Ruben Props

Platinum Recovery from Synthetic Extreme Environments by Halophilic Bacteria

Metal recycling based on urban mining needs to be established to tackle the increasing supply risk of critical metals such as platinum. Presently, efficient strategies are missing for the recovery of platinum from diluted industrial process streams, often characterized by extremely low pHs and high salt concentrations. In this research, halophilic mixed cultures were employed for the biological recovery of platinum (Pt). Halophilic bacteria were enriched from Artemia cysts, living in salt lakes, in different salt matrices (sea salt mixture and NH4Cl; 20-210 g L(-1) salts) and at low to neutral pH (pH 3-7). The main taxonomic families present in the halophilic cultures were Halomonadaceae, Bacillaceae, and Idiomarinaceae. The halophilic cultures were able to recover >98% Pt(II) and >97% Pt(IV) at pH 2 within 3-21 h (4-453 mg Ptrecovered h(-1) g(-1) biomass). X-ray absorption spectroscopy confirmed the reduction to Pt(0) and transmission electron microscopy revealed both intra- and extracellular Pt precipitates, with median diameters of 9-30 nm and 11-13 nm, for Pt(II) and Pt(IV), respectively. Flow cytometric membrane integrity staining demonstrated the preservation of cell viability during platinum recovery. This study demonstrates the Pt recovery potential of halophilic mixed cultures in acidic saline conditions.

Laboratory-scale simulation and real-time tracking of a microbial contamination event and subsequent shock-chlorination in drinking water

Rapid contamination of drinking water in distribution and storage systems can occur due to pressure drop, backflow, cross-connections, accidents, and bio-terrorism. Small volumes of a concentrated contaminant (e.g., wastewater) can contaminate large volumes of water in a very short time with potentially severe negative health impacts. The technical limitations of conventional, cultivation-based microbial detection methods neither allow for timely detection of such contaminations, nor for the real-time monitoring of subsequent emergency remediation measures (e.g., shock-chlorination). Here we applied a newly developed continuous, ultra high-frequency flow cytometry approach to track a rapid pollution event and subsequent disinfection of drinking water in an 80-min laboratory scale simulation. We quantified total (TCC) and intact (ICC) cell concentrations as well as flow cytometric fingerprints in parallel in real-time with two different staining methods. The ingress of wastewater was detectable almost immediately (i.e., after 0.6% volume change), significantly changing TCC, ICC, and the flow cytometric fingerprint. Shock chlorination was rapid and detected in real time, causing membrane damage in the vast majority of bacteria (i.e., drop of ICC from more than 380 cells μl-1 to less than 30 cells μl-1 within 4 min). Both of these effects as well as the final wash-in of fresh tap water followed calculated predictions well. Detailed and highly quantitative tracking of microbial dynamics at very short time scales and for different characteristics (e.g., concentration, membrane integrity) is feasible. This opens up multiple possibilities for targeted investigation of a myriad of bacterial short-term dynamics (e.g., disinfection, growth, detachment, operational changes) both in laboratory-scale research and full-scale system investigations in practice.

Reconciliation between operational taxonomic units and species boundaries

Abstract The development of high-throughput sequencing technologies has revolutionised the field of microbial ecology via 16S rRNA gene amplicon sequencing approaches. Clustering those amplicon sequencing reads into operational taxonomic units (OTUs) using a fixed cut-off is a commonly used approach to estimate microbial diversity. A 97% threshold was chosen with the intended purpose that resulting OTUs could be interpreted as a proxy for bacterial species. Our results show that the robustness of such a generalised cut-off is questionable when applied to short amplicons only covering one or two variable regions of the 16S rRNA gene. It will lead to biases in diversity metrics and makes it hard to compare results obtained with amplicons derived with different primer sets. The method introduced within this work takes into account the differential evolutional rates of taxonomic lineages in order to define a dynamic and taxonomic-dependent OTU clustering cut-off score. For a taxonomic family consisting of species showing high evolutionary conservation in the amplified variable regions, the cut-off will be more stringent than 97%. By taking into consideration the amplified variable regions and the taxonomic family when defining this cut-off, such a threshold will lead to more robust results and closer correspondence between OTUs and species. This approach has been implemented in a publicly available software package called DynamiC.

Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data

Detecting disturbances in microbial communities is an important aspect of managing natural and engineered microbial communities. Here, we implemented a custom-built continuous staining device in combination with real-time flow cytometry (RT-FCM) data acquisition, which, combined with advanced FCM fingerprinting methods, presents a powerful new approach to track and quantify disturbances in aquatic microbial communities. Through this new approach we were able to resolve various natural community and single-species microbial contaminations in a flow-through drinking water reactor. Next to conventional FCM metrics, we applied metrics from a recently developed fingerprinting technique in order to gain additional insight into the microbial dynamics during these contamination events. Importantly, we found that multiple community FCM metrics based on different statistical approaches were required to fully characterize all contaminations. Furthermore we found that for accurate cell concentration measurements and accurate inference from the FCM metrics (coefficient of variation ≤ 5%), at least 1000 cells should be measured, which makes the achievable temporal resolution a function of the prevalent bacterial concentration in the system-of-interest. The integrated RT-FCM acquisition and analysis approach presented herein provides a considerable improvement in the temporal resolution by which microbial disturbances can be observed and simultaneously provides a multi-faceted toolset to characterize such disturbances.

Initial evenness determines diversity and cell density dynamics in synthetic microbial ecosystems

The effect of initial evenness on the temporal trajectory of synthetic communities in comprehensive, low-volume microcosm studies remains unknown. We used flow cytometric fingerprinting and 16S rRNA gene amplicon sequencing to assess the impact of time on community structure in one hundred synthetic ecosystems of fixed richness but varying initial evenness. Both methodologies uncovered a similar reduction in diversity within synthetic communities of medium and high initial evenness classes. However, the results of amplicon sequencing showed that there were no significant differences between and within the communities in all evenness groups at the end of the experiment. Nevertheless, initial evenness significantly impacted the cell density of the community after five medium transfers. Highly even communities retained the highest cell densities at the end of the experiment. The relative abundances of individual species could be associated to particular evenness groups, suggesting that their presence was dependent on the initial evenness of the synthetic community. Our results reveal that using synthetic communities for testing ecological hypotheses requires prior assessment of initial evenness, as it impacts temporal dynamics.

Stripping flow cytometry: How many detectors do we need for bacterial identification?

Multicolor approaches are challenging for microbial flow cytometry; as flow cytometers are mainly developed for biomedical applications, modern instruments contain more detectors than needed. Some of these additional fluorescence detectors measure biological information due to spectral overlap, yet the extent to which this information is relevant for the identification of bacterial populations is ambiguous. In this paper we characterize the usefulness of these additional detectors. We propose a data‐driven detector selection method to select the smallest subset of detectors that will optimally discriminate between bacterial populations. Using a detector elimination strategy, we show that one or more detectors can be removed without loss of resolving power. A number of additional detectors are included in the final subset, which help to improve the identification of bacterial populations. Experimental data were retrieved from two types of modern cytometers with different configurations. The method reveals a clear ordering of detector importances, which depends on the instrument from which the data were retrieved. In addition, we were able to pinpoint unexpected behavior of SYBR Green I in the red spectrum. As the field of microbial flow cytometry is maturing, these results motivate the construction of a different kind of cytometric instruments for microbiologists, for which the number of detectors is reduced, but tailored toward the characteristics of microbial experiments.

Drinking water bacterial communities exhibit specific and selective necrotrophic growth

Physicochemical water disinfection methods result in the reduction of bacterial concentrations by orders of magnitude, but not in the total elimination of the bacterial community. As such, the dead bacterial biomass may act as a carbon and nutrient source for the survivor populations. The ability of bacterial strains to grow on dead bacterial cells has been described before as necrotrophy. We investigated the impact of killed bacterial biomass of two different bacterial strains on the growth potential of natural drinking water microbial communities. Many indigenous bacterial taxa could grow on dead biomass, with the total bacterial concentration increasing from 104 to 108 cells/ml. Necrotrophic growth was specific (43 enriched taxa) and selective (i.e. enriched taxa were dependent on the type of dead biomass). The potential of natural water communities to grow necrotrophically has remained underexplored. Nevertheless the phenomenon can have a big impact in water quality and deserves more attention.

Coculturing bacteria leads to reduced phenotypic heterogeneities

Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the single cell level. Because of difficulties in assessing the phenotypic heterogeneity of a single taxon in a mixed community, the importance of this deeper level of organisation remains relatively unknown for natural communities. In this study, we have used membrane-based microcosms that allow the probing of the phenotypic heterogeneity of a single taxon while interacting with a synthetic or natural community. Individual taxa were studied under axenic conditions, as members of a coculture with physical separation, and as a mixed culture. Phenotypic heterogeneity was assessed through both flow cytometry and Raman spectroscopy. Using this setup, we investigated the effect of microbial interactions on the individual phenotypic heterogeneities of two interacting drinking water isolates. We have demonstrated that interactions between these bacteria lead to an adjustment of their individual phenotypic diversities, and that this adjustment is conditional on the bacterial taxon. Importance Laboratory studies have shown the impact of phenotypic heterogeneity on the survival and functionality of isogenic populations. As phenotypic heterogeneity is known to play an important role in pathogenicity and virulence, antibiotics resistance, biotechnological applications and ecosystem properties, it is crucial to understand its influencing factors. An unanswered question is whether bacteria in mixed communities influence the phenotypic heterogeneity of their community partners. We found that coculturing bacteria leads to a reduction in their individual phenotypic heterogeneities, which led us to the hypothesis that the individual phenotypic diversity of a taxon is dependent on the community composition.

Using machine learning to associate bacterial taxa with functional groups through flow cytometry, 16S rRNA gene sequencing, and productivity data

High- (HNA) and low-nucleic acid (LNA) bacteria are two separated flow cytometry (FCM) groups that are ubiquitous across aquatic systems. HNA cell density often correlates strongly with heterotrophic production. However, the taxonomic composition of bacterial taxa within HNA and LNA groups remains mostly unresolved. Here, we associated freshwater bacterial taxa with HNA and LNA groups by integrating FCM and 16S rRNA gene sequencing using a machine learning-based variable selection approach. There was a strong association between bacterial heterotrophic production and HNA cell abundances (R2 = 0.65), but not with more abundant LNA cells, suggesting that the smaller pool of HNA bacteria may play a disproportionately large role in the freshwater carbon flux. Variables selected by the models were able to predict HNA and LNA cell abundances at all taxonomic levels, with highest accuracy at the OTU level. There was high system specificity as the selected OTUs were mostly unique to each lake ecosystem and some OTUs were selected for both groups or were rare. Our approach allows for the association of OTUs with FCM functional groups and thus the identification of putative indicators of heterotrophic activity in aquatic systems, an approach that can be generalized to other ecosystems and functioning of interest.Abstract High-(HNA) and low-nucleic acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. HNA cell density often correlates strongly with heterotrophic production, while LNA cell density does not. However, which taxa are specifically associated with these groups, and by extension, productivity has remained elusive. Here, we addressed this knowledge gap by using a machine learning-based variable selection approach that integrated FCM and 16S rRNA gene sequencing data collected from 14 freshwater lakes spanning a broad range in physicochemical conditions. There was a strong association between bacterial heterotrophic production and HNA absolute cell abundances (R2 = 0.65), but not with the more abundant LNA cells. This solidifies findings, mainly from marine systems, that HNA and LNA could be considered separate functional groups, the former contributing a disproportionately large share of carbon cycling. Taxa selected by the models could predict HNA and LNA absolute cell abundances at all taxonomic levels, with the highest performance at the OTU level. Selected OTUs ranged from low to high relative abundance and were mostly lake system-specific (89.5%-99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5%-33.3%) suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of systems-specific putative ecological indicators for heterotrophic productivity. Generally, our approach allows for the association of OTUs with specific functional groups in diverse ecosystems in order to improve our understanding of (microbial) biodiversity-ecosystem functioning relationships. Importance A major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Research is limited by the ability to readily culture most bacteria present in the environment and the difference in bacterial physiology in situ compared to in laboratory culture. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxonomic data obtained from marker gene surveys to functional groups identified by flow cytometry. This allowed us to identify the taxa that are associated with heterotrophic productivity in freshwater lakes and indicated that the key contributors were highly system-specific, regularly rare members of the community, and that some could switch between being low and high contributors. Our approach provides a promising framework to identify taxa that contribute to ecosystem functioning and can be further developed to explore microbial contributions beyond heterotrophic production.

Plant and soil microbe responses to light, warming and nitrogen addition in a temperate forest

1. Temperate forests across Europe and eastern North America have become denser since the 1950s due to less intensive forest management and global environmental changes such as nitrogen deposition and climate warming. Denser tree canopies result in lower light availability at the forest floor. This shade may buffer the effects of nitrogen deposition and climate warming on understorey plant communities. 2. We conducted an innovative in situ field experiment to study the responses of co-occurring soil microbial and understorey plant communities to nitrogen addition, enhanced light availability and experimental warming in a full-factorial design. 3. We determined the effects of multiple environmental drivers and their interactions on the soil microbial and understorey plant communities, and assessed to what extent the soil microbial and understorey plant communities covary. 4. High light led to lower biomass of the soil microbes (analysed by phospholipid fatty acids), but the soil microbial structure, i.e. the ratio of fungal biomass to bacterial biomass, was not affected by light availability. The composition of the soil bacterial community (analysed by high-throughput sequencing) was affected by both light availability and warming (and their interaction), but not by nitrogen addition. Yet, the number of unique operational taxonomic units was higher in plots with nitrogen addition, and there were significant interactive effects of light and nitrogen addition. Light availability also determined the composition of the plant community; no effects of nitrogen addition and warming were observed. The soil bacterial and plant communities were co-structured, and light availability explained a large part of the variance of this co-structure. 5. We provide robust evidence for the key role of light in affecting both the soil microbial and plant communities in forest understoreys. Our results advocate for more multifactor global change experiments that investigate the mechanism underlying the (in) direct effects of light on the plant-soil continuum in forests.