Impact of operational conditions on drinking water biofilm dynamics and coliform invasion potential.

Biofilms within drinking water distribution systems serve as a habitat for drinking water microorganisms. However, biofilms can negatively impact drinking water quality by causing water discoloration and deterioration and can be a reservoir for unwanted microorganisms. In this study, we investigated whether indicator organisms for drinking water quality, such as coliforms, can settle in mature drinking water biofilms. Therefore, a biofilm monitor consisting of glass rings was used to grow and sample drinking water biofilms. Two mature drinking water biofilms were characterized by flow cytometry, ATP measurements, confocal laser scanning microscopy, and 16S rRNA sequencing. Biofilms developed under treated chlorinated surface water supply exhibited lower cell densities in comparison with biofilms resulting from treated groundwater. Overall, the phenotypic as well as the genotypic characteristics were significantly different between both biofilms. In addition, the response of the biofilm microbiome and possible biofilm detachment after minor water quality changes were investigated. Limited changes in pH and free chlorine addition, to simulate operational changes that are relevant for practice, were evaluated. It was shown that both biofilms remained resilient. Finally, mature biofilms were prone to invasion of the coliform, Serratia fonticola. After spiking low concentrations (i.e., ±100 cells/100 mL) of the coliform to the corresponding bulk water samples, the coliforms were able to attach and get established within the mature biofilms. These outcomes emphasize the need for continued research on biofilm detachment and its implications for water contamination in distribution networks. IMPORTANCE: The revelation that even low concentrations of coliforms can infiltrate into mature drinking water biofilms highlights a potential public health concern. Nowadays, the measurement of coliform bacteria is used as an indicator for fecal contamination and to control the effectiveness of disinfection processes and the cleanliness and integrity of distribution systems. In Flanders (Belgium), 533 out of 18,840 measurements exceeded the established norm for the coliform indicator parameter in 2021; however, the source of microbial contamination is mostly unknown. Here, we showed that mature biofilms, are susceptible to invasion of Serratia fonticola. These findings emphasize the importance of understanding and managing biofilms in drinking water distribution systems, not only for their potential to influence water quality, but also for their role in harboring and potentially disseminating pathogens. Further research into biofilm detachment, long-term responses to operational changes, and pathogen persistence within biofilms is crucial to inform strategies for safeguarding drinking water quality.

Unlocking the mechanism of action: a cost-effective flow cytometry approach for accelerating antimicrobial drug development.

Antimicrobial resistance is one of the greatest challenges to global health. While the development of new antimicrobials can combat resistance, low profitability reduces the number of new compounds brought to market. Elucidating the mechanism of action is crucial for developing new antimicrobials. This can become expensive as there are no universally applicable pipelines. Phenotypic heterogeneity of microbial populations resulting from antimicrobial treatment can be captured through flow cytometric fingerprinting. Since antimicrobials are classified into limited groups, the mechanism of action of known compounds can be used for predictive modeling. We demonstrate a cost-effective flow cytometry approach for determining the mechanism of action of new compounds. Cultures of Actinomyces viscosus and Fusobacterium nucleatum were treated with different antimicrobials and measured by flow cytometry. A Gaussian mixture mask was applied over the data to construct phenotypic fingerprints. Fingerprints were used to assess statistical differences between mechanism of action groups and to train random forest classifiers. Classifiers were then used to predict the mechanism of action of cephalothin. Statistical differences were found among the different mechanisms of action groups. Pairwise comparison showed statistical differences for 35 out of 45 pairs for A. viscosus and for 32 out of 45 pairs for F. nucleatum after 3.5 h of treatment. The best-performing random forest classifier yielded a Matthews correlation coefficient of 0.92 and the mechanism of action of cephalothin could be successfully predicted. These findings suggest that flow cytometry can be a cheap and fast alternative for determining the mechanism of action of new antimicrobials.IMPORTANCEIn the context of the emerging threat of antimicrobial resistance, the development of novel antimicrobials is a commonly employed strategy to combat resistance. Elucidating the mechanism of action of novel compounds is crucial in this development but can become expensive, as no universally applicable pipelines currently exist. We present a novel flow cytometry-based approach capable of determining the mechanism of action swiftly and cost-effectively. The workflow aims to accelerate drug discovery and could help facilitate a more targeted approach for antimicrobial treatment of patients.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems.

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Pre-incubation conditions determine the fermentation pattern and microbial community structure in fermenters at mild hydrostatic pressure.

Fermentation at elevated hydrostatic pressure is a novel strategy targeting product selectivity. However, the role of inoculum history and cross-resistance, that is, acquired tolerance from incubation under distinctive environmental stress, remains unclear in high-pressure operation. In our here presented work, we studied fermentation and microbial community responses of halotolerant marine sediment inoculum (MSI) and anaerobic digester inoculum (ADI), pre-incubated in serum bottles at different temperatures and subsequently exposed to mild hydrostatic pressure (MHP; < 10 MPa) in stainless steel reactors. Results showed that MHP effects on microbial growth, activity, and community structure were strongly temperature-dependent. At moderate temperature (20°C), biomass yield and fermentation were not limited by MHP; suggesting a cross-resistance effect from incubation temperature and halotolerance. Low temperatures (10°C) and MHP imposed kinetic and bioenergetic limitations, constraining growth and product formation. Fermentation remained favorable in MSI at 28°C and ADI at 37°C, despite reduced biomass yield resulting from maintenance and decay proportionally increasing with temperature. Microbial community structure was modified by temperature during the enrichment, and slight differences observed after MHP-exposure did not compromise functionality. Results showed that the relation incubation temperature-halotolerance proved to be a modifier of microbial responses to MHP and could be potentially exploited in fermentations to modulate product/biomass ratio.

Combined Gold Recovery and Nanoparticle Synthesis in Microbial Systems Using Fractional Factorial Design.

Green synthesis of gold nanoparticles (AuNPs) using microorganisms has been generally studied aiming for high-yield production and morphologies appropriated for various applications, such as bioremediation, (bio)sensors, and (bio)catalysis. Numerous approaches showed the individual effect of factors influencing the synthesis of AuNPs with limited analysis of the governing factors enhancing the production and desired quality of the precipitates. This study proposes a fractional-factorial design to investigate the simultaneous influence of seven environmental factors (cell concentration, temperature, anoxic/oxic conditions, pH, gold concentration, electron donor type, and bacterial species) on the recovery yield and synthesis of targeted AuNPs. Various sizes and morphologies of the AuNPs were obtained by varying the environmental factors studied. The factors with significant effects (i.e., 0.2 mM Au and pH 5) were selected according to statistical analysis for optimal removal of 88.2 ± 3.5% of gold and with the production of valuable 50 nm AuNPs, which are known for their enhanced sensitivity. Implications of the cytochrome-C on the bacterial mechanisms and the provision of electron donors via an electrochemical system are further discussed. This study helps develop gold recovery and nanoparticle synthesis methods, focusing on the determining factor(s) for efficient, low-cost, green synthesis of valuable materials.

Effect of speciation and composition on the kinetics and precipitation of arsenic sulfide from industrial metallurgical wastewater.

Precipitation of arsenic as As2S3 produces little waste sludge, has the potential for low chemical consumption and for selective metal(loid) removal. In this study, arsenic removal from acidic (pH 2), metallurgical wastewater was tested in industrially relevant conditions. Sulfides added at a S:As molar ratio of 2.5 and 5 resulted in removal of 99% and 84% of As(III) and As(V). Precipitation of As2S3 from the As(III) and industrial wastewater containing 17% As(V) was nearly instantaneous. For the synthetic As(V) solution, reduction to As(III) was the rate limiting step. At a S:As ratio of 20 and an observed removal rate (k2 = 4.8 (mol L-1) h-1), two hours were required to remove of 93% of arsenic from a 1 g As L-1 solution. In the case of As(V) in industrial samples this time lag was not observed, showing that components in the industrial wastewater affected the removal and reduction of arsenate. Speciation also affected flocculation and coagulation characteristics of As2S3 particles: As(V) reduction resulted in poor coagulation and flocculation. Selective precipitation of arsenic was possible, but depended on speciation, S:As ratio and other metals present.

Raman Spectroscopy-Based Measurements of Single-Cell Phenotypic Diversity in Microbial Populations.

Microbial cells experience physiological changes due to environmental change, such as pH and temperature, the release of bactericidal agents, or nutrient limitation. This has been shown to affect community assembly and physiological processes (e.g., stress tolerance, virulence, or cellular metabolic activity). Metabolic stress is typically quantified by measuring community phenotypic properties such as biomass growth, reactive oxygen species, or cell permeability. However, bulk community measurements do not take into account single-cell phenotypic diversity, which is important for a better understanding and the subsequent management of microbial populations. Raman spectroscopy is a nondestructive alternative that provides detailed information on the biochemical makeup of each individual cell. Here, we introduce a method for describing single-cell phenotypic diversity using the Hill diversity framework of Raman spectra. Using the biomolecular profile of individual cells, we obtained a metric to compare cellular states and used it to study stress-induced changes. First, in two Escherichia coli populations either treated with ethanol or nontreated and then in two Saccharomyces cerevisiae subpopulations with either high or low expression of a stress reporter. In both cases, we were able to quantify single-cell phenotypic diversity and to discriminate metabolically stressed cells using a clustering algorithm. We also described how the lipid, protein, and nucleic acid compositions changed after the exposure to the stressor using information from the Raman spectra. Our results show that Raman spectroscopy delivers the necessary resolution to quantify phenotypic diversity within individual cells and that this information can be used to study stress-driven metabolic diversity in microbial populations.IMPORTANCE Microbial cells that live in the same community can exist in different physiological and morphological states that change as a function of spatiotemporal variations in environmental conditions. This phenomenon is commonly known as phenotypic heterogeneity and/or diversity. Measuring this plethora of cellular expressions is needed to better understand and manage microbial processes. However, most tools to study phenotypic diversity only average the behavior of the sampled community. In this work, we present a way to quantify the phenotypic diversity of microbial samples by inferring the (bio)molecular profile of its constituent cells using Raman spectroscopy. We demonstrate how this tool can be used to quantify the phenotypic diversity that arises after the exposure of microbes to stress. Raman spectroscopy holds potential for the detection of stressed cells in bioproduction.

Lacticaseibacillus casei AMBR2 modulates the epithelial barrier function and immune response in a donor-derived nasal microbiota manner.

Live biotherapeutic products (LBP) are emerging as alternative treatment strategies for chronic rhinosinusitis. The selection of interesting candidate LBPs often involves model systems that do not include the polymicrobial background (i.e. the host microbiota) in which they will be introduced. Here, we performed a screening in a simplified model system of upper respiratory epithelium to assess the effect of nasal microbiota composition on the ability to attach and grow of a potential LBP, Lacticaseibacillus casei AMBR2, in this polymicrobial background. After selecting the most permissive and least permissive donor, L. casei AMBR2 colonisation in their respective polymicrobial backgrounds was assessed in more physiologically relevant model systems. We examined cytotoxicity, epithelial barrier function, and cytokine secretion, as well as bacterial cell density and phenotypic diversity in differentiated airway epithelium based models, with or without macrophage-like cells. L. casei AMBR2 could colonize in the presence of both selected donor microbiota and increased epithelial barrier resistance in presence of donor-derived nasal bacteria, as well as anti-inflammatory cytokine secretion in the presence of macrophage-like cells. This study highlights the potential of L. casei AMBR2 as LBP and the necessity to employ physiologically relevant model systems to investigate host-microbe interaction in LBP research.

Discriminating Bacterial Phenotypes at the Population and Single-Cell Level: A Comparison of Flow Cytometry and Raman Spectroscopy Fingerprinting.

Investigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, such as flow cytometry and Raman spectroscopy, which describe optical properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth stages of three replicate Escherichia coli populations were characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high-throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single-cell level (i.e., more biochemical information is recorded). Therefore, it can identify distinct phenotypic populations when coupled with analyses tailored toward single-cell data. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose a computational workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external data set. We recommend using flow cytometry to quantify phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in-depth analysis of heterogeneity at the single-cell level. © 2019 International Society for Advancement of Cytometry.

Label-free Raman characterization of bacteria calls for standardized procedures.

Raman spectroscopy has gained relevance in single-cell microbiology for its ability to detect bacterial (sub)populations in a non-destructive and label-free way. However, the Raman spectrum of a bacterium can be heavily affected by abiotic factors, which may influence the interpretation of experimental results. Additionally, there is no publicly available standard for the annotation of metadata describing sample preparation and acquisition of Raman spectra. This article explores the importance of sample manipulations when measuring bacterial subpopulations using Raman spectroscopy. Based on the results of this study and previous findings in literature we propose a Raman metadata standard that incorporates the minimum information that is required to be reported in order to correctly interpret data from Raman spectroscopy experiments. Its aim is twofold: 1) mitigate technical noise due to sample preparation and manipulation and 2) improve reproducibility in Raman spectroscopy experiments studying microbial communities.

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. © 2017 International Society for Advancement of Cytometry.