The metabolic functional feature of gut microbiota in Mongolian patients with type 2 diabetes

doi:10.21203/rs.3.rs-3091236/v1

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The metabolic functional feature of gut microbiota in Mongolian patients with type 2 diabetes

https://doi.org/10.21203/rs.3.rs-3091236/v1

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published 29 Apr, 2024

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The accumulating evidence substantiates the indispensable role of gut microbiota in modulating the pathogenesis of type 2 diabetes. Uncovering the intricacies of the mechanism is imperative in aiding disease control efforts. Revealing key bacterial species, their metabolites and/or metabolic pathways from the vast array of gut microorganisms can significantly contribute to precise treatment of the disease. With a high prevalence of type 2 diabetes in Inner Mongolia, China, the Mongolian population was selected as subjects to investigate the relationship between gut microbiota and the disease. We recruited Volunteers of Mongolian with type 2 diabetes and control group and detected their fecal samples by metagenomic analysis and untargeted metabolomics analysis. The findings suggest that Firmicutes and Bacteroidetes phyla are the predominant gut microorganisms that exert significant influence on the pathogenesis of type 2 diabetes in Mongolian population. In the disease group, despite an increase in the quantity of most gut microbial metabolic enzymes, there was a concomitant weakening of gut metabolic function, suggesting that the gut microbiota may be in a compensatory state during the disease stage. The beta-Tocotrienol may serve as a pivotal gut metabolite produced by gut microorganisms and a potential biomarker for type 2 disease. The metabolic pathway of Ubiquinone and other terpenoid-quinone biosynthesis could be the crucial mechanism through which the gut microbiota regulates type 2 diabetes. Additionally, certain Clostridium gut species may play a pivotal role in the progression of the disease.

type 2 diabetes

gut microbiota

metabolites

Mongolian

metabolic pathway

Clostridium genus

•Clostridium genus is a main source of the key bacterial species to regulate T2D.

•In T2D stage, there is a reduction in gut metabolic function.

•Beta-Tocotrienol and its metabolic pathway may be the one of the keys to regulate T2D.

Diabetes mellitus (DM), characterized by chronic hyperglycemia, has emerged as a significant public health challenge in the 21st century, imposing substantial burdens on both human health and socioeconomic development. (Magliano DJ et al. 2021). In the context of diabetes mellitus (DM), Type 2 diabetes mellitus (T2DM/T2D) accounts for 90% of cases and is primarily attributed to genetic and environmental factors. Recent research indicates that T2D prevalence rates in China are at 11.2%, with Inner Mongolia exhibiting a higher rate of 19.9% (Li Y et al. 2020༉. The proportion of Inner Mongolia's population aged over 35 years old with diabetes was found to be 17.2% in recent studies conducted in the region of Mongolia. (Duan M et al. 2022༉. That means there is a higher prevalence rate of T2D in the Mongolian of the Inner Mongolia, China. To prevent and treat the disease, extensive research has been conducted. More and more research has established that the gut microbiota plays a pivotal role in the development of T2D ༈Zhou Z et al. 2022༉, and the gut microbiota has become an important target for prevention and treatment of T2D ༈Zhou Z et al. 2022; Wu H et al. 2020༉. On the basis of correlations between gut microbiota and T2D, identifying a mechanism at the strain and molecular level is considered the next step towards disease management. ༈Chaudhari SN et al. 2021༉. It has been revealed that Firmicutes and Bacteroidetes are the two predominant phyla in the adult human gut, accounting for over 90% of the total microbial community ༈Human Microbiome Project Consortium. 2012༉. This implies that certain strains belonging to the both phyla may have a higher likelihood of being the key strains in influencing T2D. A recognized mechanism by which the gut microbiota modulates disease is through the production of metabolites, which serve as a communication channel between the intestinal flora and host. ༈Sharma S et al. 2019; Zhu T et al. 2020༉. Certain strains of butyrate-producing bacteria have been shown to confer health benefits by producing short-chain fatty acids (SCFAs), such as butyrate, which can help reduce the risk of T2D. (Sharma S et al. 2019; Zhu T et al. 2020༉. In this study, our objective was to identify key gut bacteria and their associated metabolites involved in the progression of T2D among the Mongolian population.

Population investigation

The Mongolian volunteers in Inner Mongolia, China were recruited, with participants including newly diagnosed T2D patients and a control group with normal blood glucose levels. The inclusion and exclusion criteria were consistent with our previous research, and the other tests conducted on subjects, including fasting plasma glucose (FPG), weight, height, waist circumference, hip circumference, diastolic blood pressure (DBP), and systolic blood pressure (SBP), were also identical to those in our prior study (Liu Y et al. 2023).

Metagenomic analysis

The fresh fecal samples collected from the participants were preserved at a temperature of -80°C and subsequently sent to Wuhan Metware Biotechnology Company for DNA extraction and metagenomic analysis. The methodology employed was consistent with that used in our previous study.

Untargeted metabolomics analyses

Sample preparation and extraction

The frozen fecal sample was thawed on ice and subsequently weighed 20 mg. Then, a solution containing internal standard (Methanol: Water = 7:3, V/V) was added at a volume of 400µl and vortexed for 3 minutes. The mixture was sonicated in an ice bath for 10 minutes, followed by vortexing for 1 minute. Subsequently, it was placed at -20°C for 30 minutes and centrifuged at 12000 rpm for 10 minutes in 4°C. After removing the sediment, the supernatant was centrifuged at 12000 rpm for 3 minutes in 4°C, and a 200 µl aliquot of the supernatant was transferred for LC-MS analysis.

HPLC Conditions (T3)

All samples were obtained using the LC-MS system in accordance with machine protocols. The analytical parameters were set as follows: The column used was the Waters ACQUITY UPLC HSS T3 C18 (1.8 µm, 2.1 mm*100 mm); The column temperature was maintained at 40℃; The flow rate was 0.4 mL/min; The injection volume was 2µL; solvent system, water (0.1% formic acid): acetonitrile (0.1% formic acid); gradient program, 95:5 V/V at 0 min, 10:90 V/V at 11.0 min, 10:90 V/V at 12.0 min, 95:5 V/V at 12.1 min, 95:5 V/V at 14.0 min.

Metabolites analysis

The significantly regulated metabolites between two groups were identified based on the criteria of VIP > = 1, absolute Log2FC (fold change) > = 1, and p value < 0.05. And the OPLS-DA (Orthogonal Partial Least Squares-Discriminant Analysis) was employed to extract VIP values, the result also contained score plots and permutation plots, by using R package MetaboAnalystR. The data underwent log transformation (log2) and mean centering prior to OPLS-DA analysis. To prevent overfitting, a permutation test with 200 iterations was conducted.

The identified metabolites were annotated by referencing the KEGG Compound database (http://www.kegg.jp/kegg/compound/) and subsequently mapped to the KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html). The hypergeometric test's p-value is utilized to identify significantly enriched pathways from a given list of metabolites.

Origin analysis of differential metabolites

The differential gut microorganisms and metabolites between groups have been uploaded to MetOrigin (http://metorigin.met-bioinformatics.cn/), an integrative database that includes seven different metabolite databases, including KEGG. This database provides information on the origin of the metabolites. (Yu G et al. 2022). Additionally, the biological and statistical correlation between gut bacteria and metabolites will also be elucidated

Statistical analysis

The statistical analysis was conducted using SPSS26 and R software. The measurement data of a normal distribution was expressed as the mean ± standard deviation, satisfying the requirements for parametric testing. Differences between groups were compared using Student's t-test. The data that did not satisfy the parameter test conditions were represented by median (interquartile range), and inter-group comparisons were analyzed using Kruskal-Wallis test. The chi-square test was employed to analyze count data. Statistically significant differences were observed when the level of significance was set at *p < 0.05, **p < 0.01 and ***p < 0.001. The completion of Figs. 1 and 2 was facilitated by the utilization of Wekemo Bioincloud (https://www.bioincloud.tech).

Participants characteristics

Out of 160 volunteers, only 17 qualified participants were selected based on the inclusion and exclusion criteria. These individuals were then divided into two groups: a diabetes group (T2D) consisting of five cases with FPG levels ≥ 7.0 mmol/dl (126mg/dl), and a normal glucose control group (Control) consisting of twelve cases with FPG levels < 7.0 mmol/dl. The male-to-female sex ratio in the T2D group was 2:3, while that in the control group was 5:7. An individual case-control study was designed, in which each T2D case was matched with 2–3 control cases of the same gender and similar age. The waist circumference and waist-to-hip ratio (WHR) of the participants exhibited statistically significant differences between the T2D and Control groups, with T2D patients displaying higher values for both measures than those in the Control group. (Table 1). The comprehensive information of each participant is presented in Supplementary Table 1.

Table 1

Characteristics of the participants in both groups
Group	T2D	Control	t value	p value
FPG, mmol/L	11.58 ± 5.83	5.52 ± 0.29	3.77	0.00
Age, years	60.80 ± 8.50	55.08 ± 7.79	1.35	0.20
BMI	27.96 ± 2.38	24.97 ± 3.44	1.76	0.10
Waistlin, cm	102.60 ± 6.80	88.46 ± 7.44	3.65	0.00
Hipline, cm	105.00 ± 6.44	99.58 ± 7.01	1.48	0.16
WHR	0.98 ± 0.33	0.89 ± 0.42	4.22	0.00
SBP, mmHg	133.80 ± 18.73	129.92 ± 17.30	0.73	0.48
DBP, mmHg	83.60 ± 13.26	85.50 ± 11.34	-0.30	0.77

Differential gut microbial species identified by metagenomic analysis between T2D and Control group

The relative abundance of the seven taxonomic levels, including kingdom, phylum, class, order, family, genus and species were analyzed. Four kingdoms, Bacteria, Viruses, Archaea and Eukaryota, and 120 phyla, 101 classes, 210 orders, 433 families, 1526 genera and 6638 species from fecal samples of the participants have been detected. Although Principal Component Analysis (PCA) (Ringnér M. 2008) and Non-Metric Multidimensional Scaling (NMDS) (Young MP et al. 1995) were employed to assess the diversity differences among groups at each level, no statistically significant differences were observed. However, there were significant variations observed in the bacterial composition at both genus and species levels between group with T2D and those without. In the bacteria, Blautia genus with the highest quantity was dominant, and it was higher in T2D than Control. However, the number of most different bacteria was higher in Control than that in T2D and the Control had more abundance of microorganisms belonged to Flavobacteriia class. The detailed information of the different bacteria is shown in Supplementary table 2.

Then we measured different species among groups by rank sum test, reduced the dimension by linear discriminant analysis (LDA) (Segata N et al. 2011) and evaluated the influence of different species through LDA score. The criteria was 4, that meant the bacteria significantly impacted to the group when it’s LDA Score over 4. It is evident that, the microorganisms with higher LDA score in T2D all belonged to the Firmicutes phylum, especially the Clostridium genus, and that in Control all belonged to the Bacteroidetes phylum, especially the Flavobacteriia and Bacteroidia class (Fig. 1).

Comparative analysis of metabolic enzyme abundance by metagenomic analysis

After searching the KEGG database and conducting statistical analysis, it was found that 103 metabolic enzymes of gut microbiota exhibited significant differences in relative abundance between groups, with 80% of them increased in the T2D group. (Fig. 2).

Figure 2A cluster heat map was generated to compare the abundance of 103 metabolic enzymes between individuals with type 2 diabetes and a control group. The color red indicated an increase in enzyme abundance, while blue represented a decrease. The intensity of the color reflects the quantity of enzymes present; deeper shades indicate higher or lower quantities.

Compare the metabolic profiles between T2D and control group by Untargeted metabolomics analyses

A total of 4597 metabolites were detected, which mainly were Amino acid and its metabolites, Benzene and substituted derivatives, Heterocyclic compounds, Aldehyde, Ketones, Esters, Organic acid And Its derivatives. We analyzed the difference of metabolites between groups by OPLS-DA (Thévenot EA et al. 2015) and a significant difference was shown (Fig. 3). The OPLS-DA model evaluate was R²Y = 0.993, Q² = 0.318 and R²X = 0.441.

Figure 3 The OPLS-DA analysis revealed significant differences in metabolite profiles between the T2D and Control groups. The abscissa represented the score value of the prediction component, and the difference between groups could be seen in the abscissa direction. The ordinate represented the score value of the orthogonal component, and the ordinate direction could see the difference within the group. Percentage represented extent of explanation of components to the data set.

Differential metabolites observed in the T2D group

There were 144 metabolites had significant difference between the two groups in relative abundance, including 100 positive ions and 44 negative ions. Compared to the Control group, in the T2D group, there were 137 gut metabolites reduced including 97 positive ions and 40 negative ions and only 7 raised including three positive ions and four negative ions (Fig. 4). The detail information of all the differential metabolites was described in supplementary table 3 and the information of every sample can be got from supplementary table 4. This result was inverse of that of metabolic enzymes, that may reflect a compensatory mechanism of the disordered microbiota in that the metabolic function of the gut decreased in the T2D group.

The various sources of the differential metabolites were revealed by the Origin analysis

By using MetOrigin, eleven metabolites originated from intestinal microorganism or host were found out, and three metabolites, fusidate Sodium, 3-Hydroxyphenylacetic acid and beta-Tocotrienol (β-Tocotrienol/β-T3) were from intestinal microorganism, four of that originated from host, four of that originated from Co-Metabolism (host and microorganism co-produce) (Fig. 5). The information of the eleven metabolites was showed in Table 2, and that were all decreased in the T2D group. The detail information of all the results can be found in supplementary table 3.

Table 2

The information of the eleven metabolites from intestinal microorganism or host or Co-Metabolism
HMDBID	KEGGID	Name	Origin
HMDB0015570	C06694	Fusidate Sodium	Microbiota
HMDB0000440	C05593	3-Hydroxyphenylacetic acid	Microbiota
HMDB0030554	C14154	beta-Tocotrienol	Microbiota
HMDB0006845	C15776	4alpha-Methylfecosterol	Co-Metabolism
HMDB0000619	C00695	cholic acid	Co-Metabolism
HMDB0001337	C00909	Leukotriene A4	Co-Metabolism
HMDB0012453	C17333	3beta-Hydroxy-5-cholestenoic acid	Co-Metabolism
HMDB0000879	C13713	Tetrahydrodeoxycorticosterone	Host
HMDB0001335	C01312	Prostaglandin I2	Host
HMDB0060407	C18040	5alpha-Dihydrodeoxycorticosterone	Host
HMDB0004026	C05485	21-Hydroxypregnenolone	Host

Enrichment analysis of metabolic pathways

The differential metabolites between groups were enriched in three metabolic pathways, Ubiquinone and other terpenoid-quinone biosynthesis was from intestinal microorganism, Arachidonic acid metabolism and Steroid hormone biosynthesis were both from host. In the Arachidonic acid metabolism pathway, there were four metabolites decreased in the T2D group, which were Prostaglandin I2, Leukotriene A4, 15-Keto-Prostaglandin F2a and Trioxilin B3. In the Steroid hormone biosynthesis pathway, the 5alpha-Dihydrodeoxycorticosterone decreased in the T2D group.

Biological and statistical correlation analysis in Ubiquinone and other terpenoid-quinone biosynthesis pathway

Sankey networks from the MetOrigin can describe biological and statistical correlation of gut microbiome and metabolites. In the Ubiquinone and other terpenoid-quinone biosynthesis pathway(https://www.kegg.jp/pathway/ko00130), the beta-Tocotrienol decreased in T2D group and several gut bacteria had biological and statistical significant correlation with that. The Firmicutes, Proteobacteria, Actinobacteria phylum and the Bacilli class negatively correlated with the beta-Tocotrienol. Conversely, the Bacteroidetes phylum positively correlated with that (Fig. 6).

Correlation analysis of gut bacteria and metabolites

We analyzed the statistical correlation between the gut microorganisms and metabolites in all metabolic pathways using Spearman nonparametric inertia analysis. We found out that many species belonged to Clostridium genus were negatively correlated to most metabolites including beta-Tocotrienol (Fig. 7).

It is a complicated mechanism of gut microbiota regulating T2D (Fan Y et al. 2021), and the metabolites from the gut bacteria are no doubt the important mediates and substrate. Not only the gut microbiome could be biomarker of T2D, but also the metabolites from gut microorganisms have that capacity (Krautkramer KA et al. 2021). The metabolites from fecal sample consist of several sources including food, medicine, host, microorganisms and others (Yu G et al. 2022). In fact, most metabolites’ source is unknown, that need more research to identify that. Due to the metabolites from food and host are affected by diet easily, the metabolites from microbiome are more important in reflecting the state of both host and gut microbiota. The beta-Tocotrienol is a kind of vitamin E that has 8 isomerides, alpha (α)-, beta (β)-, gamma (γ) -, delta (δ) -Tocopherol and alpha (α)-, beta (β)-, gamma (γ)-, delta (δ)-Tocotrienol (Sun Z et al. 2022), and it is a metabolite of s-adenosyl-l-methionine. The alpha-Tocopherol has the most activity and owns the most quantity researches. Some studies proved that the alpha-Tocopherol correlates to T2D (Savolainen O et al. 2017), but there is few evidence to research the relation between the beta-Tocotrienol and T2D. A current study demonstrated that beta-Tocotrienol has anti-cancer activity by inhibiting PD-L1 expression (Sun Z et al. 2022). Furthermore, there is a study proving that delta-Tocotrienol can reduce inflammation of systemic and adipose tissue to improve T2D (Harlan L et al. 2020). Our study will give a clue of the relation between T2D and beta-Tocotrienol.

Fusidate Sodium (Fusidin) is an antibiotic used mainly for the treatment of Staphylococcus infections (Nicoletti F et al. 1995). In an animal experiment, Fusidin could ameliorate the course of diabetes (Nicoletti F et al. 2000). Although there are some other studies showed different result about the antibiotic improving diabetes (Conget I et al. 2005), it is worth to do more in-depth study to uncover the function of that to regulate diabetes. 3-Hydroxyphenylacetic acid is an intermediate metabolite of quercetin metabolized by intestinal microbiome (Zabela V et al. 2020), and lots of studies proved that quercetin has a function to treat T2D (Dhanya R. 2022). That implies the 3-Hydroxyphenylacetic acid is a potential key metabolite of affecting the course of T2D.

Our previous study has revealed the importance of gut Clostridium genus in T2D, and the hypothesis has been proved by this multi-omics study. The Clostridium genus consists of many species, including pathogenic bacteria like Clostridium hathewayi and Clostridium symbiosum and butyrate-producing bacteria such as Clostridium cluster XIVa and Clostridium indolis (Duncan SH. 2004). The Clostridium genus also affected protein hydrolysis strongly (Macfarlane S. 2006). What’s more, we found out that several species belonged to the genus significantly relate to the beta-Tocotrienol. These evidence all support that the Clostridium genus is an important source of key gut microbial strains to interfere development of T2D. Although the current data from relevant databases do not able to support the bio-association between these Clostridium sp. species and beta-Tocotrienol, the research give a clue to discover the key gut bacteria and metabolic pathway in regulating T2D.

The present study admittedly suffers from a sampling error due to the small size of samples, and further investigation is required to validate the results.

Authors contribution

YL and YG wrote the paper. YL, YG, LZ and HP performed the research. YJ and MW analyzed the data. YL designed the study. The final manuscript was reviewed and approved by all authors.

Authors contribution

YL and YG wrote the paper. YL, YG, LZ and HP performed the research. YJ and MW analyzed the data. YL designed the study. The final manuscript was reviewed and approved by all authors.

Acknowledgements

We express our gratitude to all participants who contributed to the study, and extend our appreciation to Wuhan Metware Biotechnology Co., Ltd for their invaluable assistance in sample detection.

Founding

This study was founded by Inner Mongolia Autonomous Region Natural Science Fund (2023QN08024) and "Science and Technology Million Project" of Inner Mongolia Medical University (YKD2020KJBW005).

Data availability

The raw metagenomic analysis data from this study are available for download on an open database ( http://www.ebi.ac.uk/arrayexpress/help/FAQ.html#cite) by searching “E-MTAB-11957” or the title of our previous study （Liu Y et al. 2023）. The present study utilized sample data from numbers 1, 2, 4, 5, 6 and samples numbered between 13 to 24.

Declarations

Ethics approval and consent to participate

The study protocol received approval from the Ethics Committee of Inner Mongolia Medical University (reference number: YKD2016066, signed on 07/03/2016. All participants provided written consent after being fully informed of the study's purpose and procedures. The present study was conducted in compliance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for Human Experimentation.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

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The metabolic functional feature of gut microbiota in Mongolian patients with type 2 diabetes

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Journal Publication

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Abstract

Figures

Key Points

Introduction

Materials and Methods

Population investigation

Metagenomic analysis

Untargeted metabolomics analyses

Sample preparation and extraction

HPLC Conditions (T3)

Metabolites analysis

Origin analysis of differential metabolites

Statistical analysis

Results

Participants characteristics

Differential gut microbial species identified by metagenomic analysis between T2D and Control group

Comparative analysis of metabolic enzyme abundance by metagenomic analysis

Compare the metabolic profiles between T2D and control group by Untargeted metabolomics analyses

Differential metabolites observed in the T2D group

The various sources of the differential metabolites were revealed by the Origin analysis

Enrichment analysis of metabolic pathways

Biological and statistical correlation analysis in Ubiquinone and other terpenoid-quinone biosynthesis pathway

Correlation analysis of gut bacteria and metabolites

Discussion

Authors contribution

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1