Abstract. Despite advances regarding the microbial and organic-molecular impact on nucleation, the formation of dolomite in sedimentary environments is still incompletely understood. Since 1960, apparent dolomite formation has been reported from mud sediments of the shallow, oligohaline and alkaline Lake Neusiedl, Austria. To trace potential dolomite formation or diagenetic alteration processes in its deposits, lake water samples and sediment cores were analyzed with respect to sediment composition, hydrochemistry and bacterial community composition. Sediments comprise 20 cm of homogenous mud with 60 wt % carbonate, which overlies dark-laminated consolidated mud containing 50 wt % carbonate and plant debris. Hydrochemical measurements reveal a shift from oxic lake water with pH 9.0 to anoxic sediment pore water with pH 7.5. A decrease in SO42- with a concomitant increase in ΣH2S and NH4+ from 0 to 15 cm core depth indicates anaerobic heterotrophic decomposition, including sulfate reduction. The bacterial community composition reflects the zonation indicated by the pore water chemistry, with a distinct increase in fermentative taxa below 15 cm core depth. The water column is highly supersaturated with respect to (disordered) dolomite and calcite, whereas saturation indices of both minerals rapidly approach zero in the sediment. Notably, the relative proportions of different authigenic carbonate phases and their stoichiometric compositions remain constant with increasing core depth. Hence, evidence for Ca–Mg carbonate formation or ripening to dolomite is lacking within the sediment of Lake Neusiedl. As a consequence, precipitation of high-magnesium calcite (HMC) and protodolomite does not occur in association with anoxic sediment and sulfate-reducing conditions. Instead, analytical data for Lake Neusiedl suggest that authigenic HMC and protodolomite precipitate from the supersaturated, well-mixed aerobic water column. This observation supports an alternative concept to dolomite formation in anoxic sediments, comprising Ca–Mg carbonate precipitation in the water column under aerobic and alkaline conditions.

Chemoautotrophic endosymbionts are famous for exploiting sulfur oxidization to feed marine organisms with fixed carbon. However, the physiology of thiotrophic bacteria thriving on the surface of animals (ectosymbionts) is less understood.

Globally, peatlands have been recognized as important carbon sinks while only covering approximately 3% of the earth’s land surface. Root exudates are known key drivers of C cycling in soils and rhizosphere priming effects have been studied extensively in terrestrial ecosystems. Their role for decomposition of peat still remains unclear, as little research about their fate and potential priming effects in peat exists. In this study, we aimed to evaluate pathways of root exudates and their short-term priming effects by daily determination of stable carbon isotope fluxes of CO2 and CH4. As the drainage of peatlands strongly alters processes of decomposition, we included measurements after drainage as well. Results revealed the immediate respiration of root exudates in peat, mainly as CO2, while CH4 release was associated with a lag time of several days. However, the largest proportion of added root exudates remained in the solid and liquid phase of peat. In conclusion, our findings suggest that no priming occurred as added substrates remained immobile in peat.

As the development of new drugs reaches its physical and financial limits, drug repurposing has become more important than ever. For mechanistically grounded drug repurposing, it is crucial to uncover the disease mechanisms and to detect clusters of mechanistically related diseases. Various methods for computing candidate disease mechanisms and disease clusters exist. However, in the absence of ground truth, in silico validation is challenging. This constitutes a major hurdle toward the adoption of in silico prediction tools by experimentalists who are often hesitant to carry out wet-lab validations for predicted candidate mechanisms without clearly quantified initial plausibility. To address this problem, we present DIGEST (in silico validation of disease and gene sets, clusterings or subnetworks), a Python-based validation tool available as a web interface (https://digest-validation.net), as a stand-alone package or over a REST API. DIGEST greatly facilitates in silico validation of gene and disease sets, clusterings or subnetworks via fully automated pipelines comprising disease and gene ID mapping, enrichment analysis, comparisons of shared genes and variants and background distribution estimation. Moreover, functionality is provided to automatically update the external databases used by the pipelines. DIGEST hence allows the user to assess the statistical significance of candidate mechanisms with regard to functional and genetic coherence and enables the computation of empirical $P$-values with just a few mouse clicks.

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