Effects of modified-BHI medium on the growth and metabolites of Akkermansia muciniphila

Abstract

Akkermansia muciniphila (Akk) has recently become popular due to its therapeutic effect on various diseases. However, Akk’s high-density cultivation is difficult due to its anaerobic characteristics. Therefore, Akk was cultured with modified brain–heart infusion (M-BHI) to reach 10¹¹ CFU/mL. ¹H-NMR determined the metabolites of Akk and validated them by an amino acid analyzer. Compared to the BHI, Akk significantly up-regulated lactate, histidine, fumaric acid, cytidine, threonine, arginine, and hydroxyproline in the M-BHI and significantly down-regulated methionine, trimethylamine, and sarcosine. Regarding pathway enrichment analysis, histidine metabolism, arginine and proline metabolism, cysteine and methionine metabolism mainly regulate differential metabolites. In addition, M-BHI alters the metabolic profile by affecting Akk’s involvement in amino acid metabolism remodeling. Changed metabolites showed that Akk fermentation in M-BHI may play a physiological role in regulating immune homeostasis and reducing risk factors related to diseases. Therefore, M-BHI provides a promising reference for Akk cultivation in future industrial preparation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-023-01492-x.

Introduction

Akkermansia muciniphila exists in the human gut, accounting for about 0.5–3% of the total gut microbiota in healthy individuals, and is a representative of the phylum Verrucomicrobia (Derrien et al., 2004, 2008). Akk utilizes host-secreted mucin as food and preferentially colonizes gut’s mucus layer (Aggarwal et al., 2022). As a new generation of probiotics, Akk is closely related to the intestinal barrier, nutrient metabolism, immunity, and host disease (Cani et al., 2022). Akk in the gut of healthy people were significantly higher than those of type 2 diabetes, obesity, and irritable bowel syndrome (Zhang et al., 2013). In addition to humans, Akk also shows various beneficial effects in mice. The natural ageing mice with a significantly reduced abundance of Akk can significantly improve their ageing phenotype by supplementing Akk (Ma et al., 2023). Akk can effectively alleviate S. Typhimurium-induced and DSS-induced gut inflammation disease in mice (Liu et al., 2023). Above all, the lack or decreased abundance of Akk is correlated with multiple diseases and supplements with Akk demonstrate exceptional potential in disease treatment.

The acquisition and culture of Akk bacterial bodies is the premise of its research. As a mucin-degrading bacteria, Akk has strict nutrient and culture conditions requirements. Its long culture cycle and low living bacteria count make it very difficult to cultivate in industrial high density. Akk can grow in BHI, but the growth rate is relatively slow, and the growth density is only half of that in the mucin medium (Derrien et al., 2004). As the most basic selective medium for Akk, mucin medium culture can promote the excellent growth of Akk. However, its nutrient composition from animals is easy to cause medium pollution, significantly affecting the growth observation of Akk (Jayme and Smith, 2000). PLOVIER modified the mucin medium and used glucose and N-acetylglucosamine as mixed sugar, soybean peptone and threonine to replace mucin (Plovier et al., 2017). The culture effect of Akk in this modified medium was similar to that of mucin medium. Therefore, optimising the medium without adding mucin is significant to make Akk grow rapidly and densely.

Akk can produce various substances that are beneficial to human health, such as outer membrane protein (Amuc-1100), protein P9, extracellular vesicle amevs (AmEVs), short-chain fatty acids (SCFAs), etc. Amuc-1100 and protein P9 can significantly improve obesity and diabetes (Yoon et al., 2021). AmEVs can improve intestinal barrier integrity and alleviate the DSS-induced colitis phenotype in mice (Chelakkot et al., 2018). As a SCFAs producer, Akk mainly produces acetate and propionate, which can provide benefit effects for host (Zhao et al., 2023). However, there are few reports on the global metabolic spectrum of Akk fermentation broth and its role.

Therefore, based on previous studies, this study obtained a modified BHI formula without animal-origin mucin to promote the high-density growth of Akk. Furthermore, nuclear magnetic resonance (NMR) was used to analyze differential metabolites between two groups and and validated using an amino acid analyzer. Combined with pathway enrichment analysis to explore the correlation between related metabolites and disease. Additionally, correlation heat map analysis was used to determine the role of M-BHI alters the metabolic profile of Akk. This study proves the reliability of M-BHI in the high-density culture of Akk and critical technical support for the promotion and application of Akk.

Materials and methods

Experimental strains and cultivation conditions

Akkermansia muciniphila LTA21F2 was used in this study. It was screened and isolated from the fecal of long-lived elderly and has been preserved in Guangdong microbial strain Preservation Center with the preservation number GDMCC No. 63035. The glycerol cryopreservation tube was taken out from the − 80 °C refrigerator for thawing, and 200 μL of preserved bacterial liquid was sucked into a 10 mL BHI liquid medium. Then, the culture medium was placed into the anaerobic workstation (101 kPa, 90% N₂, 5% CO₂, 5% H₂) and incubated at 37 °C for 48–72 h. After the strain regained vitality, it was inoculated with 4% (calculated by volume fraction) inoculum to BHI liquid medium for subculture. Under the above conditions, it was cultured to the logarithmic growth phase and subcultured three times for standby. All operations were carried out under sterile conditions.

Determination of growth curves and live bacterial count of Akk in BHI and M-BHI

The modified BHI formula contained 38.5 g BHI basic medium, 5.0 g N-acetylglucosamine, 5.0 g yeast extract powder, 8.0 g peptone, 0.5 g l-cysteine hydrochloride and 0.1 g hemin per litre of distilled water. BHI, yeast extract, and peptone were purchased from Hopebio (Qingdao, China). Other chemical reagents were of analytical grade. The prepared medium was sterilized by high-pressure steam at 121 °C for 20 min. l-cysteine hydrochloride and hemin were sterilized by 0.22 μm membrane filtered and sterilized, then added to the sterilized culture medium. The 200 μL subcultured Akk were inoculated into BHI and M-BHI liquid medium, and incubated an anaerobic workstation at 37 °C for 48–72 h. The absorbance of the bacterial solution at 600 nm was determined every 4 h. The growth curve was drawn with the culture time as the abscissa, and the absorbance as the ordinate, and 3 replicates were set for each measurement. The number of live bacteria was measured by the plate dilution coating method. Briefly, 1 mL bacteria cultured liquid was added to 9 ml of phosphate buffer containing l-cysteine hydrochloride for gradient dilution, and then appropriate gradients were selected for plate coating. After cultivation at an anaerobic workstation, the plate with a colony count of 30–300 was selected to count the colony count.

Extraction of metabolites from Akk fermentation broth

The extraction of metabolites from fermentation broth was done according to Chen (Chen et al., 2022) with minor modifications. Briefly, 1 mL of stable fermentation solution and 1 mL of metabolite extract solution was mixed fully. The extraction solution was a combination of NaH2PO4-K2HPO4 buffer and acetonitrile in a ratio of 1:1 (v/v). The mixture was treated with an ultrasonic ice bath to break the cells. Then, the mixture was stored at − 20 °C for 2 h to maximize the extraction of metabolites. After that, the thawed sample was centrifuged (12,000×g, 4 °C, 15 min) to obtain the metabolite-containing supernatant. Then the supernatant was transferred to a rotary evaporator to remove water and acetonitrile. Each sample was dissolved in 700 µL of tritiated water containing 0.01% TSP, and after centrifugation, 600 µL of the supernatant was transferred to a NMR tube for testing. The samples shall be stored at 4 °C for standby and no more than 24 h before NMR testing.

¹H-NMR detection of metabolites in fermentation broth

NMR spectra was conducted at 298 K by a Bruker Avance 500.13 MHz NMR Spectrometer with Prodigy liquid nitrogen cryogenic probe (Bruker, Germany). Parameters for ¹H-NMR metabolite determinations were based on previous methods (Chen et al., 2022; Wu et al., 2021). All samples were measured by the standard Bruker NOESY pulse sequence. The acquisition parameters were as follows: sampling times (64), spectral width (20 ppm), relaxation delay (2 s), number of sampling points (65,536), sampling time (3.277 s), mixing time (0.1 s), FID resolution (0.245).

NMR spectrum processing and data analysis

The baseline, phase and TSP signals were manually calibrated using MestRenova 14.2 (Mestrelab Research, Spain). The metabolite non-overlapping signal and internal reference ratio (TSP) were integrated. The water region was excluded using MestRenova and the peaks from 0.0 to 10.0 ppm were normalized to sum intensities. The peak spectra was divided into 0.01 ppm, and integral normalized data were collected for subsequent analysis.

Metaboanalyst 5.0 platform (https://www.metaboanalyst.ca/) was performed for multivariate statistical analysis on the integral data. The normalized data were scaled by unit variance conversion (UV). The principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to distinguish groups. The parameters R2 and Q2, respectively, represent the model’s fitting condition and prediction ability. Relevant metabolic pathway information mainly comes from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Verification of amino acid production in fermentation broth

Akk fermentation broth was pre-treated with sulfosalicylic acid method. Firstly, 0.2 mL fermentation broth mixed with 1.8 mL 5% (W/V) sulfosalicylic acid solution thoroughly. The mixture was extracted at 4 °C for 1 h and centrifuged (14,000 rpm, 4 °C, 15 min) to obtain the supernatant. Then, the supernatant was passed through 0.22 μm water filter membrane. Finally, the sample was placed in an amino acid analyzer for determination.

Statistical analysis

Three independent replications were set for each experiment. The experimental results were represented as mean ± standard deviation (SD). The GraphPad Prism (version 9.5) was used to draw pictures, and the Pearson correlation heat map was drawn by R studio. The IBM SPSS statistics software (version 26) was used to perform significant analysis of variance, with a significance level of p < 0.05.

Results and discussion

Comparison of growth of Akk in two culture medium

In order to determine the stable growth period and compare the growth of Akk in the two mediums, the OD600 nm value of the bacterial broth was determined. The growth curve was drawn (Fig. 1). The growth retardation period of Akk is 0–12 h, and it enters the logarithmic growth period at 15 h. In the BHI, the logarithmic growth phase of Akk was 15–27 h, and it entered the stable phase at about 30 h. The OD600 nm of the bacterial solution was 0.475 ± 0.011, and the number of live bacteria in the stable phase was (5.66 ± 0.17) × 10⁷ CFU/mL. Akk also entered the logarithmic growth phase at about 15 h and tended to be stable after 36 h in the M-BHI. Meanwhile, the number of live bacteria in the stable phase was (1.11 ± 0.02) × 10¹¹ CFU/mL in M-BHI, four orders higher than BHI. The maximum OD600 nm of bacterial solution was 0.858 ± 0.008 at 45 h, 1.82 times higher than that of BHI. It can be seen that although the M-BHI relatively prolonged the logarithmic growth period of Akk, it significantly increased the density of Akk and the number of live bacteria. The fermentation broth of Akk in two mediums was selected according to the growth curve to analyze the different metabolites between groups.

Fig. 1.

Comparison of growth curves of Akk in the two mediums. The horizontal coordinate is the sampling time, and the vertical coordinate is OD600 nm. The fermentation broth was taken at three-hour intervals to determine the absorbance at OD600 nm

Metabolic profiles of Akk strain in two medium and validation based on amino acid analyzer

Based on three NMR databases, namely, Human Metabolome Database (http://www.hmdb.ca/) and Biological Magnetic Resonance Data Bank (http://www.bmrb.wisc.edu/), ¹H-NMR spectra was analysed to identify metabolites. Related metabolomic literature were also used to identify metabolites (Chen et al., 2022; Wu et al., 2021; Zhang et al., 2023). The representative ¹H-NMR spectra of Akk fermentation in the two mediums, in which 29 metabolites were identified (Fig. 2). Table 1 shows the total metabolites with the assignment of peaks by ¹H chemical shifts. The signals mainly come from amino acids (12), organic acids (10), sugars (2), trimethylamine (TMA), uracil, cytidine, hypoxanthine and nicotinic acid salt. Since most of these signals come from amino acids, we also used amino acid analyzer to verify the free amino acids produced by Akk fermentation. It validated the amino acid signal detected by ¹H-NMR and identified another 7 free amino acids. Table S1 shows the peak time and concentration of total 18 free amino acids.

Fig. 2.

A representative ¹H-NMR spectrum of metabolites produced by Akk fermentation in two mediums. Assignments and marks of metabolites: 1. Leucine, 2. Valine, 3. Isoleucine, 4. Propionate, 5. Lactate, 6. Alanine, 7. Lysine, 8. Acetate, 9. N-acetylglucosamine, 10. Methionine, 11. 3-hydroxybutyrate, 12. Succinic acid, 13. Malic acid, 14. Sarcosine, 15. TMA, 16. Ketoisovaleric acid, 17. Glycine, 18. Glycerol, 19. Threonine, 20. Uracil, 21. Cytidine, 22. Fumaric acid, 23. Tyrosine, 24. Phenylalanine, 25. Histidine, 26. Hypoxanthine, 27. Nicotinate, 28. Anserine, 29. Formate

Table 1.

The main metabolites produced by Akk during fermentation in two different medium

Number	Metabolites	δ1H/(multiplicity)	Type	Number	Metabolites	δ1H/(multiplicity)	Type
1	Leucine	0.96 (t), 1.72 (m), 3.72 (t)	Amino acid	16	Ketoisovaleric acid	3.03 (m)	Organic acid
2	Valine	0.99 (d), 1.04 (d), 2.26 (m), 3.62 (d)	Amino acid	17	Glycine	3.57 (s)	Amino acid
3	Isoleucine	0.99 (d), 1.25 (m), 1.45 (m), 1.98 (m), 3.65 (d)	Amino acid	18	Glycerol	3.62 (d)	Saccharides
4	Propionate	1.07 (d), 2.18 (t), 2.20 (m)	Organic acid	19	Threonine	4.28 (dd)	Amino acid
5	Lactate	1.33 (d), 4.13 (q)	Organic acid	20	Uracil	7.54 (d), 5.80 (d)	Ketones
6	Alanine	1.48 (d), 3.79 (m)	Amino acid	21	Cytidine	6.05 (s)	Nucleotide
7	Lysine	1.73 (m), 1.90 (t)	Amino acid	22	Fumaric acid	6.53 (s)	Organic acid
8	Acetate	1.92 (s)	Organic acid	23	Tyrosine	6.90 (m), 7.19 (m)	Amino acid
9	N-acetylglucosamine	2.07 (d)	Saccharides	24	Phenylalanine	7.43 (m), 7.33 (m), 7.38 (m)	Amino acid
10	Methionine	2.14 (d)	Amino acid	25	Histidine	7.93 (d)	Amino acid
11	3-hydroxybutyrate	2.30 (t)	Organic acid	26	Hypoxanthine	8.19 (s), 8.21 (s)	Purine derivatives
12	Succinic acid	2.41 (s)	Organic acid	27	Nicotinate	8.24 (s)	-
13	Malic acid	2.65 (d)	Organic acid	28	Anserine	8.27 (s)	Organic acid
14	Sarcosine	2.76 (s)	Amino acid	29	Formate	8.65 (s)	Organic acid
15	TMA	2.87 (s)	Amine

s singlet, d doublet, t triplet, q quartet, m multiplet

Multivariate statistical analysis of metabolites between groups

In order to explore the differences of metabolites of Akk in the two mediums, MetaboAnalyst 5.0 was used for multivariate statistical analysis of metabolic data. Firstly, PCA was used to distinguish the metabolic components of Akk in two mediums to reflect the original state of Akk’s metabolic data in different mediums (Fig. 3A). The first two principal components explain 78.4% of the overall variance, which can reflect the metabolic differences between the two groups as a whole. According to the degree of aggregation and dispersion of samples, the scatter distribution of group BHI samples and group M-BHI samples were relatively close, showing a slight separation trend. The separation of group BHI samples within the group is greater than that of group M-BHI, and there are some overlapping parts between the two groups. Hence, the discrimination effect is not significant. Therefore, PCA results showed differences in metabolites between the two groups, but the differences were minor.

Fig. 3.

PCA (A) and PLS-DA (B) analysis of metabolites between the two groups. The PCA and PLS-DA analysis were generated with two predictive components. The PLS-DA model was validated by the cross-validation method. Symbols with red and green color denote fermented Akk in BHI and M-BHI, respectively

In order to further study the differences of metabolites of Akk in different mediums, PLS-DA analysis was performed between groups (Fig. 3B). PLS-DA analysis is a ‘supervised’ data analysis mode when the sample grouping information is known, which can better obtain the differences between sample groups (Chen et al., 2022). The contribution rates of the first and second principal components were 63.7% and 9.8%, respectively. The cross-validation method was used to estimate the prediction ability of the model. The R²X and Q² obtained based on the two principal components were 0.948 and 0.856, respectively, indicating the model has a relatively accurate prediction. The two groups can be clearly distinguished under the PLS-DA model, indicating that the metabolic spectrum of Akk in the M-BHI has changed significantly.

Differential metabolite analysis

According to the results of PLS-DA, the P value < 0.05 in the t-test and the FC value > 1.5 as standard were used to screen the metabolites with significant differences between groups. Akk produced a total of 8 metabolites with significant differences in the two mediums determined by ¹H-NMR, of which 5 were significantly up-regulated, and 3 were significantly down-regulated (Fig. 4). The changes were consistent with the results obtained by the amino acid analyzer. In addition, among the newly determined free amino acids, hydroxyproline and arginine in M-BHI were significantly up-regulated (Table S1). Lactate, histidine, fumaric acid, cytidine and threonine were significantly up-regulated. These metabolites play an essential role in the regulation of human diseases. Among them, lactate is an active host and gut microbiota metabolite, affecting immunoregulation, maintaining gut acid–base balance, and promoting calcium absorption (Zhao et al., 2018). As an example of cross-feeding of intestinal bacteria, lactate can be metabolized by lactate-utilized bacteria to produce butyrate and propionate, thus producing a variety of benefits to the body (Wang et al., 2020). Histidine can inhibit oxidative damage by metabolizing carnosine (Anderson et al., 2018). The higher circulating histidine levels are associated with lower colorectal cancer risk (Rothwell et al., 2023). Neutrophils inhibit enteritis by promoting colony-derived fumaric acid to regulate the activation of intraepithelial lymphocytes in a GSDMD-mediated cell death dependent-manner (Chen et al., 2023). The treacly tRNA synthase secreted by Akk can monitor and regulate immune homeostasis (Kim et al., 2023). The large amount of threonine produced by Akk helps to ensure that threonine can be linked to specific tRNA by enzyme catalysis, thereby affecting subsequent genetic translation process. Arginine-mediated gut microbiota enhances the immune defense function of the lungs, manifested by improved antibacterial activity and immune cell count (Kim et al., 2022). Hydroxyproline, as a sub amino acid, plays an important physiological role in antioxidant and anti-inflammatory reactions, as well as cardiovascular function (Wu, 2020). In addition, hydroxyproline is an important substance in the synthesis of collagen, which is crucial for maintaining the normal structure and strength of connective tissues such as bones, skin, cartilage, and blood vessels (Li and Wu, 2018). These results suggest that these metabolites of Akk play an essential role in inhibiting intestinal inflammation, treating colorectal cancer and regulating immune homeostasis.

Fig. 4.

Box plot of differential metabolite levels between the two groups. Metabolite peak area integrals were normalized. Box diagrams with red and green color denote fermented Akk in BHI and M-BHI, respectively

The significantly down-regulated metabolites were methionine, TMA and sarcosine. Methionine can be metabolized and converted into homocysteine in the human body, resulting in the loss of cells in the inner wall of blood vessels, causing inflammation and increasing cardiovascular diseases (Kurilshikov et al., 2019). The metabolic level of intestinal methionine is related to many coronary heart disease risk factors, such as dyslipidemia and high inflammatory factors. TMA produced by gut microbes triggers a rapid hormone-like signaling effect and promotes alcoholic liver disease (Helsley et al., 2022), which is detrimental to human health. TMA is a metabolite of intestinal bacteria, which can be metabolized into trimethylamine oxide (TMAO) by the liver, and is a typical marker of cardiovascular disease or hypertension (Tang et al., 2019). These results suggested that Akk fermentation in M-BHI could play a more significant role in the prevention and treatment of inflammation, cardiovascular disease and liver disease, as well as reduce the risk factors associated with coronary heart disease.

Correlation analysis between changes in culture medium components and differential metabolites

To explore the relationship between the changes of medium components and the differential metabolites, Pearson correlation analysis was performed between the added medium components in the M-BHI and ten significantly different metabolites (Fig. 5A). The five components added in the M-BHI were hemin, l-cysteine hydrochloride, peptone, N-acetylglucosamine and yeast extract. N-acetylglucosamine is a nitrogen-containing sugar, which is the core structure of glycosylation modification of mucin (Shuoker et al., 2023). The mixed sugar composed of N-acetylglucosamine and glucose can provide sufficient nutrients for the growth of Akk (Plovier et al., 2017). Protein is the first nutritional requirement of Akk, peptone and yeast extract provide high-quality protein for the growth of Akk. As a reductant, l-cysteine hydrochloride is widely used to cultivate and enumerate anaerobic bacteria. Correlation analysis showed that these five medium components were positively correlated with threonine, cytidine, lactate, histidine, fumaric acid, arginine and hydroxyproline while negatively correlated with methionine. Notably, the metabolites with a significant positive correlation correspond to the metabolites with a significant increase in Akk fermentation in the M-BHI. In contrast, the metabolites with a negative correlation correspond to those with a significant decrease. It indicates that the M-BHI can change the metabolic spectrum of Akk by affecting its metabolic activities and metabolic pathways.

Fig. 5.

Correlation analysis and pathway enrichment analysis of differential metabolites. (A) The Pearson correlations heatmap between increased medium components and differential metabolites. Red indicates a positive correlation, whereas blue indicates a negative correlation. The darker the color, the stronger the correlation. *represents p < 0.05, **represents p < 0.01. (B) Differential metabolites topology analysis. Horizontal coordinates represent metabolic pathway impact factors, vertical coordinates − log10 (P) represent metabolic pathway significance levels, and the colors and sizes of the plots are proportional to the impact factors and significance levels of the metabolic pathways. Pathways with an impact greater than 0.05 were marked in the figure

Enrichment analysis of KEGG metabolic pathway

In order to understand how the M-BHI affects the metabolic activities and metabolic pathways of Akk and further predict the biological functions of significant metabolites, enrichment pathways were analyzed. The KEGG pathway was enriched and analyzed to identify the metabolic and signal transduction pathways that were significantly affected. The differential metabolites produced by Akk showed significant changes involving amino acid metabolism (histidine, cysteine, methionine, glycine, serine, threonine, tyrosine, arginine, proline, β-Alanine, alanine, aspartate, glutamate), citric acid cycle (TCA cycle), pyrimidine metabolism (uracil), aminoacyl-tRNA biosynthesis, pyruvate metabolism, glycolysis/gluconeogenesis (Figure S1). Analysis of metabolic pathways and enrichment results showed that there were five metabolic pathways with impact value greater than 0.05, which were: (1) Histidine metabolism; (2) Arginine and proline metabolism; (3) Cysteine and methionine metabolism; (4) Glycine, serine and threonine metabolism; (5) Arginine biosynthesis (Fig. 5B). The enrichment of amino acid related metabolic pathways is the most significant, which may be related to Akk promoting amino acid metabolism remodeling.

Different medium components can change microbial metabolites by regulating intestinal microbial community metabolic pathways. These microbial-derived metabolites can be absorbed by gut epithelial cells or transported to the liver, thereby affecting the physiological functions of the host. There are three possible ways of histidine metabolism: the first way is to convert it into histamine, which, as a neurotransmitter, participates in the local immune response (Yu et al., 2012); The second way is to metabolize it into uric acid; The third metabolic pathway is the metabolism of alanine and histidine to produce carnosine. Carnosine has antioxidant properties that inhibits oxidative damage and protein glycosylation and eliminates age-related toxic molecules (Hipkiss, 2010). Cai found that centenarians can effectively reshape histidine metabolism to offset oxidative stress, which explains centenarians’ health and longevity from the perspective of metabolism (Cai et al., 2022). From an immune perspective, histidine and arginine metabolism are immune regulatory pathways mediated by Akk (Stražar et al., 2021). Specifically, Akk encodes a conserved enzyme called arg, which modifies host arginine as a survival mechanism. This gene family plays a role in regulating host T cell function (Bronte and Zanovello, 2005).

Methionine and cysteine are two protein-derived sulfur-containing amino acids. Healthy adults can convert methionine to cysteine through the sulfur transfer pathway. As one of the intermediates of methionine metabolism, homocysteine has been positively correlated with cardiovascular disease in recent years (Liao et al., 2021). Synb1353 is an engineered strain of probiotic Escherichia coli nissle. It can reduce the homocysteine level of patients with hyper cystinuria by consuming methionine in the gastrointestinal tract and preventing methionine from being absorbed and converted into homocysteine in the plasma. Therefore, the fermentation of Akk in the M-BHI can reduce the level of methionine production, which helps to reduce the synthesis of homocysteine from the source, thus playing a role in preventing and treating disease. A study on fasting mode promoting longevity found that glycine, serine and threonine metabolism is a critical metabolic hub related to longevity (Aon et al., 2020), and the related pathways are mainly related to detoxification, molecular turnover, repair and synthesis. It also suggests that as key metabolic hubs, glycine, serine and threonine metabolism may play an important role in Akk promoting amino acid metabolism remodeling.

The M-BHI medium can affect the production level of related metabolites by affecting the metabolic pathway of Akk. Regarding increased metabolites, Akk fermentation in the M-BHI may play a better role in regulating immunity, treating and preventing enteritis. On the other hand, the significantly decreased metabolites show that Akk may reduce the risk of cardiovascular disease, liver disease and heart disease.

In summary, this study investigated the effects of two different culture mediums on the growth and metabolites of Akk. The M-BHI could significantly increase the bacterial density and live count of Akk, reaching over 10¹¹ CFU/mL. Metabolomics showed that Akk fermentation in M-BHI significantly up-regulated the levels of lactate, histidine, fumaric acid, cytidine, threonine, arginine, and hydroxyproline and significantly down-regulated methionine, TMA, and sarcosine. KEGG enrichment analysis revealed that these differential metabolites mainly enrich 5 metabolic pathways. Combined with the correlation heat map, it indicates that the M-BHI alters the metabolic profile of Akk by influencing their participation in amino acid metabolism remodeling. The changes in the metabolic spectrum indicate that Akk fermentation in M-BHI can further play a role in preventing and treating intestinal inflammation, cardiovascular disease, and liver disease and regulate immune homeostasis to reduce risk factors related to coronary heart disease. This study provides a basis for future high-density cultivation and disease treatment research of Akk.

Supplementary Information

Below is the link to the electronic supplementary material.

Declarations

Conflict of interest

None of the authors of this study have any financial interest or conflict with industries or parties.