Abstract
Background/Aim: The leaves of Laurus nobilis have been used for culinary purposes for many years and have recently been shown to have beneficial effects on human health by altering microbiota composition. However, the effects of L. nobilis on the diversity of microbiomes in the oral cavity and gut remain unknown. Therefore, in this study, we examined the effects of an extract of L. nobilis on the diversity of microbiomes in the oral cavity and gut in mice. Materials and Methods: C57BL/6J mice were randomly divided into two groups and fed a standard diet (SD) and a standard diet containing 5% LAURESH®, a laurel extract (SDL). After 10 weeks, oral swabs and fecal samples were collected. The bacterial DNA extracted from the oral swabs and feces was used for microbiota analysis using 16S rRNA sequencing. The sequencing data were analyzed using the Quantitative Insights into Microbial Ecology 2 in the DADA2 pipeline and 16S rRNA database. Results: The α-diversity of the oral microbiome was significantly greater in the SDL group than in the SD group. The β-diversity of the oral microbiome was also significantly different between the groups. Moreover, the taxonomic abundance analysis showed that five bacteria in the gut were significantly different among the groups. Furthermore, the SDL diet increased the abundance of beneficial gut bacteria, such as Akkermansia sp. Conclusion: Increased diversity of the oral microbiome and proportion of Akkermansia sp. in the gut microbiome induced by L. nobilis consumption may benefit oral and gut health.
Laurus nobilis, commonly called Laurel, is an evergreen shrub of the Lauraceae family, and grows in subtropical and tropical areas. The natural habitat of L. nobilis is in the Mediterranean, where its leaves are often added as a seasoning to many cuisines (1). Moreover, L. nobilis has been used as a traditional medicine to treat viral infections, cough, rheumatism, impaired digestion, diarrhea, and other health conditions. Many biological properties of L. nobilis, including antimicrobial, antifungal, anticonvulsant, antioxidant, anti-inflammatory, antidiabetic, anticancer, neuroprotective, and anticholinergic activities, have been reported (2). Their biological activities can be attributed to their phenolic compounds, such as flavonoids, phenolic acids, tannins, and lignans (2). These phenolic compounds possess enormous potential for the treatment or prevention of systemic diseases including metabolic, allergic, and age-related diseases (3-5).
To date, the effect of L. nobilis on the oral environment has not been demonstrated. However, a few studies have attempted to verify the effect of L. nobilis on oral bacteria and biofilm in vitro. The antibacterial and antibiofilm activities of L. nobilis essential oils against Staphylococcus aureus strains associated with oral infections have been shown (6, 7). Little to no antimicrobial and antibiofilm activity against dental plaque bacteria has been reported (6, 8). To the best of our knowledge, no study has shown the effect of L. nobilis on the oral microbiome. Oral bacteria naturally translocate to the digestive tract, where they may form ectopic colonies and potentially cause gut dysbiosis. Alteration of the gut microbiome leads to the onset of many diseases, ranging from gastrointestinal and metabolic conditions to immunological and neuropsychiatric diseases (9-11). Thus, L. nobilis may affect systemic health by altering the oral and gut microbiomes. Therefore, the study aimed to investigate the effect of L. nobilis on the oral and gut microbiomes in mice.
Materials and Methods
Preparation of LAURESH®. LAURESH® is a laurel leaf extract that contains no less than 1.0% of deacetyl laurenobiolide (Tokiwa Phytochemical Co., Ltd., Chiba, Japan). LAURESH® was prepared according to the protocol previously published (12). Briefly, laurel leaves (100 g) were mixed with 1.4 l of 85% (v/v) ethanol, heated under reflux for 2 h, and filtered. The extracted liquid was vacuum-concentrated at 50°C and freeze-dried to yield LAURESH®. Diacetyl laurenobiolide content was analyzed by high-performance liquid chromatography using a CAPCELL PACK UG120 column (4.6 mm I.D.×250 mm, 5 μm; Shiseido, Tokyo, Japan) with an injection volume of 10 μl, column oven temperature at 40°C, using the following acetonitrile:water gradient elution sequence: 35% for 30 min, 35-100% for 20 min, and 100% for 10 min at a flow rate of 1 ml/min, with UV detection at 200 nm.
Animals. Five-week-old C57BL/6J male mice were purchased from Sankyo Laboratory Services, Japan. Mice were randomly divided into two groups (n=5 per group). The control group was fed a standard diet (SD; Oriental Yeast Co., Ltd., Tokyo, Japan). The experimental group was fed a standard diet containing 5% LAURESH® (SDL) for 10 weeks. The concentration of 5% has often been used in experiments involving natural ingredients in rodent models because it provides a balance between observing sufficient effects on the animals and avoiding the risks of side-effects or toxicity from excessively high concentrations (13, 14). We confirmed that there were no pathological alterations in the liver of the animals after the diet through a histopathological study (data not shown). After 10 weeks, the mice were anesthetized with sodium pentobarbital (50 mg/kg, intraperitoneal). Oral microbiota was collected using an oral swab (Isohelix, Kent, UK), specifically from the dorsal and ventral surfaces of the tongue, buccal mucosa, and labial mucosa, according to the method reported by Abusleme et al. (15). The swab was placed in tris-ethylenediamine-tetraacetate buffer solution and stored at −80°C until further analysis. After the oral swab was collected, the mice were sacrificed by cervical dislocation. Fecal samples were collected from the terminal part of the large intestine to extract gut microbiota and stored at −80°C until further analysis. This experiment was approved by the Animal Experimentation Committee and Ethics Committee of Health Sciences University of Hokkaido (No. 21-065).
Extraction of bacterial DNA. The bacterial DNA from the oral swab was collected as described by Paudel et al. (16). Briefly, 200 μl of lysozyme (20 mg/ml; Fujifilm Wako Pure Chemicals, Osaka, Japan) was added to the oral swab and incubated for 60 min at 37°C. Proteinase K (25 μl; Qiagen, Hilden, Germany) and 200 μl lysis buffer (Qiagen) were added and incubated overnight at 56°C. Subsequently, 200 μl of 100% ethanol was added and the contents were transferred to a spin column from a DNeasy Blood and Tissue kit (Qiagen). The DNA was then washed using wash buffers and eluted using an elution buffer from the kit. The gut bacterial DNA was extracted using DNeasy PowerSoil Pro Kit (Qiagen) according to the manufacturer’s instructions. Extracted DNA was stored at −30°C until used for next-generation sequencing library preparation.
Library preparation and sequencing. The library preparation for 16S rRNA sequencing was performed by following the 16S metagenomic sequencing library preparation protocol (Illumina, San Diego, CA, USA). The amplification of the V3-V4 region of 16S rRNA was performed by polymerase chain reaction using region-specific primers (5′-TCGTCGGCAGCGTCAGATGTGTATAAGA GACAGCCTACGGGGNGGCWGCAG and 3′-GTCTCGTGGG CTCTCGGAGAGATGTATAGAGAGACAGGACTACHVGGGTA TCTA). The polymerase chain reaction products were purified with AMPure XP beads (Beckman Coulter, Indianapolis, IN, USA). The DNA was then added to an adaptor and index for the Illumina sequencer using the Nextera XT Index kit v2 Set A (Illumina). Following purification with AMPure XP beads again, the DNA concentration was measured using a Qubit® 3.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). The DNA was normalized to 5 nM and then pooled. Following library preparation, the amplicon library was mixed with PhiX Control (Illumina) and loaded onto an Illumina MiSeq System for sequencing using MiSeqReagent Cartridge of a MiSeq Reagent Kit v3 (600 cycles; Illumina) (17, 18).
Data analysis. The sequence data were analyzed using the Quantitative Insights into Microbial Ecology 2 (QIIME2, version 2021.2; https://qiime2.org) in the DADA2 pipeline, and the 16S rRNA database (Silva v138) was used. The sequencing data were analyzed for taxonomic abundance, α-diversity, and β-diversity to compare between the groups. α-Diversity was analyzed as observed features, Faith’s phylogenetic diversity, and Shannon index, and the significant differences were evaluated using the Kruskal-Wallis test (p<0.05 was considered significant). β-Diversity was evaluated as weighted and unweighted Unifrac distances based on a principal coordinate analysis (PCoA) plot. Permutation multivariate analysis of variance (PERMANOVA) was used to test for significant differences in β-diversity (p<0.05 was considered significant). Analysis of the composition of microbiomes (ANCOM) in QIIME2 was used to analyze significant differences in the abundance of microbial taxa between the groups. The final significance was expressed as a ‘W’ value.
Functional prediction. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2; https://qiime2.org) software was used to predict the alteration in functional pathways. The statistical analysis of metagenomic profiles (STAMP; https://beikolab.cs.dal.ca/software/STAMP) software package was used to analyze the PICRUSt2 results. Welch’s t-test was used for the group comparisons, and Welch’s inverted confidence interval method was used to calculate the confidence interval. The Benjamini-Hochberg false-discovery rate method was used to calculate the adjusted p-value. Values of p<0.05 were considered statistically significant).
Results
Body weight of mice. The mice were weighed every week. The mean body weight between the two groups showed no significant differences at each week of the experiment (Figure 1).
Body weight of mice. No significant difference in the mean weight change was observed between mice fed with standard diet (SD) and mice fed a standard diet with 5% LAURESH® (SDL).
Oral microbiota analysis. In total, 327,560 sequences were amplified from the oral cavity samples of the SD and SDL groups (20,603-41,135 sequences per sample with an average of 32,756). The alterations in microbiota were analyzed for α-diversity, β-diversity, and taxonomic abundance.
The α-diversity, as measured by the observed features, was significantly greater in the oral cavities of mice fed with SDL than in those of mice fed with SD (p=0.047, q=0.047) (Figure 2A). The difference in Faith’s phylogenetic diversity was not statistically significant between the groups (Figure 2B). The Shannon index was significantly greater in the oral cavities of mice fed with SDL than in those of mice fed with SD (p=0.028, q=0.028) (Figure 2C).
Oral microbiota analysis in mice fed with standard diet (SD) and mice fed a standard diet with 5% LAURESH® (SDL). α-Diversity of the oral microbiome as evaluated with observed feature (A), Faith’s phylogenetic diversity (B) and Shannon index (C) (bar in box plot represents the median; the whiskers indicate the range). β-Diversity analysis as evaluated using weighted Unifrac distance (D) and unweighted Unifrac distance (E). From taxonomic analysis, 87 different bacterial genera were detected (F). *Significantly different at p<0.05.
The β-diversity in the oral cavity was measured using the PCoA plot for the weighted and unweighted Unifrac distance (Figure 2D and E). The PCoA plot for the weighted Unifrac distance showed separate clustering of the SD and SDL groups, indicating quantifiable differences in the overall taxonomic composition between them (p=0.022, q=0.022) (Figure 2D).
On taxonomic analysis, 87 different bacterial genera were detected (Figure 2F) and Lactobacillus was the most abundant genus in both groups. The ANCOM did not show any significant difference in the taxonomic abundance between the two groups.
Gut microbiota analysis. In total, 625,172 sequences were amplified from the guts of the SD and SDL groups (50,333-76,843 sequences per sample with an average of 62,517.2). There were no significant differences between the groups in the α-diversity of the gut microbiome as measured by observed features, Faith’s phylogenetic diversity, and Shannon index (Figure 3A-C).
Gut microbiota analysis in mice fed with standard diet (SD) and mice fed a standard diet with 5% LAURESH® (SDL). α-Diversity of the oral microbiome as evaluated with observed feature (A), Faith’s phylogenetic diversity (B) and Shannon index (C) (bar in box plot represents the median; the whiskers indicate the range. β-Diversity analysis as evaluated using weighted Unifrac distance (D) and unweighted Unifrac distance (E). From taxonomy analysis, 88 different bacterial genera were detected (F). Significantly different at: *p<0.05 and **p<0.01.
The weighted and unweighted Unifrac distance analysis for β-diversity of gut microbiome showed separate clustering of samples for the SDL and SD groups (weighted: p=0.023, q=0.023; unweighted: p=0.007, q=0.007) (Figure 3D and E).
Based on taxonomic analysis, 88 different bacterial genera were detected (Figure 3F), and Lactobacillus was the most abundant genus in both groups.
From analyzing the differences in taxonomic abundance, ANCOM showed that DNF00809, Desulfovibrio, Akkermansia, GCA-900066575, and Faecalibaculum were significantly different between the groups (Table I): The addition of LAURESH® to the diet increased the relative abundance of the genera, DNF00809, Desulfovibrio, Akkermansia and GCA-900066575 and reduced the relative abundance of Faecalibaculum.
Alteration in gut bacterial abundance evaluated by an analysis of compositions of microbiomes (ANCOM).
Functional prediction. The alteration in metabolic pathways of the oral and gut microbiota was predicted from data obtained from 16S rRNA analysis using PICRUSt2 software. The MetaCyc (http://MetaCyc.org) database was used to predict the altered pathways. Regarding the oral microbiota, the result predicted eight different metabolic pathways to be significantly altered between the SD and SDL groups. All eight pathways were predicted to be upregulated in the SDL group compared with the SD group (Figure 4A; p<0.01).
Functional pathway prediction. Alterations in the metabolic pathways of the oral and gut microbiota in mice fed with standard diet (SD) and mice fed a standard diet with 5% LAURESH® (SDL) were predicted from data obtained from 16S rRNA analysis using PICRUSt2 software. In oral microbiota, eight pathways were significantly different between the mice fed SD and mice fed SDL (A). In intestinal microbiota, 26 pathways were significantly different between the SD and SDL groups (B).
The results for gut microbiota showed that 26 metabolic pathways were predicted to be significantly different between the SD and SDL groups. Among those pathways, 21 of those were predicted to be upregulated in the SDL group compared with the SD group (Figure 4B; p<0.01).
Discussion
In this study, we performed a comprehensive analysis of oral swabs and fecal samples from mice fed with SD and SDL diets. We found significant differences in the α-diversity (observed features and Shannon index) and β-diversity of the oral microbiome between SD and SDL groups. The SDL group showed higher α-diversity than the SD group. The increased α-diversity by L. nobilis may be beneficial for oral health. Although various data on α-diversity in the oral microbiome in different oral conditions have been reported, most studies have supported the concept that increased α-diversity is beneficial for oral health. Increased diversity of oral microbiota has been observed in patients with periodontal diseases (19), and both an increase and decrease in diversity of the oral microbiome in patients with dental caries have been reported (20, 21). Reduced diversity was found in individuals with smoking habits (22, 23), oral cancer (24, 25), and inflammatory diseases of the oral mucosa (26). Furthermore, peri-implant microbiotas showed significantly lower diversity and differential abundance than periodontal microbiota in both healthy and unhealthy (with a disease) individuals (27, 28). Individuals with systemic diseases and conditions, including leukemia and obesity, had less diversity than healthy individuals (29, 30). As bacterial infection affects the diversity of oral microbiota (31, 32), the increased diversity in periodontal diseases and dental caries may be due to bacterial infections. Nevertheless, non-infectious oral pathological conditions may lead to reduced diversity. The diversity of oral microbiota is markedly lower in the modern population than in the historical population, and this lower diversity may contribute to chronic oral disease in the modern population (33). Together, increased diversity in the oral cavity may actually be a healthy condition. Therefore, the increased oral α-diversity induced by L. nobilis consumption may benefit oral health.
On the taxonomic level, Lactobacillus was dominant in the oral samples of both SD and SDL mice in the present study. Our findings of the high proportion of Lactobacillus in the oral sample were consistent with the findings of a previous study which used a similar experimental protocol for oral swabbing and bacterial DNA extraction (15). The difference in the diet and water may have contributed to the difference in the proportion of oral Lactobacillus among other studies (34, 35).
The ANCOM for oral microbiome showed no significant changes in oral bacterial abundance between the groups. However, the PICRUSt2 software predicted eight functional pathways to be increased in the oral cavity of the SDL group than in that of the SD group. Among these eight pathways, O-antigen building blocks biosynthesis and dTDP-rhamnose biosynthesis may be involved in lipopolysaccharide production (36). L-Lysine biosynthesis I, purine nucleobase degradation I (anaerobic), glycogen degradation I, superpathway of pyrimidine deoxyribonucleoside salvage, and starch degradation V may be involved in the metabolism of proteins and carbohydrates (36). It is not known how these bacterial pathways are involved in oral health. Further studies are needed to clarify the mechanism. Oral bacteria are naturally translocated to the digestive tract and can alter the gut microbiome (37). Therefore, we observed alterations in the gut microbiome following the SDL diet. No significant difference was observed in the α-diversity between the SD and SDL groups, whereas there was a significant difference in the β-diversity. ANCOM indicated that the abundance of the genus Akkermansia was significantly higher in the gut of the SDL group than in that of the SD group. Akkermansia is the most significant bacteria whose beneficial effect on gut health has been demonstrated (38). A reduced abundance of Akkermansia was observed in gut diseases, such as inflammatory bowel diseases, irritable colitis, and colon cancer (39, 40). The significant increase in Akkermansia abundance induced by L. nobilis may improve gut health. Although which component of L. nobilis contributed to the increase in Akkermansia in the gut is unknown, previous studies suggest a role of polyphenols. Combined polyphenols from tea and a mixture of grape juice and red wine were shown to increase the relative proportion of Akkermansia in the gut (41, 42). L. nobilis is rich in polyphenols and may have contributed to the increase in Akkermansia directly or indirectly (43). Further investigations are required to understand these phenomena in the gut.
A significant limitation of this study is the reliance on an animal model. The composition of microbiota and immune responses can differ markedly between humans and mice. Therefore, while the study offers valuable preliminary data, the correlation of these results with effects in humans remains uncertain. Human trials may be needed to draw definitive conclusions regarding the benefits of Laurus nobilis leaf extract on the diversity of human oral and gut microbiota.
Conclusion
We demonstrated the effect of an extract of L. nobilis in the diet on the diversity of oral and gut microbiomes in mice. Increased diversity in the oral microbiome and an increased abundance of Akkermansia in the gut microbiome induced by L. nobilis may be beneficial for oral and gut health. However, the possibility of adverse health effects induced by L. nobilis, owing to the alterations in oral and gut microbiomes, cannot be excluded. Further investigations are required to conclude whether L. nobilis benefits oral and gut health.
Acknowledgements
The Authors would like to thank Editage (www.editage.com) for English language editing.
Footnotes
Authors’ Contributions
Tetsuro Morikawa: Conceptualization, methodology, writing – original draft preparation, writing – review and editing, visualization, supervision, project administration. Durga Paudel: Methodology, writing – original draft preparation, writing – review and editing. Osamu Uehara: Conceptualization, methodology, writing – original draft preparation, writing – review and editing, visualization, supervision, project administration, funding acquisition. Dedy Ariwansa: Methodology, writing – review and editing. Yukiko Kobayashi: Conceptualization, methodology, writing – review and editing. Jinwei Yang: Conceptualization, methodology, writing – review and editing. Koki Yoshida: Writing – review and editing, project administration. Yoshihiro Abiko: Conceptualization, methodology, writing – original draft preparation, writing – review and editing supervision, project administration, funding acquisition. All Authors have read and approved the final version of the article.
Conflicts of Interest
Yukiko Kobayashi and Jinwei Yang are employees of the R&D Department of Tokiwa Phytochemical Co., Ltd., Chiba, Japan, which provided material support for the research reported in this article. This relationship had no role on the interpretation of the data or the conclusions of the research.
- Received March 8, 2024.
- Revision received April 30, 2024.
- Accepted May 10, 2024.
- Copyright © 2024 The Author(s). Published by the International Institute of Anticancer Research.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
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