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The complex interplay of a pathogen with its virulence and fitness factors, the host's immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.

Table S8. SR assembly statistics and CheckM results for 14 taxonomic bins. (PDF 35.7 kb)

Figure S1. Using Minimap2, we aligned all SR contigs against all LR reads and LR contigs. Here, we show, for a given level of average coverage of a SR contig by short reads, how many bases of the SR-contigs align to long reads only (“in LR”), or to LR contigs (“in LR contigs”), or not (“not in LR”). There are 221 SR contigs that have a coverage greater than 150 but are not shown in the plot. They cover 5.3 Mb in total, of which 49.6% is aligned to long reads and 50.4% to LR contigs. (PDF 20 kb)

Figure S2. Concordance statistics for SR contigs against LR chromosomes. In each plot, the LR chromosome is represented by the x axis, and the five panels, from top to bottom, represent: (A) the locations of alignments to the LR chromosome, (B) the corresponding percent identity, (C) the alignment-length to query-length ratio, (D) the alignment length and (E) the query length. The colors red and black are used to distinguish between alignments to different SR-bins or reference genomes, as described in the text. (PDF 72 kb)

The investigation of microbes in research has been changing with the rise of environmental sequencing from a view centered on an isolate microbe living in a laboratory setting to a broader view of microbial communities - microbiomes - as they thrive in their natural environment. Sequencing of an isolated organism generates essential insights and enables us to produce reference genomes and genomes annotation, which in turn let us compare different organisms through their genomes and study the metabolic pathways they utilize. In comparison to that, environmental sequencing does answer entirely different questions, and it does provide us with a much better view on how microbes live in their environment, what changes they undergo based on their interactions with their host, environment or with each other. It also enables us to study the genomes of microbes which previously could not be cultured in isolation and provides many more new possibilities to many different life sciences which are interested in the microbial community on earth. One of these sciences with a significant influence on our daily lives is medicine. In this work, I present multiple projects where genomics, 16S rDNA analysis and metagenomics have helped medical research to gain insights on diverse topics and under varying conditions. During these projects, I have determined a recurring need for primary analysis of environmental data, which currently often only can be done by bioinformatics specialists or at least would need time-consuming efforts to study the necessary tools for scientists from other fields. As those scientists also spend a lot of time planning and conducting the experiments which have led to the generation of the data and verifying the findings, they often do not have the time necessary to pick up the knowledge required even for many basic steps for environmental sequence analysis. To make metagenomic analysis more approachable for a variety of users, I have developed three pipelines for the fundamental analysis of environmental data - collected in the CommunAl toolkit - which require minimal hands-on effort and infrastructure to run the necessary analysis. The toolkit includes the alignment-based 16S rDNA analysis tool STARA, which is applicable on any type of sequencing available and can analyze all samples from a dataset from preprocessing over alignment to the taxonomic assignment in one run. The second member of the toolkit is MAPle, a pipeline for paired-end short-read WGS metagenomic sequencing analysis. Like STARA this also analyzes a dataset from preprocessing through to taxonomic and functional assignment. The taxonomic abundances determined by those two tools can be further investigated using TaxCo. TaxCo computes correlations between taxonomic abundances and numeric metadata and presents the results in tabular and graphic form. With these tools, I hope to enable everyone involved in environmental data analysis to generate insights from any of their datasets, which in turn will hopefully help to make environmental sequencing - especially metagenomics - a more feasible choice for everyone who could profit from the possibilities and insights it can provide.

There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce, Author Summary Microbiome sequencing projects continue to grow rapidly, both in the number of samples considered and sequencing reads collected. With MEGAN Community Edition (CE), we provide a highly efficient program for interactive analysis and comparison of such data, allowing one to explore hundreds of samples and billions of reads. While taxonomic profiling is performed based on the NCBI taxonomy, we provide a number of different functional profiling approaches such as SEED, eggNOG, KEGG, and a new InterPro2GO classification scheme. MEGAN CE also supports the use of metadata in the context of principal coordinate analysis and clustering analysis.