Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

MeganServer: facilitating interactive access to metagenomic data on a server.

MOTIVATION: Metagenomic projects often involve large numbers of large sequencing datasets (totaling hundreds of gigabytes of data). Thus, computational preprocessing and analysis are usually performed on a server. The results of such analyses are then usually explored interactively. One approach is to use MEGAN, an interactive program that allows analysis and comparison of metagenomic datasets. Previous releases have required that the user first download the computed data from the server, an increasingly time-consuming process. Here, we present MeganServer, a stand-alone program that serves MEGAN files to the web, using a RESTful API, facilitating interactive analysis in MEGAN, without requiring prior download of the data. We describe a number of different application scenarios. AVAILABILITY AND IMPLEMENTATION: MeganServer is provided as a stand-alone program tools/megan-server in the MEGAN software suite, available at https://software-ab.cs.uni-tuebingen.de/download/megan6. Source is available at: https://github.com/husonlab/megan-ce/tree/master/src/megan/ms. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and functional content of the former depend on the (details of the) latter? Here, we investigate a related technical question: Given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? We present a deep-learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make the problem more amenable to deep-learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the ToA of amicrobial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. RESULTS: Based on 7560 metagenomic profiles downloaded from MGnify, classified into 10 different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.30%. We show that adding textual information to functional features increases the accuracy. AVAILABILITY AND IMPLEMENTATION: Our approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus.

Recent genomic data have revealed multiple interactions between Neanderthals and modern humans, but there is currently little genetic evidence regarding Neanderthal behaviour, diet, or disease. Here we describe the shotgun-sequencing of ancient DNA from five specimens of Neanderthal calcified dental plaque (calculus) and the characterization of regional differences in Neanderthal ecology. At Spy cave, Belgium, Neanderthal diet was heavily meat based and included woolly rhinoceros and wild sheep (mouflon), characteristic of a steppe environment. In contrast, no meat was detected in the diet of Neanderthals from El Sidrón cave, Spain, and dietary components of mushrooms, pine nuts, and moss reflected forest gathering. Differences in diet were also linked to an overall shift in the oral bacterial community (microbiota) and suggested that meat consumption contributed to substantial variation within Neanderthal microbiota. Evidence for self-medication was detected in an El Sidrón Neanderthal with a dental abscess and a chronic gastrointestinal pathogen (Enterocytozoon bieneusi). Metagenomic data from this individual also contained a nearly complete genome of the archaeal commensal Methanobrevibacter oralis (10.2× depth of coverage)-the oldest draft microbial genome generated to date, at around 48,000 years old. DNA preserved within dental calculus represents a notable source of information about the behaviour and health of ancient hominin specimens, as well as a unique system that is useful for the study of long-term microbial evolution.

Normalising phylogenetic networks.

Rooted phylogenetic networks provide a way to describe species' relationships when evolution departs from the simple model of a tree. However, networks inferred from genomic data can be highly tangled, making it difficult to discern the main reticulation signals present. In this paper, we describe a natural way to transform any rooted phylogenetic network into a simpler canonical network, which has desirable mathematical and computational properties, and is based only on the 'visible' vertices in the original network. The method has been implemented and we demonstrate its application to some examples.

MAIRA- real-time taxonomic and functional analysis of long reads on a laptop.

BACKGROUND: Advances in mobile sequencing devices and laptop performance make metagenomic sequencing and analysis in the field a technologically feasible prospect. However, metagenomic analysis pipelines are usually designed to run on servers and in the cloud. RESULTS: MAIRA is a new standalone program for interactive taxonomic and functional analysis of long read metagenomic sequencing data on a laptop, without requiring external resources. The program performs fast, online, genus-level analysis, and on-demand, detailed taxonomic and functional analysis. It uses two levels of frame-shift-aware alignment of DNA reads against protein reference sequences, and then performs detailed analysis using a protein synteny graph. CONCLUSIONS: We envision this software being used by researchers in the field, when access to servers or cloud facilities is difficult, or by individuals that do not routinely access such facilities, such as medical researchers, crop scientists, or teachers.

Pre-Columbian mycobacterial genomes reveal seals as a source of New World human tuberculosis.

Modern strains of Mycobacterium tuberculosis from the Americas are closely related to those from Europe, supporting the assumption that human tuberculosis was introduced post-contact. This notion, however, is incompatible with archaeological evidence of pre-contact tuberculosis in the New World. Comparative genomics of modern isolates suggests that M. tuberculosis attained its worldwide distribution following human dispersals out of Africa during the Pleistocene epoch, although this has yet to be confirmed with ancient calibration points. Here we present three 1,000-year-old mycobacterial genomes from Peruvian human skeletons, revealing that a member of the M. tuberculosis complex caused human disease before contact. The ancient strains are distinct from known human-adapted forms and are most closely related to those adapted to seals and sea lions. Two independent dating approaches suggest a most recent common ancestor for the M. tuberculosis complex less than 6,000 years ago, which supports a Holocene dispersal of the disease. Our results implicate sea mammals as having played a role in transmitting the disease to humans across the ocean.

The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery.

With the rise of multi-drug resistant pathogens and the decline in number of potential new antibiotics in development there is a fervent need to reinvigorate the natural products discovery pipeline. Most antibiotics are derived from secondary metabolites produced by microorganisms and plants. To avoid suicide, an antibiotic producer harbors resistance genes often found within the same biosynthetic gene cluster (BGC) responsible for manufacturing the antibiotic. Existing mining tools are excellent at detecting BGCs or resistant genes in general, but provide little help in prioritizing and identifying gene clusters for compounds active against specific and novel targets. Here we introduce the 'Antibiotic Resistant Target Seeker' (ARTS) available at https://arts.ziemertlab.com. ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets. The aim of this web server is to automate the screening of large amounts of sequence data and to focus on the most promising strains that produce antibiotics with new modes of action. ARTS integrates target directed genome mining methods, antibiotic gene cluster predictions and 'essential gene screening' to provide an interactive page for rapid identification of known and putative targets in BGCs.

Fast and sensitive protein alignment using DIAMOND.

The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity.

Intestinal Dysbiosis, Barrier Dysfunction, and Bacterial Translocation Account for CKD-Related Systemic Inflammation.

CKD associates with systemic inflammation, but the underlying cause is unknown. Here, we investigated the involvement of intestinal microbiota. We report that collagen type 4 α3-deficient mice with Alport syndrome-related progressive CKD displayed systemic inflammation, including increased plasma levels of pentraxin-2 and activated antigen-presenting cells, CD4 and CD8 T cells, and Th17- or IFNγ-producing T cells in the spleen as well as regulatory T cell suppression. CKD-related systemic inflammation in these mice associated with intestinal dysbiosis of proteobacterial blooms, translocation of living bacteria across the intestinal barrier into the liver, and increased serum levels of bacterial endotoxin. Uremia did not affect secretory IgA release into the ileum lumen or mucosal leukocyte subsets. To test for causation between dysbiosis and systemic inflammation in CKD, we eradicated facultative anaerobic microbiota with antibiotics. This eradication prevented bacterial translocation, significantly reduced serum endotoxin levels, and fully reversed all markers of systemic inflammation to the level of nonuremic controls. Therefore, we conclude that uremia associates with intestinal dysbiosis, intestinal barrier dysfunction, and bacterial translocation, which trigger the state of persistent systemic inflammation in CKD. Uremic dysbiosis and intestinal barrier dysfunction may be novel therapeutic targets for intervention to suppress CKD-related systemic inflammation and its consequences.

SILVA, RDP, Greengenes, NCBI and OTT - how do these taxonomies compare?

BACKGROUND: A key step in microbiome sequencing analysis is read assignment to taxonomic units. This is often performed using one of four taxonomic classifications, namely SILVA, RDP, Greengenes or NCBI. It is unclear how similar these are and how to compare analysis results that are based on different taxonomies. RESULTS: We provide a method and software for mapping taxonomic entities from one taxonomy onto another. We use it to compare the four taxonomies and the Open Tree of life Taxonomy (OTT). CONCLUSIONS: While we find that SILVA, RDP and Greengenes map well into NCBI, and all four map well into the OTT, mapping the two larger taxonomies on to the smaller ones is problematic.

MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data.

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.

RiboTagger: fast and unbiased 16S/18S profiling using whole community shotgun metagenomic or metatranscriptome surveys.

BACKGROUND: Taxonomic profiling of microbial communities is often performed using small subunit ribosomal RNA (SSU) amplicon sequencing (16S or 18S), while environmental shotgun sequencing is often focused on functional analysis. Large shotgun datasets contain a significant number of SSU sequences and these can be exploited to perform an unbiased SSU--based taxonomic analysis. RESULTS: Here we present a new program called RiboTagger that identifies and extracts taxonomically informative ribotags located in a specified variable region of the SSU gene in a high-throughput fashion. CONCLUSIONS: RiboTagger permits fast recovery of SSU-RNA sequences from shotgun nucleic acid surveys of complex microbial communities. The program targets all three domains of life, exhibits high sensitivity and specificity and is substantially faster than comparable programs.

Antibiotic Selection Pressure Determination through Sequence-Based Metagenomics.

The human gut forms a dynamic reservoir of antibiotic resistance genes (ARGs). Treatment with antimicrobial agents has a significant impact on the intestinal resistome and leads to enhanced horizontal transfer and selection of resistance. We have monitored the development of intestinal ARGs over a 6-day course of ciprofloxacin (Cp) treatment in two healthy individuals by using sequenced-based metagenomics and different ARG quantification methods. Fixed- and random-effect models were applied to determine the change in ARG abundance per defined daily dose of Cp as an expression of the respective selection pressure. Among various shifts in the composition of the intestinal resistome, we found in one individual a strong positive selection for class D beta-lactamases which were partly located on a mobile genetic element. Furthermore, a trend to a negative selection has been observed with class A beta-lactamases (-2.66 hits per million sample reads/defined daily dose; P = 0.06). By 4 weeks after the end of treatment, the composition of ARGs returned toward their initial state but to a different degree in both subjects. We present here a novel analysis algorithm for the determination of antibiotic selection pressure which can be applied in clinical settings to compare therapeutic regimens regarding their effect on the intestinal resistome. This information is of critical importance for clinicians to choose antimicrobial agents with a low selective force on their patients' intestinal ARGs, likely resulting in a diminished spread of resistance and a reduced burden of hospital-acquired infections with multidrug-resistant pathogens.

Endotoxicity of lipopolysaccharide as a determinant of T-cell-mediated colitis induction in mice.

BACKGROUND & AIMS: The intestinal microbiota is an important determinant of the mucosal response. In patients with inflammatory bowel diseases, the mucosal immune system has inappropriate interactions with the intestinal microbiota. We investigated how the composition of the intestinal microbiota affects its endotoxicity and development of colitis in mice. METHODS: Germ-free C57BL/6J-Rag(1tm1Mom) (Rag1(-/-)) mice were colonized with 2 different types of complex intestinal microbiota. Colitis was induced in Rag1(-/-) mice by transfer of CD4(+)CD62L(+) T cells from C57BL/6J mice. Colonic tissues were collected and used for histologic analysis and cell isolation. Activation of lamina propria dendritic cells and T cells was analyzed by flow cytometry. RESULTS: After transfer of CD4(+)CD62L(+) T cells, mice with intestinal Endo(lo) microbiota (a low proportion of Enterobacteriaceae, high proportion of Bacteroidetes, and low endotoxicity) maintained mucosal immune homeostasis, and mice with highly endotoxic Endo(hi) microbiota (a high proportion of Enterobacteriaceae and low proportion of Bacteroidetes) developed colitis. To determine whether the effects of Endo(hi) microbiota were related to the higher endotoxic activity of lipopolysaccharide (LPS), we compared LPS from Enterobacteriaceae with that of Bacteroidetes. Administration of Escherichia coli JM83 (wild-type LPS) to the mice exacerbated colitis, and Escherichia coli JM83 + htrBPG (mutated LPS, with lower endotoxicity, similar to that of Bacteroidetes) prevented development of colitis after transfer of the T cells to mice. CONCLUSIONS: The endotoxicity of LPS produced by the intestinal microbiota is a determinant of whether mice develop colitis after transfer of CD4(+)CD62L(+) T cells. This finding might aid the design of novel biologics or probiotics to treat inflammatory bowel disease.

A poor man's BLASTX--high-throughput metagenomic protein database search using PAUDA.

SUMMARY: In the context of metagenomics, we introduce a new approach to protein database search called PAUDA, which runs ~10,000 times faster than BLASTX, while achieving about one-third of the assignment rate of reads to KEGG orthology groups, and producing gene and taxon abundance profiles that are highly correlated to those obtained with BLASTX. PAUDA requires <80 CPU hours to analyze a dataset of 246 million Illumina DNA reads from permafrost soil for which a previous BLASTX analysis (on a subset of 176 million reads) reportedly required 800,000 CPU hours, leading to the same clustering of samples by functional profiles. AVAILABILITY: PAUDA is freely available from: http://ab.inf.uni-tuebingen.de/software/pauda. Also supplementary method details are available from this website.

Integrative analysis of environmental sequences using MEGAN4.

A major challenge in the analysis of environmental sequences is data integration. The question is how to analyze different types of data in a unified approach, addressing both the taxonomic and functional aspects. To facilitate such analyses, we have substantially extended MEGAN, a widely used taxonomic analysis program. The new program, MEGAN4, provides an integrated approach to the taxonomic and functional analysis of metagenomic, metatranscriptomic, metaproteomic, and rRNA data. While taxonomic analysis is performed based on the NCBI taxonomy, functional analysis is performed using the SEED classification of subsystems and functional roles or the KEGG classification of pathways and enzymes. A number of examples illustrate how such analyses can be performed, and show that one can also import and compare classification results obtained using others' tools. MEGAN4 is freely available for academic purposes, and installers for all three major operating systems can be downloaded from www-ab.informatik.uni-tuebingen.de/software/megan.

Reference-guided assembly of four diverse Arabidopsis thaliana genomes.

We present whole-genome assemblies of four divergent Arabidopsis thaliana strains that complement the 125-Mb reference genome sequence released a decade ago. Using a newly developed reference-guided approach, we assembled large contigs from 9 to 42 Gb of Illumina short-read data from the Landsberg erecta (Ler-1), C24, Bur-0, and Kro-0 strains, which have been sequenced as part of the 1,001 Genomes Project for this species. Using alignments against the reference sequence, we first reduced the complexity of the de novo assembly and later integrated reads without similarity to the reference sequence. As an example, half of the noncentromeric C24 genome was covered by scaffolds that are longer than 260 kb, with a maximum of 2.2 Mb. Moreover, over 96% of the reference genome was covered by the reference-guided assembly, compared with only 87% with a complete de novo assembly. Comparisons with 2 Mb of dideoxy sequence reveal that the per-base error rate of the reference-guided assemblies was below 1 in 10,000. Our assemblies provide a detailed, genomewide picture of large-scale differences between A. thaliana individuals, most of which are difficult to access with alignment-consensus methods only. We demonstrate their practical relevance in studying the expression differences of polymorphic genes and show how the analysis of sRNA sequencing data can lead to erroneous conclusions if aligned against the reference genome alone. Genome assemblies, raw reads, and further information are accessible through http://1001genomes.org/projects/assemblies.html.

Self-generating autocatalytic networks: structural results, algorithms and their relevance to early biochemistry.

The concept of an autocatalytic network of reactions that can form and persist, starting from just an available food source, has been formalized by the notion of a reflexively autocatalytic and food-generated (RAF) set. The theory and algorithmic results concerning RAFs have been applied to a range of settings, from metabolic questions arising at the origin of life, to ecological networks, and cognitive models in cultural evolution. In this article, we present new structural and algorithmic results concerning RAF sets, by studying more complex modes of catalysis that allow certain reactions to require multiple catalysts (or to not require catalysis at all), and discuss the differing ways catalysis has been viewed in the literature. We also focus on the structure and analysis of minimal RAFs and derive structural results and polynomial-time algorithms. We then apply these new methods to a large metabolic network to gain insights into possible biochemical scenarios near the origin of life.

Analysis of the intestinal microbiota using SOLiD 16S rRNA gene sequencing and SOLiD shotgun sequencing.

BACKGROUND: Metagenomics seeks to understand microbial communities and assemblages by DNA sequencing. Technological advances in next generation sequencing technologies are fuelling a rapid growth in the number and scope of projects aiming to analyze complex microbial environments such as marine, soil or the gut. Recent improvements in longer read lengths and paired-sequencing allow better resolution in profiling microbial communities. While both 454 sequencing and Illumina sequencing have been used in numerous metagenomic studies, SOLiD sequencing is not commonly used in this area, as it is believed to be more suitable in the context of reference-guided projects. RESULTS: To investigate the performance of SOLiD sequencing in a metagenomic context, we compared taxonomic profiles of SOLiD mate-pair sequencing reads with Sanger paired reads and 454 single reads. All sequences were obtained from the bacterial 16S rRNA gene, which was amplified from microbial DNA extracted from a human fecal sample. Additionally, from the same fecal sample, complete genomic microbial DNA was extracted and shotgun sequenced using SOLiD sequencing to study the composition of the intestinal microbiota and the existing microbial metabolism. We found that the microbiota composition of 16S rRNA gene sequences obtained using Sanger, 454 and SOLiD sequencing provide results comparable to the result based on shotgun sequencing. Moreover, with SOLiD sequences we obtained more resolution down to the species level. In addition, the shotgun data allowed us to determine a functional profile using the databases SEED and KEGG. CONCLUSIONS: This study shows that SOLiD mate-pair sequencing is a viable and cost-efficient option for analyzing a complex microbiome. To the best of our knowledge, this is the first time that SOLiD sequencing has been used in a human sample.