N-acyl homoserine lactones (AHLs) enhanced removal of cadmium and other pollutants by algae-bacteria consortia.

Signal transduction is an important mode of algae-bacteria interaction, in which bacterial quorum sensing (QS) may affect microalgal growth and metabolism. Currently, little is known whether acyl homoserine lactones (AHLs) released by bacteria can affect the pollutant removal by algae-bacteria consortia (ABC). In this study, we constructed ABC using Chlorella vulgaris (Cv) with two AHLs-producing bacteria and investigated their performance in the removal of multiple pollutants, including chemical oxygen demand (COD), total nitrogen (TN), phosphorus (P), and cadmium (Cd). The AHLs-producing bacteria, namely Agrobacterium sp. (Ap) and Ensifer adherens (Ea), were capable of forming a symbiosis with C. vulgaris. Consortia of Cv and Ap with ratio of 2:1 (Cv2-Ap1) showed the optimal growth promotion and higher removal of Cd, COD, TN, and P compared to the C. vulgaris monoculture. Cv2-Ap1 ABC removed 36.1-47.5% of Cd, 94.5%-94.6% COD, 37.1%-56.0% TN, and 90.4%-93.5% P from the culture medium. In addition, increase of intracellular neutral lipids and extracellular protein, as well as the types of functional groups on cell surface contributed to Cd removal and tolerance in the Cv2-Ap1 ABC. Six AHLs were detected in the Cv2-Ap1 culture. Among these, 3OC8-HSL and 3OC12-HSL additions promoted the ABC growth and enhanced their Cd accumulation. These findings may contribute to further understanding of AHL-mediated communication between algae and bacteria and provide support bioremediation efforts of metal-containing wastewater.

Refining the Application of Microbial Lipids as Tracers of Staphylococcus aureus Growth Rates in Cystic Fibrosis Sputum.

Chronic lung infections in cystic fibrosis (CF) could be treated more effectively if the effects of antimicrobials on pathogens in situ were known. Here, we compared changes in the microbial community composition and pathogen growth rates in longitudinal studies of seven pediatric CF patients undergoing intravenous antibiotic administration during pulmonary exacerbations. The microbial community composition was determined by counting rRNA with NanoString DNA analysis, and growth rates were obtained by incubating CF sputum with heavy water and tracing incorporation of deuterium into two branched-chain ("anteiso") fatty acids (a-C15:0 and a-C17:0) using gas chromatography-mass spectrometry (GC/MS). Prior to this study, both lipids were thought to be specific for Staphylococcaceae; hence, their isotopic enrichment was interpreted as a growth proxy for Staphylococcus aureus Our experiments revealed, however, that Prevotella is also a relevant microbial producer of a-C17:0 fatty acid in some CF patients; thus, deuterium incorporation into these lipids is better interpreted as a more general pathogen growth rate proxy. Even accounting for a small nonmicrobial background source detected in some patient samples, a-C15:0 fatty acid still appears to be a relatively robust proxy for CF pathogens, revealing a median generation time of ∼1.5 days, similar to prior observations. Contrary to our expectation, pathogen growth rates remained relatively stable throughout exacerbation treatment. We suggest two straightforward "best practices" for application of stable-isotope probing to CF sputum metabolites: (i) parallel determination of microbial community composition in CF sputum using culture-independent tools and (ii) assessing background levels of the diagnostic metabolite.IMPORTANCE In chronic lung infections, populations of microbial pathogens change and mature in ways that are often unknown, which makes it challenging to identify appropriate treatment options. A promising tool to better understand the physiology of microorganisms in a patient is stable-isotope probing, which we previously developed to estimate the growth rates of S. aureus in cystic fibrosis (CF) sputum. Here, we tracked microbial communities in a cohort of CF patients and found that anteiso fatty acids can also originate from other sources in CF sputum. This awareness led us to develop a new workflow for the application of stable-isotope probing in this context, improving our ability to estimate pathogen generation times in clinical samples.

Data on the influence of temperature on the growth of Escherichia coli in a minimal medium containing glucose as the sole carbon source for the joint computation of growth yields and rates at each temperature from 27 to 45°C.

Temperature is a key factor influencing microbial growth rates and yields. In literature, the influence of temperature on growth is studied either on yields or rates but not both at the same time. Moreover, studies often report the influence of a specific set of temperatures using rich culture media containing complex ingredients (such as yeast extract) which chemical composition cannot be precisely specified. Here, we present a complete dataset for the growth of Escherichia coli K12 NCM3722 strain in a minimal medium containing glucose as the sole energy and carbon source for the computation of growth yields and rates at each temperature from 27 to 45°C. For this purpose, we monitored the growth of E. coli by automated optical density (OD) measurements in a thermostated microplate reader. At each temperature full OD curves were reported for 28 to 40 microbial cultures growing in parallel wells. Additionally, a correlation was established between OD values and the dry mass of E. coli cultures. For that, 21 dilutions were prepared from triplicate cultures and optical density was measured in parallel with the microplate reader (ODmicroplate) and a UV-Vis spectrophotometer (ODUV-vis) and correlated to duplicate dry biomass measurements. The correlation was used to compute growth yields in terms of dry biomass.

Quantitative stable isotope probing technique and its applications in microbial ecology.

Quantitative stable isotope probing (qSIP) is a powerful tool, which links microbial taxon with functional metabolism in ecosystems and quantitatively determines the metabolic activity or growth rate of individual microbial taxa exposed to isotope tracers in the environment. qSIP technique employs quantitative PCR, high-throughput sequencing and stable isotope probing (SIP) techniques. The procedure involves adding labeled substrates to environmental samples for cultivation, separating labeled heavy fraction from unlabeled light fraction via isopycnic ultracentrifugation, making absolute quantification and sequencing analysis for microbial populations in all fractions, and then quantifying the isotope abundance of DNA involved in uptake and transformation based on the DNA density curve of unlabeled treatment and GC content. Here, we reviewed the rationale, data analysis and application of qSIP in microbial ecology, and discussed the existing problems and prospects of qSIP.

Microbial Interactions as Drivers of a Nitrification Process in a Chemostat.

This article deals with the inclusion of microbial ecology measurements such as abundances of operational taxonomic units in bioprocess modelling. The first part presents the mathematical analysis of a model that may be framed within the class of Lotka-Volterra models fitted to experimental data in a chemostat setting where a nitrification process was operated for over 500 days. The limitations and the insights of such an approach are discussed. In the second part, the use of an optimal tracking technique (developed within the framework of control theory) for the integration of data from genetic sequencing in chemostat models is presented. The optimal tracking revisits the data used in the aforementioned chemostat setting. The resulting model is an explanatory model, not a predictive one, it is able to reconstruct the different forms of nitrogen in the reactor by using the abundances of the operational taxonomic units, providing some insights into the growth rate of microbes in a complex community.

An interaction model for the combined effect of temperature, pH and water activity on the growth rate of E. coli K12.

Previous research has indicated that more complex model structures than the commonly used gamma model are needed to obtain an accurate prediction of the effect of multiple environmental conditions on the microbial growth rate. Due to the complexity associated with the development of such model structures, it is recommended that the model structure is compatible with a modular model building method. In this research, a gamma-interaction model was built to describe the combined effect of temperature, pH and water activity on the microbial growth rate of E. coli K12 based on a dataset of 68 bioreactor experiments. This novel interaction model was compared with the standard gamma model. The model structures were tested separately for the combined effects of (i) temperature and pH, (ii) pH and water activity, (iii) temperature and water activity and (iv) temperature, pH and water activity. Based on the results of this research, it was concluded that models for the combined effect of environmental conditions need to allow for sufficient flexibility for the description of combined effects of environmental conditions to obtain accurate model predictions. In the current study, this flexibility was successfully introduced by using the gamma-interaction model. A cross-validation study also demonstrated that the predictions of the interaction model are more robust with respect to the specific data used than the gamma model. As such, the gamma-interaction model provides food producers and food safety authorities with a more accurate and reliable tool for the prediction of the microbial growth rate as a function of multiple environmental conditions.

Stability of aerobic granules during long-term bioreactor operation.

Aerobic granular sludge technology has been extensively studied over the past 20 years and is regarded as the upcoming new standard for biological treatment of domestic and industrial wastewaters. Aerobic granules (AG) are dense, compact, self-immobilized microbial aggregates that allow better sludge-water separation and thereby higher biomass concentrations in the bioreactor than conventional activated sludge aggregates. This brings potential practical advantages in terms of investment cost, energy consumption and footprint. Yet, despite the relevant advances regarding the process of AG formation, instability of AG during long-term operation is still seen as a major barrier for a broad practical application of this technology. This paper presents an up-to-date review of the literature focusing on AG stability, aiming to contribute to the identification of key factors for promoting long-term stability of AG and to a better understanding of the underlying mechanisms. Operational conditions leading to AG disintegration are described, including high organic loads, particulate substrates in the influent, toxic feed components, aerobic feeding and too short famine periods. These operational and influent wastewater composition conditions were shown to influence the micro-environment of AG, consequently affecting their stability. Granule stability is generally favored by the presence of a dense core, with microbial growth throughout the AG depth being a crucial intrinsic factor determining its structural integrity. Accordingly, possible practical solutions to improve granule long-term stability are described, namely through the promotion of minimal substrate concentration gradients and control of microbial growth rates within AG, including anaerobic, plug-flow feeding and specific sludge removal strategies.

Introducing a novel interaction model structure for the combined effect of temperature and pH on the microbial growth rate.

Efficient modelling of the microbial growth rate can be performed by combining the effects of individual conditions in a multiplicative way, known as the gamma concept. However, several studies have illustrated that interactions between different effects should be taken into account at stressing environmental conditions to achieve a more accurate description of the growth rate. In this research, a novel approach for modeling the interactions between the effects of environmental conditions on the microbial growth rate is introduced. As a case study, the effect of temperature and pH on the growth rate of Escherichia coli K12 is modeled, based on a set of computer controlled bioreactor experiments performed under static environmental conditions. The models compared in this case study are the gamma model, the model of Augustin and Carlier (2000), the model of Le Marc et al. (2002) and the novel multiplicative interaction model, developed in this paper. This novel model enables the separate identification of interactions between the effects of two (or more) environmental conditions. The comparison of these models focuses on the accuracy, interpretability and compatibility with efficient modeling approaches. Moreover, for the separate effects of temperature and pH, new cardinal parameter model structures are proposed. The novel interaction model contributes to a generic modeling approach, resulting in predictive models that are (i) accurate, (ii) easily identifiable with a limited work load, (iii) modular, and (iv) biologically interpretable.