Wenrui
Ji‡
,
Xiaomin
Xie‡
*,
Guirong
Bai
,
Yanting
He
,
Ling
Li
,
Li
Zhang
and
Dan
Qiang
Department of Endocrinology, The First People's Hospital of Yinchuan, Yinchuan, People's Republic of China. E-mail: xxm2324@126.com
First published on 30th April 2024
Many individuals with pre-diabetes eventually develop diabetes. Therefore, profiling of prediabetic metabolic disorders may be an effective targeted preventive measure. We aimed to elucidate the metabolic mechanism of progression of pre-diabetes to type 2 diabetes mellitus (T2DM) from a metabolic perspective. Four sets of plasma samples (20 subjects per group) collected according to fasting blood glucose (FBG) concentration were subjected to metabolomic analysis. An integrative approach of metabolome and WGCNA was employed to explore candidate metabolites. Compared with the healthy group (FBG < 5.6 mmol L−1), 113 metabolites were differentially expressed in the early stage of pre-diabetes (5.6 mmol L−1 ⩽ FBG < 6.1 mmol L−1), 237 in the late stage of pre-diabetes (6.1 mmol L−1 ⩽ FBG < 7.0 mmol L−1), and 245 in the T2DM group (FBG 7.0 mmol L−1). A total of 27 differentially expressed metabolites (DEMs) were shared in all comparisons. Among them, L-norleucine was downregulated, whereas ethionamide, oxidized glutathione, 5-methylcytosine, and alpha-D-glucopyranoside beta-D-fructofuranosyl were increased with the rising levels of FBG. Surprisingly, 15 (11 lyso-phosphatidylcholines, L-norleucine, oxidized glutathione, arachidonic acid, and 5-oxoproline) of the 27 DEMs were ferroptosis-associated metabolites. WGCNA clustered all metabolites into 8 modules and the pathway enrichment analysis of DEMs showed a significant annotation to the insulin resistance-related pathway. Integrated analysis of DEMs, ROC and WGCNA modules determined 12 potential biomarkers for pre-diabetes and T2DM, including L-norleucine, 8 of which were L-arginine or its metabolites. L-Norleucine and L-arginine could serve as biomarkers for pre-diabetes. The inventory of metabolites provided by our plasma metabolome offers insights into T2DM physiology metabolism.
Pre-diabetes is a transitional stage in the progression from a healthy state to diabetes, characterized by elevated blood sugar levels that are higher than normal but below the diagnostic thresholds for diabetes.4 Pre-diabetes is a reversible stage, therefore people with pre-diabetes have an opportunity to regain health5. Therefore, interrupting the progression of pre-diabetes may be an effective targeted preventive measure for T2DM. However, current diagnostic methods show various insufficiencies for the early diagnosis of T2DM. Therefore, a biomarker that can predict the risk of prediabetes progression is urgently needed. In the pre-diabetes period, insulin resistance already occurs with the increase of fasting blood glucose (FBG),6 but it is difficult to find the pathological molecular mechanism of T2DM by detecting FBG alone. Numerous studies have shown that the differential expression of metabolites analysed by metabolomics is helpful in elucidating the molecular mechanism of T2DM. For example, metabolic biomarkers based on the circulating nuclear magnetic resonance raised T2DM risk prediction.7 Plasma phosphatidylinositol and sphingomyelin were capable of discriminating healthy individuals and T2DM patients.8 Long et al. suggested that the levels of alanine, glutamate, and palmitic acid (C16:0) may be biomarkers for the progression of pre-diabetes to T2DM.9 Zhu et al. also proved that a serological metabolite panel can distinguish asymptomatic populations at risk of T2DM based on global and targeted mass spectrometry (MS)10. Although the value of metabolomics in biomarker identification of T2DM has now been repeatedly validated and applied, metabolomics studies based on FBG gradients to distinguish cohorts of subjects to uncover biomarkers for prediabetes have not been reported. Furthermore, due to the incredible diversity of metabolite network structures, it is challenging and innovative to understand how dysregulation in metabolites occurs during progression to T2DM in pre-diabetic patients.
In this study, we sought to analyse plasma metabolites in subjects with different glucose metabolism states and identify differential metabolites based on changes in FBG concentrations. Bioinformatics analysis was subsequently performed with a view to identifying pre-diabetes and T2DM-related key metabolites.
A | B | C | D | |
---|---|---|---|---|
*Kruskal–Wallis test. #One-way ANOVA with Tukey's test. *P < 0.05; **P < 0.01; ***P < 0.001; ##P < 0.01; ###P < 0.001. The significance mark represents the results of A vs. B, C, D. | ||||
Age, years | 45.15 ± 6.47 | 41.45 ± 10.06 | 47.15 ± 6.37 | 43.80 ± 5.50 |
Male sex | 10 (50%) | 14 (70%) | 9 (45%) | 11 (55%) |
BMI, kg m−2 | 23.17 ± 2.79 | 24.36 ± 3.29 | 24.10 ± 3.11 | 24.08 ± 2.03 |
Waist circumference, cm | 78.75 ± 9.68 | 83.95 ± 10.77 | 80.65 ± 8.71 | 83.80 ± 6.94 |
FBG, mmol L−1 | 4.88 ± 0.40 | 5.75 ± 0.12* | 6.42 ± 0.21*** | 11.14 ± 2.73*** |
Mean systolic BP, mmHg | 119.15 ± 8.67 | 118.75 ± 9.12 | 119.35 ± 7.91 | 118.70 ± 9.63 |
Mean diastolic BP, mmHg | 76.15 ± 7.25 | 75.85 ± 7.88 | 74.25 ± 6.62 | 74.00 ± 6.15 |
Biochemical parameters | ||||
HbA1c, ng mL−1 | 164.52 ± 25.81 | 191.31 ± 20.79## | 226.88 ± 19.55### | 251.24 ± 22.20### |
Triglycerides, mmol L−1 | 1.47(0.98, 1.88) | 1.79(1.19, 2.10) | 2.40(1.29, 3.21) | 2.69(1.29, 2.69) |
Total cholesterol, mmol L−1 | 4.80(4.15, 5.40) | 4.49(4.04, 5.30) | 5.77(4.75, 6.09) | 5.39(4.56, 6.14) |
LDL-cholesterol, mmol L−1 | 2.62 ± 0.69 | 2.60 ± 0.67 | 3.37 ± 1.44 | 3.14 ± 0.83 |
HDL-cholesterol, mmol L−1 | 1.35 ± 0.25 | 1.30 ± 0.28 | 1.32 ± 0.21 | 1.29 ± 0.23 |
ALT, U L−1 | 19.50 ± 7.98 | 21.74 ± 13.68 | 26.44 ± 14.36 | 28.35 ± 17.81 |
AST, U L−1 | 25.53 ± 3.68 | 24.84 ± 6.36 | 26.12 ± 8.66 | 25.18 ± 7.55 |
Urea, mmol L−1 | 4.95 ± 1.02 | 4.72 ± 1.25 | 4.42 ± 1.07 | 4.65 ± 1.28 |
Uric acid, μmol L−1 | 313.02 ± 86.24 | 332.57 ± 87.15 | 323.68 ± 102.2 | 279.23 ± 62.62 |
Creatinine, μmol L−1 | 65.41 ± 9.29 | 64.50 ± 10.56 | 62.49 ± 14.62 | 55.42 ± 10.35* |
Fasting insulin, mIU L−1 | 4.84 ± 0.49 | 5.40 ± 0.61## | 5.98 ± 0.53### | 6.78 ± 0.44### |
To measure the stability during instrumental detection, we evaluated the stability of the QC samples. The total ion current (TIC) map of the mixed QC sample (the spectrum obtained by adding the intensities of all ions in the mass spectrum at each time point and then continuously delineating the spectrum) is shown in ESI,† Fig. S1A. The multi-peak map of MRM metabolite detection of multi-substance extraction is shown in ESI,† Fig. S1B. The results of the TIC overlap map of the QC sample showed that the curve overlap of total ion currents detected by metabolites was high (Fig. S1C, ESI†), that is, the retention time and peak intensity were consistent, indicating that the signal stability of MS was better when the same sample was detected at different time points.
A total of 683 metabolites were detected composed of 39 metabolites from secondary metabolism, including 95 organic acids and their derivatives, 72 amino acid derivatives, and 59 nucleotides and their metabolomics (Fig. 1A and Table S2, ESI†). The principal components analysis (PCA) plot of metabolome explained 53.25% of the variation in the data (PCA1: 43.79% and PCA2: 9.46%) (Fig. 1B), indicating significant differences in overall metabolism between groups of samples. Subsequently, the biological repeatability between samples within the group was assessed by correlation analysis between samples using the Spearman rank correlation. As shown in Fig. 1C, the correlation coefficients of samples within groups were higher than those between groups, suggesting that the repeatability of samples within each group was satisfactory, and the differential metabolites obtained based on these samples will be reliable.
In addition, compared with T2DM patients of group D, a total of 113 DEMs (including 68 upregulated and 45 downregulated) were identified in the pre-diabetes group under ADA criteria (B vs. D), and 110 DEMs (including 59 upregulated and 51 downregulated) were identified in the pre-diabetes group under CDS criteria (C vs. D) (Table S3, ESI†). A total of 51 DEMs were shared in B vs. D and C vs. D (Fig. S2A, ESI†), serving as biomarkers for differentiating pre-diabetes and T2DM. Details of each DEM for the six comparison groups are presented in Table S4 (ESI†).
The DEMs of the A vs. B, B vs. C, and C vs. D comparison groups were classified, and the results showed that a total of 75 amino acids and their metabolomics, 114 lipids, 31 carboxylic acids and their derivatives (including 16 sugars, 7 phospholipids, and 3 sugar alcohols), 34 organic acid and their derivatives, etc. were obtained (Table S5, ESI†). Of these 16 sugars, the abundance of D-glucose, D-mannose, 1,6-anhydro-β-D-glucose, D-tagatose, and D-allose were significantly increased in the A vs. B comparison group. There was no difference in the B vs. C comparison group, but a significant increase was again observed in the C vs. D comparison group. However, alpha-D-glucopyranoside beta-D-fructofuranosyl, D-trehalose, lactose, lactulose, and maltose were significantly increased in the A vs. B comparison group which then decreased in the B vs. C comparison group and showed no difference in the C vs. D comparison group. These abnormal metabolisms of sugars associated with glycolysis suggest that glycolysis gradually enters a state of high throughput or even overload with the increase of FBG concentration. Of these organic acids and their derivatives, there was no difference in the abundance of α-ketoglutaric acid, methylmalonic acid, and citramalic acid in the A vs. B comparison group, but significantly increased in the B vs. C comparison group and were significantly decreased in the C vs. D comparison group; however, the abundance of pyruvate was significantly upregulated in the B vs. C comparison group but showed no difference between A vs. B and C vs. D comparison groups. These results imply that the TCA cycle is overloaded when FBG concentrations enter the range of the C group (FBG 6.1 mmol L−1).
Fig. 5 WGCNA network. (A) The clustering dendrogram identifying WGCNA modules. (B) Heatmap of modules–trait relationships. |
Next, the pathway enrichment analysis of metabolites in the eight modules was performed, and the results showed a significant annotation to insulin resistance-related pathways, such as thyroid hormone synthesis, starch and sucrose metabolism, galactose metabolism, fructose and mannose metabolism, and glycerophospholipid metabolism (Fig. 6A). Moreover, among the top 20 enrichment pathways of each module, the type 2 diabetes mellitus pathway and the thyroid hormone signaling pathway were shared by blue and yellow modules, whereas the insulin resistance pathway was unique to the turquoise module (Fig. 6B).
Biomarker | Group | Module | DEMs | Class II | A vs. B | B vs. C | C vs. D |
---|---|---|---|---|---|---|---|
B | B vs. C | Turquoise | L-Arginine | Amino acid derivatives | Unchanged | Down | Unchanged |
B | B vs. C | Turquoise | Terephthalic acid | Phenolic acids | Unchanged | Down | Unchanged |
B | B vs. C | turquoise | N8-Acetylspermidine | Amino acid derivatives | Unchanged | Down | Unchanged |
B | B vs. C | Turquoise | Bis(1-inositol)-3,1′-phosphate 1-phosphate | Alcohols | Unchanged | Down | Unchanged |
B | B vs. C | Turquoise | 3-Hydroxybenzoic acid | Organic acid and its derivatives | Unchanged | Down | Unchanged |
B | B vs. C | Turquoise | O-Succinyl-L-homoserine | Amino acid derivatives | Unchanged | Down | Unchanged |
B | B vs. C | Turquoise | Gly-Phe-Phe | Small peptide | Unchanged | down | Unchanged |
B | B vs. C | Turquoise | DL-2-Methylglutamic acid | Amino acids | Down | Up | Unchanged |
B, C, D | Turquoise | L-Norleucine | Amino acids | Down | Down | down | |
C | B vs. C | Yellow | Pyruvic acid | Organic acid and its derivatives | Unchanged | Up | Unchanged |
C | B vs. C | Yellow | L-Alanyl-L-lysine | Amino acid derivatives | unchanged | Up | Unchanged |
C | B vs. C | Yellow | Carnitine ph-C14 | CAR | Unchanged | Up | Unchanged |
D | C vs. D | Green | LPE(18:2/0:0) | LPE | Unchanged | Unchanged | Up |
D | C vs. D | Blue | SDMA | Organic acid and its derivatives | Unchanged | Unchanged | Up |
In this study, we found that L-norleucine was the only metabolite shared in all the comparison groups (A vs. B, A vs. C, A vs. D, B vs. C, B vs. D, and C vs. D), and L-norleucine abundance was negatively correlated with T2DM progression, as L-norleucine expression diminished with rising FBG (the expression of L-norleucine was ranked A > B > C > D in the four groups), indicating that the lower the expression of L-norleucine, the more severe the insulin resistance. This phenomenon may be partly attributed to the insulin secretion-regulating function of L-norleucine. Actually, L-norleucine and L-leucine could stimulate insulin release in the presence of a suitable activator of glutamate dehydrogenase.14 Early studies on mice confirmed that dietary leucine reversed high-fat diet-induced insulin resistance, obesity, and inflammation.15 These studies support our conclusion that a low expression of L-norleucine contributes to the insulin resistance involved in T2DM progression. Therefore, changes in the level of L-norleucine may also be critical to the progression of T2DM and could be a reliable biomarker of pre-diabetes. Moreover, L-norleucine is an isomer of leucine which is a branched-chain amino acid (BCAA) (contains three amino acids, leucine, isoleucine, and valine) that is linked to insulin resistance in multiple studies.16 A study of plasma samples from obese and insulin resistant people found that the main components associated with insulin resistance were BCAAs, aromatic amino acids, carnitine, etc.17 However, the role of BCAAs in insulin resistance is still controversial. On the one hand, it is believed that BCAAs have a strong positive correlation with insulin resistance in the T2DM progression,18,19 but on the other hand, BCAA supplementation did not worsen insulin resistance.20 We speculate that this may be due to the unpredictable differential effects of different leucine isomers mixed with different amino acids. Unfortunately, the current study of L-norleucine in T2DM is insufficient, so there is a lack of strong evidence to support our conclusions; therefore, we will continue to study the significance of L-norleucine abundance changes in T2DM in the future. In addition, among the 75 amino acids and their metabolomics, the number of BCAAs and their derivatives was as high as 12, four of which were already significantly altered in concentration at FBG > 5.6, and the remaining eight showed different patterns with increasing FBG (Table S5, ESI†). In summary, BCAAs are consistently abnormal during the rise in FBG concentration, thus they are involved in the progression of pre-diabetes.
Surprisingly, among 27 shared DEMs, a total of 15 DEMs (11 LPCs, GSSG, AA, L-norleucine, and 5-Oxoproline) were involved in ferroptosis. Ferroptosis is a novel non-apoptotic death form, characterized by iron-dependent, membrane damage from lipid peroxidation. The dysregulation of ferroptosis is highly associated with a variety of diseases, including diabetes-related diseases and the induction of ferroptosis is also proposed as a potential strategy for them. For example, the suppression of pancreatic iron deposition and pancreatic β cells ferroptosis by quercetin significantly alleviates T2DM.21 Inhibition of ferroptosis by cryptochlorogenic acid protects β-cells’ function from diabetes.22 Ferroptosis and ferritinophagy also contribute to the occurrence and development of diabetes complications,23 including diabetes myocardial ischemia/reperfusion,24 diabetes-induced endothelial dysfunction,25 diabetic nephropathy,26,27 and diabetic atherosclerosis.28 Therefore, ferroptosis is involved in the occurrence and development of T2DM. Here, we superficially analyzed the mechanism of ferroptosis-mediated T2DM in conjunction with the identified-DEMs. First, the LPC lipid peroxidation pathway is driven by AA as a substrate. Abnormalities in AA and LPC initiate ferroptosis leading to pre-diabetes progression. Second, the GSH/GSSG pathway involves L-norleucine as the source of occurrence. Abnormal metabolism of L-norleucine, 5-oxoproline, and GSSG in this pathway initiates ferroptosis leading to pre-diabetes progression. Third, the ROS-glycolysis pathway is implicated. ROS, a byproduct of ferroptosis, not only enhances the occurrence of ferroptosis but also impairs insulin synthesis and secretion.29 In addition, ROS acts as a signaling molecule to assist in the cellular disposal of glucose uptake.30 Fourth, there is aberrant metabolism observed in the TCA cycle starting with 5-methylcytosine and ending with carnitine. The TCA cycle is based on glycolysis as the material basis, with L-glutamate/succinate to hook up the ferroptosis pathway. Therefore, abnormalities of 5-methylcytosine and carnitine as well as glycolysis-related metabolites (ribulose-5P, D-xylulose 5P, D-piperidine acid, and 1-pyrroline-4-hydroxy-2-carboxylate) are suggestive of disturbed glucose metabolism, which may be intrinsic to the progression of pre-diabetes. Taken together, we recommend treating diabetic patients with ferroptosis blockers and controlling pre-diabetic patients with dietary restrictions such as L-norleucine and AA.
In this study, a total of 12 biomarkers were predicted (Fig. 7). Interestingly, 7 of these metabolites are arginine or arginine-related urea cycle metabolites, including L-arginine, N8-acetylspermidine, O-succinyl-L-homoserine, Gly-Phe-Phe, terephthalic acid, 3-hydroxybenzoic acid, and bis(1-inositol)-3,1′-phosphate 1-phosphate. Arginine is a multifaceted amino acid because it can act as a precursor for many biologically active substances, such as nitric oxide (NO), agmatine, and proline.31 Emerging evidence showed that arginine is involved in diabetic progression by (1) synthesizing NO through nitric oxide synthase (NOS) to regulate diabetes-related pathways.32 Induced NOS-derived NO plays a central role in the regulation of several biochemical pathways including glucose and lipid metabolism and energy metabolism under inflammatory conditions.33,34 When the body is deficient in L-arginine, the iNOS/L-arginine pathway induces increased NO production, leading to aggravated inflammation and insulin resistance, ultimately contributing to the progression of diabetes.35,36 (2) Arginine is a secretagogue that stimulates the secretion of anabolic hormones, including insulin and glucagon, somatostatin, and adrenal catecholamine in a direct or indirect manner,37–39 which are all hormones associated with the progression of diabetes. These results suggest that abnormal arginine metabolism may be an important marker for the development of T2DM. ROC results in the present study supported the conclusion that arginine can be used as a predictive biomarker for pre-diabetes.
TCA cycle defects were also found in this study due to the energy production overload from glycolysis. In this study, we found that pyruvate did not change in the A vs. B comparison group but was significantly upregulated in the B vs. C comparison group, suggesting that when FBG 6.1 mmol L−1, glycolysis overload produced excessive pyruvate. Pyruvate is an intermediate between glycolysis and TCA cycle. Excess pyruvate requires the consumption of large amounts of oxaloacetic acid to maintain the normal functioning of the TCA cycle, which is accompanied by rapid production and consumption of intermediates. We found that the abundance of TCA intermediates such as α-ketoglutaric acid, methylmalonic acid, and citramalic acid accumulated significantly at FBG 6.1 mmol L−1. The results of some studies are similar to ours. For example, mitochondrial pyruvate transporter protein activity is reduced in diabetic rats;40 pyruvate and malate are increased simultaneously in the TCA cycle after glucose loading;41 excess pyruvate in pre-diabetic Zucker (fa/fa) rats can lead to increased TCA cycle and mitochondrial overload.42 Therefore, we conclude that when FBG is elevated to 6.1, glycolytic overload produces excess pyruvate causing subsequent TCA cycle overload; however, when a diagnosis of T2DM is confirmed, that is FBG 7.0 mmol L−1, injury occurs after TCA cycle overload, and the TCA cycle exhibits a low-flow state.
Among the 324 DEMs, lipids were the most abundant species with 114, 51 of which were significantly altered at FBG 5.6 mmol L−1 (A vs. B) (Table S5, ESI†), suggesting that abnormal blood glucose can cause lipid metabolism disorder. For example, the abundance of AA (an essential fatty acid) and FFA (20:4) was significantly elevated at FBG 5.6 mmol L−1 (A vs. B). Consistent with our results, studies have demonstrated that blood glucose overload can lead to glycolytic overload, resulting in an abnormal increase in lipid metabolites.43 Abnormal lipid accumulation, such as lysophosphatidic acid, can exert negative feedback to further enhance hepatic insulin resistance.44 Moreover, the main mode of fatty acid catabolism is β-oxidation, wherein fatty acid is activated to lipid acyl coenzyme A and then transported to mitochondria via carnitine; therefore, carnitine is considered an important marker for the evaluation of metabolic disorders such as mitochondrial dysfunction and insulin resistance.45,46 In the present study, a partial imbalance in carnitine levels (such as carnitine C24:2 and DL-carnitine) was already present at FBG 5.6 mmol L−1. We hypothesize that the body's fatty acid requirements have been disrupted, and there is an overload in the transport of lipids through carnitine. In short, in the early stage of pre-diabetes (FBG 5.6 mmol L−1), increased glycolytic load initiates abnormalities in lipid metabolism, leading to further deterioration of diabetes.
Inclusion criteria: all pre-diabetes and T2DM patients were newly diagnosed and did not receive any treatment. Enrolled subjects met the 2019 US American Diabetes Association (ADA) diagnostic criteria for pre-diabetes and diabetes. According to the criteria of the Chinese Diabetes Society (CDS) about FBG, pre-diabetes was defined as 6.1 mmol L−1 ⩽ FBG < 7.0 mmol L−1, whereas according to the criteria of the ADA about FBG, pre-diabetes was defined as 5.6 mmol L−1 ⩽ FBG < 6.9 mmol L−1. Due to the difference in the diagnostic criteria between China and America, we want to explore the abnormal metabolic changes that occur in Chinese T2DM patients when their FBG 5.6 mmol L−1, to provide a theoretical basis for the early prevention and control of diabetes in China. Therefore, subjects were divided into four groups according to FBG values: group A (n = 20), FBG < 5.6 mmol L−1, healthy people with normal glucose tolerance; group B (n = 20), 5.6 mmol L−1 ⩽ FBG < 6.1 mmol L−1, the early stage of pre-diabetes patients; group C (n = 20), 6.1 mmol L−1 ⩽ FBG < 7.0 mmol L−1, the late stage of pre-diabetes patients; group D (n = 20), FBG 7.0 mmol L−1, T2DM patient. Details of each subject are shown in Table S1 (ESI†).
Exclusion criteria: Type 1 diabetes and other special types of diabetes; individuals with diabetes-related acute or chronic complications; various secondary hypertensive conditions; those with chronic kidney or liver diseases, or cancer; absence of anemia and a history of severe cardiovascular, cerebrovascular diseases, or tumors; recent lack of subjective meal management in daily life; no recent blood donation or blood product transfusion events; no history of drug abuse or other psychotropic medications; females who are pregnant or in the lactation period, and so forth.
All subjects provided signed informed consent. The study was approved by the First People's Hospital of Yinchuan Ethics Committee. All experiments were conducted in strict accordance with the relevant laws in China and followed the institutional guidelines of The First People's Hospital of Yinchuan.
For metabolite extraction, 1 mL of 80% methanol internal standard extractant was added to the plasma samples, followed by vortexing for 2 min, quick freezing in liquid nitrogen for 5 min, thawing in an ice bath for 5 min, and repeated freezing and thawing three times. Finally, the plasma sample was centrifuged at 12 000 rpm for 10 min at 4 °C, and 200 μL of the supernatant was collected for subsequent liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis (20 samples per group).
Disregarding background noise, signals with peak heights below 4000 were not picked up. Data were normalized by dividing each metabolite for each sample by the total peak area for that sample. Based on the self-built target standard database, identification analysis of molecules was carried out according to the retention time of the detected molecules, the information of the parent ion pair and the MS2 data. Each molecule was quantified using a targeted multiple reaction monitoring approach based on the peak area of the chromatographic peak on the MultiQuant software. Based on the metabolite abundance obtained above, PCA and OPLS-DA were performed for each sample using the R (v 3.3.2) package. Fold changes of molecules were determined between the different groups using the t test to assess whether differences between the groups were statistically significant. The screening criterion for differentially expressed metabolites (DEMs) was fold change >1, P < 0.05 and variable importance in the projection (VIP) > 1. GO and KEGG enrichment analysis was carried out for the DEMs using the R package of ClusterProfiler. UPLC-MS/MS and bioinformatics analysis were performed by Beijing Biomarker Technology (Beijing, China). Raw metabolite data are available in the Metabolights Database at https://www.ebi.ac.uk/metabolights/MTBLS8644.
Footnotes |
† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3mo00130j |
‡ Wenrui Ji and Xiaomin Xie contributed equally to this work. |
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