Vitamin D3 affects the gut metabolomics in lipopolysaccharide-injected mice

Abstract

Background

Although vitamin D3 (VD3) may regulate gut microbiota to play protective roles in systemic inflammation, its effects on gut metabolomics have not been clarified.

Objective

To investigate the effects of VD3 on gut metabolomics in LPS-injected mice.

Methods

After LPS-injected mice were intervened with VD3, colon contents were collected for an untargeted metabolomics analysis, with morphology and permeability of colon epithelium illustrated.

Results

The results confirmed the protective effects of VD3 against the inflammatory changes and hyperpermeability of colon epithelium in LPS-injected mice. In untargeted metabolomic analysis of colon contents, principal component analysis showed valid data. Both partial least squares-discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) showed intergroup separation of samples between the control and LPS-injected mice. Similarly, VD3 affected the gut metabolite profiling and composition in LPS-injected mice, which were also shown by PLS-DA and OPLS-DA. Furthermore, differential metabolites were identified by an univariate statistical analysis. For LPS stimulation, the gut metabolomics changed obviously, such as some lipid-related metabolites appeared increase. However, VD3 treatment had distinctive effects on the gut metabolomics, especially the induced appearance of protective Soyasaponins, with reduction in lipid-related metabolites.

Conclusions

VD3 affected the gut metabolomics and alleviated the epithelium inflammatory injuries in LPS-injected mice.

Keywords

vitamin D3 gut metabolomics Soyasaponins systemic inflammation

Introduction

Gut microbiota homeostasis and metabolism have profound influences on human health.¹ Gut dysbiosis is a common and multi-causal clinical condition marked by an abrupt shift in the gut microbiota composition and function, which is influenced by several factors, such as host genetics, dietary habits, emotional stress and infections.² In gut dysbiosis, pathogenic bacteria and their metabolites produce local inflammation and affect the gut permeability, allowing bacterial metabolites and endotoxins, such as lipopolysaccharide (LPS), to invade into the circulation and stimulate systemic inflammation.^3,4 Therefore, the bacteria-derived LPS is usually used as a classical inducer of systemic inflammation.⁵

Systemic inflammation, which is closely related with gut dysbiosis, has been recognized as a risk factor and a key character of various pathological conditions.³ As systemic inflammation brings injuries to multiple tissues and organs, there also exist inflammatory injury of intestinal epithelium.^6,7 The hyperpermeability of inflammatory intestinal epithelium allows bacteria and their harmful products to invade into blood and further deteriorate systemic inflammation, thus forming a vicious cycle.^8,9 We previously found changes of gut microbiota and injuries of intestinal epithelium in an LPS-induced systemic inflammation mouse model.⁷ However, the gut metabolomics in LPS-injected mice have not been clarified.

Vitamin D3 (VD3) that can be naturally produced in the body under ultraviolet B radiation or obtained through dietary sources, has been recognized as a steroid hormone.^10,11 Besides the regulation of calcium and phosphate metabolism, VD3 positively regulates various biological processes, such as the immune system and metabolism.¹² Usually, VD3 deficiency is associated with systemic low-grade inflammation, which can be relieved by VD3 supplement.^13,14 A serial of studies focus on the regulation of gut microbiota and their metabolomics to reveal the anti-inflammatory mechanism of vitamin D.^15,16 In mice fed with high-fat diet, VD3 supplementation affected the gut microbiota composition and metabolism, improved the gut barrier function and reduced the levels of inflammatory factors.¹⁷ Nevertheless, the changes of gut metabolomics for VD3 treatment in LPS-injected mice still need clarification.

In this study, mice were treated with LPS by intraperitoneal injection to induce systemic inflammation and intervened by oral administration of VD3. The colon epithelium permeability was measured by FITC-labeled dextran in vivo, the morphological changes of colon epithelium were illustrated by HE staining, and the gut metabolomics was analyzed by an untargeted metabolomics analysis using liquid chromatogram-mass spectrometry (LC-MS), to investigate the effects of VD3 on the gut metabolomics in LPS-injected mice.

Materials and methods

Animal experiment

As there exist strain-specific differences in VD3 response on gut homeostasis in inbred mice,¹⁸ the non-inbred ICR mice, which are widely-used for VD3 and gut metabolomics investigations, were selected in this study.^19,20 Twenty four male ICR mice (25.17 ± 1.24 g) were purchased from Charles River (Beijing, China) and kept in line with the National Guide for the Care and Use of Laboratory Animals. They were in groups of 4 in well-ventilated cages, and allowed free access to standard normal mice chow and water, with a 12 h light-12 h dark cycle.

According to the ethical policy of Hebei Medical University on the use of laboratory-bred animals (approval number: 2019102), mice were divided into three groups: control (Con) group, LPS group and LPS plus VD3 (LPS + VD3) group. Mice in the LPS and LPS + VD3 groups received an alternative-day intraperitoneal injection of 1 mg kg⁻¹ d⁻¹ LPS for 7 times to induce systemic inflammation.^7,21 Mice in the LPS + VD3 group were given 10 μg kg⁻¹ d⁻¹ VD3 (dissolved in corn oil) by oral gavage for 18 continuous days, as we previously did.⁷ At the end of the experiment, FITC-labeled dextran was used to measure intestinal epithelial permeability in vivo. After mice were killed by cervical dislocation, a humane method of euthanasia, a segment of colon tissue was collected for HE staining and colon contents were collected for the untargeted metabolomics analysis by LC-MS.

Intestinal epithelial permeability

The intestinal epithelial permeability was measured by FITC-dextran (70 kDa; Sigma Aldrich, Munich, Germany) as reported and we previously did.^21,22 After food restriction for 12 h, mice received FITC-dextran (20 mg in 0.2 ml saline) by oral gavage. Four hours later, mice were killed and colon tissue was taken out and frozen immediately. After being cut, frozen sections were observed and imaged using a fluorescence microscope (IX51; Olympus, Tokyo, Japan).

Untargeted metabolomic detection and data analysis

The untargeted metabolomics detection of fresh samples was completed by Lumingbio (Shanghai, China).²³ Briefly, the untargeted metabolomics by LC-MS was performed on AB ExionLC (SCIEX, USA) coupled with a Q Exactive Orbitrap mass spectrometer (Thermo, Shanghai, China). The LC-MS raw data were processed using the metabolomics software program Progenesis QI v2.3 (Nonlinear Dynamics, Newcastle, UK) to obtain the final data matrix for use in the formal analysis of peak detection and alignment. The data were analyzed for positive and negative ions, respectively. Meanwhile, their MS mass spectral information was searched against and matched with that in public metabolic databases, HMDB (http://www.hmdb.ca/), Metlin (https://metlin.scripps.edu/) and Lipidmaps (v2.3) to obtain each metabolite's information.

In multivariate statistical analysis, unsupervised principal component analysis (PCA), partial least squares discriminate analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were performed. And the permutation test (200 permutations) was performed to validate the OPLS-DA model. In the following univariate statistical analysis, Student's t test and fold change analysis were adopted to identify the metabolites with significant differences between groups. Differential metabolites were identified by the criteria, namely, p-value of t test < 0.05, variable importance in the projection (VIP) value of OPLS-DA > 1.

Statistical analysis

The data of untargeted metabolomics were analyzed as described, and differential metabolites between groups were identified by Student's t test and Fold change analysis, with the selection criteria p < 0 .05 and VIP > 1.

Results

VD3 alleviated the inflammatory changes and hyperpermeability of colon epithelium in LPS-injected mice

At the end of the experiment, mice were killed and colon tissues were taken out for HE staining. The morphological changes of colon epithelium were illustrated (Figure 1(A)). Relating to the normal intact colon epithelium in the Con group, discontinuous epithelium, loss of goblet cells and dramatic infiltration of inflammatory cells were observed in the LPS group. However, the inflammatory changes of colon epithelium were obviously attenuated in the LPS + VD3 group.

Figure 1.

The morphological changes and permeability of colon epithelium. (A) Representative HE staining of colon epithelium. (B) Permeating FITC fluorescence in colon wall. Scale bar, 50 μm.

The FITC-dextran was used to measure intestinal epithelial permeability in vivo. Mice received FITC-dextran orally, and 4 h later, FITC-dextran fluorescence in colon epithelium was observed (Figure 1(B)). In the Con group, little and weak FITC fluorescence was seen in colon epithelium. But brightened and concentrated FITC fluorescence was observed in the LPS group, reflecting colon epithelium hyperpermeability. However, the FITC fluorescence in colon epithelium became shrinked and weakened dramatically in the LPS + VD3 group, verifying the alleviation of colon epithelium hyperpermeability by VD3 treatment in LPS-injected mice. And the changes of colon epithelium permeability were consistent with that of colon epithelium morphology.

These results confirmed that VD3 treatment could alleviate the inflammatory changes and hyperpermeability of colon epithelium in LPS-injected mice.

VD3 affected the gut metabolite profiling and composition in LPS-injected mice

The gut metabolomics were analyzed by the LC-MS technique. From the raw data, 25,458 substance peaks and 9706 metabolites in total (including positive and negative ions) were gotten (Figure 2(A)). In multivariate statistical analysis, PCA showed that all samples in the Con and LPS groups were in confidence interval 95% (elliptic region), indicating valid data (Figure 2(B)). Similarly, PCA showed that all samples in confidence interval 95% between the LPS and LPS + VD3 groups (Figure 2(C)). Therefore, the data were used for the following analysis.

Figure 2.

Numbers of substance peaks and metabolites and data validity. (A) After Liquid Chromatograph Mass Spectrometer (LC-MS) detection, data were analyzed using Progenesis QI v2.3. Numbers of substance peaks and metabolites were shown. (B) PCA analysis of samples between the Con and LPS groups. (C) PCA analysis of samples between the LPS and LPS + VD3 groups. All samples were in confidence interval 95% (elliptic region).

Then PLS-DA and OPLS-DA analysis of samples between the Con and LPS groups were performed (Figure 3(A)). In PLS-DA, separation of samples between the Con and LPS groups were seen. And OPLS-DA ascertained the separation, with R2 = 0.999, Q2 = 0.592 in 200 permutations. The value of Q2 > 0.5 suggested that the OPLS-DA model was reliable. In the OPLS-DA loading plot, some spots stayed away from 0, and splot picture also showed scattered spots in the first and third quadrants, revealing potential differential metabolites between the Con and LPS groups (Figure 3(B)).

Figure 3.

PLS-DA and OPLS-DA analysis of samples between the Con and LPS groups. (A) PLS-DA analysis showed intergroup separation. (B) OPLS-DA analysis ascertained the separation, with R2 = 0.999, Q2 = 0.592 in 200 permutations. (C) Loading and Splot of OPLS-DA showed potential differential metabolites between the Con and LPS groups.

Meanwhile, PLS-DA and OPLS-DA analysis of samples between the LPS and LPS + VD3 groups were performed (Figure 4(A)). PLS-DA showed separation of samples between the LPS and LPS + VD3 groups, which was ascertained by OPLS-DA, with satisfactory R2 value (0.997) and Q2 value (0.579) in 200 permutations. Furthermore, the OPLS-DA loading plot and splot showed more scattered spots, indicating more potential differential metabolites between the LPS and LPS + VD3 groups (Figure 4(B)).

Figure 4.

PLS-DA and OPLS-DA analysis of samples between the LPS and LPS + VD3 groups. (A) PLS-DA analysis showed intergroup separation. (B) OPLS-DA analysis ascertained the separation, with R2 = 0.997, Q2 = 0.579 in 200 permutations. (C) Loading and Splot of OPLS-DA showed potential differential metabolites between the LPS and LPS + VD3 groups.

These results demonstrated that VD3 affected the gut metabolite profiling and composition, which changed in LPS-injected mice.

VD3 affected the gut metabolomics in LPS-injected mice

In univariate statistical analysis, differential metabolites (both up-regulated and down-regulated) between the Con and LPS groups were shown in the Volcane plot (Figure 5(A)). Under the criteria (p < 0.05, VIP > 1), 55 differential metabolites were selected. Among the top 50 differential metabolites (Figure 5(B)), some bile acid-related metabolites, such as 3b,4b,7a,12a-Tetrahydroxy-5b-cholanoic acid and 12-Ketodeoxycholic acid, as well as the unclassified metabolites 24-Nor-5beta-chol-22-ene-3alpha,7alpha,12alpha-triol, and Levothyroxine sodium anhydrous, appeared reduction. Meanwhile, several lipid-related metabolites, including Ganoderiol G and PS(18:2(9Z,12Z)/22:2(13Z,16Z)), as well as the unclassified metabolites 2-Aminoadenosine, Sodium (±)-2-(4-methoxyphenoxy)propionate, 4-Quinolone-3-Carboxamide CB2 Ligand, and 4,24-Dimethylcholest-24-en-3beta-ol, appeared increase.

Figure 5.

Volcano plot and heatmap picture of top 50 differential metabolites between the con and LPS groups. (A) Volcano plot displayed the differential metabolites. Under the criteria (p < 0.05, VIP > 1), 55 differential metabolites were selected. (B) Heatmap picture of top 50 differential metabolites. Some metabolites, such as bile acid-related metabolites, appeared decrease, and several lipid-related metabolites appeared increase in the LPS group.

With the same method, differential metabolites (both up-regulated and down-regulated) between the LPS and LPS + VD3 groups were shown in the Volcane plot (Figure 6(A)). Under the same criteria (p < 0.05, VIP > 1), 163 differential metabolites were selected and the top 50 were shown (Figure 6(B)). As illustrated, VD3 treatment induced appearance in particular in Soyasaponins, such as Soyasaponin I, Soyasaponin II, Soyasaponin V, Soyasaponin bg, but decrease in lipid-related metabolites, such as PS(18:2(9Z,12Z)/22:2(13Z,16Z)) in LPS-injected mice. Besides, VD3 treatment caused changes of unclassified metabolites, such as increase in E-64c and Aeruginopeptin 95B, and decrease in PC(14:0/0:0)[U], (25R)-12alpha-hydroxy-24R,26R-dimethyl-26,27-cyclo-cholest-4-en-3,6-dione, 3beta,12alpha-Dihydroxy-5beta-cholestan-26-oic acid, HECOGENIN ACETATE, (25S)-3-oxo-cholest-1,4-dien-26-oic acid and Tsukushinamine A.

Figure 6.

Volcano plot and heatmap picture of top 50 differential metabolites between the LPS and LPS + VD3 groups. (A) Volcano plot displayed the differential metabolites. Under the criteria (p < 0.05, VIP > 1), 163 differential metabolites were selected. (B) Heatmap picture of top 50 differential metabolites. Some metabolites, especially Soyasaponins, appeared increase, but lipid-related metabolites appeared decrease in the LPS + VD3 group.

In addition, the lipid-related metabolites Ganoderiol G, PS(18:2(9Z,12Z)/22:2(13Z,16Z)) and Hericenone E, as well as (3,4,5,6-tetrahydroxyoxan-2-yl)methyl 4-hydroxybenzoate appeared increase in the LPS group, but decrease in the LPS + VD3 groups.

These results manifested that VD3 treatment had distinctive effects on the gut metabolomics, especially the induced appearance of Soyasaponins, in LPS-injected mice.

Discussion

Gut dysbiosis is a major contributor to systemic inflammation and the related diseases.^3,24 Although VD3 has been found to regulate gut microbiota and play anti-inflammatory roles in systemic inflammation, its effects on gut metabolomics still need clarification. In this study, VD3 treatment was found to affect the gut metabolomics, especially the induced appearance of Soyasaponins, and alleviate the inflammatory changes and hyperpermeability of colon epithelium in LPS-injected mice.

Gut microbial metabolism have profound influences on human health, such as contributing enzymes for breakdown of polysaccharides and vitamins synthesis.²⁵ And the microbial metabolic products, such as short chain fatty acids and trimetlylamine oxide may have immunomodulatory and pro-inflammatory effects in the body.^25,26 In gut dysbiosis, bacterial harmful metabolites and endotoxins, such as LPS, invade into the circulation and induce systemic inflammation.^3–5 Therefore, LPS is generally believed as a classical inducer of systemic inflammation. Although the human tolerated intravenous dose of LPS is merely up to 4 ng kg⁻¹ of body weight, its intraperitoneal dosage in mouse model is mg grade.^27,28 And 1 mg kg⁻¹ LPS was selected in this study to induce systemic inflammation, as we previously did.^7,21 In addition, intestinal epithelium injury is a key link of gut dysbiosis and systemic inflammation.²⁹ In this study, obvious inflammatory changes and hyperpermeability of colon epithelium were observed in LPS-injected mice.

Mass spectrometry-based metabolomics is a key technology to detect and identify small molecules produced by gut microbiota.³⁰ Untargeted metabolomics has led to many discoveries of metabolites linked to health and diseases.³¹ For instance, metabolomics analysis found a novel tripeptide, which was generated by a functional probiotic strain to exerts beneficial effects in LPS-injected mice.³² In this study, untargeted metabolomics technology was used to analyze the changes of gut metabolomics. The results found that LPS stimulation caused obvious changes of gut metabolite profiling and composition, probably down-regulating some bile acid-related metabolites, but up-regulating several lipid-related metabolites. It has been reported that there exited changes of metabolomics in the brain to exhibit neurotoxic effects in the LPS-induced systemic inflammation.³³ Collaborating with the inflammatory changes and hyperpermeability of colon epithelium in our study, the changed gut metabolites might further deteriorate systemic inflammation. Nevertheless, whether the differential metabolites were beneficial or harmful need further identification.

Dietary supplementation with nutrients may provide safe and economical means to antagonize systemic inflammation.³⁴ Although vitamin D can be endogenously synthesized under ultraviolet B radiation in the skin, dietary supplements are still necessary.¹¹ The recommended dosage of vitamin D for humans stretches a broad range.^34,35 For example, 3000 IU daily to 50,000 IU bi-weekly vitamin D dosages were used for patients with irritable bowel syndrome.³⁵ Unsupervised high-dose vitamin D supplementation may have adverse effects such as hypercalcemia, but its moderate dose hardly induce toxicity.^34–36 In this study, the VD3 dosage used for mice is equivalent to the moderate dose for humans (4000 IU).³⁶ Studies have proved that vitamin D improves gut immune response or barrier function in inflammatory bowel disease.³⁷ In this study, VD3 was confirmed to attenuate the inflammatory changes and hyperpermeability of colon epithelium in LPS-injected mice, consistent with previous reports.^7,21

In human subjects, vitamin D supplementation has been proved to exhibit positive impacts on gut microbiota.^38,39 Serum 25-hydroxyvitamin D is reported to associate with the changes of gut microbiota and metabolites in postmenopausal women.⁴⁰ In mice fed with high-fat diet, vitamin D by intraperitoneal injection could cause changes of gut microbiota and metabolomics.¹⁷ We previously found that oral VD3 treatment could cause obvious changes of gut microbiota in LPS-injected mice.⁷ As gut microbiota and their metabolites are closely related with systemic inflammation, the effects of VD3 on gut metabolomics were explored in this study. The results found that VD3 treatment had distinctive effects on gut metabolomics, especially the induced appearance of Soyasaponins, such as Soyasaponin I, Soyasaponin II, Soyasaponin V, Soyasaponin bg in LPS-injected mice.

It's reported that Soyasaponins exhibits beneficial roles for human health, such as anti-inflammatory and antioxidant activities, and may have potentialities as bioactive food supplements.⁴¹ In mice, LPS plus D-galactosamine stimulation may cause decrease in fecal Soyasaponin II levels and Soyasaponin II treatment exhibited protected roles through its anti-inflammatory activities.⁴² In this study, the VD3-induced appearance of protective Soyasaponins was consistent with the findings that vitamin D may induce protective gut metabolites in humans.⁴³ As Soyasaponins were naturally formed in the gut and closely related with gut microbiota in humans and mice,^41,44 VD3 treatment may induce changes in gut microbiota, which produced protective Soyasaponins. However, the sources of VD3-induced Soyasaponins should be further identified. In addition, some lipid-related metabolites appeared increase for LPS stimulation, but decrease for VD3 treatment. The relationship of these changed gut metabolites and VD3 in LPS-injected mice also needs further investigation.

In summary, LPS injection caused changes of gut metabolomics, with inflammatory changes and hyperpermeability of colon epithelium in mice; while VD3 treatment had distinctive effects on gut metabolomics, particularly the induced appearance of Soyasaponins, and alleviated colon epithelium injuries in LPS-injected mice. A growing number of studies have proved the enhancement of gut health by vitamin D in humans,^38,39,43 and the present study verified positive gut metabolites induced by VD3. The results may provide evidences for the clinical use of vitamin D in management of systemic inflammation-related health problems, such as inflammatory bowel disease.⁴⁵ However, considering the differences between humans and rodents in gut microbiota, further studies in human systems or more human-relevant settings, such as gut-organ-on-chip,⁴⁶ should be conducted in the future.

Footnotes

ORCID iD

Yan-Ning Li

Author contributions

Qian Xu: Conceptualization, Investigation, Writing - original draft. Qi Geng: Conceptualization, Methodology. Yuan Liu: Conceptualization, Methodology. Yifei Sun: Investigation. Xiaoyu Tian: Methodology. Long Zheng: Conceptualization, Project administration, Validation, Writing - review & editing. Yanning Li: Conceptualization, Funding acquisition,Validation, Writing - review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the National Natural Science Foundation of China (82170846) and the Hebei Natural Science Foundation (H2020206386).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

Data, analytic methods, and study materials will be made available to other researchers upon request addressed to the corresponding author.

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