Genome-scale modeling and transcriptome analysis of Leuconostoc mesenteroides unravel the redox governed metabolic states in obligate heterofermentative lactic acid bacteria

Leuconostoc performance in soy-based fermentations - Survival, acidification, sugar metabolism, and flavor comparisons

Leuconostoc spp. is often regarded as the flavor producer, responsible for the production of acetoin and diacetyl in dairy cheese. In this study, we investigate seven plant-derived Leuconostoc strains, covering four species, in their potential as a lyophilized starter culture for flavor production in fermented soy-based cheese alternatives. We show that the process of lyophilization of Leuconostoc can be feasible using a soy-based lyoprotectant, with survivability up to 63% during long term storage. Furthermore, the storage in this media improves the subsequent growth in a soy-based substrate in a strain specific manner. The utilization of individual raffinose family oligosaccharides was strain dependent, with Leuconostoc pseudomesenteroides NFICC99 being the best consumer. Furthermore, we show that all investigated strains were able to produce a range of volatile flavor compounds found in dairy cheese products, as well as remove certain dairy off-flavors from the soy-based substrate like hexanal and 2-pentylfuran. Also here, NFICC99 was strain producing most cheese-related volatile flavor compounds, followed by Leuconostoc mesenteroides NFICC319. These findings provide initial insights into the development of Leuconostoc as a potential starter culture for plant-based dairy alternatives, as well as a promising approach for generation of stable, lyophilized cultures.

Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

Flavour by design: food-grade lactic acid bacteria improve the volatile aroma spectrum of oat milk, sunflower seed milk, pea milk, and faba milk towards improved flavour and sensory perception

BACKGROUND

The global market of plant-based milk alternatives is continually growing. Flavour and taste have a key impact on consumers' selection of plant-based beverages. Unfortunately, natural plant milks have only limited acceptance. Their typically bean-like and grassy notes are perceived as "off-flavours" by consumers, while preferred fruity, buttery, and cheesy notes are missing. In this regard, fermentation of plant milk by lactic acid bacteria (LAB) appears to be an appealing option to improve aroma and taste.

RESULTS

In this work, we systematically studied LAB fermentation of plant milk. For this purpose, we evaluated 15 food-approved LAB strains to ferment 4 different plant milks: oat milk (representing cereal-based milk), sunflower seed milk (representing seed-based milk), and pea and faba milk (representing legume-based milk). Using GC‒MS analysis, flavour changes during anaerobic fermentations were studied in detail. These revealed species-related and plant milk-related differences and highlighted several well-performing strains delivered a range of beneficial flavour changes. A developed data model estimated the impact of individual flavour compounds using sensory scores and predicted the overall flavour note of fermented and nonfermented samples. Selected sensory perception tests validated the model and allowed us to bridge compositional changes in the flavour profile with consumer response.

CONCLUSION

Specific strain-milk combinations provided quite different flavour notes. This opens further developments towards plant-based products with improved flavour, including cheesy and buttery notes, as well as other innovative products in the future. S. thermophilus emerged as a well-performing strain that delivered preferred buttery notes in all tested plant milks. The GC‒MS-based data model was found to be helpful in predicting sensory perception, and its further refinement and application promise enhanced potential to upgrade fermentation approaches to flavour-by-design strategies.

Genome Analysis of Potential Probiotic Levilactobacillus brevis AcCh91 Isolated from Indian Home-Made Fermented Milk Product (Chhurpi)

Consumption of naturally fermented milk (NFM) products is the dietary culture in India. The mountainous people of Arunachal Pradesh in India prepare the assorted artisanal home-made NFM products from cow and yak milk. Previously, we isolated and identified 76 strains of lactic acid bacteria (LAB) from NFM products of Arunachal Pradesh, viz. mar, chhurpi and churkam. We hypothesized that some of these LAB strains may possess probiotic potentials; hence, we investigated the probiotic potentials of these strains. On the basis of in vitro and genetic screening for probiotic attributes including haemolytic ability, 20 LAB strains were selected out of 76 strains, for further analysis. Using in silico analysis, viz. multivariate heatmap and PCA (principal component analysis) biplot, Levilactobacillus brevis AcCh91 was selected as the most promising probiotic strain, which was further characterized by the whole-genome analysis. Lev. brevis AcCh91 showed the highest survival rate of 93.38% in low pH and 86.68 ± 2.69% in low bile and the highest hydrophobicity average of 86.34 ± 5.53%. This strain also showed auto-aggregation and co-aggregation with antimicrobial properties against the pathogens, showed ability to produce beta-galactosidase and cholesterol reduction property and, most importantly, produced GABA, an important psychobiotic element. Genomic analysis of Lev. brevis AcCh91 showed the presence of genes corresponding to GABA, vitamins, amino acids, cholesterol reduction, immunomodulation, bioactive peptides and antioxidant activity. The absence of antimicrobial-resistant genes and virulence factors was observed. Hence, genome analysis supports the probiotic potentials of Lev. brevis AcCh91, which may be further investigated to understand its health-promoting properties.

Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen Cutibacterium acnes

Cutibacterium acnes, one of the most abundant skin microbes found in the sebaceous gland, is known to contribute to the development of acne vulgaris when its strains become imbalanced. The current limitations of acne treatment using antibiotics have caused an urgent need to develop a systematic strategy for selectively targeting C. acnes, which can be achieved by characterizing their cellular behaviors under various skin environments. To this end, we developed a genome-scale metabolic model (GEM) of virulent C. acnes, iCA843, based on the genome information of a relevant strain from ribotype 5 to comprehensively understand the pathogenic traits of C. acnes in the skin environment. We validated the model qualitatively by demonstrating its accuracy prediction of propionate and acetate production patterns, which were consistent with experimental observations. Additionally, we identified unique biosynthetic pathways for short-chain fatty acids in C. acnes compared to other GEMs of acne-inducing skin pathogens. By conducting constraint-based flux analysis under endogenous carbon sources in human skin, we discovered that the Wood-Werkman cycle is highly activated under acnes-associated skin condition for the regeneration of NAD, resulting in enhanced propionate production. Finally, we proposed potential anti-C. acnes targets by using the model-guided systematic framework based on gene essentiality analysis and protein sequence similarity search with abundant skin microbiome taxa.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

Design of a Plant-Based Yogurt-Like Product Fortified with Hemp Flour: Formulation and Characterization

Plant-based milk alternatives have gained massive popularity among consumers because of their sustainable production compared to bovine milk and because of meeting the nutritional requests of consumers affected by cow milk allergies and lactose intolerance. In this work, hemp flour, in a blend with rice flour, was used to design a novel lactose- and gluten-free yogurt-like (YL) product with suitable nutritional, functional, and sensory features. The growth and the acidification of three different lactic acid bacteria strains were monitored to better set up the biotechnological protocol for making the YL product. Hemp flour conferred the high fiber (circa 2.6 g/100 g), protein (circa 4 g/100 g), and mineral contents of the YL product, while fermentation by selected lactic acid bacteria increased the antioxidant properties (+8%) and the soluble fiber (+0.3 g/100 g), decreasing the predicted glycemic index (-10%). As demonstrated by the sensory analysis, the biotechnological process decreased the earthy flavor (typical of raw hemp flour) and increased the acidic and creamy sensory perceptions. Supplementation with natural clean-label vanilla powder and agave syrup was proposed to further decrease the astringent and bitter flavors. The evaluation of the starter survival and biochemical properties of the product under refrigerated conditions suggests an estimated shelf-life of 30 days. This work demonstrated that hemp flour might be used as a nutritional improver, while fermentation with a selected starter represents a sustainable and effective option for exploiting its potential.

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet- and strain-specific probiotic idiosyncrasies

Lactic acid bacteria (LAB) are well known to elicit health benefits in humans, but their functional metabolic landscapes remain unexplored. Here, we analyze differences in growth, intestinal persistence, and postbiotic biosynthesis of six representative LAB and their interactions with 15 gut bacteria under 11 dietary regimes by combining multi-omics and in silico modeling. We confirmed predictions on short-term persistence of LAB and their interactions with commensals using cecal microbiome abundance and spent-medium experiments. Our analyses indicate that probiotic attributes are both diet and species specific and cannot be solely explained using genomics. For example, although both Lacticaseibacillus casei and Lactiplantibacillus plantarum encode similarly sized genomes with diverse capabilities, L. casei exhibits a more desirable phenotype. In addition, "high-fat/low-carb" diets more likely lead to detrimental outcomes for most LAB. Collectively, our results highlight that probiotics are not "one size fits all" health supplements and lay the foundation for personalized probiotic design.

Functional genomics of the lactic acid bacterium Limosilactobacillus fermentum LAB-1: metabolic, probiotic and biotechnological perspectives

A genome-based systematic analysis was conducted to characterize the metabolic, probiotic, fitness, and safety properties of Limosilactobacillus fermentum LAB-1, a lactic acid bacterium demonstrating strong antimicrobial effects against clinical pathogens. Gene functional characterization revealed a large number of genes for carbohydrate metabolism and a heterofermentative system for carbon dissimilation. Genes for intact pyruvate oxidation, pentose phosphate, and PRPP biosynthetic pathways were identified. Substantial carbohydrate-active enzymes and transporters were also predicted. Metabolic reconstruction revealed complete sets of enzymes for arginine, lysine, methionine, threonine, proline, and ornithine biosynthesis. The bacterium harbors a diverse range of peptidases, and a large variety of peptide and amino acid uptake systems. It encodes restriction-modification and CRISPR-Cas systems for protection against phage infections and carries a wide spectrum of stress proteins for adaptation in the gut and industrial conditions. Genes related to the biosynthesis of B-group and K vitamins were identified allowing its application for novel bio-enriched food production. Other beneficial traits of probiotic and industrial importance such as production of flavor compounds, exopolysaccharide, acetoin, and butanediol were identified. Three antimicrobial peptides were predicted which showed >98% sequence-identity to experimentally validated bacteriocins. Negative traits such as transmissible antibiotic resistance, pathogenicity or virulence appeared to be absent suggesting the strain to be considered safe. The genome analysis will allow precisely targeted laboratory research and full exploitation of the probiotic potentials towards functional-food, biotechnology and health-related applications.

Comparative genomics reveals the organic acid biosynthesis metabolic pathways among five lactic acid bacterial species isolated from fermented vegetables

Lactic acid bacteria (LAB) comprise a widespread bacterial group, inhabiting the niches of fermented vegetables and capable of producing beneficial organic acids. In the present study, several bioinformatics approaches were used to perform whole-genome sequencing and comparative genomics of five LAB species, Lactobacillus plantarum PC1-1, Pediococcus pentosaceus PC2-1(F2), Weissella hellenica PC1A, Lactobacillus buchneri PC-C1, and Enterococcus sp. YC2-6, to enhance understanding of their different genetic functionalities and organic acid biosynthesis. The results revealed major carbohydrate-active enzymes, putative operons and unique mobile genetic elements, including plasmids, resistance genes, insertion sequences and composite transposons involved in organic acid biosynthesis. The metabolic pathways of organic acid biosynthesis emphasize the key genes encoding specific enzymes required for organic acid metabolism. The five genomes were found to contain various regions of secondary metabolite biosynthetic gene clusters, including the type III polyketide synthases (T3PKS) enriched with unique genes encoding a hydroxymethylglutaryl-CoA synthase, capable of exhibiting specific antimicrobial activity with biopreservative potential, and a cyclic AMP receptor protein (CPR) transcription factor acting as a glucose sensor in organic acid biosynthesis. This could enable the organisms to prevail in the fermentation process, suggesting potential industrial applications.

Omics Approaches to Assess Flavor Development in Cheese

Cheese is characterized by a rich and complex microbiota that plays a vital role during both production and ripening, contributing significantly to the safety, quality, and sensory characteristics of the final product. In this context, it is vital to explore the microbiota composition and understand its dynamics and evolution during cheese manufacturing and ripening. Application of high-throughput DNA sequencing technologies have facilitated the more accurate identification of the cheese microbiome, detailed study of its potential functionality, and its contribution to the development of specific organoleptic properties. These technologies include amplicon sequencing, whole-metagenome shotgun sequencing, metatranscriptomics, and, most recently, metabolomics. In recent years, however, the application of multiple meta-omics approaches along with data integration analysis, which was enabled by advanced computational and bioinformatics tools, paved the way to better comprehension of the cheese ripening process, revealing significant associations between the cheese microbiota and metabolites, as well as their impact on cheese flavor and quality.

Identifying the essential nutritional requirements of the probiotic bacteria Bifidobacterium animalis and Bifidobacterium longum through genome-scale modeling

Although bifidobacteria are widely used as probiotics, their metabolism and physiology remain to be explored in depth. In this work, strain-specific genome-scale metabolic models were developed for two industrially and clinically relevant bifidobacteria, Bifidobacterium animalis subsp. lactis BB-12® and B. longum subsp. longum BB-46, and subjected to iterative cycles of manual curation and experimental validation. A constraint-based modeling framework was used to probe the metabolic landscape of the strains and identify their essential nutritional requirements. Both strains showed an absolute requirement for pantethine as a precursor for coenzyme A biosynthesis. Menaquinone-4 was found to be essential only for BB-46 growth, whereas nicotinic acid was only required by BB-12®. The model-generated insights were used to formulate a chemically defined medium that supports the growth of both strains to the same extent as a complex culture medium. Carbohydrate utilization profiles predicted by the models were experimentally validated. Furthermore, model predictions were quantitatively validated in the newly formulated medium in lab-scale batch fermentations. The models and the formulated medium represent valuable tools to further explore the metabolism and physiology of the two species, investigate the mechanisms underlying their health-promoting effects and guide the optimization of their industrial production processes.

Effects of Lactobacillus plantarum C7 and Staphylococcus warneri S6 on flavor quality and bacterial diversity of fermented meat rice, a traditional Chinese food

Fermented meat rice (FMR) is a traditional Chinese fermented food with special flavor and abundant microorganisms. Lactobacillus and Staphylococcus species have been found to be excellent strains in FMR during fermentation. However, their roles in FMR flavor formation remain yet to be elucidated. Here, we investigated the correlation between physicochemical properties and volatile flavor components, as well as the microbial community during FMR fermentation. First, we determined pH, total titratable acids (TTA), proteins, total lipids, organic acids, free amino acids (FAAs), and volatile flavor compounds (VFCs). With increasing fermentation time, inoculation with Lactobacillus plantarum C7+ Staphylococcus warneri S6 (LP + SW) accelerated the decrease in pH, increased TTA, and reduced protein and total lipid content of FMR. In addition, LP + SW inoculation resulted in significantly (P < 0.05) higher contents of β-eudesmol, nerolidol, ethyl caproate, citronellal, lactic acid, and most FAAs (aspartic acid, glutamic acid, alanine, and lysine) in FMR compared to natural fermentation. Second, inoculated fermentation promoted the growth of Lactobacillus plantarum and/or Staphylococcus warneri and inhibited the growth of some potentially pathogenic microorganisms such as Acinetobacter and Enhydrobacter. Lactobacillus and Staphylococcus were found to be highly correlated with the physicochemical properties and VFCs (P < 0.05) of FMR as indicated by redundancy analysis (RDA) and partial least squares (PLS, VIP > 1.0) analysis. Finally, Spearman's correlation (| r | ≥ 0.7, P < 0.05) analysis of SPSS was visualized by the Cytoscape software. The findings suggest that inoculation with L. plantarum C7 and/or S. warneri S6 can significantly improve the flavor quality of FMR.

The role of dextran production in the metabolic context of Leuconostoc and Weissella Tunisian strains

High molecular weight dextrans improve the rheological properties of fermented products and have immunomodulatory and antiviral activity. We report on 5.84 × 10⁷-2.61 × 10⁸ Da dextrans produced by Leuconostoc lactis AV1n, Weissella cibaria AV2ou and Weissella confusa V30 and FS54 strains. Dextransucrases catalyze dextran synthesis by sucrose hydrolysis concomitant with fructose generation. The four bacteria have dextransucrases with molecular weight of about 160 kDa detected by zymograms. Each bacterium showed different interplay of dextran production and metabolic fluxes. All bacteria produced lactate, and AV2ou apart, synthesized mannitol from fructose. FS54 hydrolyzed dextran blue and the concentration of dextran produced by this bacterium decreased during the stationary phase. The AV1n binding to Caco-2 cells and polystyrene plates was higher under conditions for dextran synthesis. Thus, this is the first instance of a Weissella dextranase, associated with a dextransucrase ability, and of a positive influence of dextran on adhesion and aggregation properties of a bacterium.

Genome scale metabolic models and analysis for evaluating probiotic potentials

Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.

Unraveling microbial fermentation features in kimchi: from classical to meta-omics approaches

Kimchi is a traditional Korean fermented food prepared via spontaneous fermentation by various microorganisms originating from vegetables such as kimchi cabbage, radishes, and garlic. Recent advances in meta-omics approaches that integrate metataxonomics, metagenomics, metatranscriptomics, and metabolomics have contributed to explaining and understanding food fermentation processes. Kimchi microbial communities are composed of majorly lactic acid bacteria such as Leuconostoc, Lactobacillus, and Weissella and fewer eukaryotic microorganisms and kimchi fermentation are accomplished by complex microbial metabolisms to produce diverse metabolites such as lactate, acetate, CO₂, ethanol, mannitol, amino acids, formate, malate, diacetyl, acetoin, and 2, 3-butanediol, which determine taste, quality, health benefit, and safety of fermented kimchi products. Therefore, in the future, kimchi researches should be systematically performed using the meta-omics approaches to understand complex microbial metabolisms during kimchi fermentation. KEY POINTS: • Spontaneous fermentation by raw material microbes gives kimchi its unique flavor. • The kimchi microbiome is altered by environmental factors and raw materials. • Through the multi-omics approaches, it is possible to accurately analyze the diversity and metabolic characteristics of kimchi microbiome and discover potential functionalities.

A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

Background

Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data.

Results

A genome-scale metabolic model of L. reuteri JCM 1112^T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden–Meyerhof–Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden–Meyerhof–Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies.

Conclusion

We have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112^T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

A genome-scale metabolic network of the aroma bacterium Leuconostoc mesenteroides subsp. cremoris

Leuconostoc mesenteroides subsp. cremoris is an obligate heterolactic fermentative lactic acid bacterium that is mostly used in industrial dairy fermentations. The phosphoketolase pathway (PKP) is a unique feature of the obligate heterolactic fermentation, which leads to the production of lactate, ethanol, and/or acetate, and the final product profile of PKP highly depends on the energetics and redox state of the organism. Another characteristic of the L. mesenteroides subsp. cremoris is the production of aroma compounds in dairy fermentation, such as in cheese production, through the utilization of citrate. Considering its importance in dairy fermentation, a detailed metabolic characterization of the organism is necessary for its more efficient use in the industry. To this aim, a genome-scale metabolic model of dairy-origin L. mesenteroides subsp. cremoris ATCC 19254 (iLM.c559) was reconstructed to explain the energetics and redox state mechanisms of the organism in full detail. The model includes 559 genes governing 1088 reactions between 1129 metabolites, and the reactions cover citrate utilization and citrate-related flavor metabolism. The model was validated by simulating co-metabolism of glucose and citrate and comparing the in silico results to our experimental results. Model simulations further showed that, in co-metabolism of citrate and glucose, no flavor compounds were produced when citrate could stimulate the formation of biomass. Significant amounts of flavor metabolites (e.g., diacetyl and acetoin) were only produced when citrate could not enhance growth, which suggests that flavor formation only occurs under carbon and ATP excess. The effects of aerobic conditions and different carbon sources on product profiles and growth were also investigated using the reconstructed model. The analyses provided further insights for the growth stimulation and flavor formation mechanisms of the organism.

Presence of galactose in precultures induces lacS and leads to short lag phase in lactose-grown Lactococcus lactis cultures

Lactose conversion by lactic acid bacteria is of high industrial relevance and consistent starter culture quality is of outmost importance. We observed that Lactococcus lactis using the high-affinity lactose-phosphotransferase system excreted galactose towards the end of the lactose consumption phase. The excreted galactose was re-consumed after lactose depletion. The lacS gene, known to encode a lactose permease with affinity for galactose, a putative galactose–lactose antiporter, was upregulated under the conditions studied. When transferring cells from anaerobic to respiration-permissive conditions, lactose-assimilating strains exhibited a long and non-reproducible lag phase. Through systematic preculture experiments, the presence of galactose in the precultures was correlated to short and reproducible lag phases in respiration-permissive main cultivations. For starter culture production, the presence of galactose during propagation of dairy strains can provide a physiological marker for short culture lag phase in lactose-grown cultures.

Constraint-based modeling in microbial food biotechnology

Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.