Project description:Metagenome-assembled genomes (MAGs) have revealed the existence of novel bacterial and archaeal groups and provided insight into their genetic potential. However, metagenomics and even metatranscriptomics cannot resolve how the genetic potential translates into metabolic functions and physiological activity. Here, we present a novel approach for the quantitative and organism-specific assessment of the carbon flux through microbial communities with stable isotope probing-metaproteomics and integration of temporal dynamics in 13C incorporation by Stable Isotope Cluster Analysis (SIsCA). We used groundwater microcosms labeled with 13CO2 and D2O as model systems and stimulated them with reduced sulfur compounds to determine the ecosystem role of chemolithoautotrophic primary production. Raman microspectroscopy detected rapid deuterium incorporation in microbial cells from 12 days onwards, indicating activity of the groundwater organisms. SIsCA revealed that groundwater microorganisms fell into five distinct carbon assimilation strategies. Only one of these strategies, comprising less than 3.5% of the community, consisted of obligate autotrophs (Thiobacillus), with a 13C incorporation of approximately 95%. Instead, mixotrophic growth was the most successful strategy, and was represented by 12 of the 15 MAGs expressing pathways for autotrophic CO2 fixation, including Hydrogenophaga, Polaromonas and Dechloromonas, with varying 13C incorporation between 5% and 90%. Within 21 days, 43% of carbon in the community was replaced by 13C, increasing to 80% after 70 days. Of the 31 most abundant MAGs, 16 expressed pathways for sulfur oxidation, including strict heterotrophs. We concluded that chemolithoautotrophy drives the recycling of organic carbon and serves as a fill-up function in the groundwater. Mixotrophs preferred the uptake of organic carbon over the fixation of CO2, and heterotrophs oxidize inorganic compounds to preserve organic carbon. Our study showcases how next-generation physiology approach like SIsCA can move beyond metagenomics studies by providing information about expression of metabolic pathways and elucidating the role of MAGs in ecosystem functioning.

Project description:Short-amplicon 16S rRNA gene sequencing is currently the method of choice for studies investigating microbiomes. However, comparative studies on differences in procedures are scarce. We sequenced human stool samples and mock communities with increasing complexity using a variety of commonly used protocols. Short amplicons targeting different variable regions (V-regions) or ranges thereof (V1-V2, V1-V3, V3-V4, V4, V4-V5, V6-V8, and V7-V9) were investigated for differences in the composition outcome due to primer choices. Next, the influence of clustering (operational taxonomic units [OTUs], zero-radius OTUs [zOTUs], and amplicon sequence variants [ASVs]), different databases (GreenGenes, the Ribosomal Database Project, Silva, the genomic-based 16S rRNA Database, and The All-Species Living Tree), and bioinformatic settings on taxonomic assignment were also investigated. We present a systematic comparison across all typically used V-regions using well-established primers. While it is known that the primer choice has a significant influence on the resulting microbial composition, we show that microbial profiles generated using different primer pairs need independent validation of performance. Further, comparing data sets across V-regions using different databases might be misleading due to differences in nomenclature (e.g., Enterorhabdus versus Adlercreutzia) and varying precisions in classification down to genus level. Overall, specific but important taxa are not picked up by certain primer pairs (e.g., Bacteroidetes is missed using primers 515F-944R) or due to the database used (e.g., Acetatifactor in GreenGenes and the genomic-based 16S rRNA Database). We found that appropriate truncation of amplicons is essential and different truncated-length combinations should be tested for each study. Finally, specific mock communities of sufficient and adequate complexity are highly recommended.IMPORTANCE In 16S rRNA gene sequencing, certain bacterial genera were found to be underrepresented or even missing in taxonomic profiles when using unsuitable primer combinations, outdated reference databases, or inadequate pipeline settings. Concerning the last, quality thresholds as well as bioinformatic settings (i.e., clustering approach, analysis pipeline, and specific adjustments such as truncation) are responsible for a number of observed differences between studies. Conclusions drawn by comparing one data set to another (e.g., between publications) appear to be problematic and require independent cross-validation using matching V-regions and uniform data processing. Therefore, we highlight the importance of a thought-out study design including sufficiently complex mock standards and appropriate V-region choice for the sample of interest. The use of processing pipelines and parameters must be tested beforehand.

Project description:BackgroundIn the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data.ResultsIn this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software#metaSPARSimand https://gitlab.com/sysbiobig/metasparsim.ConclusionmetaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.

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Groundwater 16S rRNA sequencing

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