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Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry. Here, we aim to bring the academic and industrial communities closer to this consensus model. We developed a physiology informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. To parametrize such a large model, a parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. Additionally, we show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Such a comprehensive model could not have been developed without using steady state and glucose perturbation data sets. The resulting metabolic reconstruction and parameter estimation pipeline can be applied in the future to study other industrially-relevant scenarios. We show this by generating a hybrid CFD-metabolic model to explore intracellular glycolytic dynamics for the first time. The model suggests that all intracellular metabolites oscillate within a physiological range, except carbon storage metabolism, which is sensitive to the extracellular environment.

Arsenate-reducing microorganisms may mobilize arsenic into groundwater. Phylogenetic DNA sequence information cannot exclusively be used to infer arsenate reduction, as the spotted homologies may not translate to biochemical activities. We here develop a cultivation-based metagenomic strategy for inferring actual activities: we complement a cultivation-independent microbial community survey covering 22 arsenic-contaminated drinking water wells in Bangladesh, with the characterization of enrichments of anaerobic arsenate reducers. All investigated samples revealed microbial arsenate reduction activity. The enriched microbial communities were phylogenetically diverse within and between the enrichments. The enrichment also unveiled species: the arsenate-reducing enrichments held 16S rRNA gene sequences closest to Dechloromonas sp., Azonexus fungiphilus, Youngiibacter fragile, Methanogenic prokaryote and Fusibacter sp., in addition to arsenate-reducing Sulfurospirillum strains NP4, b10 and dissimilatory iron-reducing Geobacter species. The enriched arsenate reductase gene (arrA) sequences were closest to sequences in known arsenate reducing Sulfurospirillum barnesii. This study bridges the knowledge gap between cultivation-independent and cultivation-dependent analyses. It clarifies the mechanism for arsenic mobilization and redistribution in rural drinking water wells, as a balance between bioremediation and harmful activities. The identified, potentially dissimilatory arsenate and iron-reducing microbes, constitute a risk for arsenic bioremediation. Appropriate metagenomic and dynamic analyses should help design and maintain safer, effective bioremediation.

We studied the succession of bacterial communities during the biodegradation of 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD). The communities originated from a mesocosm with soil from Bien Hoa airbase in Vietnam heavily contaminated with herbicides and dioxins. They were grown in defined media with different carbon and Gibbs energy sources and 2,3,7,8-TCDD. Cultures with dimethyl sulfoxide (DMSO) as the sole carbon and energy source degraded about 95% of 2,3,7,8-TCDD within 60 days of cultivation. Those with an additional 1 mM of vanillin did that in roughly 90 days. Further 16S rRNA gene amplicon sequencing showed that the increase in relative abundance of members belonging to the genera Bordetella, Sphingomonas, Proteiniphilum, and Rhizobium correlated to increased biodegradation of 2,3,7,8-TCDD in these cultures. A higher concentration of vanillin slowed down the biodegradation rate. Addition of alternative carbon and Gibbs energy sources, such as amino acids, sodium lactate and sodium acetate, even stopped the degradation of 2,3,7,8-TCDD completely. Bacteria from the genera Bordetella, Achromobacter, Sphingomonas and Pseudomonas dominated most of the cultures, but the microbial profiles also significantly differed between cultures as judged by non-metric multidimensional scaling (NMDS) analyses. Our study indicates that 2,3,7,8-TCDD degradation may be stimulated by bacterial communities preadapted to a certain degree of starvation with respect to the carbon and energy source. It also reveals the succession and abundance of defined bacterial genera in the degradation process.

Oil absorbent particles made from surface-modified polypropylene can be used to facilitate the removal of oil from the environment. In this study, we investigated to what extent absorbed oil was biodegraded and how this compared to the biodegradation of oil in water. To do so, we incubated two bacterial communities originating from the Niger Delta, an area subject to frequent oil spills, in the presence and absence of polypropylene particles. One community evolved from untreated soil whereas the second evolved from soil pre-exposed to oil. We observed that the polypropylene particles stimulated the growth of biofilms and enriched species from genera Mycobacterium, Sphingomonas and Parvibaculum. Cultures with polypropylene particles degraded more crude oil than those where the oil was present in suspension regardless of whether they were pre-exposed or not. Moreover, the community pre-exposed to crude oil had a different community structure and degraded more oil than the one from untreated soil. We conclude that the biodegradation rate of crude oil was enhanced by the pre-exposure of the bacterial communities to crude oil and by the use of oil-absorbing polypropylene materials. The data show that bacterial communities in the biofilms growing on the particles have an enhanced degradation capacity for oil.

Climate change increases sugar content in grapes, resulting in unwanted increase in ethanol content of wine. Lachancea thermotolerans ferments glucose and fructose into both ethanol and lactate, decreasing final ethanol content and positively affecting wine acidity. Reported Lachancea thermotolerans strains show big variation in lactate production during fermentation. However, a mechanistic understanding of this lactate producing phenotype is currently lacking. Through a combination of metabolomics, transcriptomics, genomics and computational methods we show that the lactate production is induced by amino acid limitation in a high lactate producing strain. We found in fermentations in synthetic grape juice media that lactate production starts in the last stages of growth, marked by decreased growth rate and increased expression levels of stress related genes. This onset of lactate production is specific for the high lactate producing strain and independent of oxygen availability. The onset of lactate production was changed by increased amino acid content of the media, and it is shown by both computational methods and amino acid measurements that at the onset of lactate production amino acids become limiting for growth. This study shows that lactate production of Lachancea thermotolerans is directly linked to nitrogen availability in the media, an insight that can further aid in the improvement of wine quality.

High-resolution spectroscopy techniques play a pivotal role to validate and efficiently benchmark available methods from quantum chemistry. In this work, we analyzed the microwave spectrum of ethyl butyrate within the scope of a systematic investigation to benchmark state-of-the-art exchange-correlation functionals and ab initio methods, to accurately predict the lowest energy conformers of carbonyl compounds in their isolated state. Under experimental conditions, we observed two distinct conformers, one of Cs and one of C1 symmetry. As reported earlier in the cases of some ethyl and methyl alkynoates, structural optimizations of the most abundant conformer that exhibits a C1 symmetry proved extremely challenging for several quantum chemical levels. To probe the sensitivity of different methods and basis sets, we use the identified soft-degree of freedom in proximity to the carbonyl group as an order parameter. The results of our study provide useful insight for spectroscopists to select an adapted method for structure prediction of carbonyl compounds based on their available computational resources, suggesting a reasonable trade-off between accuracy and CPU cost. At the same time, our observations and the resulting sets of highly accurate experimental constants from high-resolution spectroscopy experiments give an appeal to theoretical groups to look further into this seemingly simple family of chemical compounds, which may prove useful for the further development and parametrization of theoretical methods in computational chemistry.

Microbes in the wild face highly variable and unpredictable environments and are naturally selected for their average growth rate across environments. Apart from using sensory regulatory systems to adapt in a targeted manner to changing environments, microbes employ bet-hedging strategies where cells in an isogenic population switch stochastically between alternative phenotypes. Yet, bet-hedging suffers from a fundamental trade-off: Increasing the phenotype-switching rate increases the rate at which maladapted cells explore alternative phenotypes but also increases the rate at which cells switch out of a well-adapted state. Consequently, it is currently believed that bet-hedging strategies are effective only when the number of possible phenotypes is limited and when environments last for sufficiently many generations. However, recent experimental results show that gene expression noise generally decreases with growth rate, suggesting that phenotype-switching rates may systematically decrease with growth rate. Such growth rate dependent stability (GRDS) causes cells to be more explorative when maladapted and more phenotypically stable when well-adapted, and we show that GRDS can almost completely overcome the trade-off that limits bet-hedging, allowing for effective adaptation even when environments are diverse and change rapidly. We further show that even a small decrease in switching rates of faster-growing phenotypes can substantially increase long-term fitness of bet-hedging strategies. Together, our results suggest that stochastic strategies may play an even bigger role for microbial adaptation than hitherto appreciated.

To secure a sustainable food supply for the rapidly growing global population, great efforts towards a plant-based diet are underway. However, the use of plant proteins comes with several challenges, such as improvement or removal of undesired flavours, and generation of desired texture properties. Fermentation holds large potential to alter these properties, but compared to dairy fermentations, our knowledge on strain properties in different plant-based substrates is still limited. Here, we explored different lactic acid bacteria for their ability to grow, produce flavour compounds, or remove off-flavour compounds from different plant proteins. For this, 151 LAB strains from dairy and non-dairy origins were cultured in plant protein plus coconut oil emulsions supplemented with glucose. Pea, chickpea, mung, fava, and soybean proteins were used in the study and bacterial strains for screening included the genera Streptococcus, Lactococcus, Lactobacillus, and Leuconostoc. Efficient, high throughput, screening on plant proteins was developed and strains were assessed for their ability to (i) acidify and decrease the pH; (ii) express key enzymes involved in the formation of amino acid derived flavours, which included PepN (aminopeptidase N), PepXP (X-prolyl dipeptidyl peptidase), EstA (esterase), BcAT (branched chain aminotransferase), CBL (cystathione beta lyase), and ArAT (aromatic aminotransferase); and (iii) improve the overall aroma profile by generating dairy/cheesy notes and decreasing off flavours. Suitable screening conditions were determined, and highlighted the importance that a sufficient heat treatment must be applied to samples containing plant proteins, prior to fermentation, as an outgrowth of spore forming Bacillus cereus was observed if the material was only pasteurised. Enzyme activities for strains measured in rich broth vs. a buffered protein solution showed little-to-no correlation, which illustrated the importance of screening conditions to obtain predictive enzyme measurements. Aroma formation analysis allowed to identify strains that were able to increase key aromas such as diacetyl, acetoin, 2- and 3-methyl butanol, and 2,3-pentanedione, as well as decrease the off-flavours hexanal, pentanal, and nonanal. Our findings illustrate the importance of strain specific differences in the assessed functionalities and how a methodical approach to screening LAB can be applied to select suitable microorganisms that show promise in fermentation of plant proteins when applied in non-dairy cheese applications.

Kinases play key roles in signaling networks that are activated by G-protein-coupled receptors (GPCRs). Kinase activities are generally inferred from cell lysates, hiding cell-to-cell variability. To study the dynamics and heterogeneity of ERK and Akt proteins, we employed high-content biosensor imaging with kinase translocation reporters. The kinases were activated with GPCR ligands. We observed ligand concentration-dependent response kinetics to histamine, α2-adrenergic and S1P receptor stimulation. By using G-protein inhibitors, we observed that Gq mediated the ERK and Akt responses to histamine. In contrast, Gi was necessary for ERK and Akt activation in response to α2-adrenergic receptor activation. ERK and Akt were also strongly activated by S1P, showing high heterogeneity at the single-cell level, especially for ERK. Cluster analysis of time series derived from 68,000 cells obtained under the different conditions revealed several distinct populations of cells that display similar response dynamics. ERK response dynamics to S1P showed high heterogeneity, which was reduced by the inhibition of Gi. To conclude, we have set up an imaging and analysis strategy that reveals substantial cell-to-cell heterogeneity in kinase activity driven by GPCRs.

Lactococcus lactis strains residing in the microbial community of a complex dairy starter culture named “Ur” are hosts to prophages belonging to the family Siphoviridae. L. lactis strains (TIFN1 to TIFN7) showed detectable spontaneous phage production and release (109 to 1010 phage particles/ml) and up to 10-fold increases upon prophage induction, while in both cases we observed no obvious cell lysis typically described for the lytic life cycle of Siphoviridae phages. Intrigued by this phenomenon, we investigated the host-phage interaction using strain TIFN1 (harboring prophage proPhi1) as a representative. We confirmed that during the massive phage release, all bacterial cells remain viable. Further, by monitoring phage replication in vivo, using a green fluorescence protein reporter combined with flow cytometry, we demonstrated that the majority of the bacterial population (over 80%) is actively producing phage particles when induced with mitomycin C. The released tailless phage particles were found to be engulfed in lipid membranes, as evidenced by electron microscopy and lipid staining combined with chemical lipid analysis. Based on the collective observations, we propose a model of phage-host interaction in L. lactis TIFN1 where the phage particles are engulfed in membranes upon release, thereby leaving the producing host intact. Moreover, we discuss possible mechanisms of chronic, or nonlytic, release of LAB Siphoviridae phages and its impact on the bacterial host. IMPORTANCE In complex microbial consortia such as fermentation starters, bacteriophages can alter the dynamics and diversity of microbial communities. Bacteriophages infecting Lactococcus lactis are mostly studied for their detrimental impact on industrial dairy fermentation processes. In this study, we describe a novel form of phage-bacterium interaction in an L. lactis strain isolated from a complex dairy starter culture: when the prophages harbored in the L. lactis genome are activated, the phage particles are engulfed in lipid membranes upon release, leaving the producing host intact. Findings from this study provide additional insights into the diverse manners of phage-bacterium interactions and coevolution, which are essential for understanding the population dynamics in complex microbial communities like fermentation starters.

Microorganisms, including the budding yeast Saccharomyces cerevisiae, express glycolytic proteins to a maximal capacity that (largely) exceeds the actual flux through the enzymes, especially at low growth rates. An open question is if this apparent expression level is really an overcapacity, or maintains the (optimal) enzyme capacity needed to carry flux at (very) low substrate availability. Here, we use computational modelling to suggest that yeast maintains a genuine excess of glycolytic enzymes at low specific growth rates. During fast fermentative growth at high glucose levels, the observed expression of the glycolytic enzymes matched the predicted optimal levels. We suggest that the excess glycolytic capacity at low glucose levels is a preparatory strategy in the adaptation to sugar fluctuations in the environment.

Vanishing white matter (VWM) is classified as a leukodystrophy with astrocytes as primary drivers in its pathogenesis. Magnetic resonance imaging has documented the progressive thinning of cortices in long-surviving patients. Routine histopathological analyses, however, have not yet pointed to cortical involvement in VWM. Here, we provide a comprehensive analysis of the VWM cortex. We employed high-resolution-mass-spectrometry-based proteomics and immunohistochemistry to gain insight into possible molecular disease mechanisms in the cortices of VWM patients. The proteome analysis revealed 268 differentially expressed proteins in the VWM cortices compared to the controls. A majority of these proteins formed a major protein interaction network. A subsequent gene ontology analysis identified enrichment for terms such as cellular metabolism, particularly mitochondrial activity. Importantly, some of the proteins with the most prominent changes in expression were found in astrocytes, indicating cortical astrocytic involvement. Indeed, we confirmed that VWM cortical astrocytes exhibit morphological changes and are less complex in structure than control cells. Our findings also suggest that these astrocytes are immature and not reactive. Taken together, we provide insights into cortical involvement in VWM, which has to be taken into account when developing therapeutic strategies.

Background Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (Km), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem). Results To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for Km estimation of kinetic modeling. First, we use a machine learning-based Km predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km values, which were close to the measured values. Conclusions MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based Km predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps, which helps modelers perform MLAGO on their own parameter estimation tasks.

MotivationCharacterizing all steady-state flux distributions in metabolic models remains limited to small models due to the explosion of possibilities. Often it is sufficient to look only at all possible overall conversions a cell can catalyze ignoring the details of intracellular metabolism. Such a characterization is achieved by elementary conversion modes (ECMs), which can be conveniently computed with ecmtool. However, currently, ecmtool is memory intensive, and it cannot be aided appreciably by parallelization.ResultsWe integrate mplrs—a scalable parallel vertex enumeration method—into ecmtool. This speeds up computation, drastically reduces memory requirements and enables ecmtool’s use in standard and high-performance computing environments. We show the new capabilities by enumerating all feasible ECMs of the near-complete metabolic model of the minimal cell JCVI-syn3.0. Despite the cell’s minimal character, the model gives rise to 4.2×109 ECMs and still contains several redundant sub-networks.Availability and implementationecmtool is available at https://github.com/SystemsBioinformatics/ecmtool.Supplementary informationSupplementary data are available at Bioinformatics online.

We steered the soil microbiome via applications of organic residues (mix of cover crop residues, sewage sludge + compost, and digestate + compost) to enhance multiple ecosystem services in line with climate-smart agriculture. Our result highlights the potential to reduce greenhouse gases (GHG) emissions from agricultural soils by the application of specific organic amendments (especially digestate + compost). Unexpectedly, also the addition of mineral fertilizer in our mesocosms led to similar combined GHG emissions than one of the specific organic amendments. However, the application of organic amendments has the potential to increase soil C, which is not the case when using mineral fertilizer. While GHG emissions from cover crop residues were significantly higher compared to mineral fertilizer and the other organic amendments, crop growth was promoted. Furthermore, all organic amendments induced a shift in the diversity and abundances of key microbial groups. We show that organic amendments have the potential to not only lower GHG emissions by modifying the microbial community abundance and composition, but also favour crop growth-promoting microorganisms. This modulation of the microbial community by organic amendments bears the potential to turn soils into more climate-smart soils in comparison to the more conventional use of mineral fertilizers.

Under carbon source transitions, the intracellular pH of Saccharomyces cerevisiae is subject to change. Dynamics in pH modulate the activity of the glycolytic enzymes, resulting in a change in glycolytic flux and ultimately cell growth. To understand how pH affects the global behavior of glycolysis and ethanol fermentation, we measured the activity of the glycolytic and fermentative enzymes in S. cerevisiae under in vivo-like conditions at different pH. We demonstrate that glycolytic enzymes exhibit differential pH dependencies, and optima, in the pH range observed during carbon source transitions. The forward reaction of GAPDH shows the highest decrease in activity, 83%, during a simulated feast/famine regime upon glucose removal (cytosolic pH drop from 7.1 to 6.4). We complement our biochemical characterization of the glycolytic enzymes by fitting the Vmax to the progression curves of product formation or decay over time. The fitting analysis shows that the observed changes in enzyme activities require changes in Vmax, but changes in Km cannot be excluded. Our study highlights the relevance of pH as a key player in metabolic regulation and provides a large set of quantitative data that can be explored to improve our understanding of metabolism in dynamic environments.

For the design of next-generation tuberculosis chemotherapy, insight into bacterial defence against drugs is required. Currently, targeting respiration has attracted strong attention for combatting drug-resistant mycobacteria. Q203 (telacebec), an inhibitor of the cytochrome bcc complex in the mycobacterial respiratory chain, is currently evaluated in phase-2 clinical trials. Q203 has bacteriostatic activity against M. tuberculosis, which can be converted to bactericidal activity by concurrently inhibiting an alternative branch of the mycobacterial respiratory chain, cytochrome bd. In contrast, non-tuberculous mycobacteria, such as Mycobacterium smegmatis, show only very little sensitivity to Q203. In this report, we investigated factors that M. smegmatis employs to adapt to Q203 in the presence or absence of a functional cytochrome bd, especially regarding its terminal oxidases. In the presence of a functional cytochrome bd, M. smegmatis responds to Q203 by increasing the expression of cytochrome bcc as well as of cytochrome bd, whereas a M. smegmatisbd-KO strain adapted to Q203 by increasing the expression of cytochrome bcc. Interestingly, single-cell studies revealed cell-to-cell variability in drug adaptation. We also investigated the role of a putative second cytochrome bd isoform postulated for M. smegmatis. Although this putative isoform showed differential expression in response to Q203 in the M. smegmatisbd-KO strain, it did not display functional features similar to the characterised cytochrome bd variant.

The fission yeast Schizosaccharomyces pombe is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker’s yeast Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually-curated high-quality reconstruction of S. pombe’s metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes, and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these use cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behaviour and the role of resource allocation in driving different strategies in fission yeast.

Horizontal gene transfer (HGT) is defined as the acquisition by an organism of hereditary material from a phylogenetically unrelated organism. This process is mostly observed among bacteria and archaea, and considered less likely between microbes and multicellular eukaryotes. However, recent studies provide compelling evidence of the evolutionary importance of HGT in eukaryotes, driving functional innovation. Here, we study an HGT event in Folsomia candida (Collembola, Hexapoda) of a carbohydrate-active enzyme homologous to glycosyl hydrase group 43 (GH43). The gene encodes an N-terminal signal peptide, targeting the product for excretion, which suggests that it contributes to the diversity of digestive capacities of the detritivore host. The predicted α-L-arabinofuranosidase shows high similarity to genes in two other Collembola, an insect and a tardigrade. The gene was cloned and expressed in Escherichia coli using a cell-free protein expression system. The expressed protein showed activity against p-nitrophenyl-α-L-arabinofuranoside. Our work provides evidence for functional activity of an HGT gene in a soil-living detritivore, most likely from a bacterial donor, with genuine eukaryotic properties, such as a signal peptide. Co-evolution of metazoan GH43 genes with the Panarthropoda phylogeny suggests the HGT event took place early in the evolution of this ecdysozoan lineage.

Fitness-enhancing adaptations of protein expression and its regulation are an important aspect of bacterial evolution. A key question is whether evolution has led to optimal protein expression that maximizes immediate growth rate (short-term fitness) in a robust manner (consistently across diverse conditions). Alternatively, they could display suboptimal short-term fitness, because they cannot do better or because they instead strive for long-term fitness maximization by, for instance, preparing for future conditions. To address this question, we focus on the ATP-producing enzyme F1F0 H+-ATPase, which is an abundant enzyme and ubiquitously expressed across conditions. Its expression is highly regulated and dependent on growth rate and nutrient conditions. For instance, during growth on sugars, when metabolism is overflowing acetate, glycolysis supplies most ATP, while H+-ATPase is the main source of ATP synthesis during growth on acetate. We tested the optimality of H+-ATPase expression in Escherichia coli across different nutrient conditions. In all tested conditions, wild-type E. coli expresses its H+-ATPase remarkably close (within a few per cent) to optimal concentrations that maximize immediate growth rate. This work indicates that bacteria can indeed achieve robust optimal protein expression for immediate growth-rate maximization.

Manganese (Mn) is an essential trace element that is supplemented in microbial media with varying benefits across species and growth conditions. We found that growth of Lactococcus cremoris was unaffected by manganese omission from the growth medium. The main proteome adaptation to manganese omission involved increased manganese transporter production (up to 2,000-fold), while the remaining 10 significant proteome changes were between 1.4- and 4-fold. Further investigation in translationally blocked (TB), nongrowing cells showed that Mn supplementation (20 mM) led to approximately 1.5 X faster acidification compared with Mn-free conditions. However, this faster acidification stagnated within 24 h, likely due to draining of intracellular NADH that coincides with substantial loss of culturability. Conversely, without manganese, nongrowing cells persisted to acidify for weeks, albeit at a reduced rate, but maintaining redox balance and culturability. Strikingly, despite being unculturable, a-keto acid-derived aldehydes continued to accumulate in cells incubated in the presence of manganese, whereas without manganese cells predominantly formed the corresponding alcohols. This is most likely reflecting NADH availability for the alcohol dehydrogenase-catalyzed conversion. Overall, manganese influences the lactococcal acidification rate, and flavor formation capacity in a redox dependent manner. These are important industrial traits especially during cheese ripening, where cells are in a non-growing, often unculturable state. IMPORTANCE In nature as well as in various biotechnology applications, microorganisms are often in a nongrowing state and their metabolic persistence determines cell survival and functionality. Industrial examples are dairy fermentations where bacteria remain active during the ripening phases that can take up to months and even years. Here we investigated environmental factors that can influence lactococcal metabolic persistence throughout such prolonged periods. We found that in the absence of manganese, acidification of nongrowing cells remained active for weeks while in the presence of manganese it stopped within 1 day. The latter coincided with the accumulation of amino acid derived volatile metabolites. Based on metabolic conversions, proteome analysis, and a reporter assay, we demonstrated that the manganese elicited effects were NADH dependent. Overall the results show the effect of environmental modulation on prolonged cell-based catalysis, which is highly relevant to nongrowing cells in nature and biotechnological applications.

Three different fungi were tested for their ability to degrade 2,4-dichlorophenoxyacetic acid and 2,4,5-trichlorophenoxyacetic acid and for the role of laccases and cytochromes P450-type in this process. We studied a white-rot fungus Rigidoporus sp. FMD21, which has a high laccase activity, for its efficiency to degrade these herbicides. A positive correlation was found between its laccase activity and the corresponding herbicide degradation rate. Even more, the doubling of the enzyme activity in this phase corresponded with a doubling of the herbicide degradation rate. It is, therefore, tempting to speculate that laccase is the most dominant enzyme in the degradation of 2,4-D and 2,4,5-T under these conditions. In addition, it was shown that Rigidoporus sp. FMD21 partly relies on cytochromes P450-type for the breakdown of the herbicides as well. Two filamentous fungi were isolated from soil contaminated with herbicides and dioxins located at Bien Hoa airbase. They belong to genera Fusarium and Verticillium of the phylum Ascomycota as judged by their 18S rRNA gene sequences. Both isolated fungi were able to degrade the herbicides but with different rates. Their laccase activity, however, was very low and did not correlate with the rate of breakdown of the herbicides. These data indicate that the white-rot fungus most likely synthesizes laccase and cytochromes P450-type for the breakdown of the herbicides, while the types of enzyme used for the breakdown of the herbicides by the two Ascomycota remain unclear. Graphical abstract

Glycogen in the female lower reproductive tract is a major carbon source for vaginal colonization and acidification by common vaginal Lactobacillus species, such as Lactobacillus crispatus. Previously we identified the pullulanase gene pulA in Lactobacillus crispatus, correlating with its ability to autonomously utilize glycogen for growth. Here we further characterize genetic variation and differential regulation of pulA affecting the presence of its gene product on the outer surface layer. We show that alpha-glucan degrading activity dissipates when Lactobacilllus crispatus is grown on glucose, maltose and maltotriose, in agreement with carbon catabolite repression elements flanking the pulA gene. Proteome analysis of the S-layer confirmed that the pullulanase protein is highly abundant in an S-layer enriched fraction, but not in a strain with a defective pullulanase variant or in a pullulanase-sufficient strain grown on glucose. In addition, we provide evidence that Lactobacillus crispatus pulA mutants are relevant in vivo, as they are commonly observed in metagenome datasets of human vaginal microbial communities. Analysis of the largest publicly available human vaginal metagenome dataset indicates that 15 out of 272 samples, containing a Lactobacillus crispatus pulA gene, contain a defective variant of this gene. Another 23 out of 272 samples show large deletions or transposon insertions. Taken together, these results demonstrate that both environmental as well as genetic factors explain variation of Lactobacillus crispatus alpha-glucosidases in the vaginal environment.

Fine-tuning cellular physiology in response to intracellular and environmental cues requires precise temporal and spatial control of gene expression. High-resolution imaging technologies to detect mRNAs and their translation state have revealed that all living organisms localize mRNAs in subcellular compartments and create translation hotspots, enabling cells to tune gene expression locally. Therefore, mRNA localization is a conserved and integral part of gene expression regulation from prokaryotic to eukaryotic cells. In this Review, we discuss the mechanisms of mRNA transport and local mRNA translation across the kingdoms of life and at organellar, subcellular and multicellular resolution. We also discuss the properties of messenger ribonucleoprotein and higher order RNA granules and how they may influence mRNA transport and local protein synthesis. Finally, we summarize the technological developments that allow us to study mRNA localization and local translation through the simultaneous detection of mRNAs and proteins in single cells, mRNA and nascent protein single-molecule imaging, and bulk RNA and protein detection methods.

Metabolic interactions between cells affect microbial community compositions and hence their function in ecosystems. It is well-known that under competition for the exchanged metabolite, concentration gradients constrain the distances over which interactions can occur. However, interaction distances are typically quantified in two-dimensional systems or without accounting for competition or other metabolite-removal, conditions which may not very often match natural ecosystems. We here analyze the impact of cell-to-cell distance on unidirectional cross-feeding in a three-dimensional aqueous system with competition for the exchanged metabolite. Effective interaction distances were computed with a reaction-diffusion model and experimentally verified by growing a synthetic consortium of 1 µm-sized metabolite producer, receiver, and competitor cells in different spatial structures. We show that receivers cannot interact with producers located on average 15 µm away from them, as product concentration gradients flatten close to producer cells. We developed an aggregation protocol and varied the receiver cells’ product affinity, to show that within producer–receiver aggregates even low-affinity receiver cells could interact with producers. These results show that competition or other metabolite-removal of a public good in a three-dimensional system reduces metabolic interaction distances to the low µm-range, highlighting the importance of concentration gradients as physical constraint for cellular interactions.

In S. cerevisiae and many other micro-organisms an increase in metabolic efficiency (i.e. ATP yield on carbon) is accompanied by a decrease in growth rate. From a fundamental point of view, studying these yield-rate trade-offs provides insight in for example microbial evolution and cellular regulation. From a biotechnological point of view, increasing the ATP yield on carbon might increase the yield of anabolic products. We here aimed to select S. cerevisiae mutants with an increased biomass yield. Serial propagation of individual cells in water-in-oil emulsions previously enabled the selection of lactococci with increased biomass yields, and adapting this protocol for yeast allowed us to enrich an engineered Crabtree-negative S. cerevisiae strain with a high biomass yield on glucose. When we started the selection with an S. cerevisiae deletion collection, serial propagation in emulsion enriched hxk2Δ and reg1Δ strains with an increased biomass yield on glucose. Surprisingly, a tps1Δ strain was highly abundant in both emulsion- and suspension-propagated populations. In a separate experiment we propagated a chemically mutagenized S. cerevisiae population in emulsion, which resulted in mutants with a higher cell number yield on glucose, but no significantly changed biomass yield. Genome analyses indicate that genes involved in glucose repression and cell cycle processes play a role in the selected phenotypes. The repeated identification of mutations in genes involved in glucose-repression indicates that serial propagation in emulsion is a valuable tool to study metabolic efficiency in S. cerevisiae.

Ready biodegradability tests (RBTs) are extensively used to screen the potential of chemicals to be biodegraded. The use of RBT protocols often results in large variations of test results that may lead to wrong interpretations. The present study aims to obtain a fundamental understanding of this variability. For this, we subjected the compounds 4-chloroaniline (4CA), carbamazepine (CBZ), metformin (MET), and N-methylpiperazine (NMP) to a variety of different test conditions. Inocula from five local wastewater treatment plants (WWTPs) were used in an attempt to enhance the Organisation for Economic Co-operation and Development (OECD) 310 biodegradability tests. The biodegradation capacity in RBTs, community composition and adaptation of the communities were compared after one week of pre-exposure in batch and four months exposure in chemostat. The results confirm that none of the test compounds is readily biodegradable in the standard OECD 310 RBT. However, when pre-exposure under either batch or chemostat conditions was included, 4CA was degraded in some cases and less variability among different inocula was observed for the transformation of MET. Bacterial communities from the five locations were found to be significantly different in composition from one another. In addition, pre-treatment performed before the RBT significantly changed the composition of each community. Results of this experiment show that short-term pre-exposure may increase the absolute number of degraders and deserves to be further investigated as a potential method to reduce the outcome variability of RBTs.

In this study, we have investigated the cheese starter culture as a microbial community through a question: can the metabolic behaviour of a co‐culture be explained by the characterized individual organism that constituted the co‐culture? To address this question, the dairy‐origin lactic acid bacteria Lactococcus lactis subsp. cremoris, Lactococcus lactis subsp. lactis, Streptococcus thermophilus and Leuconostoc mesenteroides, commonly used in cheese starter cultures, were grown in pure and four different co‐cultures. We used a dynamic metabolic modelling approach based on the integration of the genome‐scale metabolic networks of the involved organisms to simulate the co‐cultures. The strain‐specific kinetic parameters of dynamic models were estimated using the pure culture experiments and they were subsequently applied to co‐culture models. Biomass, carbon source, lactic acid and most of the amino acid concentration profiles simulated by the co‐culture models fit closely to the experimental results and the co‐culture models explained the mechanisms behind the dynamic microbial abundance. We then applied the co‐culture models to estimate further information on the co‐cultures that could not be obtained by the experimental method used. This includes estimation of the profile of various metabolites in the co‐culture medium such as flavour compounds produced and the individual organism level metabolic exchange flux profiles, which revealed the potential metabolic interactions between organisms in the co‐cultures., Co‐cultures of lactic acid bacteria were investigated using dynamic genome‐scale metabolic modelling techniques. Özcan and co‐workers estimated the metabolic capacity, microbial abundance, potential of flavour compounds production and metabolic interactions of the co‐cultures using the features of individual organisms that constituted the co‐cultures.

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation—known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells.

How the network around ROS protects against oxidative stress and Parkinson’s disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for “life-extending interventions”: mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-y, Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality., We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503)., info:eu-repo/semantics/publishedVersion

The calcium-sensing receptor (CaSR) is a G protein-coupled receptor that plays a key role in calcium homeostasis, by sensing free calcium levels in blood and regulating parathyroid hormone secretion in response. The CaSR is highly expressed in parathyroid gland and kidney where its role is well characterised, but also in other tissues where its function remains to be determined. The CaSR can be activated by a variety of endogenous ligands, as well as by synthetic modulators such as Cinacalcet, used in the clinic to treat secondary hyperparathyroidism in patients with chronic kidney disease. The CaSR couples to multiple G proteins, in a tissue-specific manner, activating several signalling pathways and thus regulating diverse intracellular events. The multifaceted nature of this receptor makes it a valuable therapeutic target for calciotropic and non-calciotropic diseases. It is therefore essential to understand the complexity behind the pharmacology, trafficking, and signalling characteristics of this receptor. This review provides an overview of the latest knowledge about the CaSR and discusses future hot topics in this field.

The understanding of miRNA target interactions is still limited due to conflicting data and the fact that high-quality validation of targets is a time-consuming process. Faster methods like high-throughput screens and bioinformatics predictions are employed but suffer from several problems. One of these, namely the potential occurrence of downstream (i.e. secondary) effects in high-throughput screens has been only little discussed so far. However, such effects limit usage for both the identification of interactions and for the training of bioinformatics tools. In order to analyse this problem more closely, we performed time-dependent microarray screening experiments overexpressing human miR-517a-3p, and, together with published time-dependent datasets of human miR-17-5p, miR-135b and miR-124 overexpression, we analysed the dynamics of deregulated genes. We show that the number of deregulated targets increases over time, whereas seed sequence content and performance of several miRNA target prediction algorithms actually decrease over time. Bioinformatics recognition success of validated miR-17 targets was comparable to that of data gained only 12 h post-transfection. We therefore argue that the timing of microarray experiments is of critical importance for detecting direct targets with high confidence and for the usability of these data for the training of bioinformatics prediction tools.

So far, only members of the bacterial phyla Proteobacteria and Verrucomicrobia are known to grow methanotrophically under aerobic conditions. Here we report that this metabolic trait is also observed within the Actinobacteria. We enriched and cultivated a methanotrophic Mycobacterium from an extremely acidic biofilm growing on a cave wall at a gaseous chemocline interface between volcanic gases and the Earth’s atmosphere. This Mycobacterium, for which we propose the name Candidatus Mycobacterium methanotrophicum, is closely related to well-known obligate pathogens such as M. tuberculosis and M. leprae. Genomic and proteomic analyses revealed that Candidatus M. methanotrophicum expresses a full suite of enzymes required for aerobic growth on methane, including a soluble methane monooxygenase that catalyses the hydroxylation of methane to methanol and enzymes involved in formaldehyde fixation via the ribulose monophosphate pathway. Growth experiments combined with stable isotope probing using 13C-labelled methane confirmed that Candidatus M. methanotrophicum can grow on methane as a sole carbon and energy source. A broader survey based on 16S metabarcoding suggests that species closely related to Candidatus M. methanotrophicum may be abundant in low-pH, high-methane environments.

Various strategies have been employed to engineer lactic acid bacteria (LAB) to improve their functionality as cell-factory or in food fermentation applications. Lactococcus lactis has long served as a paradigm organism in genetic engineering of the LAB, and the analysis of gene-specific mutant strains and/or strains overexpressing specific functions has been crucial for our increased insight in gene-regulation and metabolism of these industrially important microbes. However, application of engineered strains in food fermentations is limited by the lack of consumer acceptance of genetically engineered foods. This has led to a renewed interest in natural mechanisms of gene transfer and has stimulated the use of experimental evolution to increase specific LAB functionalities.

Overflow metabolism is ubiquitous in nature, and it is often considered inefficient because it leads to a relatively low biomass yield per consumed carbon. This metabolic strategy has been described as advantageous because it supports high growth rates during nutrient competition. Here, we experimentally evolved bacteria without nutrient competition by repeatedly growing and mixing millions of parallel batch cultures of Escherichia coli. Each culture originated from a water-in-oil emulsion droplet seeded with a single cell. Unexpectedly we found that overflow metabolism (acetate production) did not change. Instead, the numerical cell yield during the consumption of the accumulated acetate increased as a consequence of a reduction in cell size. Our experiments and a mathematical model show that fast growth and overflow metabolism, followed by the consumption of the overflow metabolite, can lead to a higher numerical cell yield and therefore a higher fitness compared with full respiration of the substrate. This provides an evolutionary scenario where overflow metabolism can be favorable even in the absence of nutrient competition.

Wine fermentation is an ancient biotechnological process mediated by different microorganisms such as yeast and bacteria. Understanding of the metabolic and physiological phenomena taking place during this process can be now attained at a genome scale with the help of metabolic models. In this chapter, we present a detailed protocol for modeling wine fermentation using genome-scale metabolic models. In particular, we illustrate how metabolic fluxes can be computed, optimized and interpreted, for both yeast and bacteria under winemaking conditions. We also show how nutritional requirements can be determined and simulated using these models in relevant test cases. This chapter introduces fundamental concepts and practical steps for applying flux balance analysis in wine fermentation, and as such, it is intended for a broad microbiology audience as well as for practitioners in the metabolic modeling field.

Some strains of Oenococcus oeni possess cinnamoyl esterase activity that can be relevant in the malolactic stage of wine production liberating hydroxycinnamic acids that are precursors of volatile phenols responsible for sensory faults. The objective of this study was to better understand the basis of the differential activity between strains. After initial screening, five commercial strains of O. oeni were selected, three were found to exhibit cinnamoyl esterase activity (CE+) and two not (CE−). Although the use of functional annotation of genes revealed genotypic variations between the strains, no specific genes common only to the three CE+ strains could explain the different activities. Pasteurized wine was used as a natural source of tartrate esters in growth and metabolism experiments conducted in MRS medium, whilst commercial trans-caftaric acid was used as substrate for enzyme assays. Detoxification did not seem to be the main biological mechanism involved in the activity since unlike its phenolic cleavage products and their immediate metabolites (trans-caffeic acid and 4-ethylcatechol), trans-caftaric acid was not toxic toward O. oeni. In the case of the two CE+ strains OenosTM and CiNeTM, wine-exposed samples showed a more rapid degradation of trans-caftaric acid than the unexposed ones. The CE activity was present in all cell-free extracts of both wine-exposed and unexposed strains, except in the cell-free extracts of the CE− strain CH11TM. This activity may be constitutive rather than induced by exposure to tartrate esters. Trans-caftaric acid was totally cleaved to trans-caffeic acid by cell-free extracts of the three CE+ strains, whilst cell-free extracts of the CE− strain CH16TM showed significantly lower activity, although higher for the strains in experiments with no prior wine exposure. The EstB28 esterase gene, found in the genomes of the 5 strains, did not reveal any difference on the upstream regulation and transport functionality between the strains. This study highlights the complexity of the basis of this activity in wine related O. oeni population. Variable cinnamoyl esterases or/and membrane transport activities in the O. oeni strains analyzed and a possible implication of wine molecules could explain this phenomenon.

During storage and ripening of fermented foods, Lactococcus cremoris is predominantly in a non-growing state. L. cremoris can become stationary due to starvation or acidification, and its metabolism in these non-growing states affects the fermented product. Available studies on the response of L. cremoris to acid and starvation stress are based on population level data. We here characterized the energetic state and the protein synthesis capacity of stationary L. cremoris cultures at the single cell level. We show that glucose starved stationary cells are energy-depleted, while acid-induced stationary cells are energized and can maintain a pH gradient over their membrane. In the absence of glucose and arginine, a small pH gradient can still be maintained. Subpopulations of stationary cells can synthesize protein without a nitrogen source, and the subpopulation size decreases with increasing stationary phase length. Protein synthesis capacity during starvation only benefits culturability after 6 days. These results highlight significant differences between glucose starved stationary and acid-induced stationary cells. Furthermore, they show that the physiology of stationary phase L. cremoris cells is multi-facetted and heterogeneous, and the presence of an energy source during stationary phase impacts the cells capacity to adapt to their environment.

This study aimed to evaluate technological (acidification, proteolysis, lipolysis, resistance to low pH, NaCl, and bile salts) and biopreservation (antimicrobial activity against foodborne pathogens) features of 1002 LAB by high throughput screening (HTS) methods. The LAB was isolated from 11 types of Brazilian artisanal cheeses (BAC) marketed in the main 5 producing regions. Remarkable intra-species variability in acidification rates have been found, which was most pronounced between isolates from Mina's artisanal cheeses, Caipira and Coalho cheeses. Lacticaseibacillus paracasei and Levilactobacillus brevis showed the fastest acidification rate; however, all isolates showed slower acidification rates than a lactococcal control strain (4.3 × lower). When testing inhibitory effects, > 75% of LAB isolates could inhibit the growth of Staphylococcus aureus ATCC 19095 and Listeria monocytogenes ATCC 7644. Two of these isolates, identified as Lactiplantibacillus plantarum and Lentilactobacillus buchneri, the sterile and neutral supernatants alone, were sufficient to inhibit L. monocytogenes growth. Principal component analysis (PCA) allowed the identification of functional groups based on proteolytic and lipolytic activity, osmotic stress resistance, and inhibition of L. monocytogenes. The type of cheese the isolates were recovered from influenced properties such as anti-listerial compounds and lipolytic enzyme production. The use of HTS and multivariate statistics allowed insights into a diverse set of LAB technological and biopreservation properties. These findings allow a profound knowledge of the heterogeneity of a large set of isolates, which can be further used to design starter cultures with varied and combined properties, such as biopreservation and technological features. Besides that, HTS makes it possible to analyze a vast panel of LAB strains, reducing costs and time within laboratory analysis, while avoiding the loss of information once all LAB are tested at the same time (differently from the traditional labor-intensive approach, in which a few numbers of strains is tested per time).

As natural selection acts on individual organisms the evolution of costly cooperation between microorganisms is an intriguing phenomenon. Introduction of spatial structure to privatize exchanged molecules can explain the evolution of cooperation. However, in many natural systems cells can also grow to low cell concentrations in the absence of these exchanged molecules, thus showing “cooperation-independent background growth”. We here serially propagated a synthetic cross-feeding consortium of lactococci in the droplets of a water-in-oil emulsion, essentially mimicking group selection with varying founder population sizes. The results show that when the growth of cheaters completely depends on cooperators, cooperators outcompete cheaters. However, cheaters outcompete cooperators when they can independently grow to only ten percent of the consortium carrying capacity. This result is the consequence of a probabilistic effect, as low founder population sizes in droplets decrease the frequency of cooperator co-localization. Cooperator-enrichment can be recovered by increasing the founder population size in droplets to intermediate values. Together with mathematical modelling our results suggest that co-localization probabilities in a spatially structured environment leave a small window of opportunity for the evolution of cooperation between organisms that do not benefit from their cooperative trait when in isolation or form multispecies aggregates.

We determined the degradation rates of the herbicides 2,4-D and 2,4,5-T by two different bacterial communities. One of these originated from soil heavily contaminated with herbicides from Bien Hoa airbase, the other from the same soil but amended with additional carbon and Gibbs energy sources. The community from the contaminated, but untreated, soil degraded both 2,4-D and 2,4,5-T within 5 days of cultivation. The one from the amended soil, however, hardly showed any degradation of the compounds throughout 23 days of cultivation. After refreshment of the medium and prolonged culturing, however, this community degraded both herbicides within 25 days with progressively increasing rates. nMDS analysis revealed a highly significant differentiation pattern of the two communities. Cultures inoculated with amended soil showed a significant increase of Bacillus and Paenibacillus upon prolonged exposure to the herbicides. The succession in the culture from untreated soil, on the other hand, was dominated by species from the Proteobacteria. We were able to isolate two of them and they were shown to be related to Bordetella petrii and Sphingomonas histidinilytica, successively. Subsequent PCR analyses of their DNA revealed the presence of key genes involved in the degradation of the herbicides. This study provides a more fundamental understanding of the biodegradation of 2,4-D and 2,4,5-T by displaying part of the bacterial community succession during their breakdown allowing a comprehensive view on potential key degraders.

In this work, we investigated the degradation of crude oil by microbial communities i) in soils from a Nigerian river delta contaminated by oil spills, ii) in mesocosms of a landfarming experiment in Nigeria, and iii) in cultures during laboratory experiments. The microbial biodegradation of hydrocarbons shows great promise to clean oil spills. However, the composition and activities of soil microbial communities are very complex so we believe that it is important to investigate the behaviour of the microbial communities during oil degradation from different perspectives. Chapter 2 is a literature review where we describe the principal genes, enzymes and metabolic pathways that are essential for the aerobic degradation of aliphatic and aromatic hydrocarbons, including the degradation of PAHs. Chapter 3 is a methodological essay where we evaluate the effects of DNA extraction methods and DNA preservation solutions on the quality of microbial community profiling to prevent bias and to improve the preservation and extraction methods. In Chapter 4, we correlate the application of different bioremediation amendments in a large-scale landfarming experiment to oil-removal efficiencies and succession of the community profiles. In Chapter 5, we evaluate a laboratory-based passive-dosing approach in the biodegradation of oil molecules. In Chapter 6, we aimed at correlating the changes of the microbial community to environmental conditions and concentrations of spilt oil during a clean-up project in a Nigerian river delta with mangrove forests. In Chapter 7, we link the degradation of alkanes and PAH to changes in the bacterial community structures to identify key species in oil degradation.

The objectives of this study were to assess the influence on microbial communities resulting from i) the physical removal of free oil (pre-treatment or post-treatment), and ii) the level of oiling within a contaminated former mangrove forest. Sediment samples were collected before and after the removal of free oil. Before the process of remediation, a highly biodiverse mangrove microbiome which had adapted to history of recurring oil spills was observed. After removing the surface oil, the microbial diversity of the sediments reduced, with members of the phyla Firmicutes and Proteobacteria becoming dominant. This indicates that while water flushing reduced overall microbial diversity, it stimulated the growth of a more specialized bacterial community reported to be involved in hydrocarbon biodegradation. These results provide new insights on microbial communities and their succession in mangrove forest sediments, that will be useful for monitoring oil cleaning programs using water flushing to remove free oil.

Poly(3-hydroxybutyrate) (PHB) granule formation in Paracoccus denitrificans Pd1222 was investigated by laser scanning confocal microscopy (LSCM) and gas chromatography analysis. Cells that had been starved for 2 days were free of PHB granules but resynthesized them within 30 min of growth in fresh medium with succinate. In most cases, the granules were distributed randomly, although in some cases they appeared in a more organized pattern. The rates of growth and PHB accumulation were analyzed within the frame of a Genome-Scale Metabolic Model (GSMM) containing 781 metabolic genes, 1403 reactions and 1503 metabolites. The model was used to obtain quantitative predictions of biomass yields and PHB synthesis during aerobic growth on succinate as sole carbon and energy sources. The results revealed an initial fast stage of PHB accumulation, during which all of the acetyl-CoA originating from succinate was diverted to PHB production. The next stage was characterized by a tenfold lower PHB production rate and the simultaneous onset of exponential growth, during which acetyl-CoA was predominantly drained into the TCA cycle. Previous research has shown that PHB accumulation correlates with cytosolic acetyl-CoA concentration. It has also been shown that PHB accumulation is not transcriptionally regulated. Our results are consistent with the mentioned findings and suggest that, in absence of cell growth, most of the cellular acetyl-CoA is channeled to PHB synthesis, while during exponential growth, it is drained to the TCA cycle, causing a reduction of the cytosolic acetyl-CoA pool and a concomitant decrease of the synthesis of acetoacetyl-CoA (the precursor of PHB synthesis). Supplementary Information The online version contains supplementary material available at 10.1186/s13568-021-01273-x.

The behaviour of microbial communities depends on environmental factors and on the interactions of the community members. This is also the case for urinary tract infection (UTI) microbial communities. Here, we devise a computational approach that uses indices of complementarity and competition based on metabolic gene annotation to rapidly predict putative interactions between pair of organisms with the aim to explain pairwise growth effects. We apply our method to 66 genomes selected from online databases, which belong to 6 genera representing members of UTI communities. This resulted in a selection of metabolic pathways with high correlation for each pairwise combination between a complementarity index and the experimentally derived growth data. Our results indicated that Enteroccus spp. were most complemented in its metabolism by the other members of the UTI community. This suggests that the growth of Enteroccus spp. can potentially be enhanced by complementary metabolites produced by other community members. We tested a few putative predicted interactions by experimental supplementation of the relevant predicted metabolites. As predicted by our method, folic acid supplementation led to the increase in the population density of UTI Enterococcus isolates. Overall, we believe our method is a rapid initial in silico screening for the prediction of metabolic interactions in microbial communities.

The first sentence under the subheading ‘Regulating developmental stages’ has been updated to clarify that the early syncytial embryos referred to are from C. elegans, and that the smFISH and MS2–MCP studies mentioned extend previous observations summarized in reference 24. These changes have been made in the HTML and PDF versions of the article.

Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories., This project has received funding from MCIU/AEI/FEDER, UE (grant references RTI2018-093744-B-C31, RTI2018-093744-B-C32, RTI2018-093744-B-C33, and PID2019-104113RB-I00) and Xunta de Galicia (IN607B 2020/03). R.M. was supported by an FPI grant from the Ministerio de Economía y Competitividad, Spain (reference BES-2016-078202). S.N.M. acknowledges funding from CONICYT Becas Chile grant 72180373. S.N.M. and B.T. acknowledge support from YogurtDesign, EraCoBioTech grant 053.80.733.

The cAMP-PKA signaling cascade in budding yeast regulates adaptation to changing environments. We developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We used this sensor with flow cytometry for high-throughput single cell-level quantification during dynamic changes in response to sudden nutrient transitions. We found that the characteristic cAMP peak differentiates between different carbon source transitions and is rather homogenous among single cells, especially for transitions to glucose. The peaks are mediated by a combination of extracellular sensing and intracellular metabolism. Moreover, the cAMP peak follows the Weber-Fechner law; its height scales with the relative, and not the absolute, change in glucose. Last, our results suggest that the cAMP peak height conveys information about prospective growth rates. In conclusion, our yEPAC-sensor makes possible new avenues for understanding yeast physiology, signaling, and metabolic adaptation.

A key question in microbial ecology is what the driving forces behind the persistence of large biodiversity in natural environments are. We studied a microbial community with more than 100 different types of species which evolved in a 15-years old bioreactor with benzene as the main carbon and energy source and nitrate as the electron acceptor. Using genome-centric metagenomics plus metatranscriptomics, we demonstrate that most of the community members likely feed on metabolic left-overs or on necromass while only a few of them, from families Rhodocyclaceae and Peptococcaceae, are candidates to degrade benzene. We verify with an additional succession experiment using metabolomics and metabarcoding that these few community members are the actual drivers of benzene degradation. As such, we hypothesize that high species richness is maintained and the complexity of a natural community is stabilized in a controlled environment by the interdependencies between the few benzene degraders and the rest of the community members, ultimately resulting in a food web with different trophic levels., Chrats Melkonian and colleagues use metagenomics and transcriptomics to study microbial dynamics in a 15-year old bioreactor. In contrast to what is expected in a system where benzene is the primary carbon and energy source, relatively few members of the microbial community can degrade this compound. The results have implications for understanding interdependencies within such a community.