Fecal microbiota transplantation, an alternative treatment method for gastrointestinal diseases, has a high recovery rate, but comes with disadvantages, such as high donor requirements and the low storability of stool. A solution to overcome these problems is the cultivation of an in vitro microbiota. However, the influence of cultivation conditions on the pH are yet unknown. In this study, the influence of the cultivation pH (6.0–7.0) on the system’s behavior and characteristics, including cell count, metabolism, and microbial composition, was investigated. With an increasing cultivation pH, an increase in cell count, total amount of SCFAs, acetate, propionate, and the abundance of Bacteroidetes and Verrucomicrobia were observed. For the concentration of butyrate and the abundance of Actinobacteria and Firmicutes, a decrease with increasing pH was determined. For the concentration of isovalerate, the abundance of Proteobacteria and diversity (richness and Shannon effective), no effect of the pH was observed. Health-promoting genera were more abundant at lower pH levels. When cultivating an in vitro microbiota, all investigated pH values created a diverse and stable system. Ultimately, therefore, the choice of pH creates significant differences in the established in vitro microbiota, but no clear recommendations for a special value can be made.

In nature, microorganisms often reside in symbiotic co-existence providing nutrition, stability, and protection for each partner by applying "division of labor." This principle may also be used for the overproduction of targeted compounds in bioprocesses. It requires the engineering of a synthetic co-culture with distributed tasks for each partner. Thereby, the competition on precursors, redox cofactors, and energy-which occurs in a single host-is prevented. Current applications often focus on unidirectional interactions, that is, the product of partner A is used for the completion of biosynthesis by partner B. Here, we present a synthetically engineered Escherichia coli co-culture of two engineered mutant strains marked by the essential interaction of the partners which is achieved by implemented auxotrophies. The tryptophan auxotrophic strain E. coli ANT-3, only requiring small amounts of the aromatic amino acid, provides the auxotrophic anthranilate for the tryptophan producer E. coli TRP-3. The latter produces a surplus of tryptophan which is used to showcase the suitability of the co-culture to access related products in future applications. Co-culture characterization revealed that the microbial consortium is remarkably functionally stable for a broad range of inoculation ratios. The range of robust and functional interaction may even be extended by proper glucose feeding which was shown in a two-compartment bioreactor setting with filtrate exchange. This system even enables the use of the co-culture in a parallel two-level temperature setting which opens the door to access temperature sensitive products via heterologous production in E. coli in a continuous manner.

Abstract. Model output statistics (MOS) methods can be used to empirically relate an environmental variable of interest to predictions from earth system models (ESMs). This variable often belongs to a spatial scale not resolved by the ESM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the ESM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In doing so, the MOS method is tested for catchments areas covering 4 orders of magnitude. Using data from the period 1981-2011, the results show that skill, with respect to climatology, is restricted on average to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduce the mean absolute error of the former in the range of 5 to 12 %, which is consistently reproduced at the subcatchment scale. An additional experiment conducted for 5-day mean streamflow indicates that the dynamical predictions help to reduce uncertainties up to about 20 days ahead, but it also reveals some shortcomings of the present MOS method.

Abstract. Based on a hindcast experiment for the period 1982–2013 in 66 sub-catchments of the Swiss Rhine, the present study compares two approaches of building a regression model for seasonal streamflow forecasting. The first approach selects a single "best guess" model, which is tested by leave-one-out cross-validation. The second approach implements the idea of bootstrap aggregating, where bootstrap replicates are employed to select several models, and out-of-bag predictions provide model testing. The target value is mean streamflow for durations of 30, 60 and 90 days, starting with the 1st and 16th day of every month. Compared to the best guess model, bootstrap aggregating reduces the mean squared error of the streamflow forecast by seven percent on average. Thus, if resampling is anyway part of the model building procedure, bootstrap aggregating seems to be a useful strategy in statistical seasonal streamflow forecasting. Since the improved accuracy comes at the cost of a less interpretable model, the approach might be best suited for pure prediction tasks, e.g. as in operational applications.