Intestinal Flora Modulates Blood Glucose via Regulating the Secretion of GLP-1 Induced by Propionate in mouse models of Gestational Diabetes Mellitus

doi:10.21203/rs.3.rs-5276950/v1

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Intestinal Flora Modulates Blood Glucose via Regulating the Secretion of GLP-1 Induced by Propionate in mouse models of Gestational Diabetes Mellitus

https://doi.org/10.21203/rs.3.rs-5276950/v1

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Purpose: Intestinal flora has been reported to be associated with metabolic homeostasis. However, the detailed functions of intestinal flora in GDM pathogenesis have not yet been fully elucidated. To investigate the roles and mechanisms of intestinal flora in GDM development.

Materials and Methods: We used high fat diet (HFD) to induce mouse models of GDM. The composition, metabolic characteristics and roles of the intestinal florawere investigated using 16S rRNA gene sequencing, targeted metabolomics, and fecal microbiota transplantation. The specific mechanism was analyzed mainly using cell cultures, transfection, western blot.

Results: We found HFD successfully induced mouse models of GDM, particularly increased the weight, blood glucose, and decreased GLP-1 concentration in C57BL/6 female mice. The composition and metabolism of intestinal flora in GDM individuals were also significantly changed, including the increased Firmicutes and reduced α-diversity and propionate levels, which negatively correlated with blood glucose. After transplanting the intestinal flora of GDM mice, propionate, GLP-1 secretion and glucose had corresponding changes. By adjusting the diet, especially increasing the intake of OFS, the composition and metabolism of gut microbiota could be reshaped, which further affected GLP-1 secretion and blood glucose. Then, we found that wnt/β-catenin/TCF7L2 signaling pathway participated in the regulation of GLP-1 by propionate.

Conclusions: The composition of intestinal flora in GDM mice changed and thereby reduced its metabolite propionate in the intestine, further inhibiting of Wnt /β-catenin/TCF7L2 signaling pathway, resulting in decrease of GLP-1 secretion and increase of blood glucose. These findings suggested gut microbiota may be used as a potential target for the treatment of GDM.

intestinal flora

propionate

glucagon-like peptide 1

gestational diabetes mellitus

pregnancy

Gestational diabetes mellitus (GDM) is a relatively common complication during pregnancy, mainly manifested by glucose intolerance first detected during pregnancy.¹ GDM is closely associated with adverse maternal and neonatal outcomes affecting approximately 16.9% of pregnancies worldwide.^{2, 3} For instance, GDM significantly increases maternal risk of gestational hypertension and surgical delivery, besides, it markedly increases the risk of fetus morbidity and their later risk for type 2 diabetes(T2DM).^{4, 5} Dietary habits are closely related to the hyperglycemia morbidity. Numerous studies have shown that obesity and high fat diets (HFD) before pregnancy have been related to a higher risk of gestational diabetes.^{6, 7} The pathogenesis of GDM is generally considered to be associated with insulin resistance and relative shortage of insulin secretion.^{8, 9} However, the underlying pathological mechanisms have not been fully elucidated.

Glucagon-like peptide 1 (GLP-1) is a gut hormone mainly secreted by enteroendocrine L cells located at distal ileum and colon.¹⁰ GLP-1 could effectively stimulate insulin secretion and inhibit glucagon release, in addition, it also suppresses appetite and increases satiety.^{11, 12} So it is regarded as a promising medicine for treating metabolic syndrome. Secretion of GLP-1 is regulated by nutrients and metabolites in the lumen of the gut. Saturated fatty acids from HFD impairs the function of L cells and reduces GLP-1 production.¹³ Short-chain fatty acids (SCFAs), derived from bacterial fermentation of dietary fiber (eg.fructo-oligosaccharide,OFS), enhance GLP-1 release though free fatty acid receptor-2 (FFAR2) which exist in L cells membrane.¹⁴ In addition, Shao W et al ¹⁵ found that wnt/β-catenin signaling pathway and its downstream effector transcription factor 7-like 2 (TCF7L2) controlled transcription of the proglucagon gene (GCG), which encodes GLP-1production. However, the specific mechanism of GLP-1 secretion is still poorly understood.

Recently, many studies have shown that intestinal flora play a pivotal role in the

development of various diseases, such as hypertension, diabetes mellitus, and autoimmune diseases.^{16, 17} The gut microbiome has been reported to differ between metabolically healthy and obese individuals during pregnancy.¹⁸ Previous studies have showed that composition and function of intestinal flora changes in GDM individuals. Xing et al ¹⁹ identified 18 differential operational taxonomic units (OTUs) mainly belonging to the phylum Firmicutes and Proteobacteria, that could distinguish GDM patients from healthy controls (HC). Our previous study found the species Bacteroides dorei were positively correlated with blood glucose during pregnancy.²⁰ Hariom et al ²¹ reported probiotic interventions could increase SCFAs producing bacteria, induced GLP-1 secretion and improve insulin resistance. These studies imply that gut dysbacteriosis is closely related the occurrence of GDM. Modulation of intestinal flora composition and function might be a promising treatment for GDM.

In this study, we investigated the specific regulatory mechanisms of intestinal flora and SCFAs on the secretion of GLP-1 in mouse models of gestational diabetes mellitus, hoping to find new intervention targets for GDM.

2.1 Animals and experimental procedures

All animal procedures in the study were approved by the Ethics Committee of the First Affiliated Hospital, Sun Yat-sen University（approval number was 2021-592）and were conducted with fully consideration of animal welfare. Mice were housed on a 12: 12 h light–dark cycle (lights on at 7:00 hours) in a temperature room (23±3°C) and were allowed free access to food and water

Experiment 1

Eight-week-old female C57BL/6 mice were purchased from Animal Centre of Sun Yat-sen University. After one week of adaptive feeding, the mice were randomly divided into three diets groups:(1) Control group, fed with regular chow diet (D12450J, Research Diets, USA); (2) GDM group, fed with high fat diet (HFD) containing 60 kcal% fat (D12492, Research Diets, USA); (3) Intervention group, fed with HFD which mixed with OFS (Macklin), the ratio of OFS is 10%. Mice were fed with three different diets for 6weeks prior to mating and continue the same diet throughout the pregnancy and weighted weekly. Females were matched to the same weeks C57BL/6 males overnight in a 1:1 ratio. Mating was confirmed through vaginal plug detection in the next morning and weight analysis in the next week. The day of plug detection was identified as day 0.5 of pregnancy. Female mice were housed five per cage before mating and individually after mating. The intraperitoneal glucose tolerance tests (IPGTT) were conducted on day 13.5 after mating. Mice received an intraperitoneal injection of glucose (2g/kg) after a 6h fast starting in the morning. The intraperitoneal insulin tolerance tests (IPITT) were performed on days 16.5 of pregnancy. Mice received an intraperitoneal injection of insulin (0.75U/kg) after a 4h fast. Blood glucose was measured before and at 15, 30, 60, 90 and 120 min after glucose or insulin injection from a tail blood sample, using a rapid glucose meter (Roche, USA). Mice were sacrificed on day 18.5 of pregnancy and blood, feces and tissue were obtained for further analysis.

Experiment 2

Eight-week-old female C57BL/6 mice were given antibiotic cocktail in drinking water everyday for 7days after one week of adaptive feeding. The antibiotic cocktail comprised vancomycin (45mg/L), metronidazole (215mg/L), Kanamycin (400mg/L), gentamycin (35mg/L), colistin B (57mg/L), erythromycin(10mg/L). Then the mice were transplanted with fecal microbiota (FMT) of GDM mouse or healthy controls and weighted weekly. The feces were collected before antibiotic use (ABs), before FMT and 4 weeks after FMT. The exhaustion and colonization of gut flora in mice was verified using 16S rRNA gene sequencing. Mice were sacrificed four weeks after FMT and blood and tissue were obtained for further analysis. The test of IPGTT and IPITT were performed as previously mentioned before mice were killed.

2.2 16S rRNA gene sequencing and Sequencing and bioinformatics analysis

The microbial total genome DNA was extracted using Mag Pure Stool DNA KF kit B (Magen, China) following the manufacturer’s instructions. DNA was quantified with a Qubit Fluorometer by using Qubit dsDNA BR Assay kit (invitrogen, USA) and the quality was checked by running aliquot on 1% agarose gel. The V3-V4 variable regions of bacterial 16S rRNA gene was amplified with degenerate polymerase chain reaction(PCR) primers, 341F(5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R(5’-GGACTACHVGGGTWTCTAAT-3’). The PCR products were purified with AmpureXP beads and eluted in Elution buffer. Libraries were qualified by the Agilent 2100 bioanalyzer (Agilent, USA), The validated libraries were used for sequencing on Illumina MiSeq platform (BGI,Shenzhen, China) and generated 2 × 300 bp paired-end reads.

Raw reads were filtered to remove low-quality and ambiguous bases, and then paired-end reads were added to tags by the Fast Length Adjustment of Short reads program (FLASH, v1.2.11) .²² The tags were clustered into OTUs with a cutoff value of 97% using UPARSE software (v7 .0.1090). ²³ Then, OTU representative sequences were taxonomically classified using Ribosomal Database Project (RDP) Classifier v.2.2. The USEARCH_global was used to compare all Tags back to OTU to get the OTU abundance statistics table of each sample. ²⁴ Alpha and beta diversity were estimated by MOTHUR (v1.31.2) ²⁵ and QIIME (v1.8.0) ²⁶at the OTU level, respectively. Sample cluster was conducted by QIIME (v1.8.0) based on UPGMA.²⁶Barplot and heatmap of different classification levels was plotted with R package v3.4.1 and R package “gplots”, respectively.

2.3 Targeted metabolomic analysis of SCFAs in feces using LC-MS/MS

The samples were processed before loaded on the equipment of liquid chromatography-tandem mass spectrometry (LC-MS/MS), 400ul methanol-acetonitrile mixture, 25mg fecal sample and magnetic beads were added to a centrifuge tube, after vortexing and shaking, the mixture was centrifuged. Seven kinds of SCFAs standard substance were taken and carried out gradient dilution to establish standard curve. After derivatization of fecal sample and standard substance, 90uL derivatized sample and 90uL H₂O were added to the filter plate, 10uL filtered liquid was loaded on Waters 6500 LC/MS system(AB SCIEX, USA). Concentrations of the SCFAs were calculated using MultiQuant software (SCIEX, USA).

2.4 Quantitative real-time PCR and Histological Analysis and Immunohistochemistry

Total RNA in proximal colon and cells were extracted using TRIzol reagent (15596018, Invitrogen) and were reversed[1]transcription using the Quantitative real-time PCR was performed using SYBR green (RR420A, Takara) on a CFX96 Real-Time PCR System. Sequences of the primers used were listed in Table S1 and Table S2. Relative gene expression was calculated by the 2^-∆∆Ctmethod.

Proximal colon, livers, pancreas and gonadal white adipose tissue (gWAT) were fixed in 4% paraformaldehyde, embedded in paraffin and sectioned at 6μm for hematoxylin and eosin(HE) staining and immunohistochemistry(IHC) analysis. The sections of livers, pancreas and gWAT were stained with hematoxylin and eosin, and the size of adipocyte and islets were measured using Aperio software. For IHC staining, antigen retrieval of proximal colon sections were performed using high-pressure steam. Endogenous peroxidases were blocked with 3% H₂O₂. Then sections were sealed with goat serum(Boster, California) and incubated with antibody against GLP-1(1:1000, Abcam, China) overnight, followed by the corresponding secondary antibody(Gene Tech, Shanghai). After the diaminobenzidine and hematoxylin staining, sections were observed with a light microscope (OLYMPUS, China).

2.5 Blood and culture medium parameters

Serum insulin was measured using a Mouse Insulin ELISA Kit (Abcam, China). Total concentrations of GLP-1 in serum and cells supernatants were detected by a Multi Species GLP-1 Total ELISA Kit (Sigma, USA). Levels of total cholesterol (TC), triglycerides (TG), high density Lipoprotein (HDL), low density Lipoprotein (LDL) were measured using an automatic biochemistry analyzer (Chemray 800, China).

2.6 Cell culture and transfection

Human NCI-H716 cells, an L-like cell line, were obtained from Cell Bank of Wuhan University (Wuhan, China) and cultured in RPMI-1640 with 10% FBS and 1% penicillin/streptomycin in 5% CO2 at 37°C. The cells were seeded in 6-well plates with a concentration of 5×10⁵ cells/well and incubated for 24 h. The cells were then washed with phosphate-buffered saline (PBS) and incubated with different concentrations of propionate (0,1,5,10,50,100mmo/L),(0,1,2,3,4,5mmol/L) for 24 hours. For transfection, RNA-vector (si-FFAR2, si-TCF7L2, si-NC) were synthesized by GIMA (China), primer sequences were listed in sTable 2, the cells were seeded in 6-well plates with a concentration of 5×10⁵ cells/well and incubated for 24 hours. The si-RNA was transfected into cells using Lipofectamine 3000 (nvitrogen, USA). Twelve hours later, the medium was replaced with fresh RPMI-1640 complete medium. After 24 hours, the cells were incubated with different concentrations of propionate for another 24 h. The cells were divided into four groups: 1.NC, 2. NC+propionate,3.NC+si-FFAR2/si-TCF7L2,4.NC+propionate+Si-FFAR2/Si-TCF7L.

2.7 Western blot

The cells were collected and lysed using RIPA lysis buffer (Cwbio, China) and the nuclear protein were extracted using a Nuclear and Cytoplasmic Protein Extraction Kit (Sangon Biotech, China). The primary antibodies used in the experiments were Rabbit antibody, contained anti-FFAR2（1:1000）, anti-β-catenin（1:1000）, anti-TCF7L2（1:1000）, anti-Lamin B （1:5000）and anti-GAPDH（1:5000）. Lamin B and GAPDH were internal reference protein to calculate expression levels of the target proteins.

2.8 Statistical analysis

GraphPad Prism 9.0 and R software (3.6.1) were used for the visualization of all data. Data were presented as the mean ± standard deviation or median ± quartile. . Statistical significances for continuous outcomes were calculated using The Mann-Whitney U test or unpaired T test. The Chi-square test or Fisher exact test were performed for categorical variables and P < 0.05 was considered as statistically significant.

3.1 Significant abnormalities in glucose tolerance, insulin resistance and GLP-1 secretion in HFD-induced GDM mice

C57BL/6 female mice were fed with three different diets for 6weeks prior to mating and continue the same diet throughout the pregnancy. Compared with control group, HFD induced a significant weight gain, higher levers of glucose, lipid and serum insulin, indicating that HFD successfully induced mouse models of GDM (Fig. 1A-H). Besides, serum total concentrations of GLP-1 in GDM group was decreased in response to HFD (Fig. 1I). However, when added to HFD, OFS successfully suppressed weight gain, induced a significant decrease of glucose and serum lipid level, improves insulin resistance. Meanwhile, OFS intervention led to an apparent increase of GLP-1levels (Fig. 1A-I). On the other hand, GDM mice had a significant increase in fat accumulation in the liver and adipocyte size in gWAT, while a lower islet area and lower level of GLP-1 in proximal colon (Figure S1A), in addition, the mRNA levels of FFAR2, TCF7L2, and GCG in proximal colon tissues of GDM mice were declined (Figure S1B-D). However, OFS feeding could reverse the above states in HFD fed mice (Figure S1A-D), suggesting that OFS maintained glucose homeostasis by regulating GLP-1 secretion.

3.2 The composition and richness of gut microbiota in GDM mice markedly changed

To reveal the role of intestinal flora in the pathogenesis of GDM, 16S rRNA gene sequencing was used to analyse fecal samples from control, GDM and OFS group. We identified a total of 1,551,042 reads in 26 fecal samples with an average sequencing depth of 47,000 reads/sample. Coverage index of each sample was over 0.99 indicating that the sequencing depth was saturated (Figure S2A). The α-diversity indexes were compared between the two group. We found that Sobs, Chao, Ace and Simpson indexes were all significantly lower in GDM group when compared with control group indicating that GDM mice existed in gut dysbacteriosis (Figure S2B-E). When adding OFS to high fat diet, Simpson index had a noticeable rise (Figure S2F), whereas Sobs, Chao, Ace indexes had no obvious change (Figure S3A-C ). This result indicated that OFS improved the richness of gut microbiota.

The principal component analysis (PCA) and partial least squares discrimination analysis (PLS-DA), which are indicators that describe the similarity and separation of microbial composition between the two groups, were performed and showed a significantly separation between the GDM and control group (Figure 2A-B). At the phylum level, compared with control group, the proportion of Bacteroidetes had a marked decrease, while Firmicutes significantly increased, thereby, the ratio of Firmicutes/Bacteroidetes (F/B) was markedly increased in GDM group. Meanwhile,

the abundance of Proteobacteria, Actinobacteria, Verrucomicrobia and Deferribacteres also remarkably changed in fecal samples of GDM mice (Fig. 2C-D). At the genus level, 30 differential intestinal bacteria were identified, the abundance of 11 bacteria of them were significant decreased while the remaining 19 bacteria were increased in GDM group (Fig. 2E). LEfSe analysis verified that the phylum Firmicutes were enriched in GDM group, however, the genus Lactobacillus belonged to Firmicutes were enriched in control group. Similarly, the phylum Bacteroidetes had an obviously decrease in GDM group, however, the genus Bacaeroides belonged to Bacteroidetes had a markedly increase in control group. (Fig. 2F). Then, the key gut microbe were analyzed and 10 differential bacteria at the genus level were identified, 4 bacteria including Bifidobacterium, Lactobacillus, Allobaculum, Barnesiella were notablely decreased in GDM group (Fig. 2G). These results revealed that the intestinal flora composition of GDM group had a significant change. When adding OFS to high fat diet, the proportion of Firmicutes and the ratio of F/B had a marked decrease (Fig. 2H-I). Meanwhile, the abundance of Actinobacteria, Verrucomicrobia, Proteobacteria and Deferribacteres also remarkably altered in fecal samples of OFS mice (Fig. 2H). At the genus level, OFS increased the abundance of four key gut microbe including Bifidobacterium, Lactobacillus, Alloprevotella and Akkermansia (Fig. 2J). These results indicated that GDM mice existed in gut dysbacteriosis and OFS could redress the imbalances of intestinal flora caused by high fat diet.

3.3 The SCFAs of gut metabolites were significantly changed in GDM mice

Gut metabolites from intestinal flora are the important bridge between the microbe and host that could influence the host physiology though its directly effects on intestinal cells or entering the blood circulation affecting distal organs. SCFAs, such as propionate, derived from bacterial fermentation of dietary fiber, are the important gut metabolites which have anti-obesity and anti-diabetic properties. Therefore, we detected the abundance of SCFAs in the fecal samples between the groups using mass spectrometry. The PCA showed a significantly separation of SCFAs between the GDM and control group (Fig. 3A). Compared with the control group, the levels of propionate had an obvious decrease, however, valerate and isovalerate had a markedly increase in GDM group (Fig. 3B). When adding OFS to high fat diet, the abundance of SCFAs had a marked change (Fig. 3C). Compared with the GDM group, the concentration of propionate and acetate increased, but the levels of valerate and isovalerate decline in OFS group (Fig. 3D). Then, correlation analysis were performed between SCFAs and fasting glucose during pregnancy, we found that propionate was significantly negatively correlated with fasting glucose (Fig. 3E-H). These results suggested that propionate was closely related to the pathogenesis of GDM.

3.4 Intestinal Flora modulates the secretion of GLP-1 and blood glucose through the pathway of propionate

To explore the relationship between intestinal flora and blood glucose, FMT were performed as previously mentioned. C57BL/6 female mice were treated with antibiotic cocktail to remove original gut microbiota and transplanted with colon contents from GDM and control mice. The result showed that the richness of gut microbiota and the α-diversity were significantly reduced by antibiotic cocktail both in control and GDM group (Fig. 4A, Figure S4B-E and Figure S5B-E). However, the abundance intestinal flora was similar between the control and GDM group at the phylum level regardless of before or after antibiotic cocktail used (Fig. 4B-C). Four weeks after FMT, The α-diversity Simpson indexe was lower in GDM group (Figure S6B), Meanwhile, the abundance of Firmicutes, Bacteroidetes, Verrucomicrobia at the phylum level and Barnesiella, Desulfovibrio, Oscillibacter, Pesudoflavonifractor at the genus level had a significant difference between the two group, these changes were consistent with the transplanted gut microbiota, suggesting that gut microbiota successfully colonized in the gut of antibiotic-treated mice (Fig. 4D-E). Then, the levels of SCFAs were detected in the fecal samples from the two group, we found that only the trend of propionate was in accordance with the transplanted mouse (Fig. 4F).

Four weeks after FMT, the mice transplanted with colon contents from GDM group showed a significant weight gain, higher levers of glucose, lipid and serum insulin and a lower GLP-1 levels (Fig. 5A-H). Meanwhile, GDM mice had a significant increase in fat accumulation in the liver and adipocyte size in gWAT, while a lower islet area and lower level GLP-1 in proximal colon (Figure S7A), in addition, the mRNA levels of FFAR2, TCF7L2, and GCG in proximal colon tissues of GDM mice were declined (Figure S7B-D). These results suggested that propionate was the bridge that intestinal flora modulates the secretion of GLP-1 and blood glucose.

3.5 Propionate promoted GLP-1 synthesis via regulating β-catenin/TCF7L2 signaling mediated GCG transcription

To further reveal the mechanism of GLP-1 secretion, NCI-H716 cells were used to measure the GLP-1 secretion and related genes and proteins expression. Propionate treatment significantly promoted the GCG transcription and GLP-1 secretion in NCI-H716 cells within a certain concentration range, and 1mmol/L was the best promoting concentration (Figure S8A-D). FFAR2 which exists in L cells membrane was a G protein coupled receptor that could be specifically recognized by propionate. Meanwhile, the wnt/β-catenin/TCF7L2 signaling pathway participates in regulation of GCG transcription. To further understand the relationship between the FFAR2 and the wnt/β-catenin/TCF7L2 signaling, si-RNA targeting FFAR2 and TCF7L2 were transfected in NCI-H716 cells. In NCI-H716 cells transfected with si-FFAR2, TCF7L2 and GCG transcriptions were suppressed (Fig. 6A-C) and the expression of cytoplasm β-catenin, TCF7L2 and nuclear β-catenin were down-regulation (Fig. 6I). Meanwhile, the secretion of GLP-1 was declined (Fig. 6D). Besides, The promoting effect of propionate was also blocked. When transfected with si-TCF7L2 in NCI-H716 cells, FFAR2 transcriptions and the expression of FFAR2, cytoplasm β-catenin and nuclear β-catenin were unchanged (Fig. 6E,6J). However, the GCG transcription and GLP-1 secretion decreased (Fig. 6G-H). These results displayed that propionate promoted GLP-1 synthesis via specific receptor FFAR2 up-regulating β-catenin/TCF7L2 signaling.

In this study, we revealed a novel mechanism that intestinal flora modulates blood glucose via regulating the secretion of GLP-1 induced by propionate in GDM. In our study, HFD successfully induced mouse models of GDM. The composition and metabolism of intestinal flora in GDM mice were significantly changed, mainly manifested as probiotic bacteria and propionate decreased. Meanwhile, the secretion of GLP-1 and insulin were altered in GDM mice. We further analyzed the correlations between the intestinal flora, propionate, GLP-1 secretion and glucose using antibiotic cocktail treated mice. After FMT, propionate, GLP-1 secretion and glucose had corresponding changes. Besides, we found propionate was significantly negatively correlated with fasting glucose. Then, we further analyzed the mechanism of effect of propionate on GLP-1 secretion in proximal colon tissues and NCI-H716 cells, we found that wnt/β-catenin/TCF7L2 signaling pathway participated in the regulation of GLP-1 by propionate. Moreover, by adjusting the diet, especially increasing the intake of OFS, the composition and metabolism of gut microbiota could be reshaped, which further affected GLP-1 secretion and blood glucose.

In recent years, many patient and animal studies had demonstrated that intestinal flora played a pivotal role in the development of various diseases, such as autoimmune diseases, obesity,and T₂DM.^{27, 28} Several previous studies had showed the gut microbial richness and composition also changed in GDM individuals. Crusell reported that gut microbiota of women with GDM was aberrant, Actinobacteria at phylum level and Collinsella, Rothia and Desulfovibrio at genus level had a higher abundance in the GDM cohort.²⁹ Xing ¹⁷ found GDM subjects were characterized with enriched Lachnospiraceae family and depleted Enterobacteriaceae and Ruminococcaceae families, and some altered gut microbes were significantly correlated with glucose values. Besides,Wang ³⁰ reported that dysbiosis of maternal gut microbiota in GDM cohort could be vertically transmitted to the neonates. DiGiulio ³¹ found GDM individuals had increased abundance of Firmicutes and reduced Bacteroidetes and Actinobacteria, which was consistent with our results. Although many studies have confirmed that gut microbiota played an important role in the development of GDM, However, few studies had elucidated the mechanism of intestinal flora in GDM. Some studies even showed opposite results. This discrepancy may due to the differences in inclusion criteria, detection methods and demographic characteristics. Moreover, Diet is the most important factor that regulate the gut microbiota, However, only a few studies had addressed the role of diet on maternal gut microbiota in GDM individuals. An observational study ³² showed that HFD diet and prebiotic fibers,such as OFS differently influenced the microbiota composition in GDM patients, which was in line with our findings.

GLP-1 is a gut hormone mainly secreted by enteroendocrine L cells located at distal ileum and colon which could effectively suppress appetite, stimulate insulin secretion and regulate glucose.^{11, 13} Few studies had focused on GLP-1 changes in GDM. Maryam ³³ reported that GLP-1 was low in GDM groups during pregnancy. Secretion of GLP-1 is regulated by nutrients and metabolites in the lumen of the gut. Paul ³⁴ found HFD impaired the function of L cells and further decreased GLP-1 production. However, L cells mainly locate in the epithelium of distal ileum and colon where most of nutrients are unlikely to reach significant concentrations. By contrast, the SCFAs produced by microbial fermentation of dietary fiber in the colon are known to reach high concentrations. Of the SCFAs, propionate is an end product of bacterial metabolism and has the highest affinity for FFAR2.³⁵ Edward ³⁶ found that propionate promoted GLP-1 secretion, reduced weight gain and and prevented the deterioration in insulin sensitivity in overweight adults. Tolhurst ¹⁴ conformed SCFAs stimulated GLP-1 secretion via FFAR2 in vitro and in vivo experiments. Another study of Edward ³⁷ reported that dietary supplementation with diet fiber improved insulin sensitivity and had distinct effects on the gut microbiota and plasma metabolites. In lines with these investigations, we found propionate was a bridge molecule which could link the diet, gut microbiota with GLP-1 secretion. Meanwhile,

we verified propionate promoted GLP-1 secretion and regulated glucose through FFAR2.

Then, we further explored the underlying mechanisms of propionate promoting GLP-1 secretion. The classical Wnt /β-catenin signaling pathway is a conserved signal transduction pathway, which is widely exist in various organisms, participating in the regulation of cell proliferation, migration, and differentiation.³⁸ The major effectors of Wnt signaling were β-catenin and TCF7L2, in addition, TCF7L2 is a downstream effector of the β-catenin. The genome-wide association studies had showed that single-nucleotide polymorphisms (SNPs) in TCF7L2 were strongly associated with the morbidity of T₂DM.³⁹ Marie ⁴⁰ reported that loss of TCF7L2 gene expression not only impaired adipocytes, but also pancreatic islet and enteroendocrine cells in mice and further illuminated the roles of TCF7L2 in glucose homeostasis. Shao ⁴¹ found GCG expression was reduced and defects in glucose homeostasis were observed after knockdown of TCF7L2 in transgenic mice, elucidating that TCF7L2 and Wnt signaling pathway controlled GCG expression and glucose homeostasis. In a large-scale case–control study⁴², genetic variants of TCF7L2 were identified and found to be associated with an increased risk of GDM. In the present study, we found the acceleration of propionate on GLP-1 secretion could be blocked by the knockdown of TCF7L2 and further confirmed that propionate regulated GLP-1 secretion through the Wnt /β-catenin/TCF7L2 signaling pathway.

Previous studies had not illustrated the specific mechanisms of intestinal flora in the development of GDM. In our study, we found changes in composition and richness of gut microbiota in GDM could affect the concentration of propionate, and further regulate GLP-1 secretion through Wnt /β-catenin/TCF7L2 signaling pathway. In addition, the regulatory effect of gut microbiota could be affected by diet. After revealing the mechanism of gut microbiota in GDM, we found gut microbiota may be used as a potential target for the treatment of GDM.

Although we had illustrated a causal relationship between intestinal dysbacteriosis and GDM and revealed the specific mechanism of gut microbiota in GDM. However, there were some limitations in our study. First, we did not identify the specific microbial species for GDM, thereby, in the antibiotic cocktail treated mice, we transplanted with the whole intestinal flora rather than single bacterial transplantation. In addition, the function of intestinal flora and the pathogenesis of GDM were extremely complicated, we just focused on the alteration of SCFAs instead of untargeted metabolomics, Maybe there were other metabolic and signal pathway participating in the development of GDM.

In summary, our study revealed a causal relationship between intestinal dysbacteriosis and GDM and a novel mechanism of gut microbiota in GDM. Namely, HFD changed the abundances of intestinal flora and its metabolite propionate in the intestine, thereby inhibiting of Wnt /β-catenin/TCF7L2 signaling pathway, resulting in decrease of GLP-1 secretion and increase of blood glucose (Fig. 10). Our findings suggested gut microbiota may be used as a potential target for the treatment of GDM.

AUTHOR CONTRIBUTIONS

Designed the experiments: ZLW, DYW and HZG. Performed the experiments: HZG, ZW and ZBP. Data analyse:YJ, SQD and SSZ. All authors read and approved the final version of the manuscript.

ACKNOWLEDGEMENTS

The authors would like to thank Animal Laboratory Center and Obstetrics and Gynecology Laboratory of the First Affiliated Hospital of Sun Yat-sen University who provided us with experimental conditions.

FUNDING INFORMATION

This work was supported by the National Key Research and Development Program of China (No.2021YFC2700700).

CONFLICT OF INTEREST STATEMENT

No potential conflicts of interest were disclosed.

DATA AVAILABILITY STATEMENT

The original data presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Catalano PM, McIntyre HD, Cruickshank JK, McCance DR, Dyer AR, Metzger BE, et al. The hyperglycemia and adverse pregnancy outcome study: associations of GDM and obesity with pregnancy outcomes. Diabetes Care (2012) 35(4):780-786. doi: 10.2337/dc11-1790
Guariguata L, Linnenkamp U, Beagley J, Whiting DR, Cho NH. Global estimates of the prevalence of hyperglycaemia in pregnancy. Diabetes Res Clin Pract (2014) 103(2):176-185. doi: 10.1016/j.diabres.2013.11.003
Metzger BE, Coustan DR, Trimble ER. Hyperglycemia and Adverse Pregnancy Outcomes. Clin Chem (2019) 65(7):937-938. doi: 10.1373/clinchem.2019.303990
Crowther CA, Hiller JE, Moss JR, McPhee AJ, Jeffries WS, Robinson JS. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N Engl J Med (2005) 352(24):2477-2486. doi: 10.1056/NEJMoa042973
Billionnet C, Mitanchez D, Weill A, Nizard J, Alla F, Hartemann A, et al. Gestational diabetes and adverse perinatal outcomes from 716,152 births in France in 2012. Diabetologia (2017) 60(4):636-644. doi: 10.1007/s00125-017-4206-6
Zhang C, Tobias DK, Chavarro JE, Bao W, Wang D, Ley SH, et al. Adherence to healthy lifestyle and risk of gestational diabetes mellitus: prospective cohort study. BMJ (2014) 349:g5450. doi: 10.1136/bmj.g5450
Bowers K, Tobias DK, Yeung E, Hu FB, Zhang C. A prospective study of prepregnancy dietary fat intake and risk of gestational diabetes. Am J Clin Nutr (2012) 95(2):446-453. doi: 10.3945/ajcn.111.026294
Kampmann U, Madsen LR, Skajaa GO, Iversen DS, Moeller N, Ovesen P. Gestational diabetes: A clinical update. World J Diabetes (2015) 6(8):1065-1072. doi: 10.4239/wjd.v6.i8.1065
Homko C, Sivan E, Chen X, Reece EA, Boden G. Insulin secretion during and after pregnancy in patients with gestational diabetes mellitus. J Clin Endocrinol Metab (2001) 86(2):568-573. doi: 10.1210/jcem.86.2.7137
Holst JJ. The physiology of glucagon-like peptide 1. Physiol Rev (2007) 87(4):1409-1439. doi: 10.1152/physrev.00034.2006
Doyle ME, Egan JM. Mechanisms of action of glucagon-like peptide 1 in the pancreas. Pharmacol Ther (2007) 113(3):546-593. doi: 10.1016/j.pharmthera.2006.11.007
Flint A, Raben A, Astrup A, Holst JJ. Glucagon-like peptide 1 promotes satiety and suppresses energy intake in humans. J Clin Invest (1998) 101(3):515-520. doi: 10.1172/JCI990
Gil-Lozano M, Wu WK, Martchenko A, Brubaker PL. High-Fat Diet and Palmitate Alter the Rhythmic Secretion of Glucagon-Like Peptide-1 by the Rodent L-cell. Endocrinology (2016) 157(2):586-599. doi: 10.1210/en.2015-1732
Tolhurst G, Heffron H, Lam YS, Parker HE, Habib AM, Diakogiannaki E, et al. Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein-coupled receptor FFAR2. Diabetes (2012) 61(2):364-371. doi: 10.2337/db11-1019
Jin T. Current Understanding on Role of the Wnt Signaling Pathway Effector TCF7L2 in Glucose Homeostasis. Endocr Rev (2016) 37(3):254-277. doi: 10.1210/er.2015-1146
Elijovich F, Laffer CL, Sahinoz M, Pitzer A, Ferguson JF, Kirabo A. The Gut Microbiome, Inflammation, and Salt-Sensitive Hypertension. Curr Hypertens Rep (2020) 22(10):79. doi: 10.1007/s11906-020-01091-9
Su X, Yin X, Liu Y, Yan X, Zhang S, Wang X, et al. Gut Dysbiosis Contributes to the Imbalance of Treg and Th17 Cells in Graves' Disease Patients by Propionic Acid. J Clin Endocrinol Metab (2020) 105(11). doi: 10.1210/clinem/dgaa511
Gomez-Arango LF, Barrett HL, Mcintyre HD, Callaway LK, Morrison M, Dekker NM. Connections Between the Gut Microbiome and Metabolic Hormones in Early Pregnancy in Overweight and Obese Women. Diabetes (2016) 65(8):2214-2223. doi: 10.2337/db16-0278
Wang X, Liu H, Li Y, Huang S, Zhang L, Cao C, et al. Altered gut bacterial and metabolic signatures and their interaction in gestational diabetes mellitus. Gut Microbes (2020) 12(1):1-13. doi: 10.1080/19490976.2020.1840765
Wu Y, Bible PW, Long S, Ming WK, Ding W, Long Y, et al. Metagenomic analysis reveals gestational diabetes mellitus-related microbial regulators of glucose tolerance. Acta Diabetol (2020) 57(5):569-581. doi: 10.1007/s00592-019-01434-2
Yadav H, Lee JH, Lloyd J, Walter P, Rane SG. Beneficial metabolic effects of a probiotic via butyrate-induced GLP-1 hormone secretion. J Biol Chem (2013) 288(35):25088-25097.doi: 10.1074/jbc.M113.452516
Magoc T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics (2011) 27(21):2957-2963. doi: 10.1093/bioinformatics/btr507
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods (2013) 10(10):996-998. doi: 10.1038/nmeth.2604
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics (2010) 26(19):2460-2461. doi: 10.1093/bioinformatics/btq461
Jelinek D, Patrick SM, Kitt KN, Chan T, Francis GA, Garver WS. Physiological and coordinate downregulation of the NPC1 and NPC2 genes are associated with the sequestration of LDL-derived cholesterol within endocytic compartments. J Cell Biochem (2009) 108(5):1102-1116. doi: 10.1002/jcb.22339
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods (2010) 7(5):335-336. doi: 10.1038/nmeth.f.303
Christovich A, Luo XM. Gut Microbiota, Leaky Gut, and Autoimmune Diseases. Front Immunol (2022),13:946248. doi: 10.3389/fimmu.2022.946248
Yang K, Niu J, Zuo T, Yang S, Zhi LX, Whitney T, et al. Alterations in the Gut Virome in Obesity and Type 2 Diabetes Mellitus. Gastroenterology (2021) 161(4):1257-1269.doi: 10.1053/j.gastro.2021.06.056
Crusell M, Hansen TH, Nielsen T, Allin KH, Ruhlemann MC, Damm P, et al. Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum. Microbiome (2018) 6(1):89. doi: 10.1186/s40168-018-0472-x
Wang J, Zheng J, Shi W, Du N, Xu X, Zhang Y, et al. Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus. Gut (2018) 67(9):1614-1625. doi: 10.1136/gutjnl-2018-315988
Digiulio DB, Callahan BJ, Mcmurdie PJ, Costello EK, Lyell DJ, Robaczewska A, et al. Temporal and spatial variation of the human microbiota during pregnancy. Proc Natl Acad Sci USA (2015) 112(35):11060-11065. doi: 10.1073/pnas.1502875112
Ferrocino I, Ponzo V, Gambino R, Zarovska A, Leone F, Monzeglio C, et al. Changes in the gut microbiota composition during pregnancy in patients with gestational diabetes mellitus (GDM). Sci Rep (2018) 8(1):12216. doi: 10.1038/s41598-018-30735-9
Mosavat M, Omar SZ, Jamalpour S, Peng CT. Serum Glucose-Dependent Insulinotropic Polypeptide (GIP) and Glucagon-Like Peptide-1 (GLP-1) in association with the Risk of Gestational Diabetes: A Prospective Case-Control Study. J Diabetes Res (2020) 2020:9072492. doi: 10.1155/2020/9072492. eCollection 2020
Richards P, Pais R, Habib AM, Brighton CA, Yeo GS, Reimann F, et al. High fat diet impairs the function of glucagon-like peptide-1 producing L-cells. Peptides (2015) 77:21-27. doi: 10.1016/j.peptides.2015.06.006
Brown AJ, Goldsworthy SM, Barnes AA, Eilert MM, Tcheang L, Daniels D, et al. The Orphan G protein-coupled receptors GPR41 and GPR43 are activated by propionate and other short chain carboxylic acids. J Biol Chem (2003) 278(13):11312-11319. doi: 10.1074/jbc.M211609200
Chambers ES, Viardot A, Psichas A, Morrison DJ, Murphy KG, Zac-Varghese SE, et al. Effects of targeted delivery of propionate to the human colon on appetite regulation, body weight maintenance and adiposity in overweight adults. Gut (2015) 64(11):1744-1754. doi: 10.1136/gutjnl-2014-307913
Chambers ES, Byrne CS, Morrison DJ, Murphy KG, Preston T, Tedford C, et al. Dietary supplementation with inulin-propionate ester or inulin improves insulin sensitivity in adults with overweight and obesity with distinct effects on the gut microbiota, plasma metabolome and systemic inflammatory responses: a randomised cross-over trial. Gut (2019) 68(8):1430-1438. doi: 10.1136/gutjnl-2019-318424
Naito AT, Shiojima I, Akazawa H, Hidaka K, Morisaki T, Kikuchi A, et al. Developmental stage-specific biphasic roles of Wnt/beta-catenin signaling in cardiomyogenesis and hematopoiesis. Proc Natl Acad Sci USA (2006) 103(52):19812-19817. doi: 10.1073/pnas.0605768103
Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet (2006) 38(3):320-323. doi: 10.1038/ng1732
Nguyen-Tu MS, Martinez-Sanchez A, Leclerc I, Rutter GA, Silva XG. Adipocyte-specific deletion of Tcf7l2 induces dysregulated lipid metabolism and impairs glucose tolerance in mice. Diabetologia (2021) 64(1):129-141. doi: 10.1007/s00125-020-05292-4
Shao W, Wang D, Chiang YT, Ip W, Zhu L, Xu F, et al. The Wnt signaling pathway effector TCF7L2 controls gut and brain proglucagon gene expression and glucose homeostasis. Diabetes (2013) 62(3):789-800. doi: 10.2337/db12-0365
Ding M, Chavarro J, Olsen S, Lin Y, Ley SH, Bao W, et al. Genetic variants of gestational diabetes mellitus: a study of 112 SNPs among 8722 women in two independent populations. Diabetologia (2018) 61(8):1758-1768. doi: 10.1007/s00125-018-4637-8.

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Intestinal Flora Modulates Blood Glucose via Regulating the Secretion of GLP-1 Induced by Propionate in mouse models of Gestational Diabetes Mellitus

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Abstract

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1 INTRODUCTION

2 METHODS

3 RESULTS

3.1 Significant abnormalities in glucose tolerance, insulin resistance and GLP-1 secretion in HFD-induced GDM mice

3.2 The composition and richness of gut microbiota in GDM mice markedly changed

3.3 The SCFAs of gut metabolites were significantly changed in GDM mice

3.4 Intestinal Flora modulates the secretion of GLP-1 and blood glucose through the pathway of propionate

3.5 Propionate promoted GLP-1 synthesis via regulating β-catenin/TCF7L2 signaling mediated GCG transcription

4 DISCUSSION

5 CONCLUSION

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