Abteilung Oberflächengewässer

Anaerobic carbon transformation in freshwater sediments drives substantial methane emissions globally, yet the microbial taxa linking complex carbon degradation to methane production remain poorly characterized. Here, we combined metagenomics with the first metatranscriptomic dataset from the anoxic sediments of meromictic Lake Cadagno (Swiss Alps) to identify the active microbial clades, metabolic pathways, and extrachromosomal elements (ecDNA) across a depth gradient within the upper 56 cm of sediment. We recovered 802 species-level metagenome-assembled genomes spanning 66 phyla and identified a Bacteroidota clade (VadinHA17) as one of the most abundant and transcriptionally active populations in the sediment. This clade encodes and transcribes a broad range of diverse glycoside hydrolases (GH), indicating a central role in complex carbohydrate degradation. Transcriptional profiles suggest that this clade ferments organic substrates to acetate and hydrogen, which are key substrates for methanogenesis. In line with this, the acetoclastic methanogen Methanothrix and hydrogenotrophic Methanoregula were among the most abundant and transcriptionally active archaea in the same depth layers as VadinHA17. Beyond microbial genomes, we detected 86 905 viral OTUs and 2136 plasmid OTUs, with free viruses and plasmids accounting for 5%–10% and 0.2% of all sequencing reads, respectively. Notably, plasmids and viruses associated with Bacteroidota VadinHA17 encode and transcribe GHs that could augment host carbohydrate-degrading capacity. Together, these findings reveal new details on how methane production in anoxic lake sediments emerges from a network spanning primary fermentation, methanogenesis, and ecDNA-mediated metabolisms.

Field-based monitoring of tree species in forests is often sparse due to logistical constraints. Remote sensing enables repeated, spatially contiguous collection of reflectance data across large areas. Tree species classification accuracy using such data is variable, likely because most studies use observational datasets where species occurrence correlates with environmental variation. We used two sites of a tree biodiversity experiment in Germany (BIOTREE: Kaltenborn and Bechstedt), where different species have been planted with high replication under controlled diversity levels, to assess how well tree species could be classified using reflectance data from airborne imaging spectroscopy and different classification methods (linear discriminant analysis, LDA, and a non-linear support vector machine, SVM). Reflectance data for 589 wavelengths between 400–2400 nm were acquired at 1 m spatial resolution during peak growing season. Reflectance spectra showed large and significant variation between taxonomic classes, orders, and species, and weak, but still significant, interactions between classes or orders and diversity levels. Classification accuracy reached 100% in training datasets, 77%–83% for the four species in Kaltenborn prediction datasets, and 31%–49% for the 16 species in Bechstedt prediction datasets. LDA provided more accurate predictions than SVM, and using similarly-spaced original wavelengths with LDA was as efficient as using principal components derived from the original data. While airborne imaging spectroscopy effectively distinguished up to four tree species in our datasets, classification accuracy was lower when 16 species had to be distinguished. In these cases, the methodology may be more useful for functional diversity monitoring than for tree species classification.

Synthetic modified oligonucleotides (mONs) are a rapidly evolving class of nucleic acid-based therapeutics with an expected increase in market approval rates. mONs are designed to resist enzymatic degradation in patients, and their biological effects arise from chemical modifications and sequence-dependent phenomena comparable to those of biologically active genetic materials. These properties and the absence of experimental evidence on the occurrence and impact of mONs emitted into aquatic environments raise questions about the need for environmental risk assessment (ERA). Here, we evaluate (i) the chemical modifications proposed for mONs and found in approved therapeutics, (ii) pharmacological data on excretion to inform emission scenarios, (iii) analytical approaches for quantification of mONs in environmental matrices, (iv) anticipated distribution and transformation processes, and (v) potential (eco)toxicological effect profiles of mONs. We propose that focusing on chemical modifications allows for a more generalized approach to assessing mON exposure in engineered and natural aquatic systems. By contrast, the evaluation of effects requires case-by-case studies of chemical modifications and nucleotide sequences in the mON products. We suggest to address the need for ERAs by monitoring mONs in wastewater with newly developed analytical methods as well as experimental studies in laboratory model systems of processes that determine mON exposure and effects.