vitamin requirements

Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires

Background: The human gastrointestinal tract harbors a diverse microbial community, in which metabolic phenotypes play important roles for the human host. Recent developments in meta-omics attempt to unravel metabolic roles of microbes by linking genotypic and phenotypic characteristics. This connection, however, still remains poorly understood with respect to its evolutionary and ecological context. Results: We generated automatically refined draft genome-scale metabolic models of 301 representative intestinal microbes in silico. We applied a combination of unsupervised machine-learning and systems biology techniques to study individual and global differences in genomic content and inferred metabolic capabilities. Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups. Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed. This finding was further substantiated by the metabolic divergence within different genera. In particular, we could distinguish three sub-type clusters based on membrane and energy metabolism within the Lactobacilli as well as two clusters within the Bifidobacteria and Bacteroides. Conclusions: We demonstrate that phenotypic differentiation within closely related species could be explained by their metabolic repertoire rather than their phylogenetic relationships. These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome