Microbially influenced corrosion and rust tubercle formation on sheet piles in freshwater systems.

The extent of how complex natural microbial communities contribute to metal corrosion is still not fully resolved, especially not for freshwater environments. In order to elucidate the key processes, we investigated rust tubercles forming massively on sheet piles along the river Havel (Germany) applying a complementary set of techniques. In-situ microsensor profiling revealed steep gradients of O2 , redox potential and pH within the tubercle. Micro-computed tomography and scanning electron microscopy showed a multi-layered inner structure with chambers and channels and various organisms embedded in the mineral matrix. Using Mössbauer spectroscopy we identified typical corrosion products including electrically conductive iron (Fe) minerals. Determination of bacterial gene copy numbers and sequencing of 16S rRNA and 18S rRNA amplicons supported a densely populated tubercle matrix with a phylogenetically and metabolically diverse microbial community. Based on our results and previous models of physic(electro)chemical reactions, we propose here a comprehensive concept of tubercle formation highlighting the crucial reactions and microorganisms involved (such as phototrophs, fermenting bacteria, dissimilatory sulphate and Fe(III) reducers) in metal corrosion in freshwaters.

Prevalence and Distribution of Potentially Human Pathogenic Vibrio spp. on German North and Baltic Sea Coasts.

Global ocean warming results in an increase of infectious diseases including an elevated emergence of Vibrio spp. in Northern Europe. The European Centre for Disease Prevention and Control reported annual periods of high to very high risks of infection with Vibrio spp. during summer months along the North Sea and Baltic Sea coasts. Based on those facts, the risk of Vibrio infections associated with recreational bathing in European coastal waters increases. To obtain an overview of the seasonal and spatial distribution of potentially human pathogenic Vibrio spp. at German coasts, this study monitored V. cholerae, V. parahaemolyticus, and V. vulnificus at seven recreational bathing areas from 2017 to 2018, including the heat wave event in summer 2018. The study shows that all three Vibrio species occurred in water and sediment samples at all sampling sites. Temperature was shown to be the main driving factor of Vibrio abundance, whereas Vibrio community composition was mainly modulated by salinity. A species-specific rapid increase was observed at water temperatures above 10°C, reaching the highest detection numbers during the heat wave event with abundances of 4.5 log10 CFU+1/100 ml of seawater and 6.5 log10 CFU+1/100 g of sediment. Due to salinity, the dominant Vibrio species found in North Sea samples was V. parahaemolyticus, whereas V. vulnificus was predominantly detected in Baltic Sea samples. Most detections of V. cholerae were associated with estuarine samples from both seas. Vibrio spp. concentrations in sediments were up to three log higher compared to water samples, indicating that sediments are an important habitat for Vibrio spp. to persist in the environment. Antibiotic resistances were found against beta-lactam antibiotics (ampicillin 31%, cefazolin 36%, and oxacillin and penicillin 100%) and trimethoprim-sulfamethoxazole (45%). Moreover, isolates harboring pathogenicity-associated genes such as trh for V. parahaemolyticus as well as vcg, cap/wcv, and the 16S rRNA-type B variant for V. vulnificus were detected. All sampled V. cholerae isolates were identified as non-toxigenic non-O1/non-O139 serotypes. To sum up, increasing water temperatures at German North Sea and Baltic Sea coasts provoke elevated Vibrio numbers and encourage human recreational water activities, resulting in increased exposure rates. Owing to a moderate Baltic Sea salinity, the risk of V. vulnificus infections is of particular concern.

Prevalence and seasonal dynamics of blaCTX-M antibiotic resistance genes and fecal indicator organisms in the lower Lahn River, Germany.

Antibiotic-resistant bacteria represent an emerging global health problem and are frequently detected in riverine environments. Analyzing the occurrence of corresponding antibiotic-resistant genes in rivers is of public interest as it contributes towards understanding the origin and dissemination of these emerging microbial contaminants via surface water. This is critical for devising strategies to mitigate the spread of resistances in the environment. Concentrations of blaCTX-M antibiotic resistance genes were quantified weekly over a 12-month period in Lahn River surface water at two sampling sites using quantitative real-time PCR. Gene abundances were statistically assessed with regard to previously determined concentrations of fecal indicator organisms Escherichia coli, intestinal enterococci and somatic coliphages, as well as influential environmental factors. Similar seasonal patterns and strong positive correlations between fecal indicators and blaCTX-M genes indicated identical sources. Accordingly, linear regression analyses showed that blaCTX-M concentrations could largely be explained by fecal pollution. E. coli provided the best estimates (75% explained variance) at the upstream site, where proportions of blaCTX-M genes in relation to fecal indicator organisms were highest. At this site, rainfall proved to be more influential, hinting at surface runoff as an emission source. The level of agricultural impact increased from downstream to upstream, linking increasing blaCTX-M concentrations after rainfall events to the degree of agricultural land use. Exposure assessment revealed that even participants in non-swimming recreational activities were at risk of incidentally ingesting blaCTX-M genes and thus potentially antibiotic resistant bacteria. Considering that blaCTX-M genes are ubiquitous in Lahn River and participants in bathing and non-bathing water sports are at risk of exposure, results highlight the importance of microbial water quality monitoring with an emphasis on antibiotic resistance not only in designated bathing waters. Moreover, E. coli might serve as a suitable estimate for the presence of respective antibiotic resistant strains.

Development of multiple linear regression models as predictive tools for fecal indicator concentrations in a stretch of the lower Lahn River, Germany.

Since rivers are typically subject to rapid changes in microbiological water quality, tools are needed to allow timely water quality assessment. A promising approach is the application of predictive models. In our study, we developed multiple linear regression (MLR) models in order to predict the abundance of the fecal indicator organisms Escherichia coli (EC), intestinal enterococci (IE) and somatic coliphages (SC) in the Lahn River, Germany. The models were developed on the basis of an extensive set of environmental parameters collected during a 12-months monitoring period. Two models were developed for each type of indicator: 1) an extended model including the maximum number of variables significantly explaining variations in indicator abundance and 2) a simplified model reduced to the three most influential explanatory variables, thus obtaining a model which is less resource-intensive with regard to required data. Both approaches have the ability to model multiple sites within one river stretch. The three most important predictive variables in the optimized models for the bacterial indicators were NH4-N, turbidity and global solar irradiance, whereas chlorophyll a content, discharge and NH4-N were reliable model variables for somatic coliphages. Depending on indicator type, the extended mode models also included the additional variables rainfall, O2 content, pH and chlorophyll a. The extended mode models could explain 69% (EC), 74% (IE) and 72% (SC) of the observed variance in fecal indicator concentrations. The optimized models explained the observed variance in fecal indicator concentrations to 65% (EC), 70% (IE) and 68% (SC). Site-specific efficiencies ranged up to 82% (EC) and 81% (IE, SC). Our results suggest that MLR models are a promising tool for a timely water quality assessment in the Lahn area.