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 first 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) . 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 five 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 flora . So the patient had been treated with an insulin pump with the weight (kg) * 0.2-0.5 u/day dose first 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 qualification 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 modified CTAB method . DNA concentration, purity was monitored and was diluted to proper concentration.
PCR amplification of 16S rRNA V1-V9 and high-throughput sequencing
The full V1-V9 region of the bacterial 16S rRNA gene was amplified using the universal primer set 27F and 1492R with Barcode by using third-generation sequencing . The PCR products were mixed and purified. The sequencing library was generated, assessed, and sequenced on the PacBio Sequel platform using standard protocols .
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) . The representative sequence for each OTU was screened for further annotation. The taxonomic information for each representative sequence was annotated by the SSUrRNA Database  of Silva Database  based on the Mothur algorithm. Alpha diversity such as Chao1, Shannon index were calculated with QIIME (Version1.9.1) and displayed with R software (Version 2.15.3). The Chao1 index was selected to identify community richness, and the Shannon index was used to identify community diversity. Tukey and Wilcox's tests were used to analyze the differences between groups，p<0.05 was considered statistically significant. Principal Coordinate Analysis (PCoA) based on unweighted unifrac distance calculated by QIIME software (Version 1.9.1) and displayed by WGCNA package, stat packages, and ggplot2 package in R software (Version 2.15.3). The significantly bacterial taxa between groups were identified by the linear discriminate analysis (LDA) effect size (LEfSe) method with a LDA threshold value of 2.018 and MetaStats at 95% confidence interval, simultaneously. Potential functional contributions of the observed microbes were inferred using PICRUSt2 . Significantly different Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KOs) were identified by t-test, P <0.05 was considered statistically significant. The corresponding enrichment pathways of significantly different KOs were predicted in KEGG（https://www.kegg.jp/kegg/tool/map_pathway2.html）.
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 significantly different between the phorbol 12-myristate 13-acetate (PMA)/ionomycin (IO)-induced intestinal explants pro-inflammatory disease model and Lactobacillus plantarum 299v treated samples, and between livers samples from Lactobacillus brevis 119-2 and control diet-administrated rat were filtered 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 final 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  was performed by String (https://string-db.org/) . The interaction results were visualized by Cytoscape .
RNA Isolation and cDNA Synthesis
Total RNA was isolated from a 1ml whole blood sample with Trizol (Invitrogen, USA) reagent and purified using RNA simple Total RNA Kit (TIANGEN, China) followed the manufacturer’s instructions. About 0.2μg of total RNA was used for first-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 (Table S9) (https://www.ncbi.nlm.nih.gov/tools/primer-blast/ and https://pga.mgh.harvard.edu/primerbank/index.html). The primer evaluation was carried out according to the reported method of literature  with modification. Standard curves of each primer pair were established using 8-fold dilution series of template cDNA.
The quantitative real-time PCR was performed using FastStart Essential DNA Green Master (Roche, Switzerland) with a Real-Time PCR System (BIO-RAD, USA). Each reaction contained 5 μL cDNA (8-time diluted), 10 μL FastStart Essential DNA Green Master, 1 μL Forward Primer (10 uM), 1 μL Reverse Primer (10 uM), and 3µL RNase-Free ddH2O water. The PCR was carried out as the following steps: predegeneration at 95°C for 10 min; 45 cycles of degeneration at 95°C for 15 s, annealing at 60°C for 15 s, and extension at 72°C for 30 s; and melting curve analysis at 65°C-95°C. RT-qPCR of each cDNA sample was carried out three times as technical replicates. Finally, the relative expression was determined by using the 2-ΔΔCt method . The Ct-value (cycle threshold) determined at the end of the reaction indicates the cycle number at which fluorescence passes a fixed threshold. The amplification of β-Actin was performed as control and reference. The differences between groups were tested by the Paired t-test. P ≤ 0.05 and 0.05 < P ≤ 0.1 are considered significant and trend, respectively.