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Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of China, Huhhot, 010018, Inner Mongolia, ChinaKey Laboratory of Dairy Products Processing, Ministry of Agricultural, Department of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of China, Huhhot, 010018, Inner Mongolia, ChinaKey Laboratory of Dairy Products Processing, Ministry of Agricultural, Department of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of China, Huhhot, 010018, Inner Mongolia, ChinaKey Laboratory of Dairy Products Processing, Ministry of Agricultural, Department of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of China, Huhhot, 010018, Inner Mongolia, ChinaKey Laboratory of Dairy Products Processing, Ministry of Agricultural, Department of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of China, Huhhot, 010018, Inner Mongolia, ChinaKey Laboratory of Dairy Products Processing, Ministry of Agricultural, Department of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of China, Huhhot, 010018, Inner Mongolia, ChinaKey Laboratory of Dairy Products Processing, Ministry of Agricultural, Department of Food Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
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.
Lactic acid bacteria (LAB) can ferment lactose to produce lactic acid and are widely distributed in nature. Lactic acid bacteria in the form of probiotics play a vital role in human health and as an important industrial microorganism. Lactic acid bacteria and their metabolites have been widely used in food, medicine, feed, and so on (
Lactic acid bacteria inevitably face various environmental stresses during their production and application, such as osmotic pressure, acid environments, oxidative stress, cold, heat, pH, and starvation. These stresses can affect the physiological state and characteristics of cells and may limit the growth of LAB in other manufacturing processes (
). Among these stresses in particular, glucose restriction has been reported as one of the most important limitations to growth. During the process of reproduction, if LAB cannot replenish glucose in time, the bacteria will enter a state of starvation and exhibit survival stress; it is worth noting that LAB can also indirectly enter a nutrient-deficient state through other stresses; LAB regulate genetic regulators of different responses associated with other stresses when faced with carbohydrate restriction (
demonstrated that Lactobacillus casei GCRL163 inhibits phosphotransferase abundance, lactose metabolism, and galactose metabolism under starvation conditions. The same process occurs during synthesis of nucleotides and proteins (
). Thus, for an experimenter, it is important to understand the conditions that are beneficial or harmful to LAB and the mechanisms that allow them to survive under stress and carry out metabolic activities.
Lactobacillus casei Zhang is a probiotic bacteria strain isolated from the traditional homemade milk products of herdsmen in Zhenglan Banner, Xilinhot, in the Inner Mongolia region of China. Lactobacillus casei Zhang is highly tolerant to acid and bile salt stress and has probiotic functions that lower blood lipids, improve immune regulation, enhance antioxidation, and antagonize the growth of intestinal pathogens (
). To assess the effects of L. casei Zhang on upper respiratory tract infections and intestinal diseases, researchers divided 137 volunteers into adult and elderly groups to perform randomized double-blind controlled trials using a daily oral dose of L. casei Zhang (9 log cfu). The administration of L. casei Zhang reduced plasma proinflammatory cytokines (IL-1), increased anti-inflammatory cytokines (IL-4 and IL-10), and upregulated the whole-blood expression levels of cluster of differentiation (CD) 4, CD8, CD44, CD27, and C-X-C motif chemokine receptor 5 (CXCR5). Additionally, the elderly group had an increased number of bowel movements per week, which alleviated red blood cell abnormalities, ameliorated the symptoms of upper respiratory tract infection in adults, and significantly shortened the duration of symptoms. These findings demonstrated the potential of L. casei Zhang for reducing the symptoms of upper respiratory tract infections and maintaining gastrointestinal health (
Probiotic Lactobacillus casei Zhang (LCZ) alleviates respiratory, gastrointestinal and RBC abnormality via immuno-modulatory, anti-inflammatory and anti-oxidative actions.
Metabolomics can be used to qualitatively and quantitatively analyze low molecular weight metabolites in cells that are produced by microbial metabolism, including peptides, AA, carbohydrates, nucleic metabolites, vitamins, organic acids, and minerals (
Novel approaches for the taxonomic and metabolic characterization of lactobacilli: Integration of 16S rRNA gene sequencing with MALDI-TOF MS and 1H-NMR.
combined 16S rRNA gene sequencing with proton nuclear magnetic resonance to determine the classification and metabolic properties of LAB and found that metabolomics can be used to correlate specific biological activities with taxonomy as well as to understand the mechanisms underlying bacteriostasis in some LAB species. Under glucose-restricted conditions, Lactobacillus plantarum H4 produces acetate in addition to acetic acid in the pH range of 3.8 to 6, and l-lactate accumulation was greater than that of d-lactate (
). These findings demonstrate that L. plantarum H4 adjusts to changes in environmental conditions by altering its metabolic pathways. There is also evidence showing that heterofermentative L. casei can degrade arginine during culture and lead to the formation of ammonia and citrulline (
). Under stress, Lactobacillus will become deficient in essential nutrients, which limits the exponential phase of microbial growth, allowing it to enter the stationary phase and engage in carbohydrate starvation, resulting in the consumption of cellular energy (
). At present, LAB, as an important fermenting microorganism, are faced with widespread carbon source stress in production applications, which will seriously affect their physiological functions and restrict the development of related products.
Ultraperformance liquid chromatography quadrupole time-of-flight MS in the MSE mode (UPLC-Q-TOF MSE) was used to analyze changes in metabolites during the different evolutionary processes between the original probiotic L. casei Zhang and other bacteria that were subcultured to generations 2,000 and 4,000 under glucose-restricted conditions. Understanding metabolic changes in L. casei Zhang under nutrient stress will provide practical guidance for the industrial production of probiotics.
MATERIALS AND METHODS
Bacterial Strain and Growth Conditions
The original strain of L. casei Zhang was deposited in the Key Laboratory of Dairy Biotechnology and Engineering, Education Ministry of China. The obtained ampoule strain was activated in De Man, Rogosa and Sharpe (MRS) broth medium (CM0359; Oxoid Ltd., Basingstoke, UK) and then subcultured twice at 37°C in an incubator (MIR-1620; Sanyo, Osaka, Japan).
Experimental Evolution
After 2 generations of cultivation (24 h/generation), L. casei Zhang was streaked on MRS agar medium (CM0361; Oxoid Ltd.). After incubation at 37°C for 72 h, 3 colonies were randomly selected and cultured in a normal 2% (vol/vol) glucose MRS broth medium and a 0.02% (vol/vol) glucose-restricted medium. Every 24 h, L. casei Zhang strains cultured in MRS broth or a glucose-restricted environment were inoculated into MRS broth and MRS containing 0.02% glucose sugar at an inoculation amount of 1% (vol/vol, 50 μL); 100-fold bacterial growth per day represents almost 6.6 generations per subculture (
). The 4,000-generation evolution in the laboratory took approximately 603 d to complete. Strains of different generations were obtained for vacuum freeze-drying preservation during the long-term cultivation (−50°C, 15.0 Pa for 48–72 h).
Sample Preparation for UPLC-Q-TOF MSE Analysis
Cultivation of L. casei Zhang
The original strain of L. casei Zhang, and the L. casei Zhang grown in the glucose-restricted medium deposited in the ampoule tube, were cultured in the MRS. The strains growing in normal or glucose-restricted environments were subcultured in MRS broth twice and the bacterial solution was transferred to fresh medium at 1% volume of inoculation every 24 h.
Sample Quenching and Metabolite Extraction
Samples were collected at the mid-exponential phase (∼8 h). The activated samples were dispensed into 2-mL centrifuge tubes and centrifuged at 7,000 × g for 5 min at 4°C; supernatant retention was performed to collect extracellular metabolic samples. Briefly, 200 μL of extracellular sample was obtained, and 800 μL of methanol/acetonitrile at the ratio of 1:1 (vol/vol) was added to each extracellular sample. Then, the sample was vortexed for 30 s and subjected to ultrasound for 10 min (4°C cold bath). Following the ultrasound, samples were allowed to stand at −20°C for 60 min and were then centrifuged at 13,000 × g for 15 min at 4°C. Next, 500 μL of the supernatant was collected, vacuum-dried at 35°C, reconstituted with 200 μL of 50% acetonitrile, vortexed for 30 s, and subjected to ultrasound for 10 min (4°C cold bath). Next, the sample was centrifuged at 13,000 × g for 15 min at 4°C and kept on the machine for testing.
The bacteria sludge cells were quickly added to a precooled 0.9% NaCl, vortexed, centrifuged at 0°C for 5 min at 5,000 × g, and washed. This process was repeated twice, and the supernatant was discarded; then, the remaining bacterial sludge was collected and −40°C precooled 60% methanol/0.9% NaCl was quickly added to the centrifuge tube for quenching using 4 times the volume of the sludge (
Determination of the phosphorylated sugars of the Embden-Meyerhoff-Parnas pathway in Lactococcus lactis using a fast sampling technique and solid phase extraction.
). The cells were then quenched for 5 min and centrifuged at 10,000 × g for 5 min at 0°C; the supernatant was then discarded. The quenched cells were subjected to liquid nitrogen milling for 20 min (a mortar was precooled at −80°C) and ground to a fine powder; 50 mg of powder was placed in a 1.5-mL centrifuge tube. Then, 1 mL of −40°C 70% methanol/water mixture was added to the tube, vortexed, fully mixed, and extracted to suspend the cells. Liquid nitrogen was used to repeatedly freeze and thaw the cells (4 times; liquid nitrogen freezing for 3 min, −20°C refrigerator melting), followed by centrifugation at 10,000 × g for 5 min at −20°C; the supernatant was then collected. The cell pellet was continuously suspended in 1 mL of −40°C 70% methanol/water, vortexed for 30 s, and centrifuged at 8,000 × g for 5 min at 0°C; the supernatants obtained by the 2 extractions were then combined (
). The supernatant was stored at −80°C until analysis; this fraction was labeled as the intracellular sample. Each sample (2 replicates per sample) was processed in parallel to yield 6 replicates.
UPLC-Q-TOF MSE Analysis
An Acquity UPLC/Xevo G2 Q TOF-MSE instrument (Waters Corp., Milford, MA) was used to extract the MS spectral peaks of the samples. Samples were filtered and placed in a sample vial for testing. When the samples in this experiment were analyzed by metabolomics, the quadrupole time-of-flight mass spectrometer carried out data scanning using positive electrospray ionization (ESI+) and negative electrospray ionization (ESI−) ion modes. The cone voltage was 40 V, the ion energy was 1, the collision energy was 6, the sample infusion flow rate was 10 µL/min, the sample fill volume was 250 µL, the LockSpray capillary was at 3 kV, and the acquisition scan range was 100 to 1,000 mass-to-charge ratio (m/z) at a scan frequency of 10.00 s.
To ensure the accuracy of data acquisition, leucine-cephalin was used as the lock mass for both positive ion mode (556.2771 [M + H]+) and negative ion mode (554.2615 [M − H]−). Pretreated samples were chromatographed using a C18-UPLC system. The column was an HSS T3 (2.1 mm × 100 mm, 1.8 µm; Waters Corp.) with a sample infusion flow rate of 0.3 µL/min; the column was maintained at 35°C. The injection volume was 4 µL.
For ESI+ ion mode, mobile phase A was an aqueous phase containing 0.1% formic acid (Sigma-Aldrich, St. Louis, MO), and mobile phase B was an organic phase containing 0.1% formic acid in acetonitrile (Optima LC/MS-grade; Fisher Scientific, Grand Island, NY). The elution gradient is shown in Table 1. For scanning in ESI− ion mode, the mobile phase consisted of water (Millipore, Bedford, MA) containing 10% acetonitrile (solution A) and 50% acetonitrile (solution B). The elution gradient was as shown in Table 2.
Table 1Ultraperformance liquid chromatography quadrupole time-of-flight MS in the MSE mode mobile phase gradient program for the analysis of samples in positive ion mode
Table 2Ultraperformance liquid chromatography quadrupole time-of-flight MS in the MSE mode mobile phase gradient program for the analysis of samples in negative ion mode
). Briefly, the data were preprocessed in MassLynx version 4.1 (Waters Corp.) and then imported into the EZinfo software (Waters Corp.) for centralization and Pareto homogenization to reduce deviation due to large-scale peaks.
Simultaneous multivariate statistical analysis was performed using SIMCA-P + 11 (Umetrics AB, Umeå, Sweden) and MetaboAnalyst (www.metaboanalyst.ca) software. For each treatment, 3 comparisons were made: 0 versus 2,000 generations, 0 versus 4,000 generations, and 2,000 versus 4,000 generations. To determine changes in metabolites between the different generations of L. casei Zhang, variables with variable importance in the projection values greater than 1 (fold change ≥2 and P ≤ 0.5) were selected as potential metabolic discriminant variables (
Ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry MSE-based untargeted milk metabolomics in dairy cows with subclinical or clinical mastitis.
). Based on these variables, the different metabolic substances were further analyzed and identified.
Identification of Differential Metabolites
The significantly different substances obtained by screening were automatically searched for in several databases, including METLIN (http://www.metlin.scripps.edu/), KEGG (www.genome.jp/kegg), ChemSpider (www.chemspider.com), and the Human Metabolome Database (www.hmdb.ca) based on their mass-to-charge ratio and retention time. Finally, correlation and metabolic pathway analyses were performed on the various retrieved metabolites.
RESULTS
Sample Principal Component Analysis Results
To compare the differences between L. casei Zhang generation 0 and glucose-restricted generation 2,000, multivariate analyses were used to segment the effective spectral area, and principal component analysis (PCA) was performed. In the PCA model, the abscissa is the first principal component and the ordinate is the second principal component. In each score graph, the addition of the 2 principal components explained more than 95% of the variance, as shown in Figure 1.
Figure 1Principal component (PC) analysis score plots of intracellular (IC) samples. Data were obtained in positive electrospray ionization (ESI+: A, B, C) and negative electrospray ionization (ESI−: D, E, F) ion modes. A and D: generation 0 versus generation 2,000; B and E: generation 0 versus generation 4,000; C and F: generation 2,000 versus generation 4,000.
In Figure 1, it can be seen that all PCA score plots have clear separation between different generations, indicating that the chemical compositions of samples differed among L. casei Zhang under stress conditions. Taking Figure 1A as an example, the 2 sets of samples are distributed in different regions, without intersection or overlap, and there is an obvious discrete phenomenon. Six parallel samples of each type are closely aggregated.
Samples between generation 0 and glucose-restricted generation 2,000 had values of 78.90 and 19.90% in the principal component 1 and principal component 2 directions, respectively, and the 2 principal components accounted for most of the variance among the different treatment samples. Principal component 1 had the largest contribution rate; this indicates that the metabolites of generations 0 and 2,000 are clearly dissociable and confirms the effectiveness of the sampling method and the reproducibility of the test. The extracellular analysis method is the same as the intracellular method; the PCA results are shown in Figure 2.
Figure 2Principal component (PC) analysis score plots of extracellular (EC) samples. Data were obtained in positive electrospray ionization (ESI+; A, B, C) and negative electrospray ionization (ESI−; D, E, F) ion modes. A and D: generation 0 versus generation 2,000; B and E: generation 0 versus generation 4,000; C and F: generation 2,000 versus generation 4,000.
Figures 2A and D and Figures 2B and E are well separated by PCA analysis; only Figures 2C and F overlap, which indicates that the PCA model cannot adequately distinguish between the 2 groups. In addition, the overall dispersion of the 2 types of sample is large, indicating that the individual differences among the samples are also relatively large. However, as can be seen from Figure 1, Figure 2, the PCA plots of both intracellular and extracellular metabolites are well separated, indicating that the metabolites of different generations will vary significantly during the laboratory evolution of L. casei Zhang in a glucose-restricted environment.
Sample Orthogonal Partial Least Squares Discriminant Analysis Results
The differences between the extracellular and intracellular metabolite samples were further examined by orthogonal partial least squares discriminant analysis (OPLS-DA). In OPLS-DA, The R2Y parameter describes the percentage of variation explained by the model and Q2 describes the predictive ability of the model. In Figure 3A, obtained through OPLS-DA in positive ion mode, the R2Y and Q2 values of the generation 0 versus generation 2,000 intracellular metabolite samples are equal to 0.996 and 0.936, respectively. This indicates that the 2 models are reliable and that the metabolic samples had significant differences. The R2Y and Q2 values of all models were relatively high, indicating that the models were reliable and that both sets of samples in each OPLS-DA score plot had metabolic differences.
Figure 3Orthogonal partial least squares discriminant analysis score plots and S-plots of the data for intracellular (IC) samples obtained in positive electrospray ionization (ESI+) ion mode. A and D: generation 0 versus 2,000; B and E: generation 0 versus 4,000; C and F: generation 2,000 versus 4,000.
In Figure 3D, each point represents a variable, and the greater the distance of a variable from the origin point, the higher the confidence level regarding its contribution to the clustering observed in the OPLS-DA score plots. The variables in each S-plot map were screened for final identification of the substance based on variable importance in the projection and fold-change values. The OPLS-DA score plots and S-plots from generation 0 to 4,000 in both positive and negative ion modes are shown in Supplemental Figures S1 through S3 (https://doi.org/10.3168/jds.2018-15702).
Identification of Different Metabolites
According to the data on retention time and mass-to-charge ratio, a total of 8,376 differentially expressed metabolites were found between generations 0 and 2,000, including intracellular and extracellular metabolites; among these, 5,298 were in ESI+ ion mode. A total of 10,234 substances differed between generations 0 and 4,000, including 7,374 in ESI+ ion mode, whereas 3,952 differentially expressed substances were found between generations 2,000 and 4,000 (of which 2,205 were in ESI+ ion mode). The differential substances ultimately conforming to the screening principle described above are discussed below, and the differential metabolites of intracellular generations 0 and 2,000 are summarized in Table 3.
Table 3Generation 0 versus generation 2,000: differential intracellular metabolites at positive and negative ions
In Table 3 we can see that, via metabolomic analysis, 53 significantly different substances between generations 0 and 2,000 were identified based on the screening principle, containing AA, organic acids, vitamins, pyrimidines, and short peptides. In total, 39 were upregulated and are involved in histidine metabolism; tryptophan metabolism; biotin metabolism; pyruvate metabolism; alanine, aspartate, and glutamate metabolism; pyrimidine metabolism; purine metabolism; valine, leucine, and isoleucine biosynthesis; and vitamin digestion and absorption. There were 14 downregulated substances involved in arginine and proline metabolism; glycine, serine, and threonine metabolism; histidine metabolism; arginine biosynthesis; and biosynthesis of AA.
In the extracellular metabolomic analysis of generations 0 and 2,000, 81 significantly different metabolites were identified under ESI+ and ESI− modes (Table 4). These 81 substances are involved in metabolic pathways including carbohydrate, energy, lipid, nucleotide, and AA metabolism. The number of significantly different metabolites was larger than that of intracellular substances, and more metabolic pathways were involved.
Table 4Generation 0 versus generation 2,000: differential extracellular metabolites at positive and negative ions
In addition, L. casei Zhang was subcultured to generation 4,000. The differential metabolites of generations 0 and 4,000 are shown in Supplemental Tables S1 and S2 (https://doi.org/10.3168/jds.2018-15702). Forty-seven different substances, including the following were found in generation 4,000 but not in generation 2,000: the AA l-ornithine, l-leucine, and l-homoserine; the organic acids salicylic acid, succinic, acid, and linolenic acid; the short peptides valine–alanine, isoleucine–proline, leucine–serine, histidine–cysteine, and tartrate; and pyridoxamine, 2-phospho-glycerate, N-acetylcytidine, guanosine, and indole. Meanwhile, 49 metabolites in generation 2,000 were not detected in generation 4,000 and hence were not screened.
Finally, we conducted a comparative analysis of the metabolites that differed between L. casei Zhang generations 2,000 and 4,000. The results are shown in Supplemental Tables S3 and S4 (https://doi.org/10.3168/jds.2018-15702). There were 66 metabolomic differences between generations 2,000 and 4,000, including both intracellular and extracellular metabolites, of which 37 were upregulated and 29 were downregulated. Figure 4 shows that 23 metabolites were shared by all 3 groups; generation 0 versus generation 2,000, generation 0 versus generation 4,000 and generation 2,000 versus generation 4,000.
Figure 4The distribution of significant metabolic differences among the 3 generations (generation 0 vs. 2,000; generation 0 vs. 4,000; generation 2,000 vs. 4,000).
The content changes of the 23 different substances in the 3 groups from generation 0 to 4,000 are shown in Figure 5. In generation 0, the contents of myristic acid, ergothioneine, lysine–threonine, and palmitamide were higher than the contents of other metabolites. As the experiment progressed and they were subcultured to generation 4,000, the levels of the substances with a high content in generation 0 decreased, which may have been due to adaptability during long-term generation. It was also obvious that the color of substances such as phosphocreatine, hypoxantine, octanoic acid, l-lysine, O-acetyl-l-serine, urocanate, l-phenylalanine, stearamide, lysine–serine, thymine, and d-ribulose changed from blue to green, which indicates that they were slightly more abundant in generation 4,000 than generation 0.
Figure 5Changes in the contents of 23 substances differentially expressed between generations 0 and 4,000.
All 175 significantly different metabolites (including intracellular and extracellular metabolites from generation 0 vs. 2,000, generation 0 vs. 4,000, and generation 2,000 vs. 4,000) were analyzed by a pathway analysis (Figure 6). As shown in Figure 6, metabolites from the 0 versus 2,000, 0 versus 4,000, and 2,000 versus 4,000 generation comparisons are involved in various metabolic processes, such as nucleotide metabolism and AA metabolism.
Figure 6Pathway analysis of significantly differentiated metabolites. (A) Generation 0 versus 2,000. 1 = alanine, aspartate, and glutamate metabolism; 2 = histidine metabolism; 3 = glycine, serine, and threonine metabolism; 4 = vitamin B6 metabolism; 5 = cysteine and methionine metabolism; 6 = lysine biosynthesis; 7 = aminoacyl-tRNA biosynthesis; and 8 = arginine and proline metabolism. (B) Generation 0 versus 4,000. 1 = histidine metabolism; 2 = alanine, aspartate, and glutamate metabolism; 3 = glycine, serine, and threonine metabolism; 4 = arginine and proline metabolism; 5 = thiamine metabolism; 6 = phenylalanine metabolism; and 7 = aminoacyl-tRNA biosynthesis. (C) Generation 2,000 versus 4,000. 1 = histidine metabolism; 2 = nicotinate and nicotinamide metabolism; 3 = lysine degradation; 4 = arginine and proline metabolism; 5 = cysteine and methionine metabolism; 6 = alanine, aspartate, and glutamate metabolism; and 7 = aminoacyl-tRNA biosynthesis.
Supplemental Figure S4 (https://doi.org/10.3168/jds.2018-15702) shows a metabolite enrichment analysis chart. In total, 32 metabolic subpathways were shared by all 3 generations, including histidine metabolism, fatty acid metabolism, arginine and proline metabolism, the urea cycle, β-alanine metabolism, glycine and serine metabolism, aspartate metabolism, purine metabolism, nicotinate and nicotinamide metabolism, glycerolipid metabolism, lysine degradation, tyrosine metabolism, and vitamin B6 metabolism.
For the generation 0 versus 2,000 comparison, 3 metabolic pathways for metabolite enrichment, histidine metabolism, arginine and proline metabolism, and vitamin B6 metabolism were observed, as shown in Figure 6. Similarly, arginine and proline metabolism as well as thiamine metabolism were observed in the 0 versus 4,000 generation comparison. For the 2,000 versus 4,000 generation comparison, lysine degradation, nicotinate and nicotinamide metabolism, and histidine metabolism were observed in both the enrichment and pathway analyses.
All of the differential substances identified in the present experiment were analyzed in terms of their metabolic pathways (Table 5). The metabolic pathway analysis revealed that the metabolites that significantly changed during subculturing of the original strain from generation 0 to 4,000 were mainly responsible for the changes in alanine, aspartate, and glutamate metabolism; glycine, serine, and threonine metabolism; histidine metabolism; cysteine and methionine metabolism; valine, leucine, and isoleucine biosynthesis; α-linolenic acid metabolism; and other metabolic pathways (Table 5).
Table 5Significant metabolic pathways of metabolites from generation 0 to 2,000 and 4,000
The metabolites of generation 0 versus those of generation 4,000 were analyzed using iPath (https://pathways.embl.de/ipath3.cgi; Figure 7). The primary metabolic pathways of the laboratory evolution of L. casei Zhang affected by the differential metabolites were AA metabolism, carbohydrate metabolism, nucleotide metabolism and cofactors, and vitamin metabolism.
Figure 7Analysis of significant metabolites using iPath (https://pathways.embl.de/ipath3.cgi) metabolic pathways between generation 0 and 4,000. Red node = metabolites; red line = interaction.
Effects of Glucose Restriction on Carbohydrate Metabolism
A glucose-restricted environment generally occurs during the stationary phase of microbial growth. Under normal culture conditions, metabolic diversity of probiotic L. casei Zhang will occur. A significant scarcity of carbon sources can cause lactobacillus to stop growing (to ensure survival), such that it enters a stable period. Because glucose is depleted during later growth stages, the cells begin to use other sugars (
Heterolactic fermentation by a homofermentative Lactobacillus sp. during glucose limitation in anaerobic continuous culture with complete cell recycle.
studied Lactobacillus sp. under limited glucose conditions in a continuous anaerobic culture with complete cell recycling and found that homofermentation transitioned into heterolactic fermentation when glucose was continuously depleted within the cell cycle such that the cells were starved. Under conditions of limited glucose, especially during cell cycle reculture, AA are widely produced and utilized, and some sulfides are produced (
Heterolactic fermentation by a homofermentative Lactobacillus sp. during glucose limitation in anaerobic continuous culture with complete cell recycle.
). This is consistent with the present experimental results.
Effects of Glucose Restriction on AA Metabolism
Amino acids are involved in many physiological functions of cells. Under stress conditions, AA can accumulate in significant amounts and function as cell-protective agents (
Metabolomic analysis of meju during fermentation by ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF MS).
). Of the 23 substances that were detected at the intersection of the 3 experimental groups, the contents of substances involved in the AA metabolic pathway were elevated. For example, urocanic acid was detected in generation 0, 2,000, and 4,000, and the relative content increased with generation time. Urocanic acid is believed to be useful as a sun-screening agent in cosmetics and pharmaceuticals (
reported that elevated levels of urocanic acid may be due to reduced folate levels. Urocanic acid is the final product of histidine metabolism, which breaks histidine down into glutamic acid in the presence of formiminoglutamic acid (
, arginine starvation in Lactococcus lactis ssp. lactis led to the induction of 15 proteins, whereas 14 proteins were induced after galactose starvation. Bacteria adapt to nutrition limitations by assuming a physiological state that is characterized by downregulation of nucleic acid and protein synthesis and simultaneous upregulation of protein degradation and AA synthesis (
). Some researchers also reported that the size and fatty acid composition of microbial cells will change under growth conditions adapted to nutrient deficiencies (
). At the same time, the rate of cellular protein synthesis decreases, and the protein expression associated with long-term survival or environmental stress increases significantly (
). In the present study, 23 unique metabolites were identified between generations 2,000 and 4,000. For example, O-phospho-l-serine was involved in glycine, serine, and threonine metabolism, glyceraldehyde-3-phosphate was involved in glycolysis and gluconeogenesis and vitamin B6 metabolism, and 2-oxo-5-aminovalerate was involved in arginine and proline metabolism. The levels of these 3 substances were elevated when subcultured in a glucose-limited environment, which corresponds to the present finding that most of the significant metabolites participated in the AA metabolic pathway. In a study investigating the restricted carbon source culturing of L. lactis IL-1403,
reported that citric acid is used preferentially when a carbon is scarce. To maintain the supply of cellular energy, the cells hydrolyze arginine to produce ornithine through the arginine deiminase pathway during the growth phase and then produce ATP to supply energy (
. The accumulation of these AA represents a stress response of the cells to carbon stress to preserve the integrity of the cells.
Effects of Glucose Restriction on Nucleotide Metabolism
Thymine was the base of deoxyribonucleic acid in the present experiment, and the thymine content increased during the subcultuing of L. casei Zhang from generation 0 to 4,000.
cultured Lactobacillus acidophilus R-26 in a medium without folic acid and found that the organism required thymine in addition to deoxynucleosides, purines, pyrimidines, and most AA. When thymine was present in the medium, the bacteria grew exponentially and without limitation. The presence of thymine can meet the demand for folic acid by Lactobacillus leichmannii (
). Uridine is a nucleoside that serves as a major component of ribonucleic acid. In the present study, uridine increased from generation 2,000 to 4,000, whereas guanosine decreased from generation 0 to 4,000. Guanosine is a proven taste enhancer when fermenting bread with Lactobacillus plantarum (
) and can provide significant growth stimulation for the Lactobacillus johnsonii NCC533 strain; however, nucleotide supplementation is required for L. plantarum growth in reconstituted skim milk (
). Metabolomic analyses were used to assess Streptococcus intermedius under aerobic and anaerobic conditions, and it was revealed that the intracellular guanosine concentration was higher during anaerobic growth and that the cells absorbed more under anaerobic conditions (
). Additionally, analyses of the presence of several typical molecules associated with the flavor and taste of foods during the fermentation of bread with Lactobacillus plantarum have detected uridine and guanosine (
) and vitamin B6 and is associated with the metabolism of pyridoxine and pyridoxamine during the metabolism of cofactors and vitamins. It has been reported that L. casei can be cultured on a medium containing riboflavin, pantothenic acid, nicotinic acid, and pyridine as AA sources. It has also been reported that L. casei can be cultured on a medium containing riboflavin, pantothenic acid, nicotinic acid, and pyridine as AA sources (
In the present study, there was a significant decrease in nicotinate, which is associated with nicotinate and nicotinamide metabolism, from generation 2,000 to 4,000. The essential growth factors needed by Lactobacillus include riboflavin, pantothenic acid, nicotinic acid, pyridoxine, biotin, and the eluate factor such as folic acid (
). Lactobacillus gasseri can produce NAD using nicotinate and nicotinamide in the presence of nicotinamidase and nicotinate phosphoribosyltransferase (
), whereas L. johnsonii and L. gasseri lack the corresponding enzymes to synthesize thiamine, biotin, pyridoxine, and other substances involved in vitamin digestion and absorption (
). For L. casei LC2W, nicotinate is an essential nutrient under anaerobic and aerobic conditions and can increase the yield of L. casei LC2W exopolysaccharide (
Although nicotinate content was reduced by the influence of glucose restriction during the process of generation in the present study, L. casei Zhang still produced beneficial substances. Therefore, when L. casei Zhang is used as a starter, it may lead to a decrease in beneficial substances when long-term glucose sources are insufficient.
Due to the current practice of application of L. casei Zhang in food and feedstuffs, it is important to study the metabolic kinetics of L. casei Zhang under glucose-restricted conditions. Metabolomic analyses of the metabolites of L. casei Zhang during long-term passage under conditions of glucose restriction will provide indispensable supporting data for further understanding the properties of L. casei Zhang.
CONCLUSIONS
We studied metabolomics-related differences between an original strain of L. casei Zhang and strains cultured in a glucose-restricted medium for 2,000 and 4,000 generations using ultraperformance liquid chromatography quadrupole time-of-flight MS. Significant changes in metabolites were observed under the glucose-restricted conditions. Changes in the metabolites, including AA, nucleotides, and short peptides, resulted in changes in metabolic pathways; the changes were more pronounced when passaged to generation 4,000. The metabolically significant metabolites significantly affected several metabolic pathways, including histidine metabolism, arginine and proline metabolism, vitamin B6 metabolism, thiamine metabolism, lysine degradation, and nicotinate and nicotinamide metabolism. Metabolomics was used to study changes in the metabolites associated with L. casei Zhang under conditions of glucose restriction and to screen for key metabolites involved in the glucose-restricted response. The present findings may provide a theoretical basis for a comprehensive understanding of the metabolic regulatory mechanisms of L. casei Zhang during glucose restriction. These findings may also lead to novel ideas and methods for assessing the glucose-restricted stress response at a metabolic level.
ACKNOWLEDGMENTS
This research was supported by National Natural Science Foundation of China (Beijing, China; Grant No. 31660450), Program Funded by University for Fostering Outstanding Young Scholars (Grant No. 2017XYQ-4), the Natural Science Foundation of Inner Mongolia, China (Grant No. 2018MS03041), and the Natural Science Foundation of Inner Mongolia, China (Grant No. 2017ZD07).
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