Anthropogenic impact on the historical phytoplankton community of Lake Constance reconstructed by multimarker analysis of sediment-core environmental DNA.

During the 20th century, many lakes in the Northern Hemisphere were affected by increasing human population and urbanization along their shorelines and catchment, resulting in aquatic eutrophication. Ecosystem monitoring commenced only after the changes became apparent, precluding any examination of timing and dynamics of initial community change in the past and comparison of pre- and postimpact communities. Peri-Alpine Lake Constance (Germany) underwent a mid-century period of eutrophication followed by re-oligotrophication since the 1980s and is now experiencing warm temperatures. We extended the period for which monitoring data of indicator organisms exist by analysing historical environmental DNA (eDNA) from a sediment core dating back some 110 years. Using three metabarcoding markers-for microbial eukaryotes, diatoms and cyanobacteria-we revealed two major breakpoints of community change, in the 1930s and the mid-1990s. In our core, the latest response was exhibited by diatoms, which are classically used as palaeo-bioindicators for the trophic state of lakes. Following re-oligotrophication, overall diversity values reverted to similar ones of the early 20th century, but multivariate analysis indicated that the present community is substantially dissimilar. Community changes of all three groups were strongly correlated to phosphorus concentration changes, whereas significant relationships to temperature were only observed when we did not account for temporal autocorrelation. Our results indicate that each microbial group analysed exhibited a unique response, highlighting the particular strength of multimarker analysis of eDNA, which is not limited to organisms with visible remains and can therefore discover yet unknown responses and abiotic-biotic relationships.

A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance.

Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.

Nonribosomal peptides protect Pseudomonas nunensis 4A2e from amoebal and nematodal predation.

The rhizosphere is a highly competitive environment forcing bacteria to evolve strategies to oppose their enemies. The production of toxic secondary metabolites allows bacteria to counteract predators. In this study, we describe the anti-predator armamentarium of the soil-derived bacterium Pseudomonas nunensis 4A2e. Based on a genome mining approach, we identified several biosynthetic gene clusters coding for nonribosomal peptide synthetases. Generation of gene deletion mutants of the respective clusters shows a loss of defense capabilities. We isolated the novel lipopeptides keanumycin D and nunapeptins B and C, and fully elucidated their structures by a combination of in-depth mass spectrometry experiments, stable isotope labelling, and chemical synthesis. Additionally, investigation of the quorum sensing-dependent biosynthesis allowed us to elucidate parts of the underlying regulation of the biosynthetic machinery. Ecology-inspired bioassays highlight the role of these peptides as a defence strategy against protozoans and led us to find a previously unknown function against the bacterivorous nematode Oscheius myriophilus.

Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic.

Major advances over the past decade in the field of ancient DNA are providing access to past paleogenomic diversity, but the diverse functions and biosynthetic capabilities of this growing paleome remain largely elusive. We investigated the dental calculus of 12 Neanderthals and 52 anatomically modern humans ranging from 100,000 years ago to the present and reconstructed 459 bacterial metagenome-assembled genomes. We identified a biosynthetic gene cluster shared by seven Middle and Upper Paleolithic individuals that allows for the heterologous production of a class of previously unknown metabolites that we name "paleofurans." This paleobiotechnological approach demonstrates that viable biosynthetic machinery can be produced from the preserved genetic material of ancient organisms, allowing access to natural products from the Pleistocene and providing a promising area for natural product exploration.

Facilitating accessible, rapid, and appropriate processing of ancient metagenomic data with AMDirT.

Background: Access to sample-level metadata is important when selecting public metagenomic sequencing datasets for reuse in new biological analyses. The Standards, Precautions, and Advances in Ancient Metagenomics community (SPAAM, https://spaam-community.org) has previously published AncientMetagenomeDir, a collection of curated and standardised sample metadata tables for metagenomic and microbial genome datasets generated from ancient samples. However, while sample-level information is useful for identifying relevant samples for inclusion in new projects, Next Generation Sequencing (NGS) library construction and sequencing metadata are also essential for appropriately reprocessing ancient metagenomic data. Currently, recovering information for downloading and preparing such data is difficult when laboratory and bioinformatic metadata is heterogeneously recorded in prose-based publications. Methods: Through a series of community-based hackathon events, AncientMetagenomeDir was updated to provide standardised library-level metadata of existing and new ancient metagenomic samples. In tandem, the companion tool 'AMDirT' was developed to facilitate rapid data filtering and downloading of ancient metagenomic data, as well as improving automated metadata curation and validation for AncientMetagenomeDir. Results: AncientMetagenomeDir was extended to include standardised metadata of over 6000 ancient metagenomic libraries. The companion tool 'AMDirT' provides both graphical- and command-line interface based access to such metadata for users from a wide range of computational backgrounds. We also report on errors with metadata reporting that appear to commonly occur during data upload and provide suggestions on how to improve the quality of data sharing by the community. Conclusions: Together, both standardised metadata reporting and tooling will help towards easier incorporation and reuse of public ancient metagenomic datasets into future analyses.