Real-time monitoring of population dynamics and physical interactions in a synthetic yeast ecosystem by use of multicolour flow cytometry

Gradient boosted regression as a tool to reveal key drivers of temporal dynamics in a synthetic yeast community

Microbial communities are vital to our lives, yet their ecological functioning and dynamics remain poorly understood. This understanding is crucial for assessing threats to these systems and leveraging their biotechnological applications. Given that temporal dynamics are linked to community functioning, this study investigated the drivers of community succession in the wine yeast community. We experimentally generated population dynamics data and used it to create an interpretable model with a gradient boosted regression tree approach. The model was trained on temporal data of viable species populations in various combinations, including pairs, triplets, and quadruplets, and was evaluated for predictive accuracy and input feature importance. Key findings revealed that the inoculation dosage of non-Saccharomyces species significantly influences their performance in mixed cultures, while Saccharomyces cerevisiae consistently dominates regardless of initial abundance. Additionally, we observed multispecies interactions where the dynamics of Wickerhamomyces anomalus were influenced by Torulaspora delbrueckii in pairwise cultures, but this interaction was altered by the inclusion of S. cerevisiae. This study provides insights into yeast community succession and offers valuable machine learning-based analysis techniques applicable to other microbial communities, opening new avenues for harnessing microbial communities.

Xylose and shikimate transporters facilitates microbial consortium as a chassis for benzylisoquinoline alkaloid production

Plant-sourced aromatic amino acid (AAA) derivatives are a vast group of compounds with broad applications. Here, we present the development of a yeast consortium for efficient production of (S)-norcoclaurine, the key precursor for benzylisoquinoline alkaloid biosynthesis. A xylose transporter enables the concurrent mixed-sugar utilization in Scheffersomyces stipitis, which plays a crucial role in enhancing the flux entering the highly regulated shikimate pathway located upstream of AAA biosynthesis. Two quinate permeases isolated from Aspergillus niger facilitates shikimate translocation to the co-cultured Saccharomyces cerevisiae that converts shikimate to (S)-norcoclaurine, resulting in the maximal titer (11.5 mg/L), nearly 110-fold higher than the titer reported for an S. cerevisiae monoculture. Our findings magnify the potential of microbial consortium platforms for the economical de novo synthesis of complex compounds, where pathway modularization and compartmentalization in distinct specialty strains enable effective fine-tuning of long biosynthetic pathways and diminish intermediate buildup, thereby leading to increases in production.

A novel strategy to construct multi-strain starter cultures: an insight to evolve from natural to directed fermentation

Some biotechnological strategies have succeeded in the attempt to imitate natural fermentation, and bioprocesses have been efficiently designed when the product is the result of a unique biological reaction. However, when the process requires more than one biological reaction, few bioprocesses have been successfully designed because the available tools to construct multi-strain starter cultures are not yet well defined. In this work, a novel experimental strategy to construct multi-strain starter cultures with selected native microorganisms from natural fermentation is proposed. The strategy analyses, selects, and defines the number and proportion of each strain that should form a starter culture to be used in directed fermentations. It was applied to evolve natural fermentation to directed fermentation in distilled agave production. The results showed that a starter culture integrated by Kluyveromyces marxianus, Clavispora lusitaniae, and Kluyveromyces marxianus var. drosophilarum in proportions of 35, 32, and 33%, respectively, allows obtaining fermented agave juice containing a 2.1% alcohol yield and a distilled product with a broad profile of aromatic compounds. Hence, the results show, for the first time, a tool that addresses the technical challenge for multi-strain starter culture construction, offering the possibility of preserving the typicity and genuineness of the original traditional product.

Predictability of the community-function landscape in wine yeast ecosystems

Predictively linking taxonomic composition and quantitative ecosystem functions is a major aspiration in microbial ecology, which must be resolved if we wish to engineer microbial consortia. Here, we have addressed this open question for an ecological function of major biotechnological relevance: alcoholic fermentation in wine yeast communities. By exhaustively phenotyping an extensive collection of naturally occurring wine yeast strains, we find that most ecologically and industrially relevant traits exhibit phylogenetic signal, allowing functional traits in wine yeast communities to be predicted from taxonomy. Furthermore, we demonstrate that the quantitative contributions of individual wine yeast strains to the function of complex communities followed simple quantitative rules. These regularities can be integrated to quantitatively predict the function of newly assembled consortia. Besides addressing theoretical questions in functional ecology, our results and methodologies can provide a blueprint for rationally managing microbial processes of biotechnological relevance.

Microbial Biofilms for Environmental Bioremediation of Heavy Metals: a Review

Heavy metal pollution caused due to various industrial and mining activities poses a serious threat to all forms of life in the environment because of the persistence and toxicity of metal ions. Microbial-mediated bioremediation including microbial biofilms has received significant attention as a sustainable tool for heavy metal removal as it is considered safe, effective, and feasible. The biofilm matrix is dynamic, having microbial cells as major components with constantly changing and evolving microenvironments. This review summarizes the bioremediation potential of bacterial biofilms for different metal ions. The composition and mechanism of biofilm formation along with interspecies communication among biofilm-forming bacteria have been discussed. The interaction of biofilm-associated microbes with heavy metals takes place through a variety of mechanisms. These include biosorption and bioaccumulation in which the microbes interact with the metal ions leading to their conversion from a highly toxic form to a less toxic form. Such interactions are facilitated via the negative charge of the extracellular polymeric substances on the surface of the biofilm with the positive charge of the metal ions and the high cell densities and high concentrations of cell-cell signaling molecules within the biofilm matrix. Furthermore, the impact of the anodic and cathodic redox potentials in a bioelectrochemical system (BES) for the reduction, removal, and recovery of numerous heavy metal species provides an interesting insight into the bacterial biofilm-mediated bioelectroremediation process. The review concludes that biofilm-linked bioremediation is a viable option for the mitigation of heavy metal pollution in water and ecosystem recovery.

Yeast biofilms on abiotic surfaces: Adhesion factors and control methods

Biofilms are highly resistant to antimicrobials and are a common problem in many industries, including pharmaceutical, food and beverage. Yeast biofilms can be formed by various yeast species, including Candida albicans, Saccharomyces cerevisiae, and Cryptococcus neoformans. Yeast biofilm formation is a complex process that involves several stages, including reversible adhesion, followed by irreversible adhesion, colonization, exopolysaccharide matrix formation, maturation and dispersion. Intercellular communication in yeast biofilms (quorum-sensing mechanism), environmental factors (pH, temperature, composition of the culture medium), and physicochemical factors (hydrophobicity, Lifshitz-van der Waals and Lewis acid-base properties, and electrostatic interactions) are essential to the adhesion process. Studies on the adhesion of yeast to abiotic surfaces such as stainless steel, wood, plastic polymers, and glass are still scarce, representing a gap in the field. The biofilm control formation can be a challenging task for food industry. However, some strategies can help to reduce biofilm formation, such as good hygiene practices, including regular cleaning and disinfection of surfaces. The use of antimicrobials and alternative methods to remove the yeast biofilms may also be helpful to ensure food safety. Furthermore, physical control measures such as biosensors and advanced identification techniques are promising for yeast biofilms control. However, there is a gap in understanding why some yeast strains are more tolerant or resistant to sanitization methods. A better understanding of tolerance and resistance mechanisms can help researchers and industry professionals to develop more effective and targeted sanitization strategies to prevent bacterial contamination and ensure product quality. This review aimed to identify the most important information about yeast biofilms in the food industry, followed by the removal of these biofilms by antimicrobial agents. In addition, the review summarizes the alternative sanitizing methods and future perspectives for controlling yeast biofilm formation by biosensors.

Expanding the diversity of Chardonnay aroma through the metabolic interactions of Saccharomyces cerevisiae cocultures

Yeast co-inoculations in winemaking are often studied in the framework of modulating the aromatic profiles of wines. Our study aimed to investigate the impact of three cocultures and corresponding pure cultures of Saccharomyces cerevisiae on the chemical composition and the sensory profile of Chardonnay wine. Coculture makes it possible to obtain completely new aromatic expressions that do not exist in the original pure cultures attributed to yeast interactions. Esters, fatty acids and phenol families were identified as affected. The sensory profiles and metabolome of the cocultures, corresponding pure cultures and associated wine blends from both pure cultures were found to be different. The coculture did not turn out to be the addition of the two pure culture wines, indicating the impact of interaction. High resolution mass spectrometry revealed thousands of cocultures biomarkers. The metabolic pathways involved in these wine composition changes were highlighted, most of them belonging to nitrogen metabolism.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

Comparing the hierarchy of inter- and intra-species interactions with population dynamics of wine yeast cocultures

In winemaking, the development of new fermentation strategies, such as the use of mixed starter cultures with Saccharomyces cerevisiae (Sc) yeast and non-Saccharomyces (NS) species, requires a better understanding of how yeasts interact, especially at the beginning of fermentation. Despite the growing knowledge on interactions between Sc and NS, few data are available on the interactions between different species of NS. It is furthermore still unclear whether interactions are primarily driven by generic differences between yeast species or whether individual strains are the evolutionarily relevant unit for biotic interactions. This study aimed at acquiring knowledge of the relevance of species and strain in the population dynamics of cocultures between five yeast species: Hanseniaspora uvarum, Lachancea thermotolerans, Starmerella bacillaris, Torulaspora delbrueckii and Sc. We performed cocultures between 15 strains in synthetic grape must and monitored growth in microplates. Both positive and negative interactions were identified. Based on an interaction index, our results showed that the population dynamics seemed mainly driven by the two species involved. Strain level was more relevant in modulating the strength of the interactions. This study provides fundamental insights into the microbial dynamics in early fermentation and contribute to the understanding of more complex consortia encompassing multiple yeasts trains.

Artificial microbial consortia for bioproduction processes

The application of artificial microbial consortia for biotechnological production processes is an emerging field in research as it offers great potential for the improvement of established as well as the development of novel processes. In this review, we summarize recent highlights in the usage of various microbial consortia for the production of, for example, platform chemicals, biofuels, or pharmaceutical compounds. It aims to demonstrate the great potential of co-cultures by employing different organisms and interaction mechanisms and exploiting their respective advantages. Bacteria and yeasts often offer a broad spectrum of possible products, fungi enable the utilization of complex lignocellulosic substrates via enzyme secretion and hydrolysis, and microalgae can feature their abilities to fixate CO2 through photosynthesis for other organisms as well as to form lipids as potential fuelstocks. However, the complexity of interactions between microbes require methods for observing population dynamics within the process and modern approaches such as modeling or automation for process development. After shortly discussing these interaction mechanisms, we aim to present a broad variety of successfully established co-culture processes to display the potential of artificial microbial consortia for the production of biotechnological products.

A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem

Nonlinear ecological interactions within microbial ecosystems and their contribution to ecosystem functioning remain largely unexplored. Higher-order interactions, or interactions in systems comprised of more than two members that cannot be explained by cumulative pairwise interactions, are particularly understudied, especially in eukaryotic microorganisms. The wine fermentation ecosystem presents an ideal model to study yeast ecosystem establishment and functioning. Some pairwise ecological interactions between wine yeast species have been characterized, but very little is known about how more complex, multispecies systems function. Here, we evaluated nonlinear ecosystem properties by determining the transcriptomic response of Saccharomyces cerevisiae to pairwise versus tri-species culture. The transcriptome revealed that genes expressed during pairwise coculture were enriched in the tri-species data set but also that just under half of the data set comprised unique genes attributed to a higher-order response. Through interactive protein-association network visualizations, a holistic cell-wide view of the gene expression data was generated, which highlighted known stress response and metabolic adaptation mechanisms which were specifically activated during tri-species growth. Further, extracellular metabolite data corroborated that the observed differences were a result of a biotic stress response. This provides exciting new evidence showing the presence of higher-order interactions within a model microbial ecosystem. IMPORTANCE Higher-order interactions are one of the major blind spots in our understanding of microbial ecosystems. These systems remain largely unpredictable and are characterized by nonlinear dynamics, in particular when the system is comprised of more than two entities. By evaluating the transcriptomic response of S. cerevisiae to an increase in culture complexity from a single species to two- and three-species systems, we were able to confirm the presence of a unique response in the more complex setting that could not be explained by the responses observed at the pairwise level. This is the first data set that provides molecular targets for further analysis to explain unpredictable ecosystem dynamics in yeast.

eGFP Gene Integration in HO: A Metabolomic Impact?

Integrating fluorescent genes including eGFP in the yeast genome is common practice for various applications, including cell visualization and population monitoring. The transformation of a commercial S. cerevisiae strain by integrating a cassette including a gene encoding an EGFP protein in the HO gene was carried out using CRISPR-Cas9 technology. Although this type of integration is often used and described as neutral at the phenotypic level of the cell, we have highlighted that under alcoholic fermentation (in a Chardonnay must), it has an impact on the exometabolome. We observed 41 and 82 unique biomarkers for the S3 and S3GFP strains, respectively, as well as 28 biomarkers whose concentrations varied significantly between the wild-type and the modified strains. These biomarkers were mainly found to correspond to peptides. Despite similar phenotypic growth and fermentation parameters, high-resolution mass spectrometry allowed us to demonstrate, for the first time, that the peptidome is modified when integrating this cassette in the HO gene.

Synthetic biology for the engineering of complex wine yeast communities

Wine fermentation is a representation of complex higher-order microbial interactions. Despite the beneficial properties that these communities bring to wine, their complexity poses challenges in predicting the nature and outcome of fermentation. Technological developments in synthetic biology enable the potential to engineer synthetic microbial communities for new purposes. Here we present the challenges and applications of engineered yeast communities in the context of a wine fermentation vessel, how this represents a model system to enable novel solutions for winemaking and introduce the concept of a 'synthetic' terroir. Furthermore, we introduce our vision for the application of control engineering.

AI-based forecasting of ethanol fermentation using yeast morphological data

Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a nonstaining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning. We found that the neural network algorithm produced the best performance, which had a coefficient of determination of >0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

Assessment of Bacillus subtilis Plasmid pLS20 Conjugation in the Absence of Quorum Sensing Repression

Bacillus subtilis conjugative plasmid pLS20 uses a quorum-sensing mechanism to control expression levels of its conjugation genes, involving the repressor Rco_pLS20, the anti-repressor Rap_pLS20, and the signaling peptide Phr*_pLS20. In previous studies, artificial overexpression of rap_pLS20 in the donor cells was shown to enhance conjugation efficiency. However, we found that the overexpression of rap_pLS20 led to various phenotypic traits, including cell aggregation and death, which might have affected the correct determination of the conjugation efficiency when determined by colony formation assay. In the current study, conjugation efficiencies were determined under different conditions using a two-color fluorescence-activated flow cytometry method and measuring a single-round of pLS20-mediated transfer of a mobilizable plasmid. Under standard conditions, the conjugation efficiency obtained by fluorescence-activated flow cytometry was 23-fold higher than that obtained by colony formation. Furthermore, the efficiency difference increased to 45-fold when rap_pLS20 was overexpressed.

The ecology of wine fermentation: a model for the study of complex microbial ecosystems

The general interest in microbial ecology has skyrocketed over the past decade, driven by technical advances and by the rapidly increasing appreciation of the fundamental services that these ecosystems provide. In biotechnology, ecosystems have many more functionalities than single species, and, if properly understood and harnessed, will be able to deliver better outcomes for almost all imaginable applications. However, the complexity of microbial ecosystems and of the interactions between species has limited their applicability. In research, next generation sequencing allows accurate mapping of the microbiomes that characterise ecosystems of biotechnological and/or medical relevance. But the gap between mapping and understanding, to be filled by "functional microbiomics", requires the collection and integration of many different layers of complex data sets, from molecular multi-omics to spatial imaging technologies to online ecosystem monitoring tools. Holistically, studying the complexity of most microbial ecosystems, consisting of hundreds of species in specific spatial arrangements, is beyond our current technical capabilities, and simpler model systems with fewer species and reduced spatial complexity are required to establish the fundamental rules of ecosystem functioning. One such ecosystem, the ecosystem responsible for natural alcoholic fermentation, can provide an excellent tool to study evolutionarily relevant interactions between multiple species within a relatively easily controlled environment. This review will critically evaluate the approaches that are currently implemented to dissect the cellular and molecular networks that govern this ecosystem. KEY POINTS: • Evolutionarily isolated fermentation ecosystem can be used as an ecological model. • Experimental toolbox is gearing towards mechanistic understanding of this ecosystem. • Integration of multidisciplinary datasets is key to predictive understanding.

Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes

Microbial mixed cultures are gaining increasing attention as biotechnological production systems, since they offer a large but untapped potential for future bioprocesses. Effects of secondary metabolite induction and advantages of labor division for the degradation of complex substrates offer new possibilities for process intensification. However, mixed cultures are highly complex, and, consequently, many biotic and abiotic parameters are required to be identified, characterized, and ideally controlled to establish a stable bioprocess. In this review, we discuss the advantages and disadvantages of existing measurement techniques for identifying, characterizing, monitoring, and controlling mixed cultures and highlight promising examples. Moreover, existing challenges and emerging technologies are discussed, which lay the foundation for novel analytical workflows to monitor mixed-culture bioprocesses.

Computational Analysis of Microbial Flow Cytometry Data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.