Reconstruction and analysis of the genome-scale metabolic model of Lactobacillus casei LC2W

Production of red beetroot juice by different methods: Kinetics of microbial growth, sugar consumption, and acid production

As a fermentation method, the utilisation of starter culture is a common practice in industrial manufacturing, although spontaneous methods have been employed since ancient times. The objective of this study was to investigate the effect of different production methods on red beetroot juice (RBJ). For this purpose, as a starter culture, the probiotic Lactibasillus paracasei (Lc. paracasei) was inoculated into the RBJ samples after pasteurization. Also, the growth of cells, acid production, and substrate utilisation were monitored throughout the fermentation process of RBJ under two different methods of fermentation. The samples produced by the addition of Lc. paracasei demonstrated a slightly lower decrease in pH values in comparison to the samples obtained by the spontaneous method. The concentration of lactic acid (LA) and acetic acid (AA) at the end of fermentation reveals that Lc. paracasei exhibits a greater capacity for both LA and AA generation compared to the spontaneous method. The ratios of LA and AA molar concentrations of RBJ were determined to be 1.7 and 3.6 for the samples produced by adding Lc. paracasei and the spontaneous method, respectively. The samples produced by adding Lc. paracasei exhibited a greater consumption of sucrose. Both fermentation methods provide LAB counts exceeding 8 log CFU/mL at the end of fermentation. Time demonstrated a significant correlation with LA and AA in the method by adding Lc. paracasei (r = 0.942 and 0.745), respectively (p < 0.01). In both methods, it was demonstrated that while sucrose content decreased during the fermentation period, fructose and glucose content remained constant (p < 0.05).

Genome-scale reconstruction of the metabolic network in Streptococcus thermophilus S-3 and assess urea metabolism

BACKGROUND

Streptococcus thermophilus is an important strain widely used in dairy fermentation, with distinct urea metabolism characteristics compared to other lactic acid bacteria. The conversion of urea by S. thermophilus has been shown to affect the flavor and acidification characteristics of milk. Additionally, urea metabolism has been found to significantly increase the number of cells and reduce cell damage under acidic pH conditions, resulting in higher activity. However, the physiological role of urea metabolism in S. thermophilus has not been fully evaluated. A deep understanding of this metabolic feature is of great significance for its production and application. Genome-scale metabolic network models (GEMs) are effective tools for investigating the metabolic network of organisms using computational biology methods. Constructing an organism-specific GEM can assist us in comprehending its characteristic metabolism at a systemic level.

RESULTS

In the present study, we reconstructed a high-quality GEM of S. thermophilus S-3 (iCH492), which contains 492 genes, 608 metabolites and 642 reactions. Growth phenotyping experiments were employed to validate the model both qualitatively and quantitatively, yielding satisfactory predictive accuracy (95.83%), sensitivity (93.33%) and specificity (100%). Subsequently, a systematic evaluation of urea metabolism in S. thermophilus was performed using iCH492. The results showed that urea metabolism reduces intracellular hydrogen ions and creates membrane potential by producing and transporting ammonium ions. This activation of glycolytic fluxes and ATP synthase produces more ATP for biomass synthesis. The regulation of fluxes of reactions involving NAD(P)H by urea metabolism improves redox balance.

CONCLUSION

Model iCH492 represents the most comprehensive knowledge-base of S. thermophilus to date, serving as a potent tool. The evaluation of urea metabolism led to novel insights regarding the role of urease. © 2023 Society of Chemical Industry.

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.

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.

Standards, dissemination, and best practices in systems biology

The reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other's work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.

The potential of lactic acid bacteria in mediating the control of plant diseases and plant growth stimulation in crop production - A mini review

The microbial diseases cause significant damage in agriculture, resulting in major yield and quality losses. To control microbiological damage and promote plant growth, a number of chemical control agents such as pesticides, herbicides, and insecticides are available. However, the rising prevalence of chemical control agents has led to unintended consequences for agricultural quality, environmental devastation, and human health. Chemical agents are not naturally broken down by microbes and can be found in the soil and environment long after natural decomposition has occurred. As an alternative to chemical agents, biocontrol agents are employed to manage phytopathogens. Interest in lactic acid bacteria (LAB) research as another class of potentially useful bacteria against phytopathogens has increased in recent years. Due to the high level of biosafety, they possess and the processes they employ to stimulate plant growth, LAB is increasingly being recognized as a viable option. This paper will review the available information on the antagonistic and plant-promoting capabilities of LAB and its mechanisms of action as well as its limitation as BCA. This review aimed at underlining the benefits and inputs from LAB as potential alternatives to chemical usage in sustaining crop productivity.

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.

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.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Proteomic analysis reveals potential factors associated with enhanced EPS production in Streptococcus thermophilus ASCC 1275

Streptococcus thermophilus ASCC 1275 has two chain length determining genes - epsC and epsD- in its eps gene cluster, and produces two times more EPS in sucrose medium than that in glucose and lactose. Hence, we investigated the influence of sugars (glucose, sucrose and lactose), at log phase (5 h) and stationary phase (10 h), on the global proteomics of S. thermophilus 1275 to understand the differentially expressed proteins (DEPs) during EPS production using isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis. Among 98 DEPs in sucrose medium, most of them were mapped into EPS biosynthesis pathway and other related metabolisms. There was an upregulation of several proteins involved in sugar transport (phosphoenolpyruvate (PEP) phosphotransferase system), EPS assembly (epsG1D) and amino acid metabolism (methionine, cysteine/arginine metabolism) in sucrose medium. This study showed that increased EPS production in S. thermophilus 1275 requires a well-co-ordinated regulation of pathway involved in both EPS assembly and amino acid metabolism along with the availability of sugars. Thus, it provided valuable insights into the biosynthesis and regulation of EPS in S. thermophilus 1275, and potential gene targets for understanding high-EPS strains.

Metabolomic analysis of significant changes in Lactobacillus casei Zhang during culturing to generation 4,000 under conditions of glucose restriction

Lactic acid bacteria are being consumed more frequently as awareness of their health benefits has increased. The industrial production of lactic acid bacteria requires a comprehensive understanding of their survival stress, especially regarding changes in metabolic substances in a glucose-limited environment. In the present study, a metabolomic approach was applied to investigate Lactobacillus casei Zhang using cultures from a common ancestor that were permitted to evolve under conditions with normal or glucose-restricted media for up to 4,000 generations. Metabolomic analyses of intracellular and extracellular differential metabolites under De Man, Rogosa and Sharpe broth (2% vol/vol glucose; Oxoid Ltd., Basingstoke, UK) and glucose-restricted (0.02% vol/vol glucose in De Man, Rogosa and Sharpe broth) conditions were performed at generations 0, 2,000, and 4,000 and revealed 23 different metabolites. Myristic acid, ergothioneine, Lys-Thr, and palmitamide contents exhibited significant reductions between 0 and 4,000 generations, whereas nicotinate, histidine, palmitic acid, l-lysine, urocanate, thymine, and other substances increased. The dynamics of the pathways involved in AA metabolism, including glycine, serine, and threonine metabolism, histidine metabolism, lysine degradation, and arginine and proline metabolism, were also a focus of the present study. There were also changes in several other metabolic pathways, including vitamin B₆, thiamine, nicotinate, and nicotinamide, according to generation time. Additionally, in the present study we screened for key metabolites involved in the glucose-restricted response and provided a theoretical basis for comprehensively revealing the regulatory mechanisms associated with L. casei Zhang glucose restriction at the metabolic level. These findings also provide novel ideas and methods for analyzing the glucose-restricted stress response at the metabolic level.

Symposium review: Genomic investigations of flavor formation by dairy microbiota

Flavor is one of the most important attributes of any fermented dairy product. Dairy consumers are known to be willing to experiment with different flavors; thus, many companies producing fermented dairy products have looked at culture manipulation as a tool for flavor diversification. The development of flavor is a complex process, originating from a combination of microbiological, biochemical, and technological aspects. A key driver of flavor is the enzymatic activities of the deliberately inoculated starter cultures, in addition to the environmental or "nonstarter" microbiota. The contribution of microbial metabolism to flavor development in fermented dairy products has been exploited for thousands of years, but the availability of the whole genome sequences of the bacteria and yeasts involved in the fermentation process and the possibilities now offered by next-generation sequencing and downstream "omics" technologies is stimulating a more knowledge-based approach to the selection of desirable cultures for flavor development. By linking genomic traits to phenotypic outputs, it is now possible to mine the metabolic diversity of starter cultures, analyze the metabolic routes to flavor compound formation, identify those strains with flavor-forming potential, and select them for possible commercial application. This approach also allows for the identification of species and strains not previously considered as potential flavor-formers, the blending of strains with complementary metabolic pathways, and the potential improvement of key technological characteristics in existing strains, strains that are at the core of the dairy industry. An in-depth knowledge of the metabolic pathways of individual strains and their interactions in mixed culture fermentations can allow starter blends to be custom-made to suit industry needs. Applying this knowledge to starter culture research programs is enabling research and development scientists to develop superior starters, expand flavor profiles, and potentially develop new products for future market expansion.

Transcriptomic Insights Into the Growth Phase- and Sugar-Associated Changes in the Exopolysaccharide Production of a High EPS-Producing Streptococcus thermophilus ASCC 1275

In a previous study, incorporation of high exopolysaccharide (EPS) producing dairy starter bacterium Streptococcus thermophilus ASCC 1275 was found to improve functionality of low fat mozzarella cheese and yogurt. This bacterium in its eps gene cluster has a unique pair of chain length determining genes, epsC- epsD, when compared to other sequenced S. thermophilus strains. Hence, the aim of this study was to understand the regulatory mechanism of EPS production in this bacterium using transcriptomic analysis to provide opportunities to improve the yield of EPS. As sugars are considered as one of the major determinants of EPS production, after preliminary screening, we selected three sugars, glucose, sucrose and lactose to identify the EPS producing mechanism of this bacterium in M17 medium. Complete RNA-seq analysis was performed using Illumina HiSeq 2000 sequencing system on S. thermophilus 1275 grown in three different sugars at two-time points, 5 h (log phase) and 10 h (stationary phase) to recognize the genes involved in sugar uptake, UDP-sugar formation, EPS assembly and export of EPS outside the bacterial cell. S. thermophilus 1275 was found to produce high amount of EPS (∼430 mg/L) in sucrose (1%) supplemented M17 medium when compared to other two sugars. Differential gene expression analysis revealed the involvement of phosphoenolpyruvate phosphotransferase system (PEP-PTS) for glucose and sucrose uptake, and lacS gene for lactose uptake. The pathways for the formation of UDP-glucose and UDP-galactose were highly upregulated in all the three sugars. In the presence of sucrose, eps1C1D2C2D were found to be highly expressed which refers to high EPS production. Protein homology study suggested the presence of Wzx/Wzy-dependent EPS synthesis and transport pathway in this bacterium. KEGG pathway and COG functional enrichment analysis were also performed to support the result. This is the first report providing the transcriptomic insights into the EPS production mechanism of a common dairy bacterium, S. thermophilus.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

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

Obligate heterofermentative lactic acid bacteria (LAB) are well-known for their beneficial health effects in humans. To delineate the incompletely characterized metabolism that currently limits their exploitation, at systems-level, we developed a genome-scale metabolic model of the representative obligate heterofermenting LAB, Leuconostoc mesenteroides (iLME620). Constraint-based flux analysis was then used to simulate several qualitative and quantitative phenotypes of L. mesenteroides, thereby evaluating the model validity. With established predictive capabilities, we subsequently employed iLME620 to elucidate unique metabolic characteristics of L. mesenteroides, such as the limited ability to utilize amino acids as energy source, and to substantiate the role of malolactic fermentation (MLF) in the reduction of pH-homeostatic burden on F0F1-ATPase. We also reported new hypothesis on the MLF mechanism that could be explained via a substrate channelling-like phenomenon mainly influenced by intracellular redox state rather than the intermediary reactions. Model simulations further revealed possible proton-symporter dependent activity of the energy efficient glucose-phosphotransferase system in obligate heterofermentative LAB. Moreover, integrated transcriptomic analysis allowed us to hypothesize transcriptional regulatory bias affecting the intracellular redox state. The insights gained here about the low ATP-yielding metabolism of L. mesenteroides, dominantly controlled by the cellular redox state, could potentially aid strain design for probiotic and cell factory applications.

Comparative genomics of Lactobacillus kefiranofaciens ZW3 and related members of Lactobacillus. spp reveal adaptations to dairy and gut environments

It is important for probiotics that are currently utilized in the dairy industry to have clear genetic backgrounds. In this study, the genetic characteristics of Lactobacillus kefiranofaciens ZW3 were studied by undertaking a comparative genomics study, and key genes for adaptation to different environments were investigated and validated in vitro. Evidence for horizontal gene transfer resulting in strong self-defense mechanisms was detected in the ZW3 genome. We identified a series of genes relevant for dairy environments and the intestinal tract, particularly for extracellular polysaccharide (EPS) production. Reverse transcription-qPCR (RT-qPCR) revealed significant increases in the relative expression of pgm, ugp, and uge during the mid-logarithmic phase, whereas the expression of pgi was higher at the beginning of the stationary phase. The enzymes encoded by these four genes concertedly regulated carbon flux, which in turn modulated the production of EPS precursors. Moreover, ZW3 tolerated pH 3.5 and 3% bile salt and retained cell surface hydrophobicity and auto-aggregation. In conclusion, we explored the potential of ZW3 for utilization in both the dairy industry and in probiotic applications. Additionally, we elucidated the regulation of the relevant genes involved in EPS production.

Probiotics, gut microbiota, and their influence on host health and disease

The gastrointestinal tract of mammals hosts a high and diverse number of different microorganisms, known as intestinal microbiota. Many probiotics were originally isolated from the gastrointestinal tract, and they were defined by the Food and Agriculture Organization of the United Nations (FAO)/WHO as "live microorganisms which when administered in adequate amounts confer a health benefit on the host." Probiotics exert their beneficial effects on the host through four main mechanisms: interference with potential pathogens, improvement of barrier function, immunomodulation and production of neurotransmitters, and their host targets vary from the resident microbiota to cellular components of the gut-brain axis. However, in spite of the wide array of beneficial mechanisms deployed by probiotic bacteria, relatively few effects have been supported by clinical data. In this regard, different probiotic strains have been effective in antibiotic-associated diarrhea or inflammatory bowel disease for instance. The aim of this review was to compile the molecular mechanisms underlying the beneficial effects of probiotics, mainly through their interaction with the intestinal microbiota and with the intestinal mucosa. The specific benefits discussed in this paper include among others those elicited directly through dietary modulation of the human gut microbiota.

Lactobacillus casei as a biocatalyst for biofuel production

Microbial fermentation of sugars from plant biomass to alcohols represents an alternative to petroleum-based fuels. The optimal biocatalyst for such fermentations needs to overcome hurdles such as high concentrations of alcohols and toxic compounds. Lactic acid bacteria, especially lactobacilli, have high innate alcohol tolerance and are remarkably adaptive to harsh environments. This study assessed the potential of five Lactobacillus casei strains as biocatalysts for alcohol production. L. casei 12A was selected based upon its innate alcohol tolerance, high transformation efficiency and ability to utilize plant-derived carbohydrates. A 12A derivative engineered to produce ethanol (L. casei E1) was compared to two other bacterial biocatalysts. Maximal growth rate, maximal optical density and ethanol production were determined under conditions similar to those present during alcohol production from lignocellulosic feedstocks. L. casei E1 exhibited higher innate alcohol tolerance, better growth in the presence of corn stover hydrolysate stressors, and resulted in higher ethanol yields.