Staphylococcus aureus-expressed acetolactate synthase enhances the biosynthesis of branched-chain amino acids and is linked to insulin resistance in type 2 diabetes in South China

Staphylococcus aureus-expressed acetolactate synthase enhances the biosynthesis of branched-chain amino acids and is linked to insulin resistance in type 2 diabetes in South China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Staphylococcus aureus-expressed acetolactate synthase enhances the biosynthesis of branched-chain amino acids and is linked to insulin resistance in type 2 diabetes in South China Tingting Liang, Tong Jiang, Zhuang Liang, Longyan Li, Lei Wu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4242450/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background An increase in branched-chain amino acid (BCAA) levels can result in insulin resistance at different stages of type 2 diabetes (T2D), however, the causes of this increase are unclear. Methods We performed metagenomics and metabolomics profiling in patients with prediabetes (PDM), newly diagnosed diabetes (NDDM), and post-medication type 2 diabetes (P2DM) to investigate whether altered gut microbes and metabolites could explain the specific clinical characteristics of different disease stages of T2D. Results Here we identify acetolactate synthase (ALS) a BCAA biosynthesis enzyme in Staphylococcus aureus as a cause of T2D insulin resistance. Compared with healthy peoples, patients with PDM, NDDM, and P2DM groups, especially in P2DM group, have increased faecal numbers of S. aureus . We also demonstrated that insulin administration may be a risk factor for S. aureus infection in T2D. The presence of ALS-positive S. aureus correlated with the levels of BCAAs and was associated with an increased fasting blood glucose (FBG) and insulin resistance. Humanized microbiota transplantation experiment indicated that ALS contributes to disordered insulin resistance mediated by S. aureus . We also found that S. aureus phage can reduced the FBG levels and insulin resistance in db/db mice. Conclusions Above all results suggest that the BCAAs biosynthesis increasing bacteria and ALS enzymes are potential intervention targets for the glucose homeostasis in T2D insulin resistance, opening a new therapeutic avenue for the prevention or treatment of diabetes. Staphylococcus aureus branched-chain amino acid acetolactate synthase insulin resistance type 2 diabetes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Type 2 diabetes (T2D), a chronic disease characterized by elevated blood glucose concentrations, is one of the leading causes of death and disability worldwide [ 1 ]. Moreover, approximately 36% of Chinese adults have prediabetes [ 2 , 3 ], a condition characterized by intermediate hyperglycemia and insulin resistance. Those with prediabetes will eventually develop overt T2D [ 4 , 5 ]. Compared with patients with prediabetes, those with treatment-naïve T2D have different microbiomes [ 3 ]. Furthermore, the interaction between antidiabetic drugs, such as metformin [ 6 ], liraglutide [ 7 ], SGLT2 inhibitors (dapagliflozin) [ 8 ], and insulin, and gut microbes, and their influence on drug functions remain of immediate research interest, making it difficult to identify bacterial biomarkers that might be related to disease progression. Therefore, clarifying the relationship between microbes and T2D incidence and development is paramount. Evidence suggests remarkable changes in the gut microbiota in T2D and prediabetes conditions [ 9 ]. Metagenomics studies have enabled the characterization of microbes in patients with T2D and provided insights into the functional gene abundance interplay between microbes and host metabolism in China and Europe [ 9 , 10 , 11 ]. In general, individuals with T2D exhibit reduced bacterial diversity and gene richness [ 12 ]. Recent studies have shown that T2D is associated with a higher ratio of Firmicutes to Bacteroidetes [ 13 ], an increase in Lactobacillus , Veillonellaceae , and Prevotella [ 14 ], and a decrease in butyrate-producing genera (e.g., Roseburia , Subdoligranulum , Clostridiales ) [ 15 ]. T2D is associated with a higher rate of Staphylococcus aureus colonization and is linked to altered glucose tolerance and elevated glucose levels [ 13 , 16 , 17 ]. There is also a significant difference in microbes between patients with prediabetes and treatment-naïve patients with T2D [ 4 ]. For example, some studies have shown that Escherichia coli was abundant in prediabetic patients, whereas Bacteroides was abundant in those with T2D [ 3 ]. In addition, it was initially reported that the glucose-lowering effect of antidiabetic drugs could be partly attributed to certain microbial species [ 18 ]. Still, no studies have analyzed the pharmacological intervention on the composition of microbes in T2D (e.g., insulin and oral hypoglycemic drugs). Furthermore, whether and how changes in fecal microbes and blood metabolites are functionally related to the host phenotype in prediabetes, newly diagnosed diabetes, and post-medication diabetes remains unclear. Alterations in the serum metabolites of branched-chain amino acids (BCAAs; valine, leucine, and isoleucine) are a channel by which microbes affect human metabolic health [ 19 ]. Studies have suggested a close link between serum BCAA levels and T2D occurrence [ 20 , 21 ], which may be the reason for increased BCAA accumulation in adipose tissues [ 22 , 23 ] and the liver [ 24 , 25 ]. T2D can increase the microbial potential for BCAA biosynthesis, such as Prevotella copri and Bacteroifes vulgatus , whereas Butyrivibrio crossotus and Eubacterium siraeum can reduce BCAA uptake [ 19 ]. The reasons for these elevated levels in obesity and T2D are not well-established. Yu et al. reported that a low isoleucine diet improves liver and adipose metabolism, increasing insulin sensitivity and preventing diabetes by activating the FGF21-UCP1 axis [ 25 ]. Charon et al. indicated that the activity of the BCAA biosynthesis enzyme, d-citramalate synthase, was the highest in patients with T2D [ 26 ]. Additionally, Qiao et al. found that Parabacteroides merdae protects against cardiovascular damage by enhancing BCAA catabolism as it expresses the porA gene that degrades BCAAs [ 27 ]. Therefore, it is necessary to explore the bacteria that promote the production or decomposition of BCAAs in T2D and analyze their possible mechanisms, including the enzymes and genes involved in BCAA metabolism. Here, we performed metagenomics sequencing and metabolomics profiling of patients with prediabetes mellitus (PDM), newly diagnosed diabetes mellitus (NDDM), and post-medication T2D (P2DM) to investigate whether the altered gut microbes and metabolites could explain the specific clinical characteristics of different disease stages of T2D. We identified diabetes subgroups associated with microbial species and their effects on BCAA profiles. Moreover, using a insulin resistance rats and db/db mice model, we uncovered evidence of causal effects of S. aureus bacteriophage, which caused a beneficial effect on gut microbes, glycemic control, and serum circulating BCAA levels in T2D. Materials And Methods T2D study population This study was approved by the Ethics Review Committee of the Guangdong Provincial People's Hospital (KY2023-675). The study participants were recruited from Guangzhou and included 12 healthy controls (H), 12 with PDM, 12 with NDDM, and 21 with P2DM. All participants were grouped according to the 2003 American Diabetes Association diagnostic criteria [28]. Patients with the following conditions were excluded: type 1 diabetes, hypertension, dyslipidemia, acute infectious disease, cirrhosis, diarrhea, pregnancy, treatment with antibiotics within 3 months, and intake of probiotics within 6 weeks. The clinical patient information is summarized in Table S1. Stool samples for metagenomics and serum samples for metabolomics were collected and then stored in a dry ice box until arrival at the laboratory. Then, they were stored in a -80℃ ultra-low temperature freezer in the laboratory until further use. DNA extraction and metagenomics sequencing Total genomic DNA was extracted from T2D stool samples using the Mag-Bind® Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.). All samples were analyzed on Illumina NovaSeq (Illumina Inc., San Diego, CA, USA) using a NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles) according to the manufacturer’s instructions (www.illumina.com). Sequence quality control and genome assembly The data were analyzed on the free online Majorbio Cloud Platform (www.majorbio.com). Briefly, adaptors were trimmed from paired-end Illumina reads, and low-quality reads were removed using fastp [29]. The reads were aligned to the human genome using BWA [30]. Metagenomics data were assembled using MEGAHIT [31]. Contigs with lengths ≥300 bp were selected as the final assembling result, and then the contigs were used for further gene predictions and annotations. Gene prediction and non-redundant gene catalog construction The open reading frames (ORFs) from each assembled contig were predicted using Prodigal [32]/MetaGene [33]. The predicted ORFs with lengths ≥100 bp were retrieved and translated into amino acid sequences using the NCBI translation table. A non-redundant gene catalog was constructed using CD-HIT [34] with 90% sequence identity and 90% coverage. High-quality reads were aligned to non-redundant gene catalogs to calculate gene abundance with 95% identity using SOAPaligner [35]. Taxonomy and functional annotation Representative sequences of non-redundant gene catalogs were aligned to the NR database with an e-value cutoff of 1e-5 using Diamond [36] for taxonomic annotations. Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation was conducted using Diamond [36] against the KEGG database with an e-value cutoff of 1e-5. Non-targeted metabolomics For metabolites extraction, serum(200 μL) was suspended in a solvent (methanol:acetonitrile=1:1, v:v, 1 mL) and vortexed for 1 min. The mixture underwent ultrasound radiation in a low-temperature environment and was then stored at -20℃ for 2 h and centrifuged at 10000 rpm for 20 min at 4℃. The supernatant was drained in a cryovacuum concentrator, and 200 μL of a complex solution (acetonitrile: H 2 O=1:1, v:v) was added for re-solution. The mixture was vortexed for 1 min and centrifuged at 10000 rpm for 30 min at 4℃. The supernatant was obtained and placed in the sample bottle for LC-MS/MS analysis. Exactly 10 μL of supernatant from each sample was mixed with QC samples to evaluate the repeatability and stability of the LC-MS analysis process. Metabolomics data processing The raw data collected from LC-MS/MS were imported into Compound Discoverer 3.1 (Thermo Fisher Scientific, USA) for data processing. The identification of metabolites was combined with several databases, including the BGI Library, mzCloud, and ChemSpider (HMDB, KEGG, and LipidMaps). Real-time qPCR Genomic DNA was extracted from H and P2DM stool samples. Primer sequences for S. aureus 16S rRNA gene (ALS, ilvG ) using the online in NCBI (https://www.ncbi.nlm.nih.gov/tools/primer-blast). Amplification of bacterial genes were determined with SYBR Green PCR master mix (Invitrogen) using the ABI 7500 real-time PCR system (Applied Biosystems). Co culture of S. aureus and its phages S. aureus 2868B2 and its phage were grown statically in tryptic soytone broth (TSB) or on TSB agar plate at 37 °C. Meanwhile, the growth curve of S. aureus alone or in combination with its phages were measured using microplate reader (Epoch2, BioTek). Animal studies The animal experimental procedures were approved by the Committee of the Institute of Microbiology, Guangdong Academy of Sciences (No. GT-IACUC202309141). Five-week-old male Wistar rats (108–116 g) were housed in a specific pathogen-free barrier facility and were free to eat and drink for 7 days, the rats were randomly divided into 3 groups. And then administration of the vancomycin (50 mg/kg), neomycin (100 mg/kg), and metronidazole (100 mg/kg) antibiotic in combination with the penbritin (1 mg/mL) once a day (10 mg/kg) to ALS-P and ALS-N rats for a total of 7 days. Stool samples from patients with H and P2DM were used for faecal transplantation in germ-free rats. Rats were gavaged with 100 μL of stool samples (1 g stool dissolved in 30 ml Luria–Bertani (LB) medium containing 15% glycerol under anaerobic conditions) for 21 days until the experiments end. The six-week-old male db/m (control) and db/db mice (diabetic model and S. aureus bacteriophage group) were fed a standard chow diet and drinking water for 7 days. And then, db/db mice were fed a standard chow diet and given S. aureus bacteriophage (1 × 10 9 PFU/mL) by gavaging for 4 weeks until the experiments end. Biochemical analyses At the end of the experiment, the blood samples were collected and centrifuged at 3000 rpm for 15 min at 4℃. Serum were collected and immediately stored at -80℃ for further analysis. Serum insulin, Hemoglobin A1C (HbA1C), total cholesterol, triglyceride, low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C), Interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), Interleukin-6 (IL-6), endotoxin, ALS, phosphatidylinositol-3-kinase (PI3K), protein kinase B (AKT), and glucose transporter 4 (GLUT4) were measured using commercial enzyme-linked immunosorbent assay (ELISA) kits (Biomerica, BMRA.US) according to the manufacturer’s instructions. Serum BCAA concentrations were determined using a high-speed amino acid analyzer (Model Hitachi 835-50). Histopathological analysis Liver, pancreas, colon, and white adipose tissues were fixed in 10% neutral buffered formalin, paraffin-embedded, cut into 5 μm sections, and subsequently stained with hematoxylin and eosin (H&E) for histopathological analysis. Statistical analysis The clinical and animal data are expressed as the means ± standard deviations. Differences between more than two groups were analyzed using Hiplot tools (https://hiplot.cn/basic and were considered significantly different at p -values < 0.05. The Kruskal–Wallis test was conducted to detect differences in bacterial diversity and relative abundance between multiple groups. Moreover, the Tukey–Kramer post hoc tests were conducted to explore the differences among the three pairwise comparisons. The composition, diversity, and functional categories of the gut microorganisms were analyzed using the QIIME II platform with R package stats (R version 4.2.3; https://www.r-project.org/). Non-targeted metabolomics was analyzed using the MetaboAnalyst 5.0 platform (https://www.metaboanalyst.ca/). Correlation analysis between the gut microbiota and clinical indices or metabolites was conducted using Spearman’s rank correlations. Results Clinical characteristics of the study population Four groups of adult participants were selected from the Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) from March 2022 to June 2022 based on their glucose levels: H group (n = 12), PDM group (n = 12), NDDM group (n = 12), and P2DM group (n = 21) (Fig. 1 A). The clinical information, including physiological and biochemical indicators, is presented in Table 1 . We found that the PDM, NDDM, and P2DM groups had higher levels of FBG and HbA1c than the H group ( p < 0.05). Moreover, the NDDM and P2DM groups had significantly higher FBG levels than the PDM group. Still, no significant difference was observed between the NDDM and P2DM groups (Fig. 1 B-C and Table 1 ). No significant differences were also observed in age; height; weight; body mass index; systolic blood pressure (SBP); diastolic blood pressure; and total cholesterol (TC), triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), albumin, and total bilirubin levels (Fig. S1A-H and Table 1 ) in the four groups. Participants in the P2DM group had a diabetes duration of approximately 4–20 years, and their fasting and postprandial C-peptide and insulin levels were different, suggesting a certain recovery of insulin secretion. The drugs used by the 21 participants in the P2DM group were recorded, including eight participants using insulin injection and others using oral hypoglycemic drugs (e.g., Metformin, Acarbose, Sitagliptin, Voglibose, Dapagliflozin, Repaglinide, Gliclazide, Kagliflozin, Alogliptin, Linagliptin, and Glimepiride) alone or in combination with insulin injection. Table 1 Anthropometric parameters and biochemical indexes among participants (n = 57). Healthy (H) (n = 12) All Diabetes P value Pre-diabetes mellitus (PDM) (n = 12) Newly diagnosed diabetes (NDDM) (n = 12) Post medication diabetes mellitus (P2DM) (n = 21) Age 60.58 ± 11.86 54.08 ± 19.64 50.33 ± 12.38 59.57 ± 14.35 > 0.05 Gender (male) 8(66.6%) 4(33.3%) 10(83.3%) 17(81.0%) - FBG (mmol/L) 5.86 ± 0.64 6.43 ± 0.34 9.22 ± 2.45 11.86 ± 5.34 < 0.001 HbA1c (%) 4.91 ± 0.49 6.78 ± 0.77 9.57 ± 2.82 9.77 ± 4.77 0.05 Weight (kg) 59.18 ± 10.22 59.43 ± 12.21 63.93 ± 10.29 60.12 ± 9.65 > 0.05 BMI (kg/m 2 ) 22.45 ± 3.23 23.48 ± 3.77 23.58 ± 3.76 22.64 ± 2.89 > 0.05 TC (mmol/L) 4.87 ± 0.81 5.14 ± 1.3 5.58 ± 1.37 5.11 ± 1.51 > 0.05 TG (mmol/L) 1.54 ± 1.02 1.23 ± 0.72 1.62 ± 0.72 1.86 ± 1.35 > 0.05 LDL-C (mmol/L) 2.9 ± 1.03 3.07 ± 0.85 3.49 ± 1.05 2.86 ± 1.02 > 0.05 HDL-C (mmol/L) 1.36 ± 0.27 1.26 ± 0.42 1.17 ± 0.23 1.18 ± 0.32 > 0.05 SBP (mmHg) 124.83 ± 10.78 130.42 ± 18.95 131.83 ± 22.65 138 ± 19.43 > 0.05 DBP (mmHg) 83.08 ± 6.5 80.08 ± 12.38 83.25 ± 17.19 81.29 ± 14.87 > 0.05 ALB (U/L) 43.54 ± 2.57 39.22 ± 5.71 36.97 ± 4.44 36.43 ± 5.77 > 0.05 TBIL (umol/L) 18.78 ± 9.47 10.3 ± 4.6 16.3 ± 6.89 11.9 ± 5.01 > 0.05 Diabete duration (Year) - - - 10.26 ± 4.93 - FC-peptide (nmol/L) - - - 0.85 ± 0.45 - F_insulin (pmol/L) - - - 46.52 ± 28.17 - PC-peptide (nmol/L) - - - 1.92 ± 1.45 - P_insulin (pmol/L) - - - 212.03 ± 182.95 - Data are means ± SD or median (interquartile range); FBG: fasting blood glucose; HbA1c: glycosylated hemoglobin; TC: total cholesterol; TG: triglyceride; LDL-C: low density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; SBP: systolic blood pressure; DBP: diastolic blood pressure; ALB: albumin; TBIL: total bilirubin; Different gut microbiome compositions in the four groups On average, 81,734,932 clean reads were obtained from the metagenomics sequencing of fecal samples from all 57 participants. The alpha and beta diversities were evaluated, which represented the intra- and inter-community diversity of the microbial community [ 37 , 38 ], respectively. No significant difference was observed in the Shannon and Chao indices among the four groups. However, the PDM group had a significantly higher Simpson index than the other three groups, suggesting that the group had a lower microbial community diversity (Fig. 2 A, Fig. S2A-B). Furthermore, based on the random forest proximity matrix, there was a significant difference in each group (Fig. 2 B), suggesting that the gut bacterial profiles of patients with PDM, NDDM, and P2DM differed from those of the healthy controls. However, non-metric multidimensional scaling principal coordinate analysis (NMDS) did not show an obvious separation in the bacterial communities among the four groups (Fig. S2C) (stress < 0.2). We also examined the gut microbiome structure of the H, PDM, NDDM, and P2DM groups and found that six phyla, 1292 genera, and 6209 ASVs were common to all samples (Fig. S2 D-E). In all four groups, Bacteroides, Firmicutes, and Proteobacteria were the most abundant phyla (Fig. 2 C-E), consistent with previous reports (39). These phyla accounted for 94.7%, 89.8%, 92.1%, and 87.7% of the phyla in the samples from the H, PDM, NDDM, and P2DM groups, respectively. The PDM, NDDM, and P2DM groups showed increased abundance of the phylum Firmicutes (48.6%, 57%, and 54.4%, respectively) and reduced abundance of the phylum Bacteroidetes (14.5%, 21.6%, and 24.7%, respectively) ( p < 0.05) compared to the healthy controls (42.8% and 49.2%, respectively). Moreover, the PDM, NDDM, and P2DM groups exhibited higher Firmicutes to Bacteroidetes ratios than the H group ( p < 0.05) (Fig. 2 F). Next, we determined the microbial composition at the genus level. We found that the P2DM group had the highest number of unique genera (237), while the H, PDM, and NDDM groups had 83, 145, and 49 unique genera, respectively (Fig. 2 G). The predominant genera in the healthy controls were Bacteroides (15.7%), Phocaeicola (16.4%), and Prevotella (5.9%). With the development of diabetes, several differentially abundant pathogenic microorganisms were identified. Escherichia (10.6%) was abundant in the PDM group, and Klebsiella (7.6%) was abundant in the NDDM group. After treatment, the relative abundance of Escherichia (3.4%) and Klebsiella (1.0%) significantly decreased (Fig. 2 H). We also investigated the differentially abundant species in the four groups (Fig. 2 I). The potential probiotics Phocaeicola vulgatus , Phocaeicola dorei , and Phocaeicola massiliensis from the genus Phocaeicola ; Prevotella copri and Prevotella pectinovora from the genus Prevotella; and Bacteroides stercoris , Bacteroides ovatus , and Bacteroides uniformis from the genus Bacteroides were reduced in the three patient groups (PDM, NDDM, and P2DM) compared with healthy controls (Fig. S3A, I-Q) ( p < 0.05). Short-chain fatty acid-producing bacteria populations, such as Roseburia inulinivorans and Alistipes putredinis , were also decreased in the PDM, NDDM, and P2DM groups (Fig. S3H, L) ( p < 0.05). However, the relative abundance of Akkermansia muciniphila and Bifidobacterium longum ( p < 0.05), previously reported to reduce weight [ 40 , 41 ] and attenuate hyperlipidemia [ 41 ], was increased in the PDM and P2DM groups compared with healthy controls. There was no significant difference in A. muciniphila abundance among the four groups (Fig. S3E, G) ( p = 0.46). We found that E. coil ( p < 0.05) and K. pneumoniae ( p = 0.07) were more prevalent in the PDM and NDDM groups, respectively (Fig. S3B, F). The NDDM group had a higher abundance of Ruminococcus torques than the other three groups. Furthermore, the NDDM and P2DM groups were enriched in Ruthenibacterium lactatiformans (Fig. S3C-D). Particularly, the relative abundance of S. aureus increased in all three patient groups compared to that of the healthy controls, especially in the P2DM group ( p < 0.05) (Fig. 2 J). S. aureus abundance in T2D and its correlation with insulin resistance To investigate whether the increase in S. aureus abundance in the PDM, NDDM, and P2DM groups is associated with the methods of pharmacological intervention, we evaluated the differences in microbial communities between the insulin injection group (Insulin group) and oral hypoglycemic drug group (N_insulin group). We found no significant difference in FBG levels between these two groups ( p > 0.05) (Fig. 2 K). As shown in Fig. S4 A-D, Firmicutes, Bacteroidetes, and Proteobacteria were predominant in both the Insulin and N_insulin groups. The Insulin group had a higher relative abundance of Firmicutes (61.3%) and a lower relative abundance of Bacteroidetes (14.4) ( p < 0.05) than the N_insulin group (49.0% and 32.5%, respectively). At the genus level (Fig. S4E), Bacteroides , Bifidobacterium , Phocaeicola , Phocaeicola , and Parabacteroides were the predominant genera in the N_insulin group. However, Enterococcus and Escherichia were predominant in the Insulin group, with relative abundances much higher than those in the N_insulin group. At the species level (Fig. S4F-N), the potential probiotics Lactobacillus nasalidis , Prevotella sp. CAG:5226, Prevotella melaninogenica , Bacteroides faecis CAG:32, and Bacteroides eggerthii CAG:109 were significantly predominant in the N_insulin group than in the Insulin group ( p < 0.05). However, the pathogenic bacteria Clostridium botulinum and Desulfovibrio sp. G11 were predominant in the Insulin group ( p 0.05). S. aureus was significantly more abundant in the Insulin group than in the N_insulin group ( p < 0.05) (Fig. 2 L). Therefore, we speculate that pharmacological intervention may be the main factor leading to increased S. aureus abundance in the feces of patients with T2D. Additionally, the causal relationship between S. aureus infection and the occurrence of T2D warrants further investigation. The four groups had different clinical indices of FBG and HbA1c levels. Thus, we constructed a microbial heatmap and co-abundance networks to investigate the correlation between gut microbes and clinical data (Fig. 3 A, B). To determine whether the unique species were related to the clinical indices, we performed Spearman’s correlations between the potential biomarker species and clinical features using all patients in the four groups (Fig. 3 C-H). The results suggest a strong correlation between the clinical indices (especially FBG and HbA1c levels) and the microbial composition. The abundance of E. coli , K. pneumoniae , and Streptococcus salivarius was more strongly correlated with blood glucose measures (FBG and HbA1c), whereas that of Roseburia intestinalis , Enteroccus faecalis , and Eubacterium rectale was more negatively correlated with the measures (Fig. 3 A). As shown in Fig. 3 B, F-H, the abundance of B. longum (R = 0.4246, p = 0.001), Enteroccus faecium (R = 0.4388, p = 0.0006), and Flavonifractor plautii (R = 0.4078, p = 0.002) increased in the patient group (PDM, NDDM, and P2DM), and was positively correlated with HbA1c, FBG, and age, respectively. Moreover, the abundance of S. aureus was positively correlated with FBG (R = 0.4123, p = 0.001), suggesting a potential relationship between S. aureus and elevated blood glucose levels. Difference in functional characterization of microbiomes in the four groups Next, we performed a KEGG pathway analysis to understand the potential functional genes associated with the different microbial communities within the four groups. The NMDS results suggested that the four groups exhibited distinct functional compositions (stress = 0.054) (Fig. 4 A). Furthermore, consistent with the microbial composition results, the P2DM group had the most abundant unique KO (reaching 220), followed by the NDDM (103), PDM (81), and H (51) groups (Fig. 4 B). The four groups were enriched in pathways associated with metabolism, environmental information processing, genetic information processing, cellular processes, human diseases, and organismal systems (Fig. 4 C). Meanwhile, level-3 KEGG functional metabolism-related genes revealed that the four groups displayed different module enrichments (Fig. 4 D-J, Fig. S5E-K). For example, microbes from patients with PDM were enriched in the phosphotransferase system, fructose and mannose metabolism, biofilm formation – E. coli , and propanoate metabolism. Microbes from the NDDM group were enriched in ABC transporters, glycerolipid metabolism, benzoate degradation, and inositol phosphate metabolism (Fig. 4 D). Meanwhile, we found that the KEGG modules involved in metabolic pathways, such as alanine, aspartate, and glutamate metabolism; the citrate cycle; and butanoate metabolism, were significantly reduced in the patient group (PDM, NDDM, and P2DM) compared to healthy controls ( p < 0.05). Tryptophan metabolism ( p < 0.05), phenylalanine metabolism ( p < 0.05), and tyrosine metabolism were increased in the patient group (Fig. S5E-K). The microbe genes involved in the “valine, leucine, and isoleucine biosynthesis” pathway were increased in the patient groups (NDDM and P2DM groups) (Fig. 4 G). A previous study reported a strong association between elevated BCAA levels and a later risk for diabetes [ 39 ], consistent with this study. We found that acetolactate synthase (EC 2.2.1.6), the first key enzyme in the synthesis of BCAAs [ 42 , 43 ], was also highly enriched in the patient group microbiome (Fig. 4 H). As shown in Fig. 4 I, insulin resistance was increased in the patient group ( p < 0.05). Moreover, by tracing the source of enzymes in the KEGG database, we found that acetolactate synthase and BCAA transaminase were secreted by S. aureus (Fig. 5 J), which possesses genes encoding valine, leucine, and isoleucine biosynthesis (K01652, gene: ilvG ). Further, BCAAs mainly played a role in the development of T2D, indicating that S. aureus can promote insulin resistance by promoting the excretion of serum BCAAs. Therefore, controlling S. aureus infection can help slow the formation of BCAAs in T2D. To explore the potential pathological effects of S. aureus on the P2DM phenotype, we evaluated microbial functions in patients with P2DM. In this study, the P2DM group had more microbial genes involved in “ S. aureus infection” than the healthy controls (Fig. 4 E-F). Liu et al. [ 44 ] reported that S. aureus infection could result in insulin resistance by producing an insulin-binding protein in the extracellular domain of LtaS , eLtaS . LtaS is a membrane-embedded enzyme (EC: 2.7.8.20) [ 45 ] that possesses genes encoding lipoteichoic acid synthesis (KEGG gene: saa: SAUSA300_0703 ; K19005) in S. aureus (Fig. S5A-C). The proportion of sequences of EC: 2.7.8.20 and K19005 were more abundant in the P2DM group compared with healthy controls ( p < 0.05). Furthermore, we found that the KEGG pathways referred to ko00552, ko01100, and ko00561. Among them, the glycerolipid metabolism pathway showed the most considerable changes in the P2DM group compared with healthy controls (Fig. S5D). Notably, the changes in enzymes (EC 1.1.1.202; EC 1.1.1.6; EC 1.2.1.3; EC 2.3.1.15; EC 2.3.1.20; EC 2.3.1.51; EC 2.4.1.208; EC 2.4.1.336; EC 2.4.1.337; EC 2.7.1.107; EC 2.7.1 121; EC 2.7.1 165; EC 2.7.1.29; EC 2.7.1.30; EC 2.7.8.20) were included in the glycerolipid metabolism pathway. Together, these observations suggest that S. aureus infection can modulate the amino acid levels correlated with glycolipid metabolism and insulin resistance in patients with T2D. The relationship of serum BCAAs with clinical parameters and S. aureus infection Perturbations in the gut microbiome influence BCAA levels, and changes in serum BCAA levels are associated with T2D [ 46 ]. Therefore, we evaluated whether serum BCAAs are involved in the S. aureus infection affecting glycolipid metabolism. We collected data from 47 patients for metabolomics profiling of serum BCAAs. The orthogonal partial least squares-discriminant analysis plot suggested that the serum metabolites of the H and PDM, PDM and NDDM, and NDDM and P2DM populations were significantly different, indicating that the serum metabolites in the PDM, NDDM, and P2DM groups were considerably different (Fig. 5 A-B, E-F, I-J). We also identified key metabolite biomarkers using the adjusted p 1 (FC: fold change). 15, 31, and 18 different metabolites were identified between the H and PDM, PDM and NDDM, and NDDM and P2DM groups, respectively (Fig. S6 A-F). Of these metabolites, there were different levels of BCAAs (leucine, isoleucine, and valine), aromatic amino acids (AAAs: phenylalanine, tyrosine, and tryptophan), and bile acids (cholic acid, 7-ketodeoxycholic acid, muricholic acid, chenodeoxycholic acid, deoxycholic acid, dehydrocholic acid, taurocholic acid, taurochenodeoxycholic acid, glycochenodeoxycholic acid, and glycocholic acid) between the two groups (Fig. 5 C, G, K, M-P; Fig. S6 G-Q). The common pathways enriched in tyrosine metabolism; primary bile acid biosynthesis; valine, leucine, and isoleucine biosynthesis; and phenylalanine metabolism were enriched between the H and PDM, PDM and NDDM, and NDDM and P2DM groups (Fig. 5 D, H, L; Fig. S6 S-R). To obtain further insights into the development of T2D in terms of BCAA biosynthesis, AAA metabolism, and bile acid biosynthesis, we quantitatively analyzed the serum metabolites related to the BCAAs, AAAs, and bile acid biosynthesis pathways (Fig. 5 M-P; Fig. S6 G-Q). In the valine, leucine, and isoleucine biosynthesis pathways, valine and BCAA levels were higher in the PDM, NDDM, and P2DM groups than in the H group ( p 0.05). Regarding AAA metabolism, tryptophan and AAA levels were higher in the P2DM group than in the H group ( p < 0.05), and the derivatives (indole-lactic acid and indole-butyric acid) of tryptophan metabolism were higher in the NDDM and P2DM groups than in the H group ( p 0.05). These results indicate that amino acid metabolism by gut microbes may modulate the levels of circulating serum BCAAs, which correlate with the severity of diabetes; thus, reducing the consumption of foods rich in BCAAs can result in a lower prevalence of T2D. To further investigate whether the alteration in valine, leucine, and isoleucine biosynthesis correlated with the clinical parameters and altered microbes, Spearman’s correlations were assessed. We found a close association among these metabolites, the altered microbiome, and clinical parameters (Fig. 6 A). Moreover, we found that BCAA and valine levels were positively correlated with FBG and HbA1c levels, isoleucine levels were positively correlated with the SBP, and tryptophan and tyrosine levels were negatively correlated with TC and LDL-C levels (Fig. 6 B). Specifically, we found that leucine levels were positively correlated with Flavonifractor plautii abundance. Valine, isoleucine, and BCAA levels were positively correlated with E. coli , K. pneumoniae , and S. aureus abundance and inversely correlated with species from B. cellulosilyticus , B. ovatus , B. xylanisolvens , B. uniformis and other potential probiotics (Fig. 6 C-M). Together, these data suggest that the T2D groups had a higher abundance of S. aureus , increased FBG and HbA1c levels, and higher BCAA levels, suggesting that the link between S. aureus and glycolipid metabolism might be mediated by BCAA levels. Transplantation of faeces from ALS-positive patients with type 2 diabetes exacerbates high fat diet-induced insulin resistance in rats To determine whether ALS contributes to disordered glycolipid metabolism mediated by S. aureus , we gavaged rats with faeces from patients with type 2 diabetes with a ALS S. aureus or a non-ALS S. aureus (Fig. 7 A, B). Compared to rats gavaged with healthy peoples faeces, rats fed with high fat diets after they were gavaged with ALS S. aureus developed more severe disordered glycolipid metabolism as indicated by a lower level of insulin, higher level of fasting blood glucose, insulin resistance and increased inflammatory cytokine levels (TNF-α and IL-6), however, there was no significant difference in serum endotoxin among three groups (Fig. 7 C-H). Rats that were fed high fat diets after they were gavaged with ALS S. aureus had significantly increased liver injury, intestinal permeability and higher levels of valine and BCAAs (Fig. 7 I-N), as compared with rats that were fed high fat diets after they were administered with non-ALS S. aureus . Altogether, the above results indicate that ALS plays an important role in promoting branched chain amino acid synthesis and inducing insulin resistance in S. aureus. The therapeutic effects of S. aureus phage that target blood glucose in db/db mice In vitro, S. aureus 2868B2 and its phage were coculture statically in TSB broth or on TSB agar plate at 37°C. The TSB broth is made transparent and zone of inhibition is formed in TSB agar plate by adding S. aureus phage, and its inhibited the growth of S. aureus (Fig. 8 A-C), which suggested that S. aureus phage can lysis of S. aureus 2868B2, and did not affect the other probiotics. Meanwhile, the gene encoding the branched chain amino acid biosynthesis enzyme in S. aureus 2868B2 was identified as ALS ( ilvG ) (Fig. 8 D). To further demonstrate the potential causative role of S. aureus for the development of high fat diets-induced type 2 diabetes insulin resistance, we investigated the effects of treatment with S. aureus phages on db/db mice (Fig. 8 E). Compared to DC mice, mice gavaged with phages that target S. aureus had lower levels of FBG, HbA1c and HOMA-IR, triglyceride and inflammation (TNF-α, IL-1β), while the body weight had a higher after gavaged with phages, but no significant difference among three groups in total cholesterol, LDL-cholesterol, and HDL-cholesterol (Fig. 8 F-L, Fig. S7A-C). At the same time, mice that were gavaged with phages that target S. aureus had significantly less liver injury, impairment of islet, steatosis, intestinal permeability and lower levels of leucine, isoleucine, valine, BCAAs and ALS (Fig. 8 M-U), as compared with the DC mice. Administration of S. aureus phage significantly increased serum levels of PI3K, AKT and GLUT4 (Fig. 8 V-X). It speculates that the mechanisms of the anti-diabetic effects in S. aureus phage are involved with activation of PI3K/AKT/GLUT4 signaling pathways expression. Therefore, S. aureus phage might have a promising potential in preventing diabetes insulin resistance. Discussion In the present study, we performed metagenomics sequencing and serum metabolomics profiling of patients with PDM, NDDM, and P2DM. We found that S. aureus abundance and BCAA biosynthesis were increased in the fecal microbes and serum metabolites of the PDM, NDDM, and P2DM groups. In particular, the P2DM (insulin injection group) group had the highest abundance of S. aureus and BCAAs and was associated with an increase in FBG and HbA1c levels. S. aureus possesses BCAA biosynthesis enzymes, especially acetolactate synthase (EC:2.2.1.6, K01652) and K19005. Furthermore, supplementation with S. aureus phage, could improve glycemic control in db/db mice and decrease BCAAs concentrations, this resulted in decreased blood glucose levels and improved insulin resistance. This study suggests a unique pathogenic microorganism and metabolite signature in patients with prediabetes, NDDM, and P2DM. Previous studies have indicated that more than 470 million people will develop pre-diabetes by 2030 [ 47 ]. Moreover, patients with prediabetes and treatment-naïve patients with T2D have distinct differences in gut microbiota [ 3 ]. Furthermore, although previous studies have shown that altered microbes are associated with T2D [ 48 ], they did not distinguish between participants with T2D who were newly diagnosed or underwent antidiabetic drug intervention. Given the importance of drug-microbiome interactions [ 49 ], whether patients with P2DM possess specific microbial and metabolite compositions remains unknown. In our study, a significant difference in the gut microbiota species was observed between the patient groups (PDM, NDDM, and P2DM) and healthy controls (Fig. 2 ). The abundance of species from the genus Bacteroides , such as Bacteroides stercoris , Bacteroides ovatus , Bacteroides uniformis , and Phocaeicola vulgatus , Phocaeicola dorei , and Phocaeicola massiliensis , was significantly depleted in the patient groups, consistent with the results of a previous study that showed that the abundance of Bacteroides was decreased in T2D [ 50 ]. Prevotella copri has been reported to improve glucose tolerance and homeostasis [ 51 ]. Moreover, we found that the relative abundances of Prevotella copri and Prevotella pectinovora were significantly reduced in the patient groups. Meanwhile, the relative abundance of Escherichia coli was significantly increased in the PDM group than in the other three groups, consistent with a recent study involving a Chinese population [ 3 ]. Nevertheless, Akkermansia muciniphila , a well-known mucin-degrading bacterium that can alleviate metabolic syndrome, was abundant in the PDM group. However, there was no significant difference among the groups, which provided conflicting findings with a previous study and may be due to the small sample size of this study [ 52 ]. In addition, the NDDM group had a higher abundance of Ruminococcus torques (Fig. S3C) in accordance with a prior study [ 53 ]. However, after treatment with antidiabetic drugs, Ruminococcus torques abundance was significantly reduced. Altogether, these results imply that some unique species follow gradual disease development from prediabetes to overt T2D and treatment. We also observed elevated S. aureus abundance in the patient groups, particularly in the P2DM group. S. aureus is a major pathogen [ 54 ], and its colonization has been widely reported in mice with insulin resistance [ 55 ] and in patients with diabetes compared with healthy individuals [ 56 ]. Notably, S. aureus abundance was positively correlated with elevated FBG levels and BCAA biosynthesis. Studies have demonstrated that increased serum BCAA levels are positively correlated with T2D [ 57 ]. We observed a significant correlation between BCAAs and FBG and a positive correlation between BCAAs and S. aureus abundance. Previous studies demonstrated that acetolactate synthase is the first key enzyme in the synthesis of BCAAs [ 58 ] and that BCAA transaminase is the last enzyme in the biosynthesis of L-isoleucine and L-valine[ 59 ]. We found that the relative abundance of valine, leucine, and isoleucine biosynthesis (EC 2.2.1.6, K00826, gene: ilvG ) in the patient group was higher than that in the healthy controls, especially in the NDDM group. In addition, our metagenomics sequencing data showed that S. aureus possesses acetolactate synthase and BCAA transaminase. We propose that S. aureus promotes the expression of BCAA synthesis-related genes, which induce BCAA biosynthesis, thereby causing elevated FBG levels and decreased insulin secretion in T2D. A recent study revealed that S. aureus infection induced insulin resistance by secreting an insulin-binding protein in the extracellular domain of LtaS , eLtaS [ 60 ]. Our study demonstrated that patients with P2DM had a high abundance of LtaS (Transferases, EC 2.7.8.20, k19005), which is related to the glycerolipid metabolism pathway, indicating that the targeted elimination of S. aureus may be a promising strategy for the treatment and prevention of T2D. Furthermore, we found that insulin injection administration may be the main factor causing the accumulation of S. aureus in the feces of patients with T2D, which requires further study. We found that the supplementation of db/db mice with S. aureus phage, which are ubiquitous in bacteria-rich environments, including the gut[ 61 ], improved FBG and insulin resistance levels. Several studies have reported that bacteriophages may regulate intestinal health and become an alternative diagnostic and therapeutic agent for metabolic diseases[ 62 , 63 ]. Rasmussen et al reported that faecal virome transplantation decreases symptoms of type 2 diabetes and obesity in a murine model[ 64 ]. It has previously been demonstrated that MS2 phages could ameliorate glucose metabolism in T2D mice[ 65 ], this consistent with our results. Further, we observed that the db/db mice had higher serum BCAA levels than the control group and that treatment with S. aureus phage decreased serum BCAA levels. Previous studies have indicated that elevated plasma branched chain amino acids (BCAAs) has been implicated in development of insulin resistance and T2D[ 66 ]. Thus we speculate that S. aureus phage regulates insulin resistance by reducing serum BCAAs levels. Karusheva et al have suggested that short-term intake of BCAAs can induce insulin resistance in humans, likely due to activation of the mechanistic target of rapamycin (mTOR) complex 1/ribosomal protein S6 kinase (p70S6K) pathway[ 67 ]. Furthermore, this study have found that take of S. aureus phage significantly increased serum levels of PI3K, AKT and GLUT4. Therefore, we speculate that the mechanisms of the anti-diabetic effects in S. aureus phage are involved with activation of PI3K/AKT/GLUT4 signaling pathways expression, and S. aureus phage might have a promising potential in preventing diabetes insulin resistance. One limitation of this study is its small sample size, therefore, a larger cohort is required to validate the relevance of our findings in humans, although we validated S. aureus changes at different stages of T2D, further studies are warranted to consider the impact of confounding factors such as diet, smoking, and alcohol abuse on the results involving microorganisms and their metabolites in patients. In additional, safety studies are required for complex populations (such as patients with type 2 diabetes insulin resistance), because phages can induce a strong immune reaction[ 68 ]. Further work is required to determine whether phages that target ALS S. aureus might be used to treat patients with type 2 diabetes insulin resistance. Conclusions In summary, this study indicates a close association between alterations in specific species and metabolites and host glucose metabolism at different disease stages of T2D. We further suggest that S. aureus and its enzymes (ALS acetolactate synthase (EC:2.2.1.6, K01652)), which are related to insulin resistance, can alter serum BCAA levels and increase blood glucose levels. We also demonstrated that insulin administration may be a risk factor causing the accumulation of S. aureus in the feces of patients with T2D. Using humanized rats that were colonized with bacteria from the faeces of patients with type 2 diabetes insulin resistance, it proved that ALS contributes to disordered insulin resistance mediated by S. aureus . This study link ALS-positive S. aureus with more severe disordered glycolipid metabolism and increased insulin resistance in patients with type 2 diabetes. We show that bacteriophages can specifically target ALS-positive S. aureus , paving the way for evaluating S. aureus phage as a prevention and treatment modality for T2D. Abbreviations BCAA branched-chain amino acid T2D type 2 diabetes PDM prediabetes NDDM newly diagnosed diabetes P2DM post-medication type 2 diabetes ALS acetolactate synthase FBG fasting blood glucose ORFs open reading frames KEGG Kyoto Encyclopedia of Genes and Genomes TSB tryptic soytone broth HbA1C Hemoglobin A1C LDL-C low density lipoprotein cholesterol HDL-C high density lipoprotein cholesterol IL-1β Interleukin-1β TNF-α tumor necrosis factor-α IL-6 Interleukin-6 PI3K phosphatidylinositol-3-kinase AKT protein kinase B GLUT4 glucose transporter 4 ELISA enzyme-linked immunosorbent assay Declarations Acknowledgements We thank Ya Chen, and Tong Chen for analyzing the composition of gut microbiota and metabolites. Author contributions Conceptualization: T.T.L., T.J, Z.L. and L.Y.L. Methodology: L.W., H.G., H.Z. and N.Z. Investigation: T.T.L., T.J., Z.L., and L.Y.L. Formal analysis: T.T.L., L.W., H.G. and H.Z. Visualization: T.T.L., T.J, Z.L., and L.Y.L. Writing-original draft: T.T.L., T.J, Z.L., and L.Y.L. Writing-review and editing: B.D., X.X.Q., Q.P.W. and B.G. All authors read, revised, and approved the final manuscript. Funding This work was supported by research grants from the Guangdong Province Basic and Applied Basic Research Fund Project (2022A1515110447), Open Fund Project of the State Key Laboratory of Applied Microbiology in South China (SKLAM006-2022), 74th batch of general funding from the China Postdoctoral Science Foundation (2023M740774), Guangdong Provincial People's Hospital, Postdoctoral Research Launch Fund (BY012022017). Availability of data and materials The data that support this study are available from the corresponding authors upon reasonable request. Ethics approval and consent to participate This study was approved by the Ethics Review Committee of the Guangdong Provincial People's Hospital (KY2023-675). Consent for publication Not applicable. 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Short-term dietary reduction of branched-chain amino acids reduces meal-induced insulin secretion and modifies microbiome composition in type 2 diabetes: a randomized controlled crossover trial. 2019;110(5):1098-107. Górski A, Dąbrowska K, Międzybrodzki R, Weber-Dąbrowska B, Łusiak-Szelachowska M, Jończyk-Matysiak E, et al. Phages and immunomodulation. 2017;12(10):905-14. Additional Declarations No competing interests reported. Supplementary Files SupplmentrayFile.docx GraphicalAbstract.jpg Graphical abstract. This study demonstrates that the biosynthesis of branched chain amino acids by gut microbes containing ALS leads to a increase in serum branched chain amino acids levels, causing insulin resistance in type 2 diabetes. Our study reveals both a novel mechanism for insulin resistance in type 2 diabetes linked to gut microbes and possible intervention targets. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4242450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290016342,"identity":"d545660f-d515-483c-823e-4d881d557055","order_by":0,"name":"Tingting 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University","correspondingAuthor":false,"prefix":"","firstName":"Ni","middleName":"","lastName":"Zhang","suffix":""},{"id":290016351,"identity":"2df54b71-ff2d-43a8-89b6-9027d2c4e01a","order_by":8,"name":"Bo Dong","email":"","orcid":"","institution":"Xi'an Jiaotong University Affiliated Honghui Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Dong","suffix":""},{"id":290016354,"identity":"a8e36c75-3446-4ce7-adb1-1a782baad7e2","order_by":9,"name":"Xinqiang Xie","email":"","orcid":"","institution":"Guangdong Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xinqiang","middleName":"","lastName":"Xie","suffix":""},{"id":290016356,"identity":"d3c30e5f-e62e-498f-b4cc-e9eebaf28bd1","order_by":10,"name":"Qingping Wu","email":"","orcid":"","institution":"Guangdong Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qingping","middleName":"","lastName":"Wu","suffix":""},{"id":290016357,"identity":"aa66151c-63b9-491d-8c02-20029688d0d9","order_by":11,"name":"Bing Gu","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2024-04-09 13:59:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4242450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4242450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54999861,"identity":"0c5153d4-0f1b-41c4-a53f-00ccd99c32b6","added_by":"auto","created_at":"2024-04-19 18:32:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":331480,"visible":true,"origin":"","legend":"\u003cp\u003eOverall study design and major clinical data among the four groups. (A) Study design. (B–C) Boxplots with major clinical outcomes for participants in the four groups. (B) FBG. (C) HbA1c. FBG, fasting blood glucose; HbA1c, glycated hemoglobin. H, healthy; PDM, prediabetes; NDDM, newly diagnosed diabetes; P2DM, post medication type 2 diabetes. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001; ns: no significant difference.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/ca4a434f904eac8ca94c2417.jpg"},{"id":55002806,"identity":"07c3713a-8070-4adf-aa9c-5ee8ce8b4490","added_by":"auto","created_at":"2024-04-19 18:40:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":467360,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the microbial community composition among the four groups. (A) Shannon index of microbial community at the species level. (B) 2D scatter map based on random forest proximity matrix. (C) Relative abundance of the microbial community at the phylum level among the four groups. (D) Comparison of Firmicutes in different groups. (E) Comparison of Bacteroidota in different groups. (F) Comparison of the ratio of Firmicutes and Bacteroidota in different groups. (G) Venn diagrams of microbial community at the species level. (H) Relative abundance of the microbial community at the genus level among the four groups. (I) Relative abundance of the microbial community at the species level among the four groups. (J) Comparison of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in different groups. (K) Comparison of FBG level after administration of insulin and non insulin. (L) Comparison of the relative abundance of \u003cem\u003eS. aureus \u003c/em\u003ebetween the insulin and non insulin group. H, healthy; PDM, prediabetes; NDDM, newly diagnosed diabetes; P2DM, post medication type 2 diabetes. **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/ed9274d81ac8a9502158c22d.jpg"},{"id":54999865,"identity":"7e6a9ce3-9b1e-4270-93c7-dcf65b9035af","added_by":"auto","created_at":"2024-04-19 18:32:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":883422,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of gut microbial species with clinical indices. (A) Heatmap of the Spearman’s rank correlation between 12 clinical indices and 34 differential species. Red, positive correlations; blue, negative correlations. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. (B) Correlation network between clinical indices and differential species. Green and red lines denoted negative correlations and positive correlations. (C-H) Scatter plots showing the correlations between gut microbial species and clinical indices. (C) \u003cem\u003eEnteroccus faecalis\u003c/em\u003e and FBG; (D) \u003cem\u003eRoseburia inulinivorans\u003c/em\u003e and HbA1c; (E) \u003cem\u003eS. aureus\u003c/em\u003e and FBG; (F) \u003cem\u003eBifidobacterium longum\u003c/em\u003e and FBG; (G) \u003cem\u003eEnteroccus faecium\u003c/em\u003e and FBG; (H) \u003cem\u003eFlavonifractor plautii\u003c/em\u003e and Age. SBP, systolic pressure; DBP, diastolic pressure; FBG, fasting blood glucose; HbA1c, glycated hemoglobin; BMI, body mass index; TC, total cholesterol; TG, triglyceride; LDL-C, low density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol. H, healthy; PDM, prediabetes; NDDM, newly diagnosed diabetes; P2DM, post medication type 2 diabetes.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/90175555329086dfed1d4d38.jpg"},{"id":54999869,"identity":"a38a8a93-f60b-4588-a1cd-959440ae820d","added_by":"auto","created_at":"2024-04-19 18:32:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":650693,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG annotation profile of the four groups microbiome. (A) NMDS analysis of the functional composition in four groups. (B) Venn diagram of functional composition at the KO level. (C) Circos diagram of functional composition at the level 1. D) Histogram of functional composition at the level 3. (E) Relative abundance of \u003cem\u003eS. aureus\u003c/em\u003e infection among four groups. (F) \u003cem\u003eS. aureus\u003c/em\u003e infection (H vs P2DM). (G) Relative abundance of valine, leucine and isoleucine biosynthesis among four groups. (H) Relative abundance of EC 2.2.1.6 (acetolactate synthase). (I) Relative abundance of insulin resistance among four groups. (J) Tracing the biosynthesis of valine, leucine and isoleucine, K01652, acetolactate synthase (gene: \u003cem\u003eilvG\u003c/em\u003e) produced by \u003cem\u003eS. aureus\u003c/em\u003e. H, healthy; PDM, prediabetes; NDDM, newly diagnosed diabetes; P2DM, post medication type 2 diabetes. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/c0b7c527815a85e3922bdf36.jpg"},{"id":54999871,"identity":"3936dc24-7569-4214-ba41-4435bfb1bd80","added_by":"auto","created_at":"2024-04-19 18:32:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":784016,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences of serum metabolites and metabolic pathway enrichment analysis among the four groups. (A, B) Score plot of the OPLS-DA model constructed in the positive and negative ion modes (H vs PDM). (C) Heat map showing the abundance of the differential metabolites for each sample in H vs PDM (VIP \u0026gt; 1, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). (D) KEGG pathway enrichment pathway analysis of differential metabolites in H vs PDM. (E, F) Score plot of the OPLS-DA model constructed in the positive and negative ion modes (PDM vs NDDM). (G) Heat map showing the abundance of the differential metabolites for each sample in PDM vs NDDM (VIP \u0026gt; 1, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05). (H) KEGG pathway enrichment pathway analysis of differential metabolites in PDM vs NDDM. (I, J) Score plot of the OPLS-DA model constructed in the positive and negative ion modes (NDDM vs P2DM). (K) Heat map showing the abundance of the differential metabolites for each sample in NDDM vs P2DM (VIP \u0026gt; 1, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05). (L) KEGG pathway enrichment pathway analysis of differential metabolites in NDDM vs P2DM. (M, N, O, P) Comparative analysis of the level of valine, leucine, isoleucine and BCAAs in different groups. H, healthy; PDM, prediabetes; NDDM, newly diagnosed diabetes; P2DM, post medication type 2 diabetes; BCAAs: branched-chain amino acids.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/5cde0f44a9e1b1cf340969ab.jpg"},{"id":54999867,"identity":"30a4b0e0-9294-4e00-afa6-b219b0801d0c","added_by":"auto","created_at":"2024-04-19 18:32:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":675209,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of clinical indicators, differential metabolites, and differential microbiota. (A) Sankey diagrams of clinical indicators, differential metabolites, and differential microbiota. (B) Correlations analysis between clinical indices and differential metabolites. (C) Correlations analysis between differential microbiota and differential metabolites. (D-M) Scatter plots showing the correlations between BCAAs (valine, leucine and isoleucine) and differential metabolites. (D) \u003cem\u003eS. aureus\u003c/em\u003e and BCAAs; (E) \u003cem\u003eEscherichia coli\u003c/em\u003e and isoleucine; (F) \u003cem\u003eBacteroides cellulosilyticus\u003c/em\u003e and BCAAs; (G) \u003cem\u003eBacteroides ovatus\u003c/em\u003e and BCAAs; (H) \u003cem\u003eBacteroides xylanisolvens\u003c/em\u003e and BCAAs; (I) \u003cem\u003eEscherichia coli\u003c/em\u003e and valine; (J) \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and isoleucine; (K) \u003cem\u003eBacteroides cellulosilyticus\u003c/em\u003e and isoleucine; (L) \u003cem\u003eBacteroides ovatus\u003c/em\u003e and isoleucine; (M) \u003cem\u003eBacteroides uniformis\u003c/em\u003e and isoleucine. BCAA, branched chain amino acid; AAAs, aromatic amino acids. H, healthy; PDM, prediabetes; NDDM, newly diagnosed diabetes; P2DM, post medication type 2 diabetes.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/c5e04c11ed61cdb3626a3261.jpg"},{"id":54999866,"identity":"a64e54b6-827c-412e-a090-8424f5e9a755","added_by":"auto","created_at":"2024-04-19 18:32:47","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1334082,"visible":true,"origin":"","legend":"\u003cp\u003eTransplantation of faeces from ALS-positive patients with type 2 diabetes exacerbates high fat diets-induced rats insulin resistance. (A) Percentage of subjects with faeces samples that was positive for ALS enzyme in controls and patients with type 2 diabetes, assessed by qPCR; (B) Design diagram of humanized gut microbiota transplantation plan; (C-H) Serum levels of FBG, insulin, HOMA-IR, TNF-α, IL-6 and endotoxins. (I-J) Representative sections of liver and colon stained with haematoxylin and eosin (H\u0026amp;E). (K-N) Serum levels of valine, leucine, isoleucine, and BCAAs. H, healthy; P2DM, post medication type 2 diabetes. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001, ns: no significant difference.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/b04b20f1b91993ca76a55000.jpg"},{"id":54999870,"identity":"c54a5dc1-d8e0-4ead-8f1b-ae1502ad5439","added_by":"auto","created_at":"2024-04-19 18:32:47","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1430096,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of \u003cem\u003eS. aureus\u003c/em\u003e phage on db/db mice. (A-B) \u003cem\u003eIn vitro\u003c/em\u003e, \u003cem\u003eS. aureus\u003c/em\u003e 2868B2 and its phage were coculture statically in TSB broth or on TSB agar plate at 37 °C. (C) The growth curve of coculture between \u003cem\u003eS. aureus\u003c/em\u003e 2868B2 and its phage. (D) The gene \u003cem\u003eilvG\u003c/em\u003e encodes ALS in \u003cem\u003eS. aureus\u003c/em\u003e 2868B2. (E) Schematic diagram of \u003cem\u003eS. aureus\u003c/em\u003e phage supplementation study design. db/db mice were given drinking water or with \u003cem\u003eS. aureus\u003c/em\u003e phage for 4 weeks. (F-L) body weight, and serum levels of FBG, HbA1c, HOMA-IR, triglyceride, TNF-α, IL-1β. (M-P) Representative sections of liver, pancreas, adipose tissue and colon stained with haematoxylin and eosin (H\u0026amp;E). (Q-U) Serum levels of valine, leucine, isoleucine, BCAAs and ALS. (V-X) Serum levels of PI3K, AKT and GLUT4. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001, ns: no significant difference.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/802f975a04541a46f5b17d72.jpg"},{"id":55005359,"identity":"3dbf20dc-ba18-4ed9-bf73-cb7cd90b6a36","added_by":"auto","created_at":"2024-04-19 18:48:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2069702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/44833bac-24fc-407f-b231-a5772e479416.pdf"},{"id":54999868,"identity":"5a0c1b4d-c0eb-453b-85ce-631a5064a733","added_by":"auto","created_at":"2024-04-19 18:32:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22668875,"visible":true,"origin":"","legend":"","description":"","filename":"SupplmentrayFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/42ae20712520376c9dfcdc4f.docx"},{"id":54999862,"identity":"226a6cb8-c51e-46dc-a097-e92b58ee351e","added_by":"auto","created_at":"2024-04-19 18:32:47","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":59131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract\u003c/strong\u003e. This study demonstrates that the biosynthesis of branched chain amino acids by gut microbes containing ALS leads to a increase in serum branched chain amino acids levels, causing insulin resistance in type 2 diabetes. Our study reveals both a novel mechanism for insulin resistance in type 2 diabetes linked to gut microbes and possible intervention targets.\u003c/p\u003e","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4242450/v1/4340f39293059578d306ef3d.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Staphylococcus aureus-expressed acetolactate synthase enhances the biosynthesis of branched-chain amino acids and is linked to insulin resistance in type 2 diabetes in South China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes (T2D), a chronic disease characterized by elevated blood glucose concentrations, is one of the leading causes of death and disability worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, approximately 36% of Chinese adults have prediabetes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], a condition characterized by intermediate hyperglycemia and insulin resistance. Those with prediabetes will eventually develop overt T2D [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Compared with patients with prediabetes, those with treatment-na\u0026iuml;ve T2D have different microbiomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, the interaction between antidiabetic drugs, such as metformin [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], liraglutide [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], SGLT2 inhibitors (dapagliflozin) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and insulin, and gut microbes, and their influence on drug functions remain of immediate research interest, making it difficult to identify bacterial biomarkers that might be related to disease progression. Therefore, clarifying the relationship between microbes and T2D incidence and development is paramount.\u003c/p\u003e \u003cp\u003eEvidence suggests remarkable changes in the gut microbiota in T2D and prediabetes conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Metagenomics studies have enabled the characterization of microbes in patients with T2D and provided insights into the functional gene abundance interplay between microbes and host metabolism in China and Europe [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In general, individuals with T2D exhibit reduced bacterial diversity and gene richness [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recent studies have shown that T2D is associated with a higher ratio of Firmicutes to Bacteroidetes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], an increase in \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eVeillonellaceae\u003c/em\u003e, and \u003cem\u003ePrevotella\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and a decrease in butyrate-producing genera (e.g., \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eClostridiales\u003c/em\u003e) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. T2D is associated with a higher rate of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e colonization and is linked to altered glucose tolerance and elevated glucose levels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. There is also a significant difference in microbes between patients with prediabetes and treatment-na\u0026iuml;ve patients with T2D [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For example, some studies have shown that \u003cem\u003eEscherichia coli\u003c/em\u003e was abundant in prediabetic patients, whereas \u003cem\u003eBacteroides\u003c/em\u003e was abundant in those with T2D [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, it was initially reported that the glucose-lowering effect of antidiabetic drugs could be partly attributed to certain microbial species [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Still, no studies have analyzed the pharmacological intervention on the composition of microbes in T2D (e.g., insulin and oral hypoglycemic drugs). Furthermore, whether and how changes in fecal microbes and blood metabolites are functionally related to the host phenotype in prediabetes, newly diagnosed diabetes, and post-medication diabetes remains unclear.\u003c/p\u003e \u003cp\u003eAlterations in the serum metabolites of branched-chain amino acids (BCAAs; valine, leucine, and isoleucine) are a channel by which microbes affect human metabolic health [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Studies have suggested a close link between serum BCAA levels and T2D occurrence [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which may be the reason for increased BCAA accumulation in adipose tissues [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and the liver [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. T2D can increase the microbial potential for BCAA biosynthesis, such as \u003cem\u003ePrevotella copri\u003c/em\u003e and \u003cem\u003eBacteroifes vulgatus\u003c/em\u003e, whereas \u003cem\u003eButyrivibrio crossotus\u003c/em\u003e and \u003cem\u003eEubacterium siraeum\u003c/em\u003e can reduce BCAA uptake [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The reasons for these elevated levels in obesity and T2D are not well-established. Yu et al. reported that a low isoleucine diet improves liver and adipose metabolism, increasing insulin sensitivity and preventing diabetes by activating the FGF21-UCP1 axis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Charon et al. indicated that the activity of the BCAA biosynthesis enzyme, d-citramalate synthase, was the highest in patients with T2D [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, Qiao et al. found that \u003cem\u003eParabacteroides merdae\u003c/em\u003e protects against cardiovascular damage by enhancing BCAA catabolism as it expresses the \u003cem\u003eporA\u003c/em\u003e gene that degrades BCAAs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, it is necessary to explore the bacteria that promote the production or decomposition of BCAAs in T2D and analyze their possible mechanisms, including the enzymes and genes involved in BCAA metabolism.\u003c/p\u003e \u003cp\u003eHere, we performed metagenomics sequencing and metabolomics profiling of patients with prediabetes mellitus (PDM), newly diagnosed diabetes mellitus (NDDM), and post-medication T2D (P2DM) to investigate whether the altered gut microbes and metabolites could explain the specific clinical characteristics of different disease stages of T2D. We identified diabetes subgroups associated with microbial species and their effects on BCAA profiles. Moreover, using a insulin resistance rats and db/db mice model, we uncovered evidence of causal effects of \u003cem\u003eS. aureus\u003c/em\u003e bacteriophage, which caused a beneficial effect on gut microbes, glycemic control, and serum circulating BCAA levels in T2D.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eT2D study population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Committee of the Guangdong Provincial People\u0026apos;s Hospital\u0026nbsp;(KY2023-675). The study participants were recruited from Guangzhou and included 12 healthy controls (H), 12 with PDM, 12 with NDDM, and 21 with\u0026nbsp;P2DM.\u0026nbsp;All participants were grouped according to\u0026nbsp;the 2003 American Diabetes Association diagnostic criteria\u0026nbsp;[28]. Patients with the following conditions were excluded: type 1 diabetes, hypertension, dyslipidemia, acute infectious disease, cirrhosis, diarrhea, pregnancy, treatment with antibiotics within 3 months, and intake of probiotics within 6 weeks. The clinical patient information is summarized in Table S1.\u003c/p\u003e\n\u003cp\u003eStool samples for metagenomics and serum samples for metabolomics were collected and then stored in a dry ice box until arrival at the laboratory. Then, they were stored in a -80℃ ultra-low temperature freezer in the laboratory until further use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDNA extraction and metagenomics sequencing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal genomic DNA was extracted from T2D stool samples using the Mag-Bind\u0026reg; Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.). All samples were analyzed on Illumina NovaSeq (Illumina Inc., San Diego, CA, USA) using a NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles) according to the manufacturer\u0026rsquo;s instructions (www.illumina.com).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSequence quality control and genome assembly\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were analyzed on the free online Majorbio Cloud Platform (www.majorbio.com). Briefly, adaptors were trimmed from paired-end Illumina reads, and low-quality reads were removed using fastp\u0026nbsp;[29]. The reads were aligned to the human genome using BWA\u0026nbsp;[30]. Metagenomics data were assembled using MEGAHIT\u0026nbsp;[31]. Contigs with lengths \u0026ge;300 bp were selected as the final assembling result, and then the contigs were used for further gene predictions and annotations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGene prediction and non-redundant gene catalog construction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe open reading frames (ORFs) from each assembled contig were predicted using Prodigal\u0026nbsp;[32]/MetaGene\u0026nbsp;[33]. The predicted ORFs with lengths \u0026ge;100 bp were retrieved and translated into amino acid sequences using the NCBI translation table. A non-redundant gene catalog was constructed using CD-HIT\u0026nbsp;[34]\u0026nbsp;with 90% sequence identity and 90% coverage. High-quality reads were aligned to non-redundant gene catalogs to calculate gene abundance with 95% identity using SOAPaligner\u0026nbsp;[35].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTaxonomy and functional annotation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative sequences of non-redundant gene catalogs were aligned to the NR database with an e-value cutoff of 1e-5 using Diamond\u0026nbsp;[36]\u0026nbsp;for taxonomic annotations. Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation was conducted using Diamond\u0026nbsp;[36]\u0026nbsp;against the KEGG database with an e-value cutoff of 1e-5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNon-targeted metabolomics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor metabolites extraction, serum(200 \u0026mu;L) was suspended in a solvent (methanol:acetonitrile=1:1, v:v, 1 mL) and vortexed for 1 min. The mixture underwent ultrasound radiation in a low-temperature environment and was then stored at -20℃ for 2 h and centrifuged at 10000 rpm for 20 min at 4℃. The supernatant was drained in a cryovacuum concentrator, and 200 \u0026mu;L of a complex solution (acetonitrile: H\u003csub\u003e2\u003c/sub\u003eO=1:1, v:v) was added for re-solution. The mixture was vortexed for 1 min and centrifuged at 10000 rpm for 30 min at 4℃. The supernatant was obtained and placed in the sample bottle for LC-MS/MS analysis. Exactly 10 \u0026mu;L of supernatant from each sample was mixed with QC samples to evaluate the repeatability and stability of the LC-MS analysis process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMetabolomics data processing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data collected from LC-MS/MS were imported into Compound Discoverer 3.1 (Thermo Fisher Scientific, USA) for data processing. The identification of metabolites was combined with several databases, including the BGI Library, mzCloud, and ChemSpider (HMDB, KEGG, and LipidMaps).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eReal-time qPCR\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from H and P2DM stool samples. Primer sequences for\u0026nbsp;\u003cem\u003eS. aureus\u003c/em\u003e 16S rRNA gene (ALS, \u003cem\u003eilvG\u003c/em\u003e) using the online in NCBI (https://www.ncbi.nlm.nih.gov/tools/primer-blast). Amplification of bacterial genes were determined with SYBR Green PCR master mix (Invitrogen) using the ABI 7500 real-time PCR system (Applied Biosystems).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCo culture of S. aureus and its phages\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e 2868B2 and its phage were grown statically in tryptic soytone broth (TSB) or on TSB agar plate at 37\u0026thinsp;\u0026deg;C. Meanwhile, the growth curve of \u003cem\u003eS. aureus\u003c/em\u003e alone or in combination with its phages were measured using microplate reader (Epoch2, BioTek).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnimal studies\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal experimental procedures were approved by the Committee of the Institute of Microbiology, Guangdong Academy of Sciences (No. GT-IACUC202309141). Five-week-old male Wistar rats (108\u0026ndash;116 g) were housed in a specific pathogen-free barrier facility and were free to eat and drink for 7 days, the rats were randomly divided into 3 groups. And then administration of the vancomycin (50 mg/kg), neomycin (100 mg/kg), and metronidazole (100 mg/kg) antibiotic in combination with the penbritin (1 mg/mL) once a day (10 mg/kg) to ALS-P and ALS-N rats for a total of 7 days. Stool samples from patients with H and P2DM were used for faecal transplantation in germ-free rats. Rats were gavaged with 100 \u0026mu;L of stool samples (1 g stool dissolved in 30 ml Luria\u0026ndash;Bertani (LB) medium containing 15% glycerol under anaerobic conditions) for 21 days until the experiments end.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe six-week-old male db/m (control) and db/db mice (diabetic model and \u003cem\u003eS. aureus\u003c/em\u003e bacteriophage group) were fed a standard chow diet and drinking water for 7 days. And then, db/db mice were fed a standard chow diet and given \u003cem\u003eS. aureus\u003c/em\u003e bacteriophage (1 \u0026times; 10\u003csup\u003e9\u0026nbsp;\u003c/sup\u003ePFU/mL) by gavaging for 4 weeks until the experiments end.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBiochemical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the end of the experiment, the blood samples were collected and centrifuged at 3000 rpm for 15 min at 4℃. Serum were collected and immediately stored at -80℃ for further analysis. Serum insulin, Hemoglobin A1C (HbA1C), total cholesterol, triglyceride, low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C), Interleukin-1\u0026beta;(IL-1\u0026beta;), tumor necrosis factor-\u0026alpha;(TNF-\u0026alpha;), Interleukin-6 (IL-6), endotoxin, ALS, phosphatidylinositol-3-kinase (PI3K), protein kinase B (AKT), and glucose transporter 4 (GLUT4) were measured using commercial enzyme-linked immunosorbent assay (ELISA) kits (Biomerica, BMRA.US) according to the manufacturer\u0026rsquo;s instructions. Serum BCAA concentrations were determined using a high-speed amino acid analyzer (Model Hitachi 835-50).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHistopathological analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiver, pancreas, colon, and white adipose tissues were fixed in 10% neutral buffered formalin, paraffin-embedded, cut into 5 \u0026mu;m sections, and subsequently stained with hematoxylin and eosin (H\u0026amp;E) for histopathological analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical and animal data are expressed as the means \u0026plusmn; standard deviations. Differences between more than two groups were analyzed using Hiplot tools (https://hiplot.cn/basic and were considered significantly different at \u003cem\u003ep\u003c/em\u003e-values \u0026lt; 0.05. The Kruskal\u0026ndash;Wallis test was conducted to detect differences in bacterial diversity and relative abundance between multiple groups. Moreover, the Tukey\u0026ndash;Kramer post hoc tests were conducted to explore the differences among the three pairwise comparisons. The composition, diversity, and functional categories of the gut microorganisms were analyzed using the QIIME II platform with R package stats (R version 4.2.3; https://www.r-project.org/). Non-targeted metabolomics was analyzed using the MetaboAnalyst 5.0 platform (https://www.metaboanalyst.ca/). Correlation analysis between the gut microbiota and clinical indices or metabolites was conducted using Spearman\u0026rsquo;s rank correlations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the study population\u003c/h2\u003e \u003cp\u003eFour groups of adult participants were selected from the Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) from March 2022 to June 2022 based on their glucose levels: H group (n\u0026thinsp;=\u0026thinsp;12), PDM group (n\u0026thinsp;=\u0026thinsp;12), NDDM group (n\u0026thinsp;=\u0026thinsp;12), and P2DM group (n\u0026thinsp;=\u0026thinsp;21) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The clinical information, including physiological and biochemical indicators, is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We found that the PDM, NDDM, and P2DM groups had higher levels of FBG and HbA1c than the H group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, the NDDM and P2DM groups had significantly higher FBG levels than the PDM group. Still, no significant difference was observed between the NDDM and P2DM groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant differences were also observed in age; height; weight; body mass index; systolic blood pressure (SBP); diastolic blood pressure; and total cholesterol (TC), triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), albumin, and total bilirubin levels (Fig. S1A-H and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in the four groups. Participants in the P2DM group had a diabetes duration of approximately 4\u0026ndash;20 years, and their fasting and postprandial C-peptide and insulin levels were different, suggesting a certain recovery of insulin secretion. The drugs used by the 21 participants in the P2DM group were recorded, including eight participants using insulin injection and others using oral hypoglycemic drugs (e.g., Metformin, Acarbose, Sitagliptin, Voglibose, Dapagliflozin, Repaglinide, Gliclazide, Kagliflozin, Alogliptin, Linagliptin, and Glimepiride) alone or in combination with insulin injection.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometric parameters and biochemical indexes among participants (n\u0026thinsp;=\u0026thinsp;57).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHealthy (H) (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAll Diabetes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-diabetes mellitus (PDM) (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNewly diagnosed diabetes (NDDM) (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePost medication diabetes mellitus (P2DM) (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.58\u0026thinsp;\u0026plusmn;\u0026thinsp;11.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.08\u0026thinsp;\u0026plusmn;\u0026thinsp;19.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.33\u0026thinsp;\u0026plusmn;\u0026thinsp;12.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.57\u0026thinsp;\u0026plusmn;\u0026thinsp;14.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(66.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.86\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.08\u0026thinsp;\u0026plusmn;\u0026thinsp;9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164.88\u0026thinsp;\u0026plusmn;\u0026thinsp;7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162.71\u0026thinsp;\u0026plusmn;\u0026thinsp;7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.43\u0026thinsp;\u0026plusmn;\u0026thinsp;12.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.93\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.42\u0026thinsp;\u0026plusmn;\u0026thinsp;18.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.83\u0026thinsp;\u0026plusmn;\u0026thinsp;22.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138\u0026thinsp;\u0026plusmn;\u0026thinsp;19.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.08\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.08\u0026thinsp;\u0026plusmn;\u0026thinsp;12.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.25\u0026thinsp;\u0026plusmn;\u0026thinsp;17.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.29\u0026thinsp;\u0026plusmn;\u0026thinsp;14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.97\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabete duration (Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.26\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC-peptide (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF_insulin (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.52\u0026thinsp;\u0026plusmn;\u0026thinsp;28.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC-peptide (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_insulin (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e212.03\u0026thinsp;\u0026plusmn;\u0026thinsp;182.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (interquartile range); FBG: fasting blood glucose; HbA1c: glycosylated hemoglobin; TC: total cholesterol; TG: triglyceride; LDL-C: low density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; SBP: systolic blood pressure; DBP: diastolic blood pressure; ALB: albumin; TBIL: total bilirubin;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDifferent gut microbiome compositions in the four groups\u003c/h2\u003e \u003cp\u003eOn average, 81,734,932 clean reads were obtained from the metagenomics sequencing of fecal samples from all 57 participants. The alpha and beta diversities were evaluated, which represented the intra- and inter-community diversity of the microbial community [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], respectively. No significant difference was observed in the Shannon and Chao indices among the four groups. However, the PDM group had a significantly higher Simpson index than the other three groups, suggesting that the group had a lower microbial community diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Fig. S2A-B).\u003c/p\u003e \u003cp\u003eFurthermore, based on the random forest proximity matrix, there was a significant difference in each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), suggesting that the gut bacterial profiles of patients with PDM, NDDM, and P2DM differed from those of the healthy controls. However, non-metric multidimensional scaling principal coordinate analysis (NMDS) did not show an obvious separation in the bacterial communities among the four groups (Fig. S2C) (stress\u0026thinsp;\u0026lt;\u0026thinsp;0.2).\u003c/p\u003e \u003cp\u003eWe also examined the gut microbiome structure of the H, PDM, NDDM, and P2DM groups and found that six phyla, 1292 genera, and 6209 ASVs were common to all samples (Fig. S2 D-E).\u003c/p\u003e \u003cp\u003eIn all four groups, Bacteroides, Firmicutes, and Proteobacteria were the most abundant phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-E), consistent with previous reports (39). These phyla accounted for 94.7%, 89.8%, 92.1%, and 87.7% of the phyla in the samples from the H, PDM, NDDM, and P2DM groups, respectively. The PDM, NDDM, and P2DM groups showed increased abundance of the phylum Firmicutes (48.6%, 57%, and 54.4%, respectively) and reduced abundance of the phylum Bacteroidetes (14.5%, 21.6%, and 24.7%, respectively) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the healthy controls (42.8% and 49.2%, respectively). Moreover, the PDM, NDDM, and P2DM groups exhibited higher Firmicutes to Bacteroidetes ratios than the H group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eNext, we determined the microbial composition at the genus level. We found that the P2DM group had the highest number of unique genera (237), while the H, PDM, and NDDM groups had 83, 145, and 49 unique genera, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). The predominant genera in the healthy controls were \u003cem\u003eBacteroides\u003c/em\u003e (15.7%), \u003cem\u003ePhocaeicola\u003c/em\u003e (16.4%), and \u003cem\u003ePrevotella\u003c/em\u003e (5.9%). With the development of diabetes, several differentially abundant pathogenic microorganisms were identified. \u003cem\u003eEscherichia\u003c/em\u003e (10.6%) was abundant in the PDM group, and \u003cem\u003eKlebsiella\u003c/em\u003e (7.6%) was abundant in the NDDM group. After treatment, the relative abundance of \u003cem\u003eEscherichia\u003c/em\u003e (3.4%) and \u003cem\u003eKlebsiella\u003c/em\u003e (1.0%) significantly decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eWe also investigated the differentially abundant species in the four groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). The potential probiotics \u003cem\u003ePhocaeicola vulgatus\u003c/em\u003e, \u003cem\u003ePhocaeicola dorei\u003c/em\u003e, and \u003cem\u003ePhocaeicola massiliensis\u003c/em\u003e from the genus \u003cem\u003ePhocaeicola\u003c/em\u003e; \u003cem\u003ePrevotella copri\u003c/em\u003e and \u003cem\u003ePrevotella pectinovora\u003c/em\u003e from the genus \u003cem\u003ePrevotella;\u003c/em\u003e and \u003cem\u003eBacteroides stercoris\u003c/em\u003e, \u003cem\u003eBacteroides ovatus\u003c/em\u003e, and \u003cem\u003eBacteroides uniformis\u003c/em\u003e from the genus \u003cem\u003eBacteroides\u003c/em\u003e were reduced in the three patient groups (PDM, NDDM, and P2DM) compared with healthy controls (Fig. S3A, I-Q) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Short-chain fatty acid-producing bacteria populations, such as \u003cem\u003eRoseburia inulinivorans\u003c/em\u003e and \u003cem\u003eAlistipes putredinis\u003c/em\u003e, were also decreased in the PDM, NDDM, and P2DM groups (Fig. S3H, L) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the relative abundance of \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e and \u003cem\u003eBifidobacterium longum\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), previously reported to reduce weight [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and attenuate hyperlipidemia [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], was increased in the PDM and P2DM groups compared with healthy controls. There was no significant difference in \u003cem\u003eA. muciniphila\u003c/em\u003e abundance among the four groups (Fig. S3E, G) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46). We found that \u003cem\u003eE. coil\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003eK. pneumoniae\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07) were more prevalent in the PDM and NDDM groups, respectively (Fig. S3B, F). The NDDM group had a higher abundance of \u003cem\u003eRuminococcus torques\u003c/em\u003e than the other three groups. Furthermore, the NDDM and P2DM groups were enriched in \u003cem\u003eRuthenibacterium lactatiformans\u003c/em\u003e (Fig. S3C-D). Particularly, the relative abundance of \u003cem\u003eS. aureus\u003c/em\u003e increased in all three patient groups compared to that of the healthy controls, especially in the P2DM group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eS. aureus abundance in T2D and its correlation with insulin resistance\u003c/h2\u003e \u003cp\u003eTo investigate whether the increase in \u003cem\u003eS. aureus\u003c/em\u003e abundance in the PDM, NDDM, and P2DM groups is associated with the methods of pharmacological intervention, we evaluated the differences in microbial communities between the insulin injection group (Insulin group) and oral hypoglycemic drug group (N_insulin group). We found no significant difference in FBG levels between these two groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK). As shown in Fig. S4 A-D, Firmicutes, Bacteroidetes, and Proteobacteria were predominant in both the Insulin and N_insulin groups. The Insulin group had a higher relative abundance of Firmicutes (61.3%) and a lower relative abundance of Bacteroidetes (14.4) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than the N_insulin group (49.0% and 32.5%, respectively). At the genus level (Fig. S4E), \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003ePhocaeicola\u003c/em\u003e, \u003cem\u003ePhocaeicola\u003c/em\u003e, and \u003cem\u003eParabacteroides\u003c/em\u003e were the predominant genera in the N_insulin group. However, \u003cem\u003eEnterococcus\u003c/em\u003e and \u003cem\u003eEscherichia\u003c/em\u003e were predominant in the Insulin group, with relative abundances much higher than those in the N_insulin group. At the species level (Fig. S4F-N), the potential probiotics \u003cem\u003eLactobacillus nasalidis\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e sp. CAG:5226, \u003cem\u003ePrevotella melaninogenica\u003c/em\u003e, \u003cem\u003eBacteroides faecis\u003c/em\u003e CAG:32, and \u003cem\u003eBacteroides eggerthii\u003c/em\u003e CAG:109 were significantly predominant in the N_insulin group than in the Insulin group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the pathogenic bacteria \u003cem\u003eClostridium botulinum\u003c/em\u003e and \u003cem\u003eDesulfovibrio sp. G11\u003c/em\u003e were predominant in the Insulin group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eK. pneumoniae\u003c/em\u003e were abundant in the Insulin and N_insulin groups, respectively; however, there were no significant differences between them (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). \u003cem\u003eS. aureus\u003c/em\u003e was significantly more abundant in the Insulin group than in the N_insulin group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL). Therefore, we speculate that pharmacological intervention may be the main factor leading to increased \u003cem\u003eS. aureus\u003c/em\u003e abundance in the feces of patients with T2D. Additionally, the causal relationship between \u003cem\u003eS. aureus\u003c/em\u003e infection and the occurrence of T2D warrants further investigation.\u003c/p\u003e \u003cp\u003eThe four groups had different clinical indices of FBG and HbA1c levels. Thus, we constructed a microbial heatmap and co-abundance networks to investigate the correlation between gut microbes and clinical data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). To determine whether the unique species were related to the clinical indices, we performed Spearman\u0026rsquo;s correlations between the potential biomarker species and clinical features using all patients in the four groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-H). The results suggest a strong correlation between the clinical indices (especially FBG and HbA1c levels) and the microbial composition. The abundance of \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eK. pneumoniae\u003c/em\u003e, and \u003cem\u003eStreptococcus salivarius\u003c/em\u003e was more strongly correlated with blood glucose measures (FBG and HbA1c), whereas that of \u003cem\u003eRoseburia intestinalis\u003c/em\u003e, \u003cem\u003eEnteroccus faecalis\u003c/em\u003e, and \u003cem\u003eEubacterium rectale\u003c/em\u003e was more negatively correlated with the measures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, F-H, the abundance of \u003cem\u003eB. longum\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.4246, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), \u003cem\u003eEnteroccus faecium\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.4388, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006), and \u003cem\u003eFlavonifractor plautii\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.4078, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) increased in the patient group (PDM, NDDM, and P2DM), and was positively correlated with HbA1c, FBG, and age, respectively. Moreover, the abundance of \u003cem\u003eS. aureus\u003c/em\u003e was positively correlated with FBG (R\u0026thinsp;=\u0026thinsp;0.4123, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), suggesting a potential relationship between \u003cem\u003eS. aureus\u003c/em\u003e and elevated blood glucose levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDifference in functional characterization of microbiomes in the four groups\u003c/h2\u003e \u003cp\u003eNext, we performed a KEGG pathway analysis to understand the potential functional genes associated with the different microbial communities within the four groups. The NMDS results suggested that the four groups exhibited distinct functional compositions (stress\u0026thinsp;=\u0026thinsp;0.054) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Furthermore, consistent with the microbial composition results, the P2DM group had the most abundant unique KO (reaching 220), followed by the NDDM (103), PDM (81), and H (51) groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The four groups were enriched in pathways associated with metabolism, environmental information processing, genetic information processing, cellular processes, human diseases, and organismal systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Meanwhile, level-3 KEGG functional metabolism-related genes revealed that the four groups displayed different module enrichments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-J, Fig. S5E-K). For example, microbes from patients with PDM were enriched in the phosphotransferase system, fructose and mannose metabolism, biofilm formation \u0026ndash; \u003cem\u003eE. coli\u003c/em\u003e, and propanoate metabolism. Microbes from the NDDM group were enriched in ABC transporters, glycerolipid metabolism, benzoate degradation, and inositol phosphate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Meanwhile, we found that the KEGG modules involved in metabolic pathways, such as alanine, aspartate, and glutamate metabolism; the citrate cycle; and butanoate metabolism, were significantly reduced in the patient group (PDM, NDDM, and P2DM) compared to healthy controls (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Tryptophan metabolism (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), phenylalanine metabolism (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and tyrosine metabolism were increased in the patient group (Fig. S5E-K).\u003c/p\u003e \u003cp\u003eThe microbe genes involved in the \u0026ldquo;valine, leucine, and isoleucine biosynthesis\u0026rdquo; pathway were increased in the patient groups (NDDM and P2DM groups) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). A previous study reported a strong association between elevated BCAA levels and a later risk for diabetes [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], consistent with this study. We found that acetolactate synthase (EC 2.2.1.6), the first key enzyme in the synthesis of BCAAs [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], was also highly enriched in the patient group microbiome (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI, insulin resistance was increased in the patient group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, by tracing the source of enzymes in the KEGG database, we found that acetolactate synthase and BCAA transaminase were secreted by \u003cem\u003eS. aureus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ), which possesses genes encoding valine, leucine, and isoleucine biosynthesis (K01652, gene: \u003cem\u003eilvG\u003c/em\u003e). Further, BCAAs mainly played a role in the development of T2D, indicating that \u003cem\u003eS. aureus\u003c/em\u003e can promote insulin resistance by promoting the excretion of serum BCAAs. Therefore, controlling \u003cem\u003eS. aureus\u003c/em\u003e infection can help slow the formation of BCAAs in T2D.\u003c/p\u003e \u003cp\u003eTo explore the potential pathological effects of \u003cem\u003eS. aureus\u003c/em\u003e on the P2DM phenotype, we evaluated microbial functions in patients with P2DM. In this study, the P2DM group had more microbial genes involved in \u0026ldquo;\u003cem\u003eS. aureus\u003c/em\u003e infection\u0026rdquo; than the healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F). Liu et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] reported that \u003cem\u003eS. aureus\u003c/em\u003e infection could result in insulin resistance by producing an insulin-binding protein in the extracellular domain of \u003cem\u003eLtaS\u003c/em\u003e, \u003cem\u003eeLtaS\u003c/em\u003e. \u003cem\u003eLtaS\u003c/em\u003e is a membrane-embedded enzyme (EC: 2.7.8.20) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] that possesses genes encoding lipoteichoic acid synthesis (KEGG gene: \u003cem\u003esaa: SAUSA300_0703\u003c/em\u003e; K19005) in \u003cem\u003eS. aureus\u003c/em\u003e (Fig. S5A-C). The proportion of sequences of EC: 2.7.8.20 and K19005 were more abundant in the P2DM group compared with healthy controls (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, we found that the KEGG pathways referred to ko00552, ko01100, and ko00561. Among them, the glycerolipid metabolism pathway showed the most considerable changes in the P2DM group compared with healthy controls (Fig. S5D). Notably, the changes in enzymes (EC 1.1.1.202; EC 1.1.1.6; EC 1.2.1.3; EC 2.3.1.15; EC 2.3.1.20; EC 2.3.1.51; EC 2.4.1.208; EC 2.4.1.336; EC 2.4.1.337; EC 2.7.1.107; EC 2.7.1 121; EC 2.7.1 165; EC 2.7.1.29; EC 2.7.1.30; EC 2.7.8.20) were included in the glycerolipid metabolism pathway. Together, these observations suggest that \u003cem\u003eS. aureus\u003c/em\u003e infection can modulate the amino acid levels correlated with glycolipid metabolism and insulin resistance in patients with T2D.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eThe relationship of serum BCAAs with clinical parameters and S. aureus infection\u003c/h2\u003e \u003cp\u003ePerturbations in the gut microbiome influence BCAA levels, and changes in serum BCAA levels are associated with T2D [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Therefore, we evaluated whether serum BCAAs are involved in the \u003cem\u003eS. aureus\u003c/em\u003e infection affecting glycolipid metabolism. We collected data from 47 patients for metabolomics profiling of serum BCAAs. The orthogonal partial least squares-discriminant analysis plot suggested that the serum metabolites of the H and PDM, PDM and NDDM, and NDDM and P2DM populations were significantly different, indicating that the serum metabolites in the PDM, NDDM, and P2DM groups were considerably different (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B, E-F, I-J). We also identified key metabolite biomarkers using the adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2(FC)| \u0026gt; 1 (FC: fold change). 15, 31, and 18 different metabolites were identified between the H and PDM, PDM and NDDM, and NDDM and P2DM groups, respectively (Fig. S6 A-F). Of these metabolites, there were different levels of BCAAs (leucine, isoleucine, and valine), aromatic amino acids (AAAs: phenylalanine, tyrosine, and tryptophan), and bile acids (cholic acid, 7-ketodeoxycholic acid, muricholic acid, chenodeoxycholic acid, deoxycholic acid, dehydrocholic acid, taurocholic acid, taurochenodeoxycholic acid, glycochenodeoxycholic acid, and glycocholic acid) between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, G, K, M-P; Fig. S6 G-Q).\u003c/p\u003e \u003cp\u003eThe common pathways enriched in tyrosine metabolism; primary bile acid biosynthesis; valine, leucine, and isoleucine biosynthesis; and phenylalanine metabolism were enriched between the H and PDM, PDM and NDDM, and NDDM and P2DM groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, H, L; Fig. S6 S-R). To obtain further insights into the development of T2D in terms of BCAA biosynthesis, AAA metabolism, and bile acid biosynthesis, we quantitatively analyzed the serum metabolites related to the BCAAs, AAAs, and bile acid biosynthesis pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eM-P; Fig. S6 G-Q). In the valine, leucine, and isoleucine biosynthesis pathways, valine and BCAA levels were higher in the PDM, NDDM, and P2DM groups than in the H group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Isoleucine levels were higher in the PDM and NDDM groups. No significant difference in leucine levels was observed between these groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Regarding AAA metabolism, tryptophan and AAA levels were higher in the P2DM group than in the H group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the derivatives (indole-lactic acid and indole-butyric acid) of tryptophan metabolism were higher in the NDDM and P2DM groups than in the H group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant differences were observed in the biosynthesis of the main bile acids between the two groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These results indicate that amino acid metabolism by gut microbes may modulate the levels of circulating serum BCAAs, which correlate with the severity of diabetes; thus, reducing the consumption of foods rich in BCAAs can result in a lower prevalence of T2D.\u003c/p\u003e \u003cp\u003eTo further investigate whether the alteration in valine, leucine, and isoleucine biosynthesis correlated with the clinical parameters and altered microbes, Spearman\u0026rsquo;s correlations were assessed. We found a close association among these metabolites, the altered microbiome, and clinical parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Moreover, we found that BCAA and valine levels were positively correlated with FBG and HbA1c levels, isoleucine levels were positively correlated with the SBP, and tryptophan and tyrosine levels were negatively correlated with TC and LDL-C levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Specifically, we found that leucine levels were positively correlated with \u003cem\u003eFlavonifractor plautii\u003c/em\u003e abundance. Valine, isoleucine, and BCAA levels were positively correlated with \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eK. pneumoniae\u003c/em\u003e, and \u003cem\u003eS. aureus\u003c/em\u003e abundance and inversely correlated with species from \u003cem\u003eB. cellulosilyticus\u003c/em\u003e, \u003cem\u003eB. ovatus\u003c/em\u003e, \u003cem\u003eB. xylanisolvens\u003c/em\u003e, \u003cem\u003eB. uniformis\u003c/em\u003e and other potential probiotics (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-M). Together, these data suggest that the T2D groups had a higher abundance of \u003cem\u003eS. aureus\u003c/em\u003e, increased FBG and HbA1c levels, and higher BCAA levels, suggesting that the link between \u003cem\u003eS. aureus\u003c/em\u003e and glycolipid metabolism might be mediated by BCAA levels.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransplantation of faeces from ALS-positive patients with type 2 diabetes exacerbates high fat diet-induced insulin resistance in rats\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo determine whether ALS contributes to disordered glycolipid metabolism mediated by \u003cem\u003eS. aureus\u003c/em\u003e, we gavaged rats with faeces from patients with type 2 diabetes with a ALS \u003cem\u003eS. aureus\u003c/em\u003e or a non-ALS \u003cem\u003eS. aureus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Compared to rats gavaged with healthy peoples faeces, rats fed with high fat diets after they were gavaged with ALS \u003cem\u003eS. aureus\u003c/em\u003e developed more severe disordered glycolipid metabolism as indicated by a lower level of insulin, higher level of fasting blood glucose, insulin resistance and increased inflammatory cytokine levels (TNF-α and IL-6), however, there was no significant difference in serum endotoxin among three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-H). Rats that were fed high fat diets after they were gavaged with ALS \u003cem\u003eS. aureus\u003c/em\u003e had significantly increased liver injury, intestinal permeability and higher levels of valine and BCAAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI-N), as compared with rats that were fed high fat diets after they were administered with non-ALS \u003cem\u003eS. aureus\u003c/em\u003e. Altogether, the above results indicate that ALS plays an important role in promoting branched chain amino acid synthesis and inducing insulin resistance in \u003cem\u003eS. aureus.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eThe therapeutic effects of S. aureus phage that target blood glucose in db/db mice\u003c/h2\u003e \u003cp\u003e \u003cem\u003eIn vitro, S. aureus\u003c/em\u003e 2868B2 and its phage were coculture statically in TSB broth or on TSB agar plate at 37\u0026deg;C. The TSB broth is made transparent and zone of inhibition is formed in TSB agar plate by adding \u003cem\u003eS. aureus\u003c/em\u003e phage, and its inhibited the growth of \u003cem\u003eS. aureus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-C), which suggested that \u003cem\u003eS. aureus\u003c/em\u003e phage can lysis of \u003cem\u003eS. aureus\u003c/em\u003e 2868B2, and did not affect the other probiotics. Meanwhile, the gene encoding the branched chain amino acid biosynthesis enzyme in \u003cem\u003eS. aureus\u003c/em\u003e 2868B2 was identified as ALS (\u003cem\u003eilvG\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). To further demonstrate the potential causative role of \u003cem\u003eS. aureus\u003c/em\u003e for the development of high fat diets-induced type 2 diabetes insulin resistance, we investigated the effects of treatment with \u003cem\u003eS. aureus\u003c/em\u003e phages on db/db mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). Compared to DC mice, mice gavaged with phages that target \u003cem\u003eS. aureus\u003c/em\u003e had lower levels of FBG, HbA1c and HOMA-IR, triglyceride and inflammation (TNF-α, IL-1β), while the body weight had a higher after gavaged with phages, but no significant difference among three groups in total cholesterol, LDL-cholesterol, and HDL-cholesterol (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF-L, Fig. S7A-C). At the same time, mice that were gavaged with phages that target \u003cem\u003eS. aureus\u003c/em\u003e had significantly less liver injury, impairment of islet, steatosis, intestinal permeability and lower levels of leucine, isoleucine, valine, BCAAs and ALS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eM-U), as compared with the DC mice. Administration of \u003cem\u003eS. aureus\u003c/em\u003e phage significantly increased serum levels of PI3K, AKT and GLUT4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eV-X). It speculates that the mechanisms of the anti-diabetic effects in \u003cem\u003eS. aureus\u003c/em\u003e phage are involved with activation of PI3K/AKT/GLUT4 signaling pathways expression. Therefore, \u003cem\u003eS. aureus\u003c/em\u003e phage might have a promising potential in preventing diabetes insulin resistance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we performed metagenomics sequencing and serum metabolomics profiling of patients with PDM, NDDM, and P2DM. We found that \u003cem\u003eS. aureus\u003c/em\u003e abundance and BCAA biosynthesis were increased in the fecal microbes and serum metabolites of the PDM, NDDM, and P2DM groups. In particular, the P2DM (insulin injection group) group had the highest abundance of \u003cem\u003eS. aureus\u003c/em\u003e and BCAAs and was associated with an increase in FBG and HbA1c levels. \u003cem\u003eS. aureus\u003c/em\u003e possesses BCAA biosynthesis enzymes, especially acetolactate synthase (EC:2.2.1.6, K01652) and K19005. Furthermore, supplementation with \u003cem\u003eS. aureus\u003c/em\u003e phage, could improve glycemic control in db/db mice and decrease BCAAs concentrations, this resulted in decreased blood glucose levels and improved insulin resistance.\u003c/p\u003e \u003cp\u003eThis study suggests a unique pathogenic microorganism and metabolite signature in patients with prediabetes, NDDM, and P2DM. Previous studies have indicated that more than 470\u0026nbsp;million people will develop pre-diabetes by 2030 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, patients with prediabetes and treatment-na\u0026iuml;ve patients with T2D have distinct differences in gut microbiota [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, although previous studies have shown that altered microbes are associated with T2D [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], they did not distinguish between participants with T2D who were newly diagnosed or underwent antidiabetic drug intervention. Given the importance of drug-microbiome interactions [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], whether patients with P2DM possess specific microbial and metabolite compositions remains unknown. In our study, a significant difference in the gut microbiota species was observed between the patient groups (PDM, NDDM, and P2DM) and healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The abundance of species from the genus \u003cem\u003eBacteroides\u003c/em\u003e, such as \u003cem\u003eBacteroides stercoris\u003c/em\u003e, \u003cem\u003eBacteroides ovatus\u003c/em\u003e, \u003cem\u003eBacteroides uniformis\u003c/em\u003e, and \u003cem\u003ePhocaeicola vulgatus\u003c/em\u003e, \u003cem\u003ePhocaeicola dorei\u003c/em\u003e, and \u003cem\u003ePhocaeicola massiliensis\u003c/em\u003e, was significantly depleted in the patient groups, consistent with the results of a previous study that showed that the abundance of \u003cem\u003eBacteroides\u003c/em\u003e was decreased in T2D [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. \u003cem\u003ePrevotella copri\u003c/em\u003e has been reported to improve glucose tolerance and homeostasis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Moreover, we found that the relative abundances of \u003cem\u003ePrevotella copri\u003c/em\u003e and \u003cem\u003ePrevotella pectinovora\u003c/em\u003e were significantly reduced in the patient groups. Meanwhile, the relative abundance of \u003cem\u003eEscherichia coli\u003c/em\u003e was significantly increased in the PDM group than in the other three groups, consistent with a recent study involving a Chinese population [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Nevertheless, \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e, a well-known mucin-degrading bacterium that can alleviate metabolic syndrome, was abundant in the PDM group. However, there was no significant difference among the groups, which provided conflicting findings with a previous study and may be due to the small sample size of this study [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In addition, the NDDM group had a higher abundance of \u003cem\u003eRuminococcus torques\u003c/em\u003e (Fig. S3C) in accordance with a prior study [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. However, after treatment with antidiabetic drugs, \u003cem\u003eRuminococcus torques\u003c/em\u003e abundance was significantly reduced. Altogether, these results imply that some unique species follow gradual disease development from prediabetes to overt T2D and treatment.\u003c/p\u003e \u003cp\u003eWe also observed elevated \u003cem\u003eS. aureus\u003c/em\u003e abundance in the patient groups, particularly in the P2DM group. \u003cem\u003eS. aureus\u003c/em\u003e is a major pathogen [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and its colonization has been widely reported in mice with insulin resistance [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and in patients with diabetes compared with healthy individuals [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Notably, \u003cem\u003eS. aureus\u003c/em\u003e abundance was positively correlated with elevated FBG levels and BCAA biosynthesis. Studies have demonstrated that increased serum BCAA levels are positively correlated with T2D [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. We observed a significant correlation between BCAAs and FBG and a positive correlation between BCAAs and \u003cem\u003eS. aureus\u003c/em\u003e abundance. Previous studies demonstrated that acetolactate synthase is the first key enzyme in the synthesis of BCAAs [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] and that BCAA transaminase is the last enzyme in the biosynthesis of L-isoleucine and L-valine[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. We found that the relative abundance of valine, leucine, and isoleucine biosynthesis (EC 2.2.1.6, K00826, gene: \u003cem\u003eilvG\u003c/em\u003e) in the patient group was higher than that in the healthy controls, especially in the NDDM group. In addition, our metagenomics sequencing data showed that \u003cem\u003eS. aureus\u003c/em\u003e possesses acetolactate synthase and BCAA transaminase. We propose that \u003cem\u003eS. aureus\u003c/em\u003e promotes the expression of BCAA synthesis-related genes, which induce BCAA biosynthesis, thereby causing elevated FBG levels and decreased insulin secretion in T2D.\u003c/p\u003e \u003cp\u003eA recent study revealed that \u003cem\u003eS. aureus\u003c/em\u003e infection induced insulin resistance by secreting an insulin-binding protein in the extracellular domain of \u003cem\u003eLtaS\u003c/em\u003e, \u003cem\u003eeLtaS\u003c/em\u003e [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Our study demonstrated that patients with P2DM had a high abundance of \u003cem\u003eLtaS\u003c/em\u003e (Transferases, EC 2.7.8.20, k19005), which is related to the glycerolipid metabolism pathway, indicating that the targeted elimination of \u003cem\u003eS. aureus\u003c/em\u003e may be a promising strategy for the treatment and prevention of T2D. Furthermore, we found that insulin injection administration may be the main factor causing the accumulation of \u003cem\u003eS. aureus\u003c/em\u003e in the feces of patients with T2D, which requires further study.\u003c/p\u003e \u003cp\u003eWe found that the supplementation of db/db mice with \u003cem\u003eS. aureus\u003c/em\u003e phage, which are ubiquitous in bacteria-rich environments, including the gut[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], improved FBG and insulin resistance levels. Several studies have reported that bacteriophages may regulate intestinal health and become an alternative diagnostic and therapeutic agent for metabolic diseases[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Rasmussen et al reported that faecal virome transplantation decreases symptoms of type 2 diabetes and obesity in a murine model[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. It has previously been demonstrated that MS2 phages could ameliorate glucose metabolism in T2D mice[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], this consistent with our results. Further, we observed that the db/db mice had higher serum BCAA levels than the control group and that treatment with \u003cem\u003eS. aureus\u003c/em\u003e phage decreased serum BCAA levels. Previous studies have indicated that elevated plasma branched chain amino acids (BCAAs) has been implicated in development of insulin resistance and T2D[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Thus we speculate that \u003cem\u003eS. aureus\u003c/em\u003e phage regulates insulin resistance by reducing serum BCAAs levels. Karusheva et al have suggested that short-term intake of BCAAs can induce insulin resistance in humans, likely due to activation of the mechanistic target of rapamycin (mTOR) complex 1/ribosomal protein S6 kinase (p70S6K) pathway[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Furthermore, this study have found that take of \u003cem\u003eS. aureus\u003c/em\u003e phage significantly increased serum levels of PI3K, AKT and GLUT4. Therefore, we speculate that the mechanisms of the anti-diabetic effects in \u003cem\u003eS. aureus\u003c/em\u003e phage are involved with activation of PI3K/AKT/GLUT4 signaling pathways expression, and \u003cem\u003eS. aureus\u003c/em\u003e phage might have a promising potential in preventing diabetes insulin resistance.\u003c/p\u003e \u003cp\u003eOne limitation of this study is its small sample size, therefore, a larger cohort is required to validate the relevance of our findings in humans, although we validated \u003cem\u003eS. aureus\u003c/em\u003e changes at different stages of T2D, further studies are warranted to consider the impact of confounding factors such as diet, smoking, and alcohol abuse on the results involving microorganisms and their metabolites in patients. In additional, safety studies are required for complex populations (such as patients with type 2 diabetes insulin resistance), because phages can induce a strong immune reaction[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Further work is required to determine whether phages that target ALS \u003cem\u003eS. aureus\u003c/em\u003e might be used to treat patients with type 2 diabetes insulin resistance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study indicates a close association between alterations in specific species and metabolites and host glucose metabolism at different disease stages of T2D. We further suggest that \u003cem\u003eS. aureus\u003c/em\u003e and its enzymes (ALS acetolactate synthase (EC:2.2.1.6, K01652)), which are related to insulin resistance, can alter serum BCAA levels and increase blood glucose levels. We also demonstrated that insulin administration may be a risk factor causing the accumulation of \u003cem\u003eS. aureus\u003c/em\u003e in the feces of patients with T2D. Using humanized rats that were colonized with bacteria from the faeces of patients with type 2 diabetes insulin resistance, it proved that ALS contributes to disordered insulin resistance mediated by \u003cem\u003eS. aureus\u003c/em\u003e. This study link ALS-positive \u003cem\u003eS. aureus\u003c/em\u003e with more severe disordered glycolipid metabolism and increased insulin resistance in patients with type 2 diabetes. We show that bacteriophages can specifically target ALS-positive \u003cem\u003eS. aureus\u003c/em\u003e, paving the way for evaluating \u003cem\u003eS. aureus\u003c/em\u003e phage as a prevention and treatment modality for T2D.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBCAA branched-chain amino acid\u003c/p\u003e\n\u003cp\u003eT2D type 2 diabetes\u003c/p\u003e\n\u003cp\u003ePDM prediabetes\u003c/p\u003e\n\u003cp\u003eNDDM newly diagnosed diabetes\u003c/p\u003e\n\u003cp\u003eP2DM post-medication type 2 diabetes\u003c/p\u003e\n\u003cp\u003eALS\u0026nbsp;acetolactate synthase\u003c/p\u003e\n\u003cp\u003eFBG fasting blood glucose\u003c/p\u003e\n\u003cp\u003eORFs open reading frames\u003c/p\u003e\n\u003cp\u003eKEGG Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eTSB tryptic soytone broth\u003c/p\u003e\n\u003cp\u003eHbA1C Hemoglobin A1C\u003c/p\u003e\n\u003cp\u003eLDL-C low density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eHDL-C high density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eIL-1\u0026beta; Interleukin-1\u0026beta;\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha; tumor necrosis factor-\u0026alpha;\u003c/p\u003e\n\u003cp\u003eIL-6 Interleukin-6\u003c/p\u003e\n\u003cp\u003ePI3K phosphatidylinositol-3-kinase\u003c/p\u003e\n\u003cp\u003eAKT protein kinase B\u003c/p\u003e\n\u003cp\u003eGLUT4 glucose transporter 4\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eELISA enzyme-linked immunosorbent assay\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Ya Chen, and Tong Chen for analyzing the composition of gut microbiota and metabolites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: T.T.L., T.J, Z.L. and L.Y.L. Methodology: L.W., H.G., H.Z. and N.Z. Investigation: T.T.L., T.J., Z.L., and L.Y.L. Formal analysis: T.T.L., L.W., H.G. and H.Z. Visualization: T.T.L., T.J, Z.L., and L.Y.L. Writing-original draft: T.T.L., T.J, Z.L., and L.Y.L. Writing-review and editing: B.D., X.X.Q., Q.P.W. and B.G. All authors read, revised, and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by research grants from the Guangdong Province Basic and Applied Basic Research Fund Project (2022A1515110447), Open Fund Project of the State Key Laboratory of Applied Microbiology in South China (SKLAM006-2022), 74th batch of general funding from the China Postdoctoral Science Foundation (2023M740774), Guangdong Provincial People\u0026apos;s Hospital, Postdoctoral Research Launch Fund (BY012022017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Committee of the Guangdong Provincial People\u0026apos;s Hospital\u0026nbsp;(KY2023-675).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOng KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, et al. 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Phages and immunomodulation. 2017;12(10):905-14.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Staphylococcus aureus, branched-chain amino acid, acetolactate synthase, insulin resistance, type 2 diabetes","lastPublishedDoi":"10.21203/rs.3.rs-4242450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4242450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e An increase in branched-chain amino acid (BCAA) levels can result in insulin resistance at different stages of type 2 diabetes (T2D), however, the causes of this increase are unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e We performed metagenomics and metabolomics profiling in patients with prediabetes (PDM), newly diagnosed diabetes (NDDM), and post-medication type 2 diabetes (P2DM) to investigate whether altered gut microbes and metabolites could explain the specific clinical characteristics of different disease stages of T2D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Here we identify acetolactate synthase (ALS) a BCAA biosynthesis enzyme in \u003cem\u003eStaphylococcus aureus\u003c/em\u003e as a cause of T2D insulin resistance. Compared with healthy peoples, patients with PDM, NDDM, and P2DM groups, especially in P2DM group, have increased faecal numbers of \u003cem\u003eS. aureus\u003c/em\u003e. We also demonstrated that insulin administration may be a risk factor for \u003cem\u003eS. aureus\u003c/em\u003e infection in T2D. The presence of ALS-positive \u003cem\u003eS. aureus\u003c/em\u003ecorrelated with the levels of BCAAs and was associated with an increased fasting blood glucose (FBG) and insulin resistance. Humanized microbiota transplantation experiment indicated that ALS contributes to disordered insulin resistance mediated by \u003cem\u003eS. aureus\u003c/em\u003e. We also found that \u003cem\u003eS. aureus\u003c/em\u003e phage can reduced the FBG levels and insulin resistance in db/db mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eAbove all results suggest that the BCAAs biosynthesis increasing bacteria and ALS enzymes are potential intervention targets for the glucose homeostasis in T2D insulin resistance, opening a new therapeutic avenue for the prevention or treatment of diabetes.\u003c/p\u003e","manuscriptTitle":"Staphylococcus aureus-expressed acetolactate synthase enhances the biosynthesis of branched-chain amino acids and is linked to insulin resistance in type 2 diabetes in South China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:32:42","doi":"10.21203/rs.3.rs-4242450/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fdcdb43-6d65-44a6-9eeb-817bc519381c","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-19T18:32:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:32:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4242450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4242450","identity":"rs-4242450","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}