El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Serum samples collected from 88 Peruvians with unexplained fever were analyzed for viral sequences using metagenomics. Nucleic acids of anelloviruses, pegivirus A (GBV-C), HIV, Dengue virus, and Oropouche virus were detected. We also characterized from two sera the RNA genomes of new species of partitivirus and dicistrovirus belonging to viral families known to infect fungi or arthropod, respectively. Genomic DNA of a putative fungal cellular host could be PCR amplified from the partitivirus-containing serum sample. The detection in human serum of nucleic acids from viral families not known to infect vertebrates may indicate contamination during sample collection and aliquoting or human infection by their presumed cellular host, here a fungus. The role, if any, of the non-vertebrate infecting viruses detected in serum in inducing fever is unknown. / Revisión por pares

Yes / Assigning metagenomic reads to taxa presents significant challenges. Existing approaches address some issues, but are mostly limited to metabarcoding or optimized for microbial data. We present PIA (Phylogenetic Intersection Analysis): a taxonomic binner that works from standard BLAST output while mitigating key effects of incomplete databases. Benchmarking against MEGAN using sedaDNA suggests that, while PIA is less sensitive, it can be more accurate. We use known sequences to estimate the accuracy of PIA at up to 96% when the real organism is not represented in the database. For ancient DNA, where taxa of interest are frequently over-represented domesticates or absent, poorly-known organisms, more accurate assignment is critical, even at the expense of sensitivity. PIA offers an approach to objectively filter out false positive hits without the need to manually remove taxa and so make presuppositions about past environments and their palaeoecologies. / European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (ERC funded project no. 670518 LOST FRONTIER

Background: Studying the infant gut microbiome during the period of solid food introduction may provide valuable insight into gut colonization, microbial evolution, and the ecological role of bacterial metabolic pathways in microbial succession. However, since infant gut microbial communities are made of bacterial genera with high relative abundance, within-genus and within-species diversity, the efficacy of current computational tools in elucidating strain-specific differences is not known. Methods: 34 infant gut metagenomic samples were simulated with the CAMI-Simulator, using 16S rRNA gene profiles from subjects of the Baby & Mi study as a reference. Raw simulated reads were trimmed, assembled, and binned into metagenome-assembled genomes (MAGs) using mg_workflow, a Snakemake-based pipeline of current metagenomic analysis protocols. Results were compared to gold-standard references in order to benchmark the success of current computational methods in retrieving strain-level MAGs from the gut, and in predicting bacterial carbohydrate active enzymes. Real metagenomic samples from the Baby, Food & Mi cohort were processed through the bfm_mg_flow pipeline to study the taxonomic and metabolic changes in the infant gut microbiome during the solid food introduction period. Post-pipeline analyses were conducted in R. Results: Misassemblies were significantly impacted by sample community composition, including Shannon diversity, number of strains in the sample, and relative abundance of the most dominant strain. MAG completeness, contamination, quality, and reference coverage were significantly impacted by choice of assembly software, and choice of single- or co-sample assembly. Different assemblies yielded different MAGs from the same samples. Reference coverage of MAGs recovered from co-assemblies were lower than for those from single assemblies and CAZyme predictions were more accurate from MetaSPAdes than from MEGAHIT assemblies at both the assembly-level and the MAG-level. Based on these results, we propose the MetAGenomic PIpelinE (MAGPIE), with recommendations for ensemble methods for assembly, binning, and gene predictions. Using these methods, we identified changes in microbial community composition before and after solid food introduction in real Baby & Mi infant gut samples. These changes included an increase in bacteria that can digest a wide variety of carbohydrates, such as Bacteroides, and a decrease in Bifidobacterium. Conclusions: In this study, we characterized the current state of tools for genome-resolved metagenomics, and contributed a framework to tailor metagenomic data analysis for the unique composition of the infant gut microbiome. We further used this framework to study bacterial metabolism in the infant gut microbiome before and after the introduction of solid foods. / Thesis / Master of Science (MSc) / Solid food introduction to the infant diet brings new glycans to the gut environment, driving the selection of bacteria that are able to digest these compounds. Studying the gut microbiome during this timepoint is essential to deciphering how and when beneficial bacteria colonize, how they evolve, and how the infant gut matures to an adult-like state. A widely used method to characterize microbial identity and metabolic function in the gut is metagenomic sequencing. However, dominant bacterial genera in the infant gut often have multiple closely related species and strains, making it difficult to decipher the essential metabolic differences between them. In this study, we simulated an infant gut metagenomic dataset to understand how the structure of the infant gut impacts commonly used metagenomic tools, and to quantify the quality of genomes and metabolic predictions at the end of common metagenomic analyses. We found that gut microbial community composition and metagenomic assembler choice both impact the quality of final genomes retrieved from the data, and the accuracy of metabolic gene predictions. Based on these results, we make several recommendations to use ensemble methods to improve metagenomic data analysis, and additionally propose a metagenomic pipeline to analyze infant gut data over the period of solid food introduction.

Postmortem microbial communities are being extensively studied for their utility in forensic science investigations. Microbial communities associated with decomposition, necrobiome, have been shown to react in a predictable manner to the postmortem interval or time since death. These communities are affected by environmental factors such as temperature and humidity which can cause variabilities in the community structural and functional turnover. However, the transmigration patterns across organs and functional activity as decomposition progresses is still relatively unknown in a highly controlled system. This study aims to describe the community structural changes that take place during a highly controlled decomposition in mice along with one of the first representations of visualizing transmigration and detecting functional pathway differences between postmortem times. Although, postmortem microbial communities have been viewed under the scope of forensics, there are also extended uses of early postmortem microbiome communities that represent the antemortem microbiome for health research. Additionally, in this study we aim to provide evidence for the use of the early postmortem microbiome as a public health surveillance tool by detecting antibiotic resistance determinants with their corresponding bacterial genera in human autopsies. These results have provided important baseline microbial community structure and function data for forensic research in murine models and have identified antibiotic resistance determinants of high public health concern in human autopsy samples.

Metagenomics is the study of the totality of the complete genetic elements discovered from a defined environment. Different from traditional microbiology study, which only analyzes a small percent of microbes that could survive in laboratory, metagenomics allows researchers to get entire genetic information from all the samples in the communities. So metagenomics enables understanding of the target environments and the hidden relationships between bacteria and diseases. In order to efficiently analyze the metagenomics data, cutting-edge technologies for analyzing the relationships among microbes and communities are required. To overcome the challenges brought by rapid growth in metagenomics datasets, advances in novel methodologies for interpreting metagenomics data are clearly needed. The first two chapters of this dissertation summarize and compare the widely-used methods in metagenomics and integrate these methods into pipelines. Properly analyzing metagenomics data requires a variety of bioinformatcis and statistical approaches to deal with different situations. The raw reads from sequencing centers need to be processed and denoised by several steps and then be further interpreted by ecological and statistical analysis. So understanding these algorithms and combining different approaches could potentially reduce the influence of noises and biases at different steps. And an efficient and accurate pipeline is important to robustly decipher the differences and functionality of bacteria in communities. Traditional statistical analysis and machine learning algorithms have their limitations on analyzing metagenomics data. Thus, rest three chapters describe a new phylogeny based machine learning and feature selection algorithm to overcome these problems. The new method outperforms traditional algorithms and can provide more robust candidate microbes for further analysis. With the frowing sample size, deep neural network could potentially describe more complicated characteristic of data and thus improve model accuracy. So a deep learning framework is designed on top of the shallow learning algorithm stated above in order to further improve the prediction and selection accuracy. The present dissertation work provides a powerful tool that utilizes machine learning techniques to identify signature bacteria and key information from huge amount of metagenomics data.

Recent developments in DNA sequencing technology allow the study of microbial ecology at unmatched detail. To fully embrace this revolution, an important avenue of research is the development of bioinformatic tools that enable scientists to leverage and manipulate the exceedingly large amounts of data produced. In this thesis, several bioinformatic tools were developed in order to process and analyze metagenomic sequence data. Subsequently, the tools were applied to the study of microbial biogeography and microbial systems biology. A targeted metagenomics pipeline automating quality filtering, joining and taxonomic annotation was developed to assess the diversity of bacteria, archaea and eukaryotes permitting the study of biogeographic patterns in great detail. Next, a second software package which provides annotation based on environmental ontology terms was coded aiming to exploit the cornucopia of information available in public databases. It was applied to resource tracking, paleontology, and biogeography. Indeed, both these tools have already found broad applications in extending our understanding of microbial diversity in inland waters and have contributed to the development of conceptual frameworks for microbial biogeography in lotic systems. The programs were used for analyzing samples from several environments such as alkaline soda lakes and ancient sediment cores. These studies corroborated the view that the dispersal limitations of microbes are more or less non-existant as environmental properties dictating their distribution and that dormant microbes allow the reconstruction of the origin and history of the sampled community. Furthermore, a shotgun metagenomics analysis pipeline for the characterization of total DNA extraction from the environment was put in place. The pipeline included all essential steps from raw sequence processing to functional annotation and reconstruction of prokaryotic genomes. By applying this tool, we were able to reconstruct the biochemical processes in a selection of systems representative of the tens of millions of lakes and ponds of the boreal landscape. This revealed the genomic content of abundant and so far undescribed prokaryotes harboring important functions in these ecosystems. We could show the presence of organisms with the capacity for photoferrotrophy and anaerobic methanotrophy encoded in their genomes, traits not previously detected in these systems. In another study, we showed that microbes respond to alkaline conditions by adjusting their energy acquisition and carbon fixation strategies. To conclude, we demonstrated that the "reverse ecology" approach in which the role of microbes in elemental cycles is assessed by genomic tools is very powerful as we can identify novel pathways and obtain the partitioning of metabolic processes in natural environments.

La peau l’un des plus grands organes du corps humain avec une superficie moyenne de 1,5m2 à 2m2 est à l’interface avec le monde extérieur et régule les échanges entre les deux milieux. Avec ses nombreuses invaginations et des apports nutritifs constants cet écosystème favorise la colonisation par des microorganismes. Les études taxonomiques basées sur le séquençage du gène rrs après amplification PCR à partir de l’ADN extrait ont permis de découvrir la diversité des différentes populations microbiennes qui colonisent la peau humaine dont peu sont cultivables in vitro. A côté des espèces communes à tous les individus une certaine spécificité individuelle a été trouvée de même qu’il a été montré que la surface du corps humain se différencie en régions avec des spécificités physico-chimiques propres pour lesquelles des correspondances taxonomiques du microbiote ont été détectées. Ce travail de thèse, réalisé dans le cadre d’un contrat CIFRE avec la société LibraGen a eu pour but d’aborder le volet fonctionnel du microbiote cutané grâce à l’application de l’approche séquençage haut débit de l’ADN metagénomique bactérien extrait de la peau humaine. Cet objectif a nécessité le développement d’une méthode de prélèvement‐extraction de l’ADN afin de remédier aux contraintes spécifiques de l’écosystème étudié, une faible densité microbienne et la putative présence de contamination par l’ADN humain. Les données de séquence obtenues nous ont permis de caractériser le potentiel fonctionnel du microbiote cutané des différents sites cutanés et, par comparaisons inter-environnementales de déterminer les fonctions spécifiquement rencontrées dans ce microbiote lié à l’homme. Les applications de ces travaux sont importantes comme par exemple la démonstration d’un effet sur le microbiote d’une application quotidienne d’onctions qui modifie tant la diversité taxonomique que le potentiel fonctionnel du microbiote cutané. / Skin is one of the largest human organs, with an average surface of 1.8 m2. At the interface between the human body and its external environment, skin is continually exposed to chemical and biological aggression and provides efficient protection of the human body. Its appearance is a good indicator of internal health, as skin reacts very rapidly to any change. Historically, skin microbiology was limited to the study of microorganisms isolated from skin pathologies, including those that provoke the pathological state and those that result from the pathology. However, the presence of skin microorganisms extends far beyond these pathological aspects, as non-pathological bacteria are detected everywhere on the skin, with up to 107 bacterial cells by cm2 in some areas. Skin is not a sterile human organ, but acts as an ecological niche for commensal microor- ganisms whose presence participates in protection against pathogens by preventing their colonization. Skin microbiology had been studied long before consid- ering the commensal skin bacteria, the majority of which could not be cultivated in vitro and the role of which was neglected until these new approaches, based on amplification and sequencing of DNA directly extracted from skin, were applied. Metagenomics revolutionized the study of microbiology in numerous environments where most bacteria are difficult to isolate. The first result of this approach was a more accurate estimation of microbial diversity, which had been underestimated by traditional approaches. However, interest in metagenomics exceeds a simple inventory of bacteria by providing information on their activity, function and interrelationship with other organisms.Our objective in this work realized in a CIFRE collaboration with Libragen company, was to study the functional profiles of the human skin microbiome with the application of metagenomic tools. This approach required first developing a method for the sampling and the dna extraction in order to overcome the constraints specific to this microbiome. Then, the sequencing data allowed characterizing the functional potential of the skin microorganisms in comparison to other environmental metagenome, and to find specificities of their local adaptations among three different skin sites. Applications of this work can be very important and one application tried in this thesis was the demonstration of the effect of several cutaneous cosmetic applications that modified the microflora at the taxonomic level and at the functional potential level.

Lignocellulose is composed of lignin, hemicellulose, and cellulose. Lignocellulose waste is a sustainable and renewable resource available for use in biotechnological applications. Efficient enzyme production and enzymes with high catalytic activity are needed for the use of lignocellulose. The study of cellulases and xylanases that degrade cellulose and xylan into constituent monosaccharides is required to advance industrial application of these enzymes. The use of a traditional pure culture approach to discover and characterize cellulases and xylanases from novel actinomycete isolates and the use of metagenomics to uncover previously unidentified cellulase genes was undertaken. Actinomycetes were cultivated from soil samples and the isolate with the best cellulase and xylanase activity was subjected to strain improvement through protoplast fusion. Enhanced enzymatic activity was found in one fusant. Differential release of sugars from xylan was observed through gas chromatographic analysis between the parental and fusant cultures. Genome shuffling was observed in 16S rRNA genes after protoplast fusion. Finally, one putative endo-β-1,4-glucanase was discovered in a metagenomic library created from cellulose-enriched potting soil. / UOIT

For many years, natural products have been a primary source of new molecules for the treatment of disease, and microorganisms have been a prolific source of these molecules. Recent studies have indicated, however, that many biosynthetic pathways are present in organisms for which no natural product can be associated, and only a small fraction of the microbial life present in the environment can be grown in culture. This indicates that if methods could be developed for the isolation of these pathways and production of their target molecules in heterologous hosts, great numbers of potentially valuable compounds might be discovered. In these investigations, large insert libraries of two microorganisms were constructed, one a bacterial artificial chromosome (BAC) library, the other a fosmid library, and two large insert fosmid libraries were constructed with DNA isolated from marine environmental samples. A mathematical formula was derived to estimate probabilities of cloning intact biosynthetic pathways with large insert genomic libraries and tested with a computer simulation. This indicated that even large pathways could be cloned intact in large insert libraries, provided there was an adequate size difference between the target pathway and the library inserts, and there was a concomitant increase in the size of the library with the targeting of these larger pathways. In addition, an investigation into a mixed marine culture sample lead to the identification of an unusual relationship between two bacteria for which extended co-culture leads to the production of pyocyanin. However, no useful biosynthetic pathways were located within the genomic libraries. It is concluded that significant improvements would be required to make this approach feasible for larger scale investigations. It is further concluded, on the basis of recent developments in the field, including a reduction in the cost of sequencing, improvements in techniques of whole-genome shotgun sequencing, and the development of recombination based cloning, that the employment of mass sequencing efforts and sequence-driven, recombinationbased cloning, might prove to be a more fruitful and efficient alternative to large-insert library construction for the isolation and expression of these pathways. A possible paradigm for the cloning of pathways on the basis of this technology is proposed.

>Magister Scientiae - MSc / Heterologous metagenomic screening strategies have relied largely on the construction of DNA libraries and screening in Escherichia coli to access novel enzymes. There is an increased demand for the identification of novel lignocellulose degrading enzymes with enhanced biochemical properties which are suitable for applications in industrial processes; biofuels being one of them. The use of heterologous gene expression in function based metagenomic studies has resulted in the discovery of enormous novel bioactive compounds. However, there are limitations associated with using E. coli as a heterologous host which does not allow transcription and translation of all genes in the metagenome. E. coli can only express 40% of the environmental DNA because of promoter recognition, codon usage, and host toxicity of gene products. Therefore alternative strategies for expressing or producing novel enzymes are needed, which can also be employed in metagenomic gene discovery. In vitro protein synthesis is an important tool in molecular biology and used to obtain proteins from genes for functional and expression studies. These systems may hold the key to unlock more of the potential in metagenomic DNA. The broader aim of the study is to develop non- E. coli based cell-free protein synthesis systems to further the metagenomics screening. In this study, Rhodococcus erythropolis H8 was evaluated for its suitability in cell-free expression. Crude extracts containing the macromolecular components (70S or 80S ribosomes, tRNAs, initiation, elongation and termination factors) fromR. erythropolis were prepared using existing crude extract based cell-free protein synthesis (CFPS) protocols. Three genes were selected and used as templates for synthesis: cell11, xp12 and acetyl xylan esterase (axe10), all previously isolated from metagenomic libraries screened inE. coli. As judged by zymograms and enzyme assays, all enzymes were successfully expressedfrom their native promoters and in recombinants clones using the PtipA promoter, and wereactive. Furthermore, the amounts of XP12 protein produced using pFos-XP_12 was 1.2mg/mlfrom E. coli and 1.67mg/ml from R. erythropolis CFPS, showing that the R. erythropolismachinery was more efficient in the expression of XP12 than the E. coli machinery. To the best of our knowledge this is the first demonstration of a cell-free expression using an actinomycete.