The global pandemic of diet-related non-communicable diseases and the fact that global food production represents one of the largest contributors to greenhouse gas emissions, have identified unhealthy and unsustainably food chains as a major societal health challenge and a risk to ecosystem stability. This thesis aimed to investigate if digestion of nutritious, less highly processed foods could lead to health-promoting changes in the gut microbiota. Our modern Western-style diet (MWD) is characterized by high intake of extremely processed foods, which contain significant concentrations of inflammatory advanced glycation end-products (AGE) implicated in metabolic disease development. Novel observations in this thesis showed that chronic exposure to dietary AGE modulated gut microbiota (GM) community structure rendering it more similar to the GM previously observed in diabetic/obese mice. Further, I demonstrated that elevated systemic inflammatory markers could be mediated by AGE induced changes in GM composition. Measuring the potential of whole plant foods to improve gut health, a local broccoli ecotype (Broccolo of Torbole, BR) and Moringa oleifera were investigated using in vitro models of the human GM and intestinal epithelium. BR significantly reduced bacterial richness and evenness, increased Escherichia-Shigella relative abundance and decreased Alistipes and Ruminococcus 1. The GM extensively metabolized BR polyphenols and increased concentrations of short chain fatty acids. However, BR did not impact on intestinal permeability, using a Caco-2 monolayer model and trans-epithelial electrical resistance (TEER). This thesis provided novel insights on the fate of Moringa glucosinolates and polyphenols during faecal fermentation and on their potential beneficial activity on gut health, with glucomoringin significantly increasing TEER. Microbial communities are also involved in healthy and sustainable food production. Characterizing the successional development of local organic sauerkraut production, this thesis established a culture collection of sauerkraut lactic acid bacteria of potential future biotechnological evaluation and measured metabolite production during sauerkraut fermentation. Sauerkraut water improved immune response of a Caco-2-peripheral blood mononuclear cell (PBMC) in vitro model of the gut associated immune system upon inflammatory LPS challenge. Finally, since sustainable diets rely on sustainable and nutritious foods, I analyzed the role of the GM in improving the sustainability of farmed trout. Novel sustainable feeds containing poultry by-products (P) or insect protein (Hermetia illucens (H) meal), were investigated for their potential impact on fish growth performance, GM composition and inflammatory biomarkers. P increased the relative abundance of protein-degraders Paeniclostridium and Bacteroidales, while H increased chitin-degraders Actinomyces and Bacillus. This study also provided evidence of feed-chain microbiome transmission of Actinomyces from insect H feed to trout GM. The analysis of gut microbiomes therefore represents an innovative strategy to define healthy reference diets, to characterize the potential health effects of local and traditionally produced foods, to identify new sustainable and nutritious crops, and to drive the urgently needed transformation of the global food system. In order to obtain more sustainable, healthy and nutritious food production systems a better understanding and management of microbiomes along the food chain has never been more important.

Bisphenols are used in the process of polymerization of polycarbonate plastics and epoxy resins. Bisphenols can easily migrate out of plastic products and enter the gastrointestinal system. By increasing colonic inflammation in mice, disrupting the intestinal bacterial community structure and altering the microbial membrane transport system in zebrafish, bisphenols seem to interfere with the gut microbiome. The highly abundant human commensal bacterium Bacteroides thetaiotaomicron was exposed to bisphenols (Bisphenol A (BPA), Bisphenol F (BPF), Bisphenol S (BPS)), to examine the mode of action, in particular of BPF. All chemicals caused a concentration-dependent growth inhibition and the half-maximal effective concentration (EC50) corresponded to their individual logP values, a measure of their hydrophobicity. B. thetaiotaomicron exposed to BPF decreased membrane fluidity with increasing BPF concentrations. Physiological changes including an increase of acetate concentrations were observed. On the proteome level, a higher abundance of several ATP synthase subunits and multidrug efflux pumps suggested an increased energy demand for adaptive mechanisms after BPF exposure. Defense mechanisms were also implicated by a pathway analysis that identified a higher abundance of members of resistance pathways/strategies to cope with xenobiotics (i.e., antibiotics). Here, we present further insights into the mode of action of bisphenols in a human commensal gut bacterium regarding growth inhibition, and the physiological and functional state of the cell. These results, combined with microbiota-directed effects, could lead to a better understanding of host health disturbances and disease development based on xenobiotic uptake.

Plant roots are associated with a diverse microbial community of bacteria and fungi. The root microbiome communities associate with the root surface or penetrate the superficial layers of root tissues. It is of high agronomic interest to characterize root microbiomes and identify microbes with beneficial effects on plant defense against pathogens. Soybean taproot decline (TRD) is an emerging soil-borne disease caused by a fungus classified as Xylaria necrophora, challenging to control and with devastating effects on yield. Our group has initiated a study of the soybean root microbiome with three primary experiments: (I) perform a comparative study of root microbiomes and mycobiomes from healthy and TRD-affected soybean, (II) isolate, screen and (III) identify soybean root bacterial endophytes with anti-X. necrophora and anti-TRD properties. Our long-term objective is to discover and implement microbiome-based management techniques that enhance the health and yield of soybeans.

Archaeological dental calculus represents a depositional environment that entraps oral microbes, and debris of dietary, environmental, and cultural material that entered the mouth throughout the host’s life. Hence, they represent valuable archives of information about the host’s lifestyle, health, and environment. The aim of this study was to identify if the farming transition and its’ associated change in diet composition, may have influenced species composition in the oral cavity. To shed some light into the evolution of ancient oral microbiomes from Africa, 3 novel Iron Age dental calculus metagenomes together with a comparative dataset of 18 archaeological dental calculus metagenomes from North African Upper Palaeolithic, Later Stone Age, Iron Age, and 18th-19th century populations where analysed. Shotgun sequencing data was used to reconstruct 21 oral metagenomes from the past 15,000 years. This study found an oral microbiome that has been maintained from the Upper Palaeolithic (North Africa) to the 19th Century. However, closer examination to the relative abundance of three keystone species of the subgingival plaque, portray a chronological evolution that reflects that of its host during the major dietary and cultural transition that occurred during the farming revolution in the Iron Age.

Obesity is a common disease among all ages that has threatened human health and has become a global concern. Gut microbiota can affect human metabolism and thus may modulate obesity. Certain mixes of gut microbiota can protect the host to be healthy or predispose the host to obesity. Modern next-generation sequencing technique allows accessing huge amount of genetic information underlying microbiota and thus provides new insights into the functionality of these micro-organisms and their interactions with the host. Multiple previous studies have demonstrated that the microbiome might contribute to obesity by increasing dietary energy harvest, promoting fat deposition and triggering systemic inflammation. However, these researches are either based on lab cultivation studies or basic statistical analysis. In order to further explore how gut microbiota affect obesity, this thesis utilize a series of machine learning methods to analyze large amount of metagenomics data from human gut microbiome. The publicly available HMP (Human Microbiome Project) metagenomic sequencing data, contain microbiome data for healthy adults, including overweight and obese individuals, were used for this study. HMP gut data were organized based on two different feature definitions: taxonomic information and metabolic reconstruction information. Several widely used classification algorithms: namely Naive Bayes, Random Forest, SVM and elastic net logistic regression were applied to predict healthy or obese status of the subjects based on the cross-validation accuracy. Furthermore, the corresponding feature selection algorithms were used to identify signature features in each dataset that lead to the differences between healthy and obese samples. The results showed that these algorithms perform poorly on taxonomic data than metabolic pathway data though lots of selected taxa are still supported by literature. Among all the combinations between different algorithms and data, elastic net logistic regression has the best cross-validation performance and thus becomes the best model. In this model, several important features are found and some of these are consistent with the previous studies. Rerunning classifiers by using features selected by elastic net logistic regression again further improved the performance of the classifiers. On the other hand, this study uncovered some new features that haven't been supported by previous studies. The new features could also be the potential target to distinguish obese and healthy subjects. The present thesis work compares the strengths and weaknesses of different machine learning techniques with different types of features originating from the same metagenomics data. The features selected by these models could provide a deep understanding of the metabolic mechanisms of micro-organisms. It is therefore worth to comprehensively understand the differences of gut microbiota between healthy and obese subjects, and particularly how gut microbiome affects obesity.

The relationship between biodiversity and ecosystem functioning (BEF) is a central issue in soil and microbial ecology. To date, most belowground BEF studies focus on the diversity of microbes analyzed by barcoding on total DNA, which targets both active and inactive microbes. This approach creates a bias as it mixes the part of the microbiome currently steering processes that provide actual ecosystem functions with the part not directly involved. Using experimental extensive grasslands under current and future climate, we used the bromodeoxyuridine (BrdU) immunocapture technique combined with pair-end Illumina sequencing to characterize both total and active microbiomes (including both bacteria and fungi) in the rhizosphere of Trifolium pratense. Rhizosphere function was assessed by measuring the activity of three microbial extracellular enzymes (β-glucosidase, N-acetyl-glucosaminidase, and acid phosphatase), which play central roles in the C, N, and P acquisition. We showed that the richness of overall and specific functional groups of active microbes in rhizosphere soil significantly correlated with the measured enzyme activities, while total microbial richness did not. Active microbes of the rhizosphere represented 42.8 and 32.1% of the total bacterial and fungal taxa, respectively, and were taxonomically and functionally diverse. Nitrogen fixing bacteria were highly active in this system with 71% of the total operational taxonomic units (OTUs) assigned to this group detected as active. We found the total and active microbiomes to display different responses to variations in soil physicochemical factors in the grassland, but with some degree of resistance to a manipulation mimicking future climate. Our findings provide critical insights into the role of active microbes in defining soil ecosystem functions in a grassland ecosystem. We demonstrate that the relationship between biodiversity-ecosystem functioning in soil may be stronger than previously thought.

Tropical coral reefs are highly productive ecosystems thriving in oligotrophic waters, a phenomenon facilitated by efficient but delicate biogeochemical cycling within reef communities. Global climate change and local stressors are driving phase shifts from coral- to non-calcifier-dominated states in reefs worldwide, substantially altering reef biogeochemical functioning. While major benthic players such as coral and macroalgae have been investigated in detail regarding carbon and nutrient dynamics, the less conspicuous “reef cryptobiome” (sensu Carvalho et al., 2019) – comprising most of reef diversity – has only recently gained attention. Autonomous Reef Monitoring Structures (ARMS) have recently been developed to sample coral reef cryptobenthic communities in a non-destructive and standardised way, allowing exploration of these often overlooked biota. Here, 16 ARMS were deployed for seven months in four distinct habitats dominated by different benthic players (i.e., four units per habitat) in a nearshore Red Sea coral reef to investigate the cryptobiome associated with proxies of varying benthic states. Two of these habitats were coral-dominated, and one each dominated by turf algae or coral rubble. To assess the biogeochemical fluxes of pioneering cryptobenthic communities, ARMS were incubated in situ prior to retrieval using customised chambers. Subsequently, 16S rRNA gene amplicon and shotgun metagenomic sequencing of the ARMS sessile (i.e., encrusting) fractions were performed to link observed fluxes with prokaryotic taxonomic and functional profiles, particularly regarding nitrogen cycling. The results show that the pioneering cryptobiome represents a significant source of inorganic nutrients and that its associated microbial communities facilitate the mineralisation and assimilation of organic matter and provide crucial genetic functional pathways for nitrogen cycling. Functional similarities among habitats suggested functional redundancy despite variation in bacterial community composition. Hence, the reef cryptobiome can be considered an important biogeochemical player in coral reefs, actively shaping the abiotic conditions within niches of the reef framework and driving the recruitment and persistence of crytobenthic and other reef organisms. As communities associated with the algae-dominated reef habitat were most distinct compositionally and biogeochemically, and as non-calcifiers are becoming more dominant in many reefs, this has implications for intensifying phase shifts in coral reefs worldwide. Future ARMS studies will also benefit from adjustment of sample processing and molecular protocols, resulting in higher sample throughput and lower costs in times of increased application of ARMS.

Using a novel, adipose-liver, two-organ, human-on-a-chip system, the metabolic disease non-alcoholic fatty liver disease was modeled. This model was then used to test the effects of the gut microbiome on NAFLD progression. Two products of the gut microbiome, Trimethylamine-n-oxide and butyrate, were selected as representatives of potentially harmful and potentially beneficial compounds. A dose response, adipocyte and hepatocyte monocultures controls, and HoaC systems were run for 14 days. Through this experimentation, it was found that a dysbiosis of the gut microbiome could be influencing NAFLD progression. Additionally, further development and discovery regarding adipose-liver systems was added to the ongoing conversation of HoaC systems and their usages.

Cystic fibrosis (CF) is an autosomal recessive genetic disorder involving a mutation in the CF transmembrane conductance regulator protein (CFTR), which causes dysfunctional transport of chloride ions across cell membranes. CF affects multiple body systems and a few of its symptoms include chronic cough, difficulty breathing, obstructive airway disease, bacterial pulmonary infections, maldigestion, malabsorption, pancreatitis, and male infertility. Until recently, treatment options have been limited to alleviating symptoms, but a new classification of drugs, CFTR modulators, provide an opportunity to slow the progression of the disease and improve clinical outcomes. The effect of CFTR modulators may be attributed to the reduction of persistently colonizing bacteria in CF lungs. Though, the effects of modulators on microbial communities colonizing the CF lung remains unknown, specifically with common respiratory pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. Particularly, previous CF studies have been limited in scope due to focusing on only one type of modulator and by using low-yield sequencing techniques. To address this gap, we seek to study the changes in CF respiratory pathogens of patients initiating CFTR modulator therapy at Nemours Hospital using long-read metagenomic sequencing (Oxford Nanopore) of longitudinally collected respiratory samples. We have optimized a protocol for host DNA depletion and microbial metagenomic sequencing to characterize the respiratory microbiome. This study focuses on utilizing these sequencing data to compare the microbiome among two healthy controls to pre-CFTR-treatment microbial communities of two recruited pediatric CF patients.

Metagenomics is a cultivation-independent approach for obtaining the genomic composition of microbial communities. Microbial communities are ubiquitous in nature. Microbes which are associated with the human body play important roles in human health and disease. These roles span from protecting us against infections from other bacteria, to being the causes of these diseases. A deeper understanding of these communities and how they function inside our bodies allows for advancements in treatments and preventions for these diseases. Recent developments in metagenomics have been driven by the emergence of Next-Generation Sequencing technologies and Third-Generation Sequencing technologies that have enabled cost-effective DNA sequencing and the generation of large volumes of genomic data. These technologies have allowed for the introduction of hybrid DNA assembly techniques to recover the genomes of the constituent microbes. While Next-Generation Sequencing technologies use paired-end sequencing reads from DNA fragments into short reads and have a relatively lower error rate, Third-Generation Sequencing technologies use much longer DNA fragments to generate longer reads, bringing contigs together for larger scaffolds with a higher error rate. Hybrid assemblers leverage both short and long read sequencing technologies and can be a critical step in the advancements of metagenomics, combining these technologies to allow for longer assemblies of DNA with lower error rates. We evaluate the strengths and weaknesses of the hybrid assembly framework using several state-of-the-art assemblers and simulated human microbiome datasets. Our work provides insights into metagenomic assembly and genome recovery, an important step towards a deeper understanding of the microbial communities that influence our well-being.