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

Harnessing the polysaccharide production potential to optimize and expand the application of probiotics

Certain probiotic microorganisms can synthesize important bioproducts, including polysaccharides as components of cellular structure or extracellular matrix. Probiotic-derived polysaccharides have been widely applied in food, pharmaceutical, and medical fields due to their excellent properties and biological activities. The development of polysaccharide production potential has become a driving force for facilitating biotechnological applications of probiotics. Based on technical advances in synthetic biology, significant progress has recently been made in engineering probiotics with efficient biosynthesis of polysaccharides. Herein, this review summarizes probiotics chassis and genetic tools used for polysaccharide production. Then, probiotic polysaccharides and relevant biosynthesis mechanisms are also clearly described. Next, we introduce strategies for preparing high-yield, controllable molecular weight or non-native polysaccharides by adjusting metabolic pathways and integrating expression elements in probiotics. Finally, some prospective and well-established contributions of exogenous and in situ polysaccharides in probiotics' stability, bioactivity, and therapeutic effects are presented. Our viewpoints on advancing the efficient biomanufacturing of valuable biopolymers in probiotics and engineering probiotics with customized features are provided to exploit probiotics' industrial and biomedical applications.

Limosilactobacillus reuteri AJCR4: A Potential Probiotic in the Fight Against Oral Candida spp. Biofilms

Oral candidiasis is one of the most common infections in the immunocompromised. Biofilms of Candida species can make treatments difficult, leading to oral infection recurrence. This research aimed to isolate a Lactobacillus with anti-Candida effects from the oral cavity. An oral Lactobacillus was isolated in caries-free individuals. The best isolate was evaluated against Candida spp. planktonic and biofilm forms. The bacterial impacts on Candida biofilms' adhesion to acrylic discs were analyzed through an in vitro test. L. reuteri AJCR4 had the best anti-Candida activity in the preliminary screening. Results were promising in both planktonic and biofilms, particularly with C. albicans SC5314 and C. tropicalis ATCC750, where no viable cells were detected when using the cell-free supernatant (undiluted). In C. glabrata ATCC2001 and C. parapsilosis ATCC22019 biofilms, reductions of 3 Log10 and more than 2 Log10, respectively, were noted when using a cell suspension of L. reuteri ACJR4 (108 CFU/mL). On polymethyl methacrylate acrylic discs, the cell-free supernatant reduced Candida adhesion, resulting in no viable cell detection on the surface. In conclusion, L. reuteri AJCR4 demonstrated notable antifungal activity against Candida biofilms. This oral isolate and its postbiotic can be a potential alternative strategy to oral candidiasis, especially to treat recalcitrant infections.

Pangenome reconstruction of Lactobacillaceae metabolism predicts species-specific metabolic traits

Strains across the Lactobacillaceae family form the basis for a trillion-dollar industry. Our understanding of the genomic basis for their key traits is fragmented, however, including the metabolism that is foundational to their industrial uses. Pangenome analysis of publicly available Lactobacillaceae genomes allowed us to generate genome-scale metabolic network reconstructions for 26 species of industrial importance. Their manual curation led to more than 75,000 gene-protein-reaction associations that were deployed to generate 2,446 genome-scale metabolic models. Cross-referencing genomes and known metabolic traits allowed for manual metabolic network curation and validation of the metabolic models. As a result, we provide the first pangenomic basis for metabolism in the Lactobacillaceae family and a collection of predictive computational metabolic models that enable a variety of practical uses.IMPORTANCELactobacillaceae, a bacterial family foundational to a trillion-dollar industry, is increasingly relevant to biosustainability initiatives. Our study, leveraging approximately 2,400 genome sequences, provides a pangenomic analysis of Lactobacillaceae metabolism, creating over 2,400 curated and validated genome-scale models (GEMs). These GEMs successfully predict (i) unique, species-specific metabolic reactions; (ii) niche-enriched reactions that increase organism fitness; (iii) essential media components, offering insights into the global amino acid essentiality of Lactobacillaceae; and (iv) fermentation capabilities across the family, shedding light on the metabolic basis of Lactobacillaceae-based commercial products. This quantitative understanding of Lactobacillaceae metabolic properties and their genomic basis will have profound implications for the food industry and biosustainability, offering new insights and tools for strain selection and manipulation.

Metagenomic analysis of the intestinal microbiome reveals the potential mechanism involved in Bacillus amyloliquefaciens in treating schistosomiasis japonica in mice

Schistosomiasis japonica is one of the neglected tropical diseases characterized by chronic hepatic, intestinal granulomatous inflammation and fibrosis, as well as dysbiosis of intestinal microbiome. Previously, the probiotic Bacillus amyloliquefaciens has been shown to alleviate the pathological injuries in mice infected with Schistosoma japonicum by improving the disturbance of the intestinal microbiota. However, the underlying mechanisms involved in this process remain unclear. In this study, metagenomics sequencing and functional analysis were employed to investigate the differential changes in taxonomic composition and functional genes of the intestinal microbiome in S. japonicum-infected mice treated with B. amyloliquefaciens. The results revealed that intervention with B. amyloliquefaciens altered the taxonomic composition of the intestinal microbiota at the species level in infected mice and significantly increased the abundance of beneficial bacteria. Moreover, the abundance of predicted genes in the intestinal microbiome was also significantly changed, and the abundance of xfp/xpk and genes translated to urease was significantly restored. Further analysis showed that Limosilactobacillus reuteri was positively correlated with several KEGG Orthology (KO) genes and metabolic reactions, which might play important roles in alleviating the pathological symptoms caused by S. japonicum infection, indicating that it has the potential to function as another effective therapeutic agent for schistosomiasis. These data suggested that treatment of murine schistosomiasis japonica by B. amyloliquefaciens might be induced by alterations in the taxonomic composition and functional gene of the intestinal microbiome in mice. We hope this study will provide adjuvant strategies and methods for the early prevention and treatment of schistosomiasis japonica.

IMPORTANCE

Targeted interventions of probiotics on gut microbiome were used to explore the mechanism of alleviating schistosomiasis japonica. Through metagenomic analysis, there were significant changes in the composition of gut microbiota in mice infected with Schistosoma japonicum and significant increase in the abundance of beneficial bacteria after the intervention of Bacillus amyloliquefaciens. At the same time, the abundance of functional genes was found to change significantly. The abundance of genes related to urease metabolism and xfp/xpk related to D-erythrose 4-phosphate production was significantly restored, highlighting the importance of Limosilactobacillus reuteri in the recovery and abundance of predicted genes of the gut microbiome. These results indicated potential regulatory mechanism between the gene function of gut microbiome and host immune response. Our research lays the foundation for elucidating the regulatory mechanism of probiotic intervention in alleviating schistosomiasis japonica, and provides potential adjuvant treatment strategies for early prevention and treatment of schistosomiasis japonica.

Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning

Limosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied the independent component analysis (ICA) to 117 RNA-seq data sets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. Our findings indicate that the ICA provides a qualitative advancement and captures nuanced relationships within gene clusters that other methods may miss. This study uncovers the fundamental properties of L. reuteri's TRN and deepens our understanding of its arginine metabolism and the co-regulation of riboflavin metabolism and fatty acid conversion. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and allows for the speculation of the potential role of isoprenoid biosynthesis in L. reuteri's adaptive response to environmental changes. By integrating transcriptomics and machine learning, we provide a system-level understanding of L. reuteri's response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production.

IMPORTANCE

We have studied Limosilactobacillus reuteri, a beneficial probiotic microbe that plays a significant role in our health and production of sustainable foods, a type of foods that are nutritionally dense and healthier and have low-carbon emissions compared to traditional foods. Similar to how humans adapt their lifestyles to different environments, this microbe adjusts its behavior by modulating the expression of genes. We applied machine learning to analyze large-scale data sets on how these genes behave across diverse conditions. From this, we identified 35 unique patterns demonstrating how L. reuteri adjusts its genes based on 50 unique environmental conditions (such as various sugars, salts, microbial cocultures, human milk, and fruit juice). This research helps us understand better how L. reuteri functions, especially in processes like breaking down certain nutrients and adapting to stressful changes. More importantly, with our findings, we become closer to using this knowledge to improve how we produce more sustainable and healthier foods with the help of microbes.

mergem: merging, comparing, and translating genome-scale metabolic models using universal identifiers

Numerous methods exist to produce and refine genome-scale metabolic models. However, due to the use of incompatible identifier systems for metabolites and reactions, computing and visualizing the metabolic differences and similarities of such models is a current challenge. Furthermore, there is a lack of automated tools that can combine the strengths of multiple reconstruction pipelines into a curated single comprehensive model by merging different drafts, which possibly use incompatible namespaces. Here we present mergem, a novel method to compare, merge, and translate two or more metabolic models. Using a universal metabolic identifier mapping system constructed from multiple metabolic databases, mergem robustly can compare models from different pipelines, merge their common elements, and translate their identifiers to other database systems. mergem is implemented as a command line tool, a Python package, and on the web-application Fluxer, which allows simulating and visually comparing multiple models with different interactive flux graphs. The ability to merge, compare, and translate diverse genome scale metabolic models can facilitate the curation of comprehensive reconstructions and the discovery of unique and common metabolic features among different organisms.

Advances in modeling techniques for the production and purification of biomolecules: A comprehensive review

In response to the growing demand for therapeutic biomolecules, there is a need for continuous and cost-effective bio-separation techniques to enhance extraction yield and efficiency. Aqueous biphasic extractive fermentation has emerged as an integrated downstream processing technique, offering selective partitioning, high productivity, and preservation of biomolecule integrity. However, the dynamic nature of this technique requires a comprehensive understanding of the underlying separation mechanisms. Unfortunately, the analysis of parameters influencing this dynamic behavior can be challenging due to limited resources and time. To address this, mathematical modeling approaches can be employed to minimize the tedious trial-and-error experimentation process. This review article presents mathematical modeling approaches for both upstream and downstream processing techniques, focusing on the production of biomolecules which can be used in pharmaceutical industries in a cost-effective manner. By leveraging mathematical models, researchers can optimize the production and purification processes, leading to improved efficiency and processing cost reduction in biomolecule production.

Plasmid-encoded toxin defence mediates mutualistic microbial interactions

Gut environments harbour dense microbial ecosystems in which plasmids are widely distributed. Plasmids facilitate the exchange of genetic material among microorganisms while enabling the transfer of a diverse array of accessory functions. However, their precise impact on microbial community composition and function remains largely unexplored. Here we identify a prevalent bacterial toxin and a plasmid-encoded resistance mechanism that mediates the interaction between Lactobacilli and Enterococci. This plasmid is widespread across ecosystems, including the rumen and human gut microbiota. Biochemical characterization of the plasmid revealed a defence mechanism against reuterin, a toxin produced by various gut microbes, such as Limosilactobacillus reuteri. Using a targeted metabolomic approach, we find reuterin to be prevalent across rumen ecosystems with impacts on microbial community structure. Enterococcus strains carrying the protective plasmid were isolated and their interactions with L. reuteri, the toxin producer, were studied in vitro. Interestingly, we found that by conferring resistance against reuterin, the plasmid mediates metabolic exchange between the defending and the attacking microbial species, resulting in a beneficial relationship or mutualism. Hence, we reveal here an ecological role for a plasmid-coded defence system in mediating a beneficial interaction.

Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions

Multi-strain probiotics are widely regarded as effective products for improving gut microbiota stability and host health, providing advantages over single-strain probiotics. However, in general, it is unclear to what extent different strains would cooperate or compete for resources, and how the establishment of a common biofilm microenvironment could influence their interactions. In this work, we develop an integrative experimental and computational approach to comprehensively assess the metabolic functionality and interactions of probiotics across growth conditions. Our approach combines co-culture assays with genome-scale modelling of metabolism and multivariate data analysis, thus exploiting complementary data- and knowledge-driven systems biology techniques. To show the advantages of the proposed approach, we apply it to the study of the interactions between two widely used probiotic strains of Lactobacillus reuteri and Saccharomyces boulardii, characterising their production potential for compounds that can be beneficial to human health. Our results show that these strains can establish a mixed cooperative-antagonistic interaction best explained by competition for shared resources, with an increased individual exchange but an often decreased net production of amino acids and short-chain fatty acids. Overall, our work provides a strategy that can be used to explore microbial metabolic fingerprints of biotechnological interest, capable of capturing multifaceted equilibria even in simple microbial consortia.

Biocatalytic gateway to convert glycerol into 3-hydroxypropionic acid in waste-based biorefineries: Fundamentals, limitations, and potential research strategies

Microbial conversion of bioenergy-derived waste glycerol into value-added chemicals has emerged as an important bioprocessing technology due to its eco-friendliness, feasible technoeconomics, and potential to provide sustainability in biodiesel and bioethanol production. Glycerol is an abundant liquid waste from bioenergy plants with a projected volume of 6 million tons by 2025, accounting for about 10% of biodiesel and 2.5% of bioethanol yields. 3-Hydroxypropionic acid (3-HP) is a major product of glycerol bioconversion, which is the third largest biobased platform compound with expected market size and value of 3.6 million tons/year and USD 10 billion/year, respectively. Despite these biorefinery values, 3-HP biosynthesis from glycerol is still at an immature stage of commercial exploitation. The main challenges behind this immaturity are the toxic effects of 3-HPA on cells, the distribution of carbon flux to undesirable pathways, low tolerance of cells to glycerol and 3-HP, co-factor dependence of enzymes, low enzyme activity and stability, and the problems of substrate inhibition and specificity of enzymes. To address these challenges, it is necessary to understand the fundamentals of glycerol bioconversion and 3-HP production in terms of metabolic pathways, related enzymes, cell factories, midstream process configurations, and downstream 3-HP recovery, as discussed in this review critically and comprehensively. It is equally important to know the current challenges and limitations in 3-HP production, which are discussed in detail along with recent research efforts and remaining gaps. Finally, possible research strategies are outlined considering the recent technological advances in microbial biosynthesis, aiming to attract further research efforts to achieve a sustainable and industrially exploitable 3-HP production technology. By discussing the use of advanced tools and strategies to overcome the existing challenges in 3-HP biosynthesis, this review will attract researchers from many other similar biosynthesis technologies and provide a common gateway for their further development.

Microbial exopolysaccharides-β-glucans-as promising postbiotic candidates in vaccine adjuvants

The urgent task of creating new, enhanced adjuvants is closely related to our comprehension of their mechanisms of action. A few adjuvants have shown sufficient efficacy and low toxicity to be allowed for use in human vaccines, despite the fact that they have a long history and an important function. Adjuvants have long been used without a clear understanding of how precisely they augment the immune response. The rational production of stronger and safer adjuvants has been impeded by this lack of information, which necessitates more mechanistic research to support the development of vaccines. Carbohydrate structures-polygalactans, fructans, β-D-glucans, α-D-glucans, D-galactose, and D-glucose-are desirable candidates for the creation of vaccine adjuvants and immunomodulators because they serve important functions in nature and are often biocompatible, safe, and well tolerated. In this review, we have discussed recent advances in microbial-derived carbohydrate-based adjuvants, their immunostimulatory activity, and the implications of this for vaccine development, along with the critical view on the microbial sources, chemical composition, and biosynthetic pathways.

Long-Term Lacticaseibacillus rhamnosus GG Storage at Ambient Temperature in Vegetable Oil: Viability and Functional Assessments

Vegetable oils with varying saturated fat levels were inoculated with Lacticaseibacillus rhamnosus GG (LGG), subjected to different heat treatments in the absence and presence of inulin and stored for 12 months at room temperature. After storage, the heat-treated probiotics actively grew to high concentrations after removal of the oils and reculturing. The bacterial samples, regardless of aerobic or anaerobic conditions and treatment methods, showed no changes in their growth behavior. The random amplified polymorphic DNA-polymerase chain reaction, antimicrobial, morphology, and motility tests also showed no major differences. Samples of LGG treated with a higher antioxidant content (Gal400) showed reduced inflammatory and anti-inflammatory properties. These findings have been confirmed by metabolite and genome sequencing studies, indicating that Gal400 showed lower concentrations and secretion percentages and the highest number of single nucleotide polymorphisms. We have shown proof of concept that LGG can be stored in oil with minimum impact on probiotic in vitro viability.

In Silico Genomic and Metabolic Atlas of Limosilactobacillus reuteri DSM 20016: An Insight into Human Health

Probiotics are bacterial strains that are known to provide host health benefits. Limosilactobacillus reuteri is a well-documented lactic acid bacterium that has been cultured from numerous human sites. The strain investigated was L. reuteri DSM 20016, which has been found to produce useful metabolites. The strain was explored using genomic and proteomic tools, manual searches, and databases, including KEGG, STRING, BLAST Sequence Similarity Search, and UniProt. This study located over 200 key genes that were involved in human health benefit pathways. L. reuteri DSM 20016 has metabolic pathways to produce acetate, propionate, and lactate, and there is evidence of a pathway for butanoate production through a FASII mechanism. The bacterium produces histamine through the hdc operon, which may be able to suppress proinflammatory TNF, and the bacterium also has the ability to synthesize folate and riboflavin, although whether they are secreted is yet to be explored. The strain can bind to human Caco2 cells through srtA, mapA/cnb, msrB, and fbpA and can compete against enteric bacteria using reuterin, which is an antimicrobial that induces oxidative stress. The atlas could be used for designing metabolic engineering approaches to improve beneficial metabolite biosynthesis and better probiotic-based cures.

Probing Genome-Scale Model Reveals Metabolic Capability and Essential Nutrients for Growth of Probiotic Limosilactobacillus reuteri KUB-AC5

Limosilactobacillus reuteri KUB-AC5 displays the hallmark features of probiotic properties for food and feed industries. Optimization of cultivation condition for the industrial production is important to reach cell concentration and cost reduction. Considering the strain-specific growth physiology, metabolic capability, and essential nutrients of L. reuteri KUB-AC5, the genome-scale metabolic model (GSMM) of L. reuteri KUB-AC5 was developed. Hereby, the GSMM of iTN656 was successfully constructed which contained 656 genes, 831 metabolites, and 953 metabolic reactions. The iTN656 model could show a metabolic capability under various carbon sources and guide potentially 14 essential single nutrients (e.g., vitamin B complex and amino acids) and 2 essential double nutrients (pairwise glutamine-glutamate and asparagine-aspartate) for L. reuteri KUB-AC5 growth through single and double omission analysis. Promisingly, the iTN656 model was further integrated with transcriptome data suggesting that putative metabolic routes as preferable paths e.g., sucrose uptake, nucleotide biosynthesis, urea cycle, and glutamine transporter for L. reuteri KUB-AC5 growth. The developed GSMM offers a powerful tool for multi-level omics analysis, enabling probiotic strain optimization for biomass overproduction on an industrial scale.

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.

Two-species community design of lactic acid bacteria for optimal production of lactate

Microbial communities that metabolise pentose and hexose sugars are useful in producing high-value chemicals, resulting in the effective conversion of raw materials to the product, a reduction in the production cost, and increased yield. Here, we present a computational analysis approach called CAMP (Co-culture/Community Analyses for Metabolite Production) that simulates and identifies appropriate communities to produce a metabolite of interest. To demonstrate this approach, we focus on the optimal production of lactate from various Lactic Acid Bacteria. We used genome-scale metabolic models (GSMMs) belonging to Lactobacillus, Leuconostoc, and Pediococcus species from the Virtual Metabolic Human (VMH; https://vmh.life/) resource and well-curated GSMMs of L. plantarum WCSF1 and L. reuteri JCM 1112. We analysed 1176 two-species communities using a constraint-based modelling method for steady-state flux-balance analysis of communities. Flux variability analysis was used to detect the maximum lactate flux in the communities. Using glucose or xylose as substrates separately or in combination resulted in either parasitism, amensalism, or mutualism being the dominant interaction behaviour in the communities. Interaction behaviour between members of the community was deduced based on variations in the predicted growth rates of monocultures and co-cultures. Acetaldehyde, ethanol, acetate, among other metabolites, were found to be cross-fed between community members. L. plantarum WCSF1 was found to be a member of communities with high lactate yields. In silico community optimisation strategies to predict reaction knock-outs for improving lactate flux were implemented. Reaction knock-outs of acetate kinase, phosphate acetyltransferase, and fumarate reductase in the communities were found to enhance lactate production.

Genome-scale insights into the metabolic versatility of Limosilactobacillus reuteri

BACKGROUND

Limosilactobacillus reuteri (earlier known as Lactobacillus reuteri) is a well-studied lactic acid bacterium, with some specific strains used as probiotics, that exists in different hosts such as human, pig, goat, mouse and rat, with multiple body sites such as the gastrointestinal tract, breast milk and mouth. Numerous studies have confirmed the beneficial effects of orally administered specific L. reuteri strains, such as preventing bone loss and promoting regulatory immune system development. L. reuteri ATCC PTA 6475 is a widely used strain that has been applied in the market as a probiotic due to its positive effects on the human host. Its health benefits may be due, in part, to the production of beneficial metabolites. Considering the strain-specific effects and genetic diversity of L. reuteri strains, we were interested to study the metabolic versatility of these strains.

RESULTS

In this study, we aimed to systematically investigate the metabolic features and diversities of L. reuteri strains by using genome-scale metabolic models (GEMs). The GEM of L. reuteri ATCC PTA 6475 was reconstructed with a template-based method and curated manually. The final GEM iHL622 of L. reuteri ATCC PTA 6475 contains 894 reactions and 726 metabolites linked to 622 metabolic genes, which can be used to simulate growth and amino acids utilization. Furthermore, we built GEMs for the other 35 L. reuteri strains from three types of hosts. The comparison of the L. reuteri GEMs identified potential metabolic products linked to the adaptation to the host.

CONCLUSIONS

The GEM of L. reuteri ATCC PTA 6475 can be used to simulate metabolic capabilities and growth. The core and pan model of 35 L. reuteri strains shows metabolic capacity differences both between and within the host groups. The GEMs provide a reliable basis to investigate the metabolism of L. reuteri in detail and their potential benefits on the host.

Modelling microbial communities: Harnessing consortia for biotechnological applications

Microbes propagate and thrive in complex communities, and there are many benefits to studying and engineering microbial communities instead of single strains. Microbial communities are being increasingly leveraged in biotechnological applications, as they present significant advantages such as the division of labour and improved substrate utilisation. Nevertheless, they also present some interesting challenges to surmount for the design of efficient biotechnological processes. In this review, we discuss key principles of microbial interactions, followed by a deep dive into genome-scale metabolic models, focussing on a vast repertoire of constraint-based modelling methods that enable us to characterise and understand the metabolic capabilities of microbial communities. Complementary approaches to model microbial communities, such as those based on graph theory, are also briefly discussed. Taken together, these methods provide rich insights into the interactions between microbes and how they influence microbial community productivity. We finally overview approaches that allow us to generate and test numerous synthetic community compositions, followed by tools and methodologies that can predict effective genetic interventions to further improve the productivity of communities. With impending advancements in high-throughput omics of microbial communities, the stage is set for the rapid expansion of microbial community engineering, with a significant impact on biotechnological processes.

Probiogenomics of Lactobacillus delbrueckii subsp. lactis CIDCA 133: In Silico, In Vitro, and In Vivo Approaches

Lactobacillus delbrueckii subsp. lactis CIDCA 133 (CIDCA 133) has been reported as a potential probiotic strain, presenting immunomodulatory properties. This study investigated the possible genes and molecular mechanism involved with a probiotic profile of CIDCA 133 through a genomic approach associated with in vitro and in vivo analysis. Genomic analysis corroborates the species identification carried out by the classical microbiological method. Phenotypic assays demonstrated that the CIDCA 133 strain could survive acidic, osmotic, and thermic stresses. In addition, this strain shows antibacterial activity against Salmonella Typhimurium and presents immunostimulatory properties capable of upregulating anti-inflammatory cytokines Il10 and Tgfb1 gene expression through inhibition of Nfkb1 gene expression. These reported effects can be associated with secreted, membrane/exposed to the surface and cytoplasmic proteins, and bacteriocins-encoding genes predicted in silico. Furthermore, our results showed the genes and the possible mechanisms used by CIDCA 133 to produce their beneficial host effects and highlight its use as a probiotic microorganism.

Culture conditions affect Lactobacillus reuteri DSM 17938 ability to perform glycerol bioconversion into 3-hydroxypropionic acid

The platform molecule 3-hydroxypropionic acid (3-HP) can be produced using Lactobacillus reuteri through a two-step bioprocess that involves a growth phase followed by a bioconversion phase. The bioproduction is performed by resting cells that convert glycerol into 3-HP and 1,3-propanediol in fed-batch mode. This work aimed at studying the effect of the growth conditions of L. reuteri DSM 17938 during the first step, on the glycerol bioconversion into 3-HP during the second step. A Plackett and Burman design was carried out to test, in controlled bioreactors, the effect of 11 growth conditions simultaneously, at fixed bioconversion conditions. The supplementation of the growth medium with vitamin B12 and cysteine displayed a negative effect on the 3-HP bioproduction. The addition of glucose, phytone peptone, Tween 80, 1,2-propanediol and betaine in the growth medium, together with a low temperature and an optimal pH of 6.0 during the growth phase increased the bioconversion duration from 56 h to 89 h at a glycerol feeding rate of 0.5 g·h^-1. A validating experiment displayed that the 3-HP titer, 3-HP production yield and 3-HP specific production rate were significantly improved by 25 %, 150 % and 61 %, respectively.

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.

Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

Quantitative Connection between Cell Size and Growth Rate by Phospholipid Metabolism

The processes involved in cell growth are extremely complicated even for a single cell organism such as Escherichia coli, while the relationship between growth rate and cell size is simple. We aimed to reveal the systematic link between them from the aspect of the genome-scale metabolic network. Since the growth rate reflects metabolic rates of bacteria and the cell size relates to phospholipid synthesis, a part of bacterial metabolic networks, we calculated the cell length from the cardiolipin synthesis rate, where the cardiolipin synthesis reaction is able to represent the phospholipid metabolism of Escherichia coli in the exponential growth phase. Combined with the flux balance analysis, it enables us to predict cell length and to examine the quantitative relationship between cell length and growth rate. By simulating bacteria growing in various nutrient media with the flux balance analysis and calculating the corresponding cell length, we found that the increase of the synthesis rate of phospholipid, the cell width, and the protein fraction in membranes caused the increase of cell length with growth rate. Different tendencies of phospholipid synthesis rate changing with growth rate result in different relationships between cell length and growth rate. The effects of gene deletions on cell size and growth rate are also examined. Knocking out the genes, such as Δ tktA, Δ tktB, Δ yqaB, Δ pgm, and Δ cysQ, affects growth rate largely while affecting cell length slightly. Results of this method are in good agreement with experiments.