Background: Sequencing of 16S rRNA genes has become a powerful technique to study microbial communities and their responses towards changing environmental conditions in various ecosystems. Several tools have been developed for the prediction of functional profiles from 16S rRNA gene sequencing data, because numerous questions in ecosystem ecology require knowledge of community functions in addition to taxonomic composition. However, the accuracy of these tools relies on functional information derived from genomes available in public databases, which are often not representative of the microorganisms present in the studied ecosystem. In addition, there is also a lack of tools to predict functional gene redundancy in microbial communities. Results: To address these challenges, we developed Tax4Fun2, an R package for the prediction of functional profiles and functional gene redundancies of prokaryotic communities from 16S rRNA gene sequences. We demonstrate that functional profiles predicted by Tax4Fun2 are highly correlated to functional profiles derived from metagenomes of the same samples. We further show that Tax4Fun2 has higher accuracies than PICRUSt and Tax4Fun. By incorporating user-defined, habitat-specific genomic information, the accuracy and robustness of predicted functional profiles is substantially enhanced. In addition, functional gene redundancies predicted with Tax4Fun2 are highly correlated to functional gene redundancies determined for simulated microbial communities. Conclusions: Tax4Fun2 provides researchers with a unique tool to predict and investigate functional profiles of prokaryotic communities based on 16S rRNA gene sequencing data. It is easy-to-use, platform-independent and highly memory-efficient, thus enabling researchers without extensive bioinformatics knowledge or access to high-performance clusters to predict functional profiles. Another unique feature of Tax4Fun2 is that it allows researchers to calculate the redundancy of specific functions, which is a potentially important measure of how resilient a community will be to environmental perturbation. Tax4Fun2 is implemented in R and freely available at https://github.com/bwemheu/Tax4Fun2.

Dissolved organic matter (DOM) is the main substrate and energy source for heterotrophic bacterioplankton. To understand the interactions between DOM and the bacterial community (BC), it is important to identify the key factors on both sides in detail, chemically distinct moieties in DOM and the various bacterial taxa. Next-generation sequencing facilitates the classification of millions of reads of environmental DNA and RNA amplicons and ultrahigh-resolution mass spectrometry yields up to 10 000 DOM molecular formulae in a marine water sample. Linking this detailed biological and chemical information is a crucial first step toward a mechanistic understanding of the role of microorganisms in the marine carbon cycle. In this study, we interpreted the complex microbiological and molecular information via a novel combination of multivariate statistics. We were able to reveal distinct relationships between the key factors of organic matter cycling along a latitudinal transect across the North Sea. Total BC and DOM composition were mainly driven by mixing of distinct water masses and presumably retain their respective terrigenous imprint on similar timescales on their way through the North Sea. The active microbial community, however, was rather influenced by local events and correlated with specific DOM molecular formulae indicative of compounds that are easily degradable. These trends were most pronounced on the highest resolved level, that is, operationally defined ‘species’, reflecting the functional diversity of microorganisms at high taxonomic resolution.

The diverse microbial communities in agricultural biogas fermenters are assumed to be well adapted for the anaerobic transformation of plant biomass to methane. Compared to natural systems, biogas reactors are limited in their hydrolytic potential. The reasons for this are not understood. In this paper, we show that a typical industrial biogas reactor fed with maize silage, cow manure, and chicken manure has relatively lower hydrolysis rates compared to feces samples from herbivores. We provide evidence that on average, 2.5 genes encoding cellulolytic GHs/Mbp were identified in the biogas fermenter compared to 3.8 in the elephant feces and 3.2 in the cow rumen data sets. The ratio of genes coding for cellulolytic GH enzymes affiliated with the Firmicutes versus the Bacteroidetes was 2.8:1 in the biogas fermenter compared to 1:1 in the elephant feces and 1.4:1 in the cow rumen sample. Furthermore, RNA-Seq data indicated that highly transcribed cellulases in the biogas fermenter were four times more often affiliated with the Firmicutes compared to the Bacteroidetes, while an equal distribution of these enzymes was observed in the elephant feces sample. Our data indicate that a relatively lower abundance of bacteria affiliated with the phylum of Bacteroidetes and, to some extent, Fibrobacteres is associated with a decreased richness of predicted lignocellulolytic enzymes in biogas fermenters. This difference can be attributed to a partial lack of genes coding for cellulolytic GH enzymes derived from bacteria which are affiliated with the Fibrobacteres and, especially, the Bacteroidetes. The partial deficiency of these genes implies a potentially important limitation in the biogas fermenter with regard to the initial hydrolysis of biomass. Based on these findings, we speculate that increasing the members of Bacteroidetes and Fibrobacteres in biogas fermenters will most likely result in an increased hydrolytic performance.

Phytoplankton blooms exhibit a severe impact on bacterioplankton communities as they change nutrient availabilities and other environmental factors. In the current study, the response of a bacterioplankton community to a Phaeocystis globosa spring bloom was investigated in the southern North Sea. For this purpose, water samples were taken inside and reference samples outside of an algal spring bloom. Structural changes of the bacterioplankton community were assessed by amplicon-based analysis of 16S rRNA genes and transcripts generated from environmental DNA and RNA, respectively. Several marine groups responded to bloom presence. The abundance of the Roseobacter RCA cluster and the SAR92 clade significantly increased in bloom presence in the total and active fraction of the bacterial community. Functional changes were investigated by direct sequencing of environmental DNA and mRNA. The corresponding datasets comprised more than 500 million sequences across all samples. Metatranscriptomic data sets were mapped on representative genomes of abundant marine groups present in the samples and on assembled metagenomic and metatranscriptomic datasets. Differences in gene expression profiles between non-bloom and bloom samples were recorded. The genome-wide gene expression level of Planktomarina temperata, an abundant member of the Roseobacter RCA cluster, was higher inside the bloom. Genes that were differently expressed

Volcanic regions contain a variety of environments suitable for extremophiles. This study was focused on assessing and exploiting the prokaryotic diversity of two microbial communities derived from different Kamchatkian thermal springs by metagenomic approaches. Samples were taken from a thermoacidophilic spring near the Mutnovsky Volcano and from a thermophilic spring in the Uzon Caldera. Environmental DNA for metagenomic analysis was isolated from collected sediment samples by direct cell lysis. The prokaryotic community composition was examined by analysis of archaeal and bacterial 16S rRNA genes. A total number of 1235 16S rRNA gene sequences were obtained and used for taxonomic classification. Most abundant in the samples were members ofThaumarchaeota,Thermotogae, andProteobacteria. The Mutnovsky hot spring was dominated by the Terrestrial Hot Spring Group,Kosmotoga, andAcidithiobacillus. The Uzon Caldera was dominated by uncultured members of the Miscellaneous Crenarchaeotic Group andEnterobacteriaceae. The remaining 16S rRNA gene sequences belonged to theAquificae,Dictyoglomi,Euryarchaeota,Korarchaeota,Thermodesulfobacteria,Firmicutes, and some potential new phyla. In addition, the recovered DNA was used for generation of metagenomic libraries, which were subsequently mined for genes encoding lipolytic and proteolytic enzymes. Three novel genes conferring lipolytic and one gene conferring proteolytic activity were identified.