Degradation effects and mechanisms of Limosilactobacillus fermentum on ethanol

Abstract

Acute heavy drinking can lead to a rapid increase in blood ethanol concentration, resulting in dizziness, liver damage, and other adverse effects. Although lactic acid bacteria possess the ability to degrade ethanol, the mechanisms remain unclear. For the first time, our study revealed that Limosilactobacillus fermentum DACN611, derived from traditional Chinese fermented yogurt, exhibited superior ethanol degradation capability, achieving a 90.87% ± 8.12% reduction in ethanol concentration in a 2.5% (v/v) ethanol MRS broth over 24 h, among fifty lactic acid bacteria strains. Notably, transcriptome analysis of DACN611 under ethanol stress conditions revealed that DACN611 degraded ethanol by adjusting the cell cycle, promoting protein synthesis, maintaining oxidative metabolic homeostasis, and modulating cell wall and membrane synthesis along with other metabolic pathways. Additionally, DACN611 showed excellent resistance to gastric acid and bile salts, along with a safe profile. In the acute heavy drinking Kunming mouse model, DACN611 significantly increased the latency of the loss of righting reflex (LORR) and reduced the LORR duration. Serum ethanol and acetaldehyde concentrations decreased by 35.36% and 33.56%, respectively. The gastric and hepatic activities of alcohol dehydrogenase (ADH) and acetaldehyde dehydrogenase (ALDH) increased by 1.98-fold and 1.95-fold, and 1.79-fold and 1.70-fold, respectively. In addition, DACN611 decreased serum alanine aminotransferase and aspartate aminotransferase levels, and reduced hepatic cytochrome P450 2E1 expression. It also alleviated pathological liver changes, demonstrating protective effects against alcoholic liver injury in mice. In conclusion, DACN611 significantly degraded ethanol through adaptive metabolic changes under ethanol stress conditions and the promotion of ADH and ALDH activities in gastric and hepatic tissues.

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1. Introduction

Ethanol is the primary ingredient in alcoholic beverages. Moderate consumption of alcohol may be beneficial to health,^1,2 but excessive and prolonged abuse poses serious risks, including gastrointestinal disorders, liver damage, and even cancer.^3,4 Research has identified certain chemicals, such as disulfiram, naltrexone, and acamprosate, that counteract ethanol's effects. However, these drugs can cause side effects like dizziness, vomiting, and allergies.^5–7 Consequently, there is an urgent need to develop natural and safe alternatives to alleviate the harmful effects of ethanol.

Probiotics, beneficial live microorganisms, are known to offer health advantages when consumed in appropriate quantities.⁸ Their long-established safety in food products and various clinical trials suggest that probiotics generally promote health without significant side effects.⁹ Notably, lactic acid bacteria have been found to reduce blood ethanol levels effectively. Zhang et al. developed an acute liver injury model in mice using 52% (v/v) white wine and discovered that Bifidobacterium animalis subsp. lactis KV9 could decrease blood ethanol and acetaldehyde concentrations by enhancing the activities of liver alcohol dehydrogenase (ADH) and acetaldehyde dehydrogenase (ALDH).¹⁰ Similarly, Lim et al. found that a combination of four probiotics, including Lactobacillus gasseri CBT LGA1, Lactobacillus casei CBT LC5, Bifidobacterium lactis CBT BL3, and Bifidobacterium breve CBT BR3, significantly lowered blood ethanol and acetaldehyde levels in ethanol-intoxicated mice.¹¹ Moreover, Bacillus coagulans TCI711 achieved a 28.21% ethanol degradation rate in a 5% (v/v) ethanol MRS medium, followed by a reduction in ethanol levels in people's breath after drinking 75 mL of whiskey one week after ingesting Bacillus coagulans TCI711 capsules.¹² However, although the ability of certain lactic acid bacteria to degrade ethanol has been documented, the mechanism by which these bacteria degrade ethanol is not yet clear.

The study screened fifty strains of edible lactic acid bacteria for their ethanol degradation capabilities. The strain with the optimal ethanol degradation was assessed for its tolerance to gastric acid and bile salts, as well as its safety. The underlying mechanism of ethanol degradation by this strain was elucidated through transcriptome analysis. Subsequent animal experiments demonstrated the strain's effectiveness in degrading ethanol in mice subjected to acute ethanol consumption. The findings reveal, for the first time, that a strain named Limosilactobacillus fermentum DACN611 possesses exceptional properties for ethanol degradation, elucidating the underlying mechanism of its action.

2. Materials and methods

2.1 Materials

The primary (GB 115524) and secondary (GB 21303) antibodies for detecting cytochrome P450 2E1 (CYP2E1) expression were provided by Wuhan Servicebio Technology Co., Ltd (Wuhan, Hubei Province, China). de Man, Rogosa, and Sharpe (MRS) broth medium was obtained from Beijing Land Bridge Technology Co., Ltd (Beijing, China). tert-Butanol (M 89186) was acquired from Meryer Chemical Technology Co., Ltd (Shanghai, China). Ethanol (M056195) and acetaldehyde (A800494) standards were purchased from Beijing MREDA Technology Co., Ltd (Beijing, China) and Shanghai Macklin Biochemical Technology Co., Ltd (Shanghai, China), respectively.

2.2 Isolation and identification of lactic acid bacteria

Traditional fermented food samples were randomly procured from various regions across China. Simultaneously, fecal specimens were collected from a cohort of healthy human subjects. All samples were aseptically transferred into sterilized centrifuge tubes and transported to Southwest University for further analysis.

Lactic acid bacteria were isolated and identified using a slightly modified version of a previously established method.¹³ Briefly, 1 g of each sample was mixed with 9 mL of sterile saline, followed by shaking and thorough mixing. The mixture was then serially diluted. Appropriate dilutions were plated on MRS agar for the isolation and purification of lactic acid bacteria. After three rounds of purification, individual colonies were selected and identified based on their morphological characteristics and Gram staining. For further identification, 16S rDNA analysis was performed. The DNA from the isolated strains was extracted using the TIANamp Bacteria DNA Kit (Tiangen Biotech Co., Ltd, Beijing, China). The DNA was then amplified by polymerase chain reaction (PCR) and sent to Sangon Biotech Co., Ltd (Shanghai, China) for sequencing. The sequencing results were analyzed for homology using Mega11 software, and a phylogenetic tree was constructed.

2.3 Determination of ethanol degradation rate of strain in vitro

The ethanol degradation rate of the strain was determined as previously described.^12,14 The bacterial concentration was adjusted to 5 × 10⁸ colony-forming units (CFU) per milliliter. MRS broth containing 5% (v/v) ethanol was inoculated with 2% (v/v) of the fresh bacterial culture for the experimental group, and 2% (v/v) sterile saline was used for the control group. Both groups were then incubated at 37 °C for 8 h. Subsequently, the MRS broth was centrifuged at 6000g for 10 min at 4 °C, and the supernatant was collected. The supernatant (1 mL) was mixed with potassium dichromate solution (25 mL, 0.136 mol L⁻¹) and concentrated sulfuric acid (4 mL, 18.3 mol L⁻¹). This mixture was then heated in a boiling water bath for 10 min. After cooling, the absorbance of the solution at 610 nm was measured. The ethanol degradation rate (%) of the strain was calculated using the following equation.
where A represents the absorbance of the solution at 610 nm.

2.4 Gastrointestinal resistance, hemolytic properties and antibiotic susceptibility testing of strains

The survival rate of the strains in the simulated gastrointestinal fluid was detected according to a previous method.^13,15 Briefly, one milliliter of the bacterial suspension was combined with 9 mL of artificial gastric fluid (pH = 3). After an incubation period of 3 h at 37 °C, the bacterial count was determined using the plate counting method. Subsequently, artificial intestinal fluid with 0.3% bile salt was mixed with 2% (v/v) of the activated bacterial solution and incubated in a thermostatic oscillator at 37 °C for 24 h. After incubation, the OD₆₀₀ value of the suspension culture was determined in an enzyme plate to calculate the tolerance of the strain to bile salts.

The hemolytic properties of the strains were determined using a previously described method with slight modifications.¹⁶ Briefly, an inoculating loop was dipped into the bacterial suspension and streaked onto a blood agar plate. The plates were then incubated at 37 °C for 24 h. After incubation, the colonies were examined for the presence of hyaline rings around the colonies, indicating hemolysis. Staphylococcus aureus was used as a positive control.

The antibiotic susceptibility of the strain was assessed following an established method, with slight modifications.^17,18 After culturing the strain for 24 h, 10 μL of the bacterial suspension was applied to an MRS agar plate. A drug-impregnated paper disk was then gently placed in the center of the plate using tweezers. After 48 h of incubation, the diameter of the inhibition zone surrounding the paper disk was measured with a vernier caliper. Twenty antibiotics were tested in this experiment, supplied by Hunan Bickman Biotechnology Co., Ltd (Changsha, Hunan Province, China). These included ampicillin, penicillin, tetracycline, erythromycin, ceftriaxone, cefuroxime sodium, amikacin, cefoperazone, gentamicin, vancomycin, kanamycin, minocycline, polymyxin, streptogramin, doxycycline, cefazolin, piperacillin sodium, lincomycin, ceftazidime, and cefalexin. The sensitivity of the strain was determined by the diameter of the inhibition zone: Sensitive >20 mm, Intermediate resistance 15–19 mm, Resistant ≤14 mm.

2.5 Transcriptome analysis

Transcriptome sequencing of DACN611 was performed.¹⁹ DACN611 was cultured in MRS medium containing 0% and 5% (v/v) ethanol for 8 h, establishing a control group and a treatment group. Each group included three parallel samples. The samples were centrifuged to obtain the bacterial pellet, rapidly frozen, and then stored at −80 °C. Bacterial RNA was extracted using a kit from Vazyme Biotech Co., Ltd (Nanjing, Jiangsu Province, China). Sequencing analysis was conducted on an Illumina NovaSeqXPlus platform by Majorbio Bio-Pharm Technology Co., Ltd (Shanghai, China). Individual raw data were normalized using the TMM method, and differential expression of components was analyzed with the criteria of P < 0.05 and |log 2 fold change| > 1.

2.6 Animal experiments

Animal experiments were authorized by the Experimental Animal Welfare Ethics Review Committee of Southwest University (IACUC-20231222-03, Chongqing, China). Male specific-pathogen-free Kunming (KM) mice (30–34 g, 8 weeks old) were acquired from Hunan SJA Laboratory Animal Co., Ltd (Changsha, Hunan Province, China). The experimental mice were housed in a standardized laboratory with a room temperature of 25 °C ± 2 °C, a relative humidity of 50% ± 5%, and a 12 h light/12 h dark cycle.

The animal experiments were conducted as described, but with some minor changes.²⁰ Briefly, 32 mice were randomly and equally allocated into four groups: a control group, a model group, a positive group, and the DACN611 group. At 10:00 am, the mice in the control and model groups were given saline. The mice in the positive group were given bifendate (Shanghai Jinbuhuan Lankao Pharmaceutical Co., Ltd, Shanghai, China) at 150 mg per kg bw, and the mice in the DACN611 group were given DACN611 at 5 × 10¹⁰ CFU per kg bw. At 11:00 am, the mice in the control group were administered saline via oral gavage, whereas the mice in the model and positive groups were treated with ethanol (Red Star Er Guo Tou White Spirit, 56% (v/v), Beijing Red Star Co., Ltd, Beijing, China) at 12 mL per kg bw. The mice's body weights were monitored daily throughout the experiment. All mice were fasted for 12 h before euthanasia. After the blood from the mice was collected, it was placed in a refrigerator at 4 °C for 30 min. The blood was then centrifuged at 4000g for 15 min at 4 °C. The serum was carefully aspirated into a new centrifuge tube and frozen at −80 °C for later experiments. In addition, the liver and stomach tissues of mice were collected for subsequent experiments.

2.7 Loss of righting reflex (LORR) analysis

LORR experiments were performed in mice on the first day of ethanol gavage, as previously described.²¹ Briefly, the mice were placed in a V-shaped tank, and LORR was defined as the inability of mice to straighten themselves three times within 30 seconds of ethanol treatment. The latency of LORR was the time interval from the ethanol gavage to the onset of LORR, while the duration of LORR was the time from the onset of LORR to the return of the righting reflex. Both the latency and duration of LORR were recorded.

2.8 Measurement of serum biochemical indicators

Serum ethanol and acetaldehyde concentrations were determined in the same manner as previously described, with necessary modifications.²² Briefly, after filtering the serum with a 0.45 μm filter tip, 100 μL of serum was added to 500 μL of tert-butanol solution (0.013 mol L⁻¹). The solution was then centrifuged for 10 min at 3000g and analyzed by gas chromatography. Serum ethanol and acetaldehyde concentrations were measured using a GC-2010 plus system (Shimadzu, Kyoto, Japan) equipped with an SH-Rtx-Wax capillary column (30 m × 0.25 mm, 0.25 μm). High-purity nitrogen was used as the carrier gas. The injector temperature was maintained at 200 °C with a split ratio of 20 : 1. The initial temperature was set at 65 °C and held for 3 min. The temperature was then increased to 110 °C at a rate of 6 °C per minute. Detection was performed using a flame ionization detector, with the detector temperature maintained at 200 °C. Following the previous method,²³ the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in serum were determined using commercial kits from Nanjing Jiancheng Bioengineering Institute (Nanjing, Jiangsu Province, China).

2.9 Determination of liver and stomach biochemical indices and liver index

The activities of ADH and ALDH enzymes in the gastric and hepatic tissues of mice were evaluated using commercial assay kits, following the supplier's instructions (Nanjing Jiancheng Bioengineering Institute, Nanjing, Jiangsu Province, China).

Fresh livers from the mice were washed with sterile saline, then placed on a balance and weighed. The liver index of the mice was calculated according to the following equation from a previous study.²⁰

2.10 Histological and immunohistochemical analysis

Hematoxylin and eosin (H&E) staining was performed on the liver samples of mice, as described in a previous report with slight modifications.²⁴ Briefly, fresh liver tissue samples were taken from the same liver lobe of each mouse, immediately fixed with 4% (v/v) paraformaldehyde, dehydrated in ethanol, cleared with xylene, and then embedded in paraffin. Tissue sections with a thickness of 5 μm were prepared and stained with H&E. The pathological changes in the liver tissue were viewed under a light microscope (Olympus, Tokyo, Japan).

The immunohistochemistry (IHC) of the liver in mice was conducted based on a previously described method with slight modifications.²⁵ Briefly, paraffin-embedded liver sections were dewaxed in an environmentally friendly dewaxing solution and then hydrated in graded ethanol and water. After blocking with bovine serum albumin (BSA), the sections were incubated overnight at 4 °C with the primary antibody against CYP2E1 (1 : 200). Following washing with PBS (0.01 M, pH = 7.4), the secondary antibody (1 : 300) was applied and incubated at room temperature for 50 min. The sections were developed with diaminobenzidine solution and counterstained with hematoxylin. The staining of sections was observed using a microscope (Olympus, Tokyo, Japan) and quantified with ImageJ software (National Institutes of Health, Bethesda, Maryland, USA).

2.11 Statistical analysis

One-way analysis of variance was conducted using GraphPad Prism 8 (GraphPad Software, San Diego, California, USA), followed by Tukey's test. Data were expressed as mean ± standard deviation. P < 0.05 was considered to indicate a statistically significant difference.

3. Results

3.1 Effect of DNCA611 on ethanol degradation in vitro

Fifty strains of lactic acid bacteria, collected from traditional Chinese fermented foods or healthy human feces, were incubated in 5% (v/v) ethanol MRS broth medium for 8 h to determine their ethanol degradation rates. As shown in Table 1, these strains exhibited different ethanol degradation properties, with rates ranging from 0.04% ± 4.24% to 30.93% ± 2.80%. DACN611 had the highest ethanol degradation rate among these strains, at 30.93% ± 2.80%, and was therefore selected for further experiments.

Table 1 Ethanol reduction rate (%) in de Man, Rogosa and Sharpe (MRS) broth by fifty strains of lactic acid bacteria. N = 3

Strain	Source	Ethanol degradation rate (%)
Lacticaseibacillus rhamnosus	Sichuan handmade yogurt	14.22 ± 0.72
Lactiplantibacillus plantarum	Healthy human feces	23.15 ± 2.70
Lactiplantibacillus plantarum	Sichuan handmade yogurt	16.01 ± 1.87
Limosilactobacillus fermentum	Sichuan handmade pickle	16.04 ± 2.80
Lacticaseibacillus paracasei	Guizhou handmade sour soup	14.24 ± 6.03
Lactiplantibacillus plantarum	Sichuan handmade yogurt	19.05 ± 1.67
Lactobacillus kefiri	Tibetan kefir grains	14.61 ± 1.59
Lactiplantibacillus plantarum	Sichuan handmade pickle	18.13 ± 8.91
Lactobacillus acidophilus	Sichuan handmade pickle	8.57 ± 2.49
Pediococcus pentosaceus	Sichuan handmade pickle	9.14 ± 1.96
Lacticaseibacillus paracasei	Sichuan handmade pickle	10.15 ± 2.21
Lactobacillus acidophilus	Guizhou handmade sour soup	23.16 ± 1.88
Pediococcus pentosaceus	Yunnan handmade yogurt	12.21 ± 2.54
Pediococcus pentosaceus	Yunnan handmade yogurt	8.10 ± 0.69
Limosilactobacillus fermentum	Sichuan handmade yogurt	15.14 ± 1.32
Lacticaseibacillus rhamnosus	Healthy human feces	4.18 ± 0.70
Lacticaseibacillus rhamnosus	Qinghai handmade yogurt	9.39 ± 0.91
Lacticaseibacillus rhamnosus	Qinghai handmade yogurt	5.89 ± 2.14
Lactiplantibacillus plantarum	Sichuan handmade yogurt	11.66 ± 3.48
Lactiplantibacillus plantarum	Guizhou handmade sour soup	10.43 ± 4.28
Lacticaseibacillus paracasei	Sichuan handmade yogurt	8.14 ± 1.12
Lacticaseibacillus paracasei	Healthy human feces	9.48 ± 1.40
Lacticaseibacillus rhamnosus	Healthy human feces	13.45 ± 1.99
Ligilactobacillus salivarius	Healthy human feces	15.69 ± 2.35
Lactiplantibacillus plantarum	Sichuan handmade pickle	8.83 ± 0.62
Lacticaseibacillus rhamnosus	Healthy human feces	11.39 ± 2.26
Ligilactobacillus salivarius	Chongqing handmade yogurt	3.71 ± 1.07
Limosilactobacillus fermentum	Sichuan handmade yogurt	0.04 ± 4.24
Limosilactobacillus fermentum	Sichuan handmade yogurt	3.50 ± 1.74
Limosilactobacillus fermentum	Sichuan handmade yogurt	0.72 ± 2.57
Lactobacillus helveticus	Yunnan handmade cheese	5.32 ± 0.14
Lacticaseibacillus paracasei	Sichuan handmade yogurt	13.41 ± 5.28
Lactiplantibacillus plantarum	Sichuan handmade yogurt	17.88 ± 2.07
Lactiplantibacillus plantarum	Sichuan handmade pickle	15.43 ± 0.29
Ligilactobacillus salivarius	Xinjiang handmade yogurt	18.64 ± 2.93
Limosilactobacillus fermentum	Xinjiang handmade yogurt	14.74 ± 13.22
Limosilactobacillus fermentum	Xinjiang handmade yogurt	15.39 ± 0.86
Lactiplantibacillus plantarum	Yunnan handmade yogurt	17.98 ± 3.15
Lactiplantibacillus plantarum	Yunnan handmade yogurt	8.30 ± 5.30
Lactococcus lactis subsp. lactis	Qinghai yak cheese	5.13 ± 5.74
Limosilactobacillus fermentum	Qinghai yak cheese	10.17 ± 5.59
Lactococcus lactis subsp. lactis	Qinghai handmade yogurt	17.27 ± 0.95
Limosilactobacillus fermentum	Tibetan handmade yogurt	30.93 ± 2.80
Limosilactobacillus fermentum	Guizhou handmade sour soup	7.19 ± 0.77
Lactococcus lactis subsp. lactis	Qinghai handmade yogurt	17.09 ± 2.03
Lactococcus lactis subsp. lactis	Qinghai handmade yogurt	12.01 ± 2.14
Limosilactobacillus fermentum	Sichuan handmade yogurt	1.33 ± 4.20
Limosilactobacillus fermentum	Sichuan handmade yogurt	2.11 ± 11.13
Lactococcus lactis subsp. lactis	Qinghai handmade yogurt	2.84 ± 6.15
Lactiplantibacillus plantarum	Xinjiang handmade yogurt	17.41 ± 3.96

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The ethanol degradation rate of DACN611 was determined at different inoculum concentrations after 8 h of incubation in 5% (v/v) ethanol MRS broth. As shown in Fig. 1A, the ethanol degradation rate increased with the bacterial inoculum concentration, reaching its peak at 51.47% ± 6.87% with a concentration of 5 × 10⁹ CFU mL⁻¹, which was chosen for subsequent experiments. Furthermore, the ethanol degradation rate of DACN611 at various incubation times was assessed. As depicted in Fig. 1B, this rate increased over time, peaking at 55.88% ± 4.13% after 24 h, which was then established as the incubation period for further study. Finally, the impact of different ethanol concentrations on DACN611's ethanol degradation rate was evaluated. As illustrated in Fig. 1C, the degradation rate decreased with increasing ethanol concentrations, achieving a maximum of 90.87% ± 8.12% at a 2.5% (v/v) ethanol concentration. In conclusion, the ethanol degradation ability of DACN611 was influenced by the strain inoculum, incubation time, and ethanol concentration.

Fig. 1 Effect of different conditions on the ethanol degradation rate of Limosilactobacillus fermentum DACN611. (A) Inoculation amount, (B) time, and (C) ethanol concentration. Different letters mean significant differences. N = 3.

3.2 Identification of DACN611 and measurement of its gastrointestinal resistance and safety properties

As shown in Fig. 2A, DACN611 colonies on MRS solid plates were creamy white, raised, and had a smooth surface. The strain DACN611 was a Gram-positive bacterium and appeared as short rods under the microscope. The 16S rRNA sequence analysis, as depicted in Fig. 2B, revealed that DACN611 was highly similar to L. fermentum NBRC15885. We then assessed the survival rates of DACN611 in artificial gastric fluid at pH 3.0 and in 0.3% (v/v) bile salts, finding survival rates of 84.02% ± 2.71% and 19.37% ± 2.04%, respectively, as shown in Fig. 2C. Hemolytic activity and antibiotic sensitivity of DACN611 were also determined; Fig. 2D illustrates that unlike Staphylococcus aureus, which produced clear zones on blood agar plates, DACN611 did not. Furthermore, as detailed in Table 2, DACN611 was sensitive to 14 antibiotics, including ampicillin, penicillin, tetracycline, erythromycin, ceftiaxone, cefuroxime sodium, cefoperazone, minocycline, doxycycline, cefazolin, piperacillin sodium, lincomycin, ceftazidime, and cefalexin. It showed intermediate sensitivity to gentamicin and was resistant to five antibiotics, namely amikacin, vancomycin, kanamycin, polymyxin, and streptogramin. In conclusion, DACN611 was identified as L. fermentum and demonstrated excellent gastrointestinal resistance and safety properties.

Fig. 2 The biological characteristics of Limosilactobacillus fermentum DACN611. (A) Colonial morphology and microscopic image, (B) the UPGMA dendrogram constructed based on 16S rDNA gene sequence, (C) in vitro tolerance to simulated gastric juice and bile salt, and (D) hemolytic activity. N = 3.

Table 2 Antibiotic susceptibility test for Limosilactobacillus fermentum DACN611. N = 3

Antibiotics	Zone diameter of inhibition (mm)	Concentration (per disc)	Susceptibility
Ampicillin	40.00 ± 2.96	10 μg	Sensitive
Penicillin	31.87 ± 3.81	10 U	Sensitive
Tetracyclines	24.20 ± 2.04	30 μg	Sensitive
Erythromycin	28.10 ± 0.95	15 μg	Sensitive
Ceftriaxone	31.27 ± 0.59	30 μg	Sensitive
Cefuroxime sodium	30.93 ± 2.08	30 μg	Sensitive
Amikacin	13.00 ± 0.62	30 μg	Resistant
Cefoperazone	35.10 ± 0.61	75 μg	Sensitive
Gentamicin	17.00 ± 0.72	10 μg	Intermediate
Vancomycin	—	30 μg	Resistant
Kanamycin	—	30 μg	Resistant
Minocycline	29.40 ± 1.21	30 μg	Sensitive
Polymyxin	—	300 IU	Resistant
Streptogramin	—	10 μg	Resistant
Doxycycline	26.07 ± 1.19	30 μg	Sensitive
Cefazolin	33.23 ± 2.10	30 μg	Sensitive
Piperacillin sodium	40.03 ± 2.14	100 μg	Sensitive
Lincomycin	28.73 ± 1.76	2 μg	Sensitive
Ceftazidime	26.97 ± 1.48	30 μg	Sensitive
Cefalexin	21.07 ± 2.10	30 μg	Sensitive

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3.3 Transcriptome analysis of DACN611 under ethanol treatment

As shown in Fig. 3A and B, ethanol treatment resulted in differentially expressed genes (DEGs) in DACN611, with 442 DEGs being up-regulated and 501 DEGs being down-regulated.

Fig. 3 Transcriptomic analysis of Limosilactobacillus fermentum DACN611 in ethanol degradation. (A) Volcano plot of differentially expressed genes (DEGs). (B) Number of DEGs. (C) Gene Ontology (GO) enrichment analysis of up-regulated DEGs. (D) GO enrichment analysis of down-regulated DEGs. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of up-regulated DEGs. (F) KEGG analysis of down-regulated DEGs.

Gene Ontology (GO) enrichment analysis revealed the top twenty GO terms with up-regulated DEGs in ethanol-treated DACN611, encompassing biological processes (BP), cellular components (CC), and molecular functions (MF). In terms of BP, the GO term most significantly enriched for up-regulated DEGs was amino acid activation. For CC, the most enriched GO term was ribosome, and for MF, it was ligase activity for the formation of carbon-oxygen bonds (Fig. 3C). Conversely, down-regulated genes in ethanol-treated DACN611 were enriched in both BP and MF categories. The most significantly enriched BP GO term for down-regulated DEGs was DNA-templated transcription, while the most enriched MF GO term was oxidoreductase activity (Fig. 3D).

KEGG enrichment analysis revealed that the up-regulated DEGs in ethanol-treated DACN611 were significantly enriched in 11 pathways, including ribosome, aminoacyl-tRNA biosynthesis, peptidoglycan biosynthesis, teichoic acid biosynthesis, D-amino acid metabolism, fatty acid biosynthesis, pentose phosphate pathway, Staphylococcus aureus infection, cell cycle- Caulobacter, carbon fixation pathways in prokaryotes, and nucleotide metabolism (Fig. 3E). Among these, 42 DEGs were enriched in the ribosomal pathway, 22 in aminoacyl-tRNA biosynthesis, and 15 in nucleotide metabolism, including genes such as guaA, nrdE, and pyrH. In the peptidoglycan biosynthesis pathway, 14 DEGs were enriched, including mraY, murG, murD, murB, and murC. For the pentose phosphate pathway, 11 DEGs were enriched, including zwf and rbsk. In the teichoic acid biosynthesis pathway, 9 DEGs were enriched, including dltA, dltC, and dltD. Seven DEGs were enriched in carbon fixation pathways in prokaryotes, including guaD, hpt, and nrdF. In the D-amino acid metabolism pathway, 8 DEGs were enriched, including dapF, lysA, and udk. The fatty acid biosynthesis pathway included 8 enriched DEGs, such as fabZ, fabF, and accC. The cell cycle regulation pathway had 6 enriched DEGs, including rseP, ftsA, and ftsZ. Four DEGs were enriched in the Staphylococcus aureus infection pathway, such as dltA and dltC (Fig. 4A). The down-regulated DEGs in ethanol-treated DACN611 were significantly enriched in 8 pathways, including nitrogen metabolism, fluid shear stress and atherosclerosis, NOD-like receptor signaling pathway, Parkinson's disease, alanine, aspartate and glutamate metabolism, histidine metabolism, salmonella infection, and lipoic acid metabolism (Fig. 3F). Among these, 10 DEGs were enriched in alanine, aspartate and glutamate metabolism, including carB, carA, and asnA. In nitrogen metabolism, 8 DEGs were enriched, such as narL and arcC. In histidine metabolism, 7 DEGs were enriched, including hisC, hisF, and hisH. Five DEGs were enriched in lipoic acid metabolism, including lpdA and pdhA. Four DEGs were enriched in fluid shear stress and atherosclerosis, such as P8770_RS08970 and trxA. The gene trxA was also enriched in the NOD-like receptor signaling pathway, Parkinson's disease, and salmonella infection (Fig. 4B).

Fig. 4 Functional chord diagram of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes (DEGs) in Limosilactobacillus fermentum DACN611 treated with ethanol. (A) Functional chord diagram of KEGG enrichment analysis of up-regulated DEGs. (B) Functional chord diagram of KEGG enrichment analysis of down-regulated DEGs.

The results indicate an adaptive mechanism of DACN611 to ethanol degradation at the molecular level, involving multiple aspects of amino acid metabolism, protein synthesis, maintenance of cellular structure, transcriptional regulation, and redox homeostasis.

3.4 Effect of DACN611 on body weight and liver index in mice

The body weight and liver index in mice were measured and are presented in Fig. 5A and B. After ethanol intervention, the body weight of mice in the model group significantly reduced compared to the control group (P < 0.05). There was no significant difference in the body weight of mice in the positive and DACN611 groups compared to the model group (P > 0.05). As shown in Fig. 5C, the liver indices of mice in the model group were markedly higher than those in the control group (P < 0.05). In contrast, the liver indices of mice in the positive and DACN611 groups did not significantly differ from those in the model group (P > 0.05). These results indicate that ethanol intervention can cause weight loss and an increase in liver index in mice, but the dietary interventions tested did not significantly affect these changes.

Fig. 5 Effects of Limosilactobacillus fermentum DACN611 on body weight and liver index in mice. (A) Body weight, (B) body weight on the last day, and (C) liver index. Different letters mean significant differences. N = 8.

3.5 Effects of DACN611 on ethanol degradation in mice

As shown in Fig. 6A, compared with the control group, the latency of LORR in the model group of mice was notably elevated. The DACN611 and positive groups exhibited increases in LORR latency by 140.45% and 137.08%, respectively, compared to the model group. Fig. 6B demonstrates that the duration of LORR in the model group was 209.10 ± 30.41 min after ethanol intervention. However, the duration of LORR was reduced by 29.94% and 30.50% in the DACN611 and positive groups, respectively.

Fig. 6 Effect of Limosilactobacillus fermentum DACN611 on ethanol degradation in mice. (A) The latency of the loss of righting reflex (LORR) and (B) the duration of LORR, (C) serum ethanol, and (D) serum acetaldehyde. Different letters mean significant differences. N = 8.

On the eighth day, serum ethanol and acetaldehyde levels were measured one hour after the last ethanol ingestion. As depicted in Fig. 6C and D, in comparison to the control group, serum ethanol and acetaldehyde concentrations in the model group were considerably higher. However, the DACN611 and positive groups showed a decrease in serum ethanol levels by 35.36% and 42.89%, respectively. Additionally, these groups decreased serum acetaldehyde levels by 33.22% and 25.28%, respectively. These results suggest that DACN611 could positively influence ethanol degradation in vivo.

3.6 Effects of DACN611 on ethanol metabolizing enzymes in mice

The effects of ethanol exposure on ADH and ALDH activity in the liver and stomach were further investigated. As shown in Fig. 7A and B, ethanol treatment decreased the ADH levels in the stomach and liver (P < 0.05). In the DACN611 and positive groups, ADH in the stomach increased by 0.98-fold and 1.40-fold, respectively, compared with the model group. Similarly, liver ADH increased by 0.79-fold and 0.78-fold, respectively, in these groups. Furthermore, as illustrated in Fig. 7C and D, the ALDH activities in the liver and stomach were significantly decreased in the model group compared with the control group (P < 0.05), but DACN611 treatment significantly mitigated this decline. Specifically, DACN611 increased the gastric and hepatic ALDH activities by 0.95-fold and 0.70-fold, respectively. These results indicate that DACN611 enhances the activity of ethanol-metabolizing enzymes in the liver and stomach.

Fig. 7 Effects of Limosilactobacillus fermentum DACN611 on ethanol metabolizing enzymes in mice. (A) Alcohol dehydrogenase (ADH) activity in the liver, (B) ADH activity in the stomach, (C) acetaldehyde dehydrogenase (ALDH) activity in the liver, and (D) ALDH activity in the stomach. Different letters mean significant differences. N = 8.

3.7 Effects of DACN611 on liver injury in mice

The levels of serum AST and ALT in mice were determined. As depicted in Fig. 8A and B, the serum AST and ALT levels in ethanol-exposed mice were markedly elevated. However, after treatment with DACN611 and the positive drug, serum AST levels were reduced to 0.48-fold and 0.29-fold, respectively, and serum ALT levels to 0.42-fold and 0.39-fold, respectively, in comparison to the model group. The liver pathology of mice was further examined; Fig. 8C shows that the hepatic cords in the control group were radially arranged from the central vein, and the hepatocyte structure was intact. In contrast, mice in the model group exhibited disrupted hepatic cords, inflammatory cell infiltration, and hepatocyte vacuolization. Mice treated with DACN611 and the positive drug demonstrated reduced inflammatory infiltration and less hepatocyte vacuolization compared to the model group. As illustrated in Fig. 9A and B, hepatic CYP2E1 expression was significantly increased in the model group compared to the control group (P < 0.05). However, in the DACN611 and positive groups, hepatic CYP2E1 expression was reduced by 0.63-fold and 1.28-fold, respectively, compared with the model group. These findings suggest that DACN611 reduced serum transaminase levels and hepatic CYP2E1 expression, and mitigated ethanol-induced hepatic pathology, thereby providing protection against ethanol-induced liver toxicity.

Fig. 8 Effect of Limosilactobacillus fermentum DACN611 on serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels and liver histopathology in mice. (A) ALT levels, (B) AST levels, and (C) hematoxylin and eosin staining of the liver (magnification ×200, ×400). Different letters mean significant differences. N = 8.

Fig. 9 Effects of Limosilactobacillus fermentum DACN611 on hepatic cytochrome P450 2E1 (CYP2E1) expression in mice. (A) Immunohistochemistry of liver tissues showed CYP2E1 expression as brownish-yellow, with nuclei stained blue by hematoxylin (scale bar = 20 μm, magnification ×400), and (B) percentage of the CYP2E1 positive area. Different letters mean significant differences. N = 6.

4. Discussion

Ethanol, the main ingredient in alcoholic beverages, can lead to disturbances in ethanol metabolism and adversely affect host health when consumed excessively.²⁶ Some bacteria with ethanol-degrading properties have been reported to promote in vivo ethanol degradation. Huang et al. reported that the ethanol degradation rate of Bacillus coagulans TCI711 in MRS broth with 5% ethanol was 28.21%, which subsequently increased ethanol metabolism in humans after one week of administering Bacillus coagulans TCI711 capsules in a clinical trial.¹² Additionally, recombinant Bacillus subtilis fmb8 has demonstrated good ethanol degradation properties in vitro, with experiments in mice showing that it reduces alcoholic impairment.²⁷ Consistent with these findings, our study discovered that DACN611 has excellent ethanol degradation ability, degrading 90.87% ± 8.12% of ethanol in 2.5% (v/v) ethanol MRS broth over 24 h. Moreover, L. fermentum DACN611, a strain isolated from traditional Chinese fermented yogurt, was identified for the first time as having ethanol-degrading properties.

The transcriptome analysis of lactic acid bacteria plays a crucial role in exploring the mechanisms underlying ethanol degradation. By comparing gene expression differences in the presence and absence of ethanol, a series of genes related to ethanol metabolism can be identified. In this study, ethanol treatment caused significant changes in the expression profile of the DACN611 gene, with 442 DEGs being up-regulated and 501 DEGs being down-regulated. This differential expression indicates the strong genomic response of DACN611 to ethanol exposure and may reveal its mechanism of metabolizing and detoxifying ethanol. GO enrichment analysis further revealed the functional significance of these DEGs. Up-regulated DEGs were mainly associated with amino acid activation, ribosome assembly, and ligase activities forming carbon-oxygen bonds. These enrichments suggest that ethanol treatment enhances protein synthesis machinery and metabolic processes related to the utilization of certain amino acids and bond formation, which are crucial for the adaptation and survival of cells under ethanol stress.^28,29 Conversely, down-regulated DEGs were significantly enriched in DNA-templated transcription and oxidoreductase activities. The down-regulation of genes related to DNA-templated transcription may reflect the cellular response to environmental changes, adjusting the growth rate to adapt to different nutritional or stress conditions. The reduction in oxidoreductase activity may imply a protective adaptation mechanism adopted by DACN611 to mitigate oxidative stress caused by ethanol metabolism.³⁰ KEGG enrichment analysis further elucidates the involvement of DEGs in cellular metabolic pathways and biological functions. It is well known that ethanol penetrates the lactic acid bacteria cell membrane, compromising its integrity and inhibiting its function.³¹ In this study, the expression of DEGs (such as murD, murB, and murC) in the peptidoglycan synthesis pathway of DACN611 was significantly up-regulated in the ethanol-treated group. Simultaneously, genes encoding the biosynthetic enzymes of teichoic acid (dltA, dltC, dltD, etc.) were significantly up-regulated. Consistent with previous studies, Zhao et al. found that Oenococcus responds to ethanol toxicity by strengthening the expression of peptidoglycan biosynthesis genes (aimA, dacC, etc.).³² Similarly, Lactobacillus plantarum WCFS1 under ethanol stress induces the expression of dlt and subsequently upregulates genes tagE5 and tagE6, involved in cellular teichoic acid synthesis, thereby enhancing ethanol environment adaptability.³³ Additionally, in the fatty acid biosynthesis pathway, the levels of genes involved in fatty acid biosynthesis (fabZ, fabF, accC, etc.) increased in ethanol-treated DACN611. Consistent with previous studies, Huang et al. found that Lactobacillus plantarum ZDY 2013 activates genes encoding fatty acid elongation proteins (fabG, fabZ, fabF, etc.) to adapt to changes in cell membrane structure under acid stress.³⁴ Therefore, DACN611 maintains cellular stability by adjusting cell membrane and cell wall synthesis pathways, indirectly sustaining the activity and stability of ethanol-metabolizing enzymes within the cells. Furthermore, the ribosome is the site of protein synthesis, and aminoacyl-tRNA biosynthesis is essential in the protein synthesis process. The ribosome and aminoacyl-tRNA synthesis pathways of ethanol-treated DACN611 were up-regulated, potentially enhancing protein synthesis capacity to meet the increased metabolic demands during ethanol degradation. The pentose phosphate pathway, which decomposes glucose-6-phosphate into ribulose-5-phosphate while generating reducing power NADPH from NADP+, plays a key antioxidant defense role. In this study, the genes zwf and rbsk in ethanol-treated DACN611 were significantly upregulated. The cell cycle regulation and nucleotide metabolism pathways play key roles in the growth, division, gene expression, energy metabolism, and response to environmental stress of lactic acid bacteria. The normal operation of these pathways is crucial for the survival and function of lactic acid bacteria. In the ethanol treatment group, the genes in the cell cycle regulation and nucleotide metabolism pathways of DACN611 were upregulated. Therefore, DACN611 regulates the cell cycle and nucleotide metabolism pathways to better adapt to the environmental pressure caused by ethanol, optimize resource allocation, and improve the efficiency of ethanol degradation. In the KEGG pathways enriched with downregulated DEGs, the alanine, aspartate, and glutamate metabolism, nitrogen metabolism, and histidine metabolism pathways had the largest number of enriched DEGs. Under ethanol treatment, the downregulation of gene expression in these metabolic pathways may be related to the cell's adaptive response to ethanol toxicity. Our results showed that ethanol treatment caused significant changes in DACN611 gene expression, particularly upregulating genes related to amino acid activation, ribosome assembly, carbon-oxygen bond-forming enzyme activity, and cell cycle and nucleotide metabolism. These changes likely enhanced the bacteria's ability to synthesize proteins and increased metabolic flexibility in the presence of ethanol, while maintaining cellular stability by adjusting cell wall and membrane synthesis. This, in turn, improves the efficiency of ethanol degradation.

Ethanol, a small molecule organic solvent, easily penetrates most biological barriers, including the blood-brain barrier, leading to ethanol concentrations in all aqueous spaces, including the brain, closely mirroring those in blood plasma.³⁵ Acetaldehyde, a secondary metabolite of ethanol, can form adducts with DNA and proteins, damaging the liver.^27,36 Studies have shown that the blood ethanol and acetaldehyde levels in mice peak one hour after white wine administration.³⁷Bifidobacterium Animalis subsp. Lactis KV9 was reported to reduce blood ethanol concentration by 15.01% in C57BL/6 mice treated with a 52% (v/v) alcohol solution.¹⁰ In our study, treatment with 56% white wine in KM mice, followed by DACN611 administration, resulted in a 35.35% reduction in serum ethanol concentration and a 33.56% decrease in acetaldehyde levels. Compared to previous strains, DACN611 demonstrated superior efficacy in degrading blood ethanol in vivo, likely due to its strain specificity and its effect on regulating key host alcohol metabolizing enzymes. Ethanol, once ingested, is absorbed mainly in the gastrointestinal tract; a portion undergoes first-pass metabolism in the stomach, while the remainder enters the liver through the portal vein for metabolism.^38,39 The principal ethanol metabolizing enzymes are ADH and ALDH, with ADH converting ethanol to acetaldehyde, and ALDH converting acetaldehyde to acetic acid, which is then oxidized to carbon dioxide and water for excretion.³⁵ Previous studies have shown that white wine treatment decreases gastric and hepatic ADH and ALDH activities in mice.^20,37 Consistently, our study observed reduced ADH and ALDH activities in ethanol-treated mice, whereas DACN611-treated mice exhibited increased activities of these enzymes in both the liver and stomach, thus enhancing the degradation of serum ethanol and acetaldehyde. Furthermore, assessing the resistance of probiotics to gastric acid and bile salts, is important for evaluating their viability in the digestive tract, and functional performance. DACN611 exhibited robust gastrointestinal tolerance, indicating its ability to traverse the gastrointestinal tract effectively and degrade ethanol in the host.

It is widely recognized that excessive ethanol consumption can lead to liver damage, including conditions such as fatty liver and liver inflammation. ALT is predominantly found in the cytoplasm of hepatocytes, whereas AST is located in both the cytoplasm and mitochondria. When hepatocytes are damaged, these enzymes are released into the bloodstream, leading to elevated serum ALT and AST levels.⁴⁰ Several studies have demonstrated that excess ethanol consumption alters liver pathology in mice, characterized by inflammatory infiltration and hepatocyte vacuolization, and results in increased serum AST and ALT levels.⁴¹ Wang et al. reported that Lactobacillus casei Zhang, isolated from traditional horse milk, significantly reduced serum ALT and AST levels and improved liver pathology in mice, alleviating LPS/D-GalN-induced liver injury.⁴² Fang et al. found that pretreatment with Lactobacillus plantarum CMU995 before ethanol exposure reduced serum ALT and AST levels and ameliorated hepatic steatosis and liver injury.⁴³ Furthermore, CYP2E1, a component of the cytochrome P450 enzyme system, is widely expressed in the liver.⁴⁴ It primarily involved in drug metabolism and oxidative reactions of endogenous substances. Overexpression of CYP2E1 catalyzes oxidative reactions, leading to the production of reactive oxygen species (ROS), such as hydroxyl radicals, hydrogen peroxide, and superoxide anion, which in high concentrations can induce oxidative stress and cellular damage.⁴⁵ Sun et al. reported that extract of Jasminum grandiflorum L. mitigated hepatic injury by reducing hepatic CYP2E1 expression.⁴⁶ Fructose 1,6-diphosphate was reported to ameliorate alcohol-induced liver injury through the down-regulation of hepatic CYP2E1 in mice.²⁵ In our study, ethanol-treated mice exhibited elevated serum aminotransferases and hepatic CYP2E1 protein expression, alongside fatty microvacuolization and significant inflammatory infiltration in the liver. However, DACN611 treatment reduced serum transaminase levels, hepatic CYP2E1 expression, and pathological changes in the liver. These findings indicate that DACN611 can significantly alleviate alcoholic liver injury.

5. Conclusions

In summary, we identified a strain named Limosilactobacillus fermentum DACN611 from traditional Chinese fermented foods. This strain exhibited good gastrointestinal tolerance and safety. DACN611 significantly degraded ethanol through adaptive metabolic changes under ethanol stress conditions and by promoting ADH and ALDH activities in gastric and hepatic tissues. The results of this study are significant for elucidating the mechanisms by which lactic acid bacteria metabolize ethanol and for the development of functional lactic acid bacteria.

Author contributions

Lingling Zhang: methodology, visualization, investigation, data analysis, writing original draft. Yuhong Zhang: funding acquisition. Shijian Liu: formal analysis. Jiajia Song: conceptualization, methodology, supervision, writing original draft, writing review and editing. Huayi Suo: conceptualization, supervision, project administration, funding acquisition.

Data availability

The datasets generated during the current study are not publicly available due to an ongoing patent submission. However, they are available from the corresponding author upon reasonable request.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This work was supported by the Sichuan Province Key Research and Development Program (2024YFHZ0077), the Major Science and Technology Special Projects in Tibet Autonomous Region (No. XZ202201ZD0001N), the University Innovation Research Group in Chongqing (No. CXQT21007), the Key Construction Disciplines of Traditional Chinese Medicine in Chongqing (2021-4322190044), and the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202300204).

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