The Co-Regulation of the Gut Microbiome and Host Genes Might Play Important Roles in Metformin Intolerance

Metformin is commonly considered the rst-line therapy for type 2 diabetes (T2D) and also had potential treating utility in other areas; however, ~20% of patients experience intolerance with unclear underlying mechanisms. In the present study, we performed the full-length 16S rRNA (V1-V9) for the fecal samples and bioinformatics analysis to study the mechanisms of the metformin intolerance combining the gut ora and host. The results showed that Barnesiella (p=0.046) and Parabacteroides goldsteinii (p=0.016), which transforming primary into secondary bile acid (SBA), were higher in the TS than T group, and were eliminated in the TSa group, which might lead to the accumulation of primary bile acids (PBA) such as cholic acid (CA), the change of GLI1 gene, and following diarrhea in the TSa group. Lactobacillus brevis (p=0.024) and Lactobacillus plantarum (p=0.026) were up-regulated in TSa than TS group. The two ora might cause the changes of genes including FOXA2, HTR7, GADPH, and intolerance relief, which might be a worthwhile future direction for preventing metformin intolerance. hinted


Conclusions
These results hinted that the differential ora and co-regulation of them with the host might be intolerance-related. Our results partly provided theoretical support for intolerance prevention.

Background
Metformin is commonly considered as the rst-line therapy for type 2 diabetes (T2D) and also had potential treating utility in obesity, metabolic dysfunction, and some cancers. However, ~20% of patients experience gastrointestinal (GI) intolerance, including diarrhea, nausea, and bloating with unclear underlying mechanisms and a lack of effective management strategies that warrant discontinuation of metformin treatment [1]. Researches have uncovered that the modi cation of intestinal microbiota and host DNA methylation might be both involved in GI intolerance, and the increase of Escherichia abundance might be one of the reasons in the European population. However, the Escherichia have not been modi ed in the Chinese population [2], which implied that different mechanisms might employed in Chinese.
In present study, we collected fecal samples from T2D patients who had no GI intolerance after metformin administration (before (T) and after (Ta) taking metformin, respectively) and who had GI intolerance after metformin administration (before (TS) and after (TSa) taking metformin, respectively) and health subjects (N). Then, we carried out 16S rRNA sequencing on fecal samples and bioinformatics analysis to explore the mechanisms of metformin intolerance.

Results And Discussion
We found that the Dorea longicatena was up-regulated in both T and TS groups (LDA Score>2.0) and also in the merging data than N group ( Figure S 1a-c). The K03386, K01809, K03321, and K13016 are enriched in the T group, and K02398 is enriched in the N group (Table 1). The alpha diversity was not differed before vs. after taking metformin (Table S2). These patterns had been con rmed by previous studies [3,4], which proved the high con dence of our research. and Parabacteroides goldsteinii (p=0.016), which transforming primary into secondary bile acids (SBA) [5,6] were far higher in the TS than T group. However, the two ora were eliminated in the TSa group (Figure 1a), which implied that the eradication of Barnesiella and P. goldsteinii might lead to the accumulation of PBA such as CA and following diarrhea in the TSa group ( Figure 2 inside (1) dashed rectangle). Six agella assembly-relevant Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KOs) signi cantly higher enriched in TS than T group were also found (Table 1), which might cause a higher risk of in ammation in the TS group for the pro-in ammatory potential of agellin [7]. The TS and T groups exhibited different patterns of microbial communities from TSa and Ta groups (Figure 1b).
There are 26 and 10 signi cantly differential ora in total between the TS and TSa group, and the T and Ta group, respectively ( Figure 1c, Table S2,3). Among them, Leuconostocaceae (p=0.048), Leuconostoc (p=0.041), Weissella confusa (p=0.037), Weissella paramesenteroides (p=0.008), Lactobacillus brevis (p=0.024), Lactobacillus plantarum (p=0.026) were up-regulated in TSa group (Figure 1d). These ora belongs to lactic acid bacteria. In addition, the alpha-galactosidase, a protective factor of GI intolerance [8], was higher enriched in Ta than T group but not in the TSa group. In contrast, beta-galactosidase, also a protective factor of GI intolerance [9], was down-regulated in TSa compared with the TS group (Table  1). It may be one reason for GI intolerance ( Besides, by bioinformatics analysis as that showed in Supplementary, we found that metformin intolerance might be the result of co-regulation of intestinal ora and host genes. There is a closeinteraction among the L. plantarum, L. brevis, cholic acid (CA) and, metformin glycemic response and intolerance-related genes ( Figure 1e, Table S4-6). According to GEO data and previous reports, we found that the CA and FOXA2 could both modulate the expression of GLI1 [10]. In contrast, the L. plantarum and L. brevis could respectively modulate the expression of HTR7 and GAPDH. By RT-qPCR check, we found the expression of GLI1 was down-regulated (p<0.05), and the expression of FOXA2 showed an upward trend (p<0.10) in TSa than TS group (Figure 2, Figure S3, Table S7). The downregulation of GLI1 might cause bloating, diarrhea and nausea by sharing the same pathogenesis and symptoms of in ammatory bowel disease (IBD), which might lead to GI intolerance [11]. On the contrary, we found expression of HTR7 and GADPH showed a downward trend (p<0.10) in TSa than TS group ( Figure 2, Figure S3, Table  S7), which might be related to the relief of GI intolerance, for opposite changes of these genes are corresponding to IBD [12,13]. The effect of ora on these genes seem to restore a healthy state after being disturbed, which might be one of the reasons why some intolerance individuals become more tolerant to metformin after persisting on it for a period, which might be a worthwhile future direction for the prevention of metformin GI intolerance.

Conclusion
In general, the mechanism of metformin intolerance in Chinese might be different from that in Europeans.
It might be caused by the eradication of PBA degrading-related ora in gut microbiota and also the coregulation of intestinal ora and host genes. However, the relatively small sample size might affect the results to a certain extent. However, fortunately, some of the results in our study are consistent with large cohort researches, which guaranteeing the accuracy of the results. More importantly, the selection of dose regimen and treatment duration was based on the hospital's conventional treatment process to avoid interference treatment and better re ect the actual clinical phenomenon. Therefore, our results could still provide theoretical support for intolerance prevention. Further experiments with larger sample sizes and animal models are required to verify those results to reveal the mechanism involved so that more people could bene t from metformin in the future.

Subjects
This study was carried out at the First People's Hospital of Qujing City, Yunnan Province China, from February 2019 to August 2019. The inclusion criteria were as follows: the patients were newly diagnosed with T2D with the fasting plasma (blood) glucose higher than 7.0 mmol/L, were between 40 to 70 years old, were able to communicate, had volunteered to participate in this study, and were willing to provide informed consent. Subjects did not take ion pump inhibitor drugs, antibiotics, steroid hormones, or Chinese herbal medicine, including oral, intramuscular, or intravenous injections within the three months before collecting fecal samples, did not take other glucose-lowering medications. Subjects did not take other medicine except drugs used in this study during the experiment, did not occur diarrhea on the day of the rst sampling. Those excluded were the patients who had severe conditions, including indigestion, renal failure, hepatic failure, severe gallbladder, stroke, pancreatic diseases, malignant tumors, or unstable cardiovascular diseases (such as myocardial infarction, ketosis, or hyperthyroidism) [14]. Age-matched healthy volunteers were included as above.

Medication Strategy and Samples Collection
We recommend the use of an oral glucose tolerance test (OGTT) (consisting of a fasting and 2-hour glucose level using a 75-g oral glucose load) to screen for impaired glucose tolerance (IGT) and T2D. Feces and blood samples were collected, and the OGTT experiment was completed in the early morning of the next day after the patient was admitted to the hospital. The blood was used to detect other indicators such as fasting blood glucose, serum C-peptide. Then the patient took metformin hydrochloride sustained-release tablet (Qingdao Huanghai Pharmaceutical Co., Ltd.) orally at a dose of 500 mg/time, two times/day. When the patient had intestinal side effects, they stopped metformin treatment, collected stool, and measured fasting blood glucose the next morning. When the patient had no side effects after metformin administration, the stool was collected ve days later, and fasting blood glucose was measured. The feces of each subject were immediately stored at -80°C after collection until the next step. According to hospital clinical experience, insulin combined with metformin treatment can achieve a better hypoglycemic effect, and insulin will not change the composition of intestinal ora [2]. So the patient had been treated with an insulin pump with the weight (kg) * 0.2-0.5 u/day dose rst for a day, when whose random blood glucose was greater than 16.8 mmol/L on admission. Feces and blood samples were collected from the healthy subjects only one time, respectively.
Isolation and quali cation of fecal bacterial DNA Eighteen stool samples were collected from twelve subjects (Table S1), as follows: six samples from three patients who had no intestinal side effects after metformin administration (before (T) and after (Ta) taking metformin, respectively). Six samples from three patients who had intestinal side effects after metformin administration (before (TS) and after (TSa) taking metformin, respectively). Six samples from six health subjects (N). Genomic DNA from human stool samples clinically collected was extracted by a modi ed CTAB method [15]. DNA concentration, purity was monitored and was diluted to proper concentration.
PCR ampli cation of 16S rRNA V1-V9 and high-throughput sequencing The full V1-V9 region of the bacterial 16S rRNA gene was ampli ed using the universal primer set 27F and 1492R with Barcode by using third-generation sequencing [16]. The PCR products were mixed and puri ed. The sequencing library was generated, assessed, and sequenced on the PacBio Sequel platform using standard protocols [17].

Processing of sequencing data
The original sequences were registered in the NCBI SRA database (registration number: PRJNA725340). The clean reads were acquired by removing the barcodes and primers, low-quality reads, and chimera sequences from raw data [18,19]. Sequences with ≥97% similarity were assigned to the same OTUs by Uparse software (Uparse v7.0.1001) [20]. The representative sequence for each OTU was screened for further annotation. The taxonomic information for each representative sequence was annotated by the

Bioinformatics analysis
In order to explore the effects of intolerance-related differential bacteria and primary bile acid on the body, we used the GEO database to search the research on them. As a result, we found the GSE23630 data set related to Lactobacillus plantarum 299v the GSE41734 data related to Lactobacillus brevis 119-2, the GSE55443 data related to cholic acid. Besides, Using related scores >1.0 as the cutoff we searched metformin intolerance symptoms, including bloating, diarrhea, and nausea in GeneCards (https://www.genecards.org/) to collect intolerance symptoms related genes.
Next, we used GEO2R to analyze the above two data sets related to the differential bacteria and carry out the T-test separately. Using P<0.05 as the cutoff, genes signi cantly different between the phorbol 12myristate 13-acetate (PMA)/ionomycin (IO)-induced intestinal explants pro-in ammatory disease model and Lactobacillus plantarum 299v treated samples, and between livers samples from Lactobacillus brevis 119-2 and control diet-administrated rat were ltered out. If there are multiple transcripts for the same gene, we multiplied the P-value and used the square root of the product as the nal P-value.
Because there is only 1 sample in the case and control groups in the GSE55443 data set, we could not carry out statistical analysis. So, we selected the top 100 different genes with the maximum value and the top 100 genes with the minimum value of LogFC between CA and vehicle-treated intestinal epithelial cells of mice. Then, we intersected the differential genes obtained from the above analysis with the genes related to intolerance symptoms.
After that, Protein interaction analysis on differential genes from the above analysis, and seven genes related to metformin glycemic response, and four genes related to metformin intolerance retrieved from the literature [24] was performed by String (https://string-db.org/) [25]. The interaction results were visualized by Cytoscape [26].

RNA Isolation and cDNA Synthesis
Total RNA was isolated from a 1ml whole blood sample with Trizol (Invitrogen, USA) reagent and puri ed using RNA simple Total RNA Kit (TIANGEN, China) followed the manufacturer's instructions. About 0.2μg of total RNA was used for rst-strand cDNA synthesis by using Mix in FastKing RT Kit (With gDNase) (TIANGEN, China) according to the manufacturer's instructions.

Primer Design and Evaluation
The primer pairs of FOXA2, GLI1, HTR7, and GAPDH were designed according to their sequences by using the online program Primer-BLAST (

Declarations
Ethics approval and consent to participate The research activities were approved by the local ethics committee of First People's Hospital of Qujing City. All clinical data collection and genetic diagnoses were performed after obtaining consent from the patients. The objective, materials, and methods of this research and the rights and obligations of the patients have informed patients in oral and written form. Make sure the parents understand all information, then the informed consent was signed before study inclusion. All informed consents were collected and delivered to the study coordinators by the doctor concerned.

Consent for publication
Not applicable.

Availability of data and materials
The original sequences were registered in the NCBI SRA database (PRJNA725340). Other data generated or analyzed during this study are included in this article and its additional les. Comparison of intestinal ora before and after taking metformin between people who had intestinal side effects and those without intestinal side effects after taking metformin, and the interaction analysis of the differential ora and side effects related genes. a,d. Relative abundance of differential intestinal ora among four groups. b,c. Principal coordinate analysis (PCoA) and linear discriminant analysis (LDA) effect size (LEfSe) analysis among groups. The red represents before taking metformin, and the green represents after taking metformin. e. Interaction analysis of the glycemic response and intolerancerelated genes reported in the literature, differential intestinal ora, and Cholic acid. CA Cholic acid, CALP Cholic acid and Lactobacillus plantarum, SEG side effects related genes, GRG glycemic response-related genes, LB Lactobacillus brevis, LBLP Lactobacillus brevis, and Lactobacillus plantarum.

Figure 2
Diagram of the possible gut microbial mechanism of intestinal side effects caused by metformin. The black arrow indicates the result of the GEO analysis and RT-qPCR test, and the blue arrow indicates the results from the literature analysis.

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download.