Antibiotics-mediated intestinal microbiome perturbation aggravates tacrolimus-induced glucose disorders in mice

Abstract

Both immunosuppressants and antibiotics (ABX) are indispensable for transplant patients. However, the former increases the risk of new-onset diabetes, whereas the latter impacts intestinal microbiota (IM). It is still unclear whether and how the interaction between immunosuppressants and ABX alters the IM and thus leads to glucose metabolism disorders. This study examined the alterations of glucose and lipid metabolism and IM in mice exposed to tacrolimus (TAC) with or without ABX. We found that ABX further aggravated TAC-induced glucose tolerance and increased insulin secretion. Combined treatment resulted in exacerbated lipid accumulation in the liver. TAC-altered microbial community was further amplified by ABX administration, as characterized by reductions in phylum Firmicutes, family Lachnospiraceae, and genus Coprococcus. Analyses based on the metagenomic profiles revealed that ABX augmented the effect of TAC on microbial metabolic function mostly related to lipid metabolism. The altered components of gut microbiome and predicted microbial functional profiles showed significant correlation with hepatic lipid accumulation and glucose disorders. In conclusion, ABX aggravated the effect of TAC on the microbiome and its metabolic capacities, which might contribute to hepatic lipid accumulation and glucose disorders. These findings suggest that the ABX-altered microbiome can amplify the diabetogenic effect of TAC and could be a novel therapeutic target for patients.

Keywords

antibiotics / tacrolimus / glucose disorders / microbiome

Introduction

Immunosuppressive agents help many transplant patients to survive. However, immunosuppressive treatment also causes side effects, e.g., new-onset diabetes mellitus (NODM), which seriously impacts the life quality of postoperative patients, and is associated with elevated risks of cardiovascular disease, graft dysfunction, and mortality [¹–³]. The use of immunosuppressive agents, especially tacrolimus (TAC), is considered as the primary and controllable risk factor of NODM [⁴,⁵]. Recent studies showed that intestinal microbiota (IM) might be involved in the occurrence of NODM relevant to TAC [⁵,⁶]. In addition, antibiotics (ABX)-mediated gut dysbiosis increases the incidence of diabetes and accelerates its development in mice [⁷]. In clinical practice, the use of ABX is unavoidable for transplant patients because of the high incidence of opportunistic infections due to adoption of immunosuppressants [⁸,⁹]. Thus, we speculated that the combination of ABX and TAC might alter IM, which lead to glucose disorders and even diabetes.

Commensal bacteria are implicated in human health and disease [¹⁰]. They are even considered as an important metabolic organ and a key regulator of multiple metabolic pathways, including those relevant to the host’s energy balance and glucose metabolism [¹¹,¹²]. IM are highly associated with type 2 diabetes (T2DM) [¹³–¹⁵]. Patients with T2DM are characterized by decreased abundance of butyrate-producing bacteria and increased opportunistic pathogens, which lead to sulfate reduction and oxidative stress resistance [¹⁴]. Our previous study also found the critical role of IM as an important player of NODM in transplant recipients [⁵]. Modulation of IM by probiotics partially restores intestinal homeostasis, improves microbial metabolic function, attenuates pancreatic inflammation, and maintains glucose homeostasis (our unpublished data). We also found that patients receiving liver transplantation had a significant decrease of butyrate-producing bacteria and an increase of opportunistic pathogens [¹⁶], which was similar to patients with diabetes [¹⁴,¹⁷]. Although these studies indicated that intestinal dysbiosis might contribute to the development of TAC-induced diabetes, it remains unknown whether and how ABX contribute to TAC-induced diabetes.

Here, we performed high-throughput sequencing targeting the 16S rRNA sequences (V3–V4 region) to characterize the fecal microbial communities in TAC-treated mice with or without ABX. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was applied to predict function shift of IM. We also tried to identify specific microbial lineages and potential bacterial biomarkers that may play important roles in the promoting process of glucose disorders. This is the first evaluation of the combined effect of ABX and TAC on glucose metabolism mediated by gut microbiota. Our findings provide a more comprehensive understanding of the combined effect of ABX and TAC treatment on the composition and function of IM and its potential influence on the glucose metabolism, and facilitate therapeutic efforts to target the microbiota for patients with such clinical conditions.

Materials and methods

Animal experiments

Wild-type male C57BL/6 mice (8 weeks old, n = 30; Shanghai, Chinese Academy of Sciences, China) weighing 22–25 g were used in the experiments. Mice were housed in groups of five per cage with a strict 12:12 h light–dark cycle (lights on at 7 AM) at 20 °C and had free access to water and food ad libitum in a controlled SPF environment [¹⁸]. Body weight (bw) of mice was recorded once a week. All mice were acclimated for 1 week and then were randomly divided into three groups: (1) TAC group, intraperitoneally treated daily with TAC (Astellas Pharma, Inc., Tokyo, Japan) in PBS (2 mg/kg per bw, TAC, n = 10); (2) TAC+ ABX group, treated with TAC and ABX (ampicillin, vancomycin, neomycin, and metronidazole, each at 1 g/L in sterile water [¹⁹], TAC+ ABX, n = 10); and (3) blank control group, intraperitoneally treated daily with equivalent volume of saline (Control, n = 10). After 4-week treatment, mice were euthanized via intraperitoneal injection of 3% sodium pentobarbital solution at 2.5 mL/kg bw. Blood samples were collected through cardiac puncture immediately, and the plasma was frozen immediately at −80 °C until it was assayed. Stool sample was collected and stored at −80 °C until use.

Glucose tolerance test (GTT) and glucose-stimulated insulin secretion test

After 4-week treatment, we performed GTT in mice. Mice were fasted for 16 h with free access to water overnight. Then, they were intraperitoneally injected with D-glucose (1 g/kg bw) [²⁰]. Glucose from tail vein blood was monitored before (0 min) and after (15, 30, 60, and 120 min) glucose injection using Accu-Chek Go (Roche Diagnostics GmbH, Basel, Switzerland). The plasma insulin levels were assessed at 0, 30, and 120 min after glucose injection by Rat/Mouse Insulin ELISA Kit (Millipore, Watford, UK).

Bacterial genomic DNA extraction and 16S rRNA Illumina sequencing

Total bacterial genomic DNA was extracted from stool samples using QIAamp^® DNA Stool Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions with modifications [²¹]. Then, the bacterial genomic DNA concentration and purity were determined using the Nano-Drop 1000 Spectrophotometer (NanoDrop Technologies, Thermo Scientific, USA). The V3–V4 region of the bacterial 16S rRNA gene was amplified by PCR with the barcoded forward fusion primers (5′-CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGT GTGCTCTTCCGATCT-3′) in combination with the reverse fusion primer (5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTC- CGATCT-3′). PCR products were gel-purified (AXYGEN Gel Extraction Kit, AXYGEN). Then, PCR products were pooled in equimolar concentrations and sequenced using an Illumina^® MiSeq platform according to the manufacturer’s recommendations. All reads were clustered into operational taxonomic units (OTUs) at a sequence identity of 97%, and the taxonomic affiliation of the OTUs was determined with QIIME (version 1.8.0) against the Greengenes database version 13.8 [²²]. The following downstream data analyses were conducted in R software. Analysis of variance (ANOVA) was performed to evaluate α diversity (Shannon index, Simpson index, Chao index, ACE index) among the different groups in 16S sequencing data. PERMANOVA (Adonis) was used to test for microbial community clustering (PCoA) using unweighted UniFrac distance matrices and Bray–Curtis distance matrices. The linear discriminant analysis (LDA) effect size (LEfSe) method was performed to characterize the taxa with statistical significance and biological relevance [²³]. For LEfSe analysis, the Kruskal–Wallis rank sum test (α value of 0.05) and LDA score of>2 were used as thresholds. Pairwise comparisons were analyzed with Mann–Whitney U test. Then, a predicted functional composition of the intestinal microbiome was inferred for each sample using PICRUSt, which can accurately predict the abundance of gene families from the 16S rRNA information based on the close link between phylogeny and function [²³]. Briefly, metagenome inference was carried out with 16S rRNA gene data clustered at a 97% identity cut-off using close reference of the Greengenes database. The resulting OTU table was normalized by its gene copy number, and finally predicted gene family abundance of each sample was inferred. In addition, the graphical representation of the results was done with STAMP [²⁴], and significant functional differences among mice in different groups were assessed with Welch’s t-test using a P value<0.05.

Detection of triglyceride (TG) levels in the liver and blood

Hepatic TG content was measured using the Triglyceride Quantification Kit (Abcam, Cambridge, UK) according to the manufacturer’s instructions as previously reported [²⁵]. Briefly, fresh liver sample (100 mg) was suspended and homogenized in 1 mL of 5% Nonidet P-40/ddH₂O solution. The samples were slowly heated to 80 °C–100 °C until the solution became cloudy and cooled down to room temperature. Then, the samples were centrifuged at 2000 g for 2 min. The supernatant was collected. Finally, the absorbance of the samples was quantified with a spectrophotometer (BioTek, Vermont, USA) at 570 nm. The TG values were expressed as mg/g of liver. The blood TG was measured using a Hitachi 7600-210 Automatic Analyzer (Hitachi, Tokyo, Japan).

Statistical analysis

To determine the statistical significance for the difference among groups, one-way ANOVA for data with normal distribution or Kruskal–Wallis test for data with abnormal distribution was used. Spearman’s or Pearson’s correlation tests were employed to address the correlation between microbiomes and metabolic parameters. P values<0.05 were considered significant. Statistical analysis was performed with GraphPad Prism software (version 6.00; San Diego, California, USA) and SPSS software (version 16.0, Chicago, USA)

Results

ABX exacerbated the effect of TAC on hepatic lipid deposition and glucose disorders in mice

To evaluate the influence of TAC and ABX on glucose metabolism, intraperitoneal GTT was performed on controls and TAC-treated mice with or without ABX. Among the three groups, TAC+ ABX mice showed the highest blood glucose levels from 30 min (TAC+ ABX vs. Control: 24.74 vs. 15.61, P<0.0001; TAC vs. Control: 21.60 vs. 15.61, P<0.0001; TAC+ ABX vs. TAC: 24.74 vs. 21.60, P<0.01) to 60 min (TAC+ ABX vs. Control: 19.51 vs. 11.01, P<0.0001; TAC vs. Control: 15.36 vs. 11.01, P<0.001; TAC+ ABX vs. TAC: 19.51 vs. 15.36, P<0.01) after receiving glucose and the area under the curve (AUC) (TAC+ ABX vs. Control: 2010 vs. 1447, P<0.0001; TAC vs. Control: 1691 vs. 1447, P<0.05; TAC+ ABX vs. TAC: 2010 vs. 1691, P<0.01) (Fig. 1A and 1B). The results suggested that ABX significantly improved glucose tolerance induced by TAC treatment. The insulin levels in TAC mice were elevated at fasting state (25.07 vs. 39.39, P<0.0001) and at 120 min (35.41 vs. 60.86, P<0.0001) following glucose treatment but decreased at 30 min (72.67 vs. 52.81, P<0.0001) after glucose injection compared with control mice (Fig. 1C). The results demonstrated that the peak of insulin secretion was delayed and insulin levels were lowered by TAC. Unexpectedly, a significant improvement in insulin secretion was observed in TAC+ ABX mice compared with TAC mice (P<0.05) (Fig. 1C).

Given that TAC also disturbs lipid metabolism [²⁶], we further detected TG levels in the liver and blood of mice with different treatments. The blood levels of TG in TAC mice were significantly increased (1.69 vs. 2.17, P<0.001) compared with the controls and slightly decreased after ABX treatment (Fig. 1D). Interestingly, the liver levels of TG in TAC mice were significantly increased compared with the controls (6.30 vs. 16.50, P<0.5) and further increased in TAC+ ABX mice (TAC vs. TAC+ ABX: 16.50 vs. 23.00, P<0.0001, Fig. 2). These results suggested that ABX increased hepatic lipid deposition in TAC-treated mice.

Impact of ABX on intestinal microbiomes of TAC-treated mice

To examine the effect of ABX on microbial community in TAC-treated mice, we performed high-throughput Illumina® MiSeq sequencing targeting the bacterial 16S rRNA genes. Detailed statistical characteristics of pyrosequencing data are summarized in Supplementary Table S1. Although the Simpson and Shannon index showed no significant difference among the mice with different treatments, TAC markedly increased commensal diversity as indicated by elevated ACE (Control vs. TAC: 15 100 vs. 18 750, P<0.05) and Chao 1 indexes (Control vs. TAC: 14 026 vs. 17 679, P<0.05), whereas ABX decreased α diversity (ACE indexes, TAC+ ABX vs. TAC: 15 209 vs. 18 750, P<0.05, Table S1). IM in different groups of mice were clearly separated by the PCoA plot analysis (PERMANOVA, unweighted UniFrac, TAC vs. Control: P<0.01, r= 0.509; TAC+ ABX vs. Control: P<0.01, r= 0.488; TAC+ ABX vs. TAC: P<0.05, r= 0.435, Fig. 3A). Similar results were obtained using the Bray–Curtis distance matrices (TAC vs. Control: P<0.05, r= 0.680; TAC+ ABX vs. Control: P<0.01, r= 0.652; TAC+ ABX vs. TAC: P<0.05, r= 0.602, Figure S1). Administration of ABX altered the relative abundance of predominant microbiota at the phylum level, including Firmicutes, Verrucomicrobia, and Actinobacteria (Fig. 3B).

We identified the distinguished bacteria in mice with different treatment by LEfSe analysis. Compared with controls, the relative abundance of phylum Firmicutes (44.71% vs. 25.78%, P<0.01), family Lachnospiraceae (4.14% vs. 1.38%, P<0.05), and genus Coprococcus (9.72% vs. 0.76%, P<0.01) and the ratio of Firmicutes to Bacteroidetes (1.062 vs. 0.4180, P<0.05) were significantly reduced in TAC+ ABX-treated mice (Fig. 3C and 3D; Fig. 4A, 4B, 4D, and 4F). However, the abundance of phylum Verrucomicrobia (0.70% vs. 4.38%, P<0.0001), family Verrucomicrobiaceae (1.15% vs. 4.90%, P<0.0001), and genus Akkermansia (5.67% vs. 20.06%, P<0.0001) was enhanced in TAC+ ABX-treated mice (Fig. 3C and 3D; Fig. 4C, 4E, and 4G).

Altered key microbiota relevant to ABX and TAC were associated with glucose disorders

We assessed the correlation between ABX- and TAC-altered microbiota and glucose metabolism. Fig. 4 shows that the AUC and peak level of glucose in GTT negatively correlated with the ratio of Firmicutes to Bacteroidetes and the relative abundances of Firmicutes, Lachnospiraceae, and Coprococcus and positively correlated with the relative abundances of Bacteroidetes (P<0.05). Notably, those altered microbiota were associated with liver TG levels (P<0.05, Fig. 4H). These findings indicated that ABX-dependent IM dysbiosis might contribute to glucose disorders in TAC mice.

Predicted function pathways of microbiome altered by ABX and TAC significantly correlated with glucose disorders

The potential functional changes of gut microbiome in mice with different treatment were predicted with PICRUSt using Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs. Statistical analysis revealed that compared with those of controls and TAC mice, the gut microbiome of TAC+ ABX mice was significantly enriched in functional categories relevant to lipid metabolism, metabolism of cofactors, biosynthesis and biodegradation of secondary metabolites and vitamins, amino acid metabolism, and xenobiotic biodegradation (P<0.05, Fig. 5A). Compared with controls, the gut microbiome in TAC+ ABX mice had significantly reduced carbohydrate metabolism-related functional categories (P<0.05, Fig. 5A). The altered functional categories in level three KEGG pathways are summarized in Supplementary Table S2.

Further analyses showed that the altered functional genes of inferred metagenome induced by ABX were correlated with glucose metabolism. The AUC of GTT was positively related with microbial genes associated with lipid metabolism and biosynthesis and biodegradation of secondary metabolites genes (P<0.05, Fig. 5C). The levels of blood glucose at 30 min of GTT were positively related with lipid metabolism, biosynthesis and biodegradation of secondary metabolites, amino acid metabolism, and metabolism of cofactors and vitamins genes (P<0.05, Fig. 5C). The values at 15 min of GTT were positively related with metabolism of cofactors and vitamins genes (P<0.05, Fig. 5C).

Moreover, we found that the IM functional categories related to lipid metabolism, biosynthesis and biodegradation of secondary metabolites, amino acid metabolism, and metabolism of cofactors and vitamins genes were significantly associated with liver TG levels (P<0.05, Fig. 5C). These results suggested that the microbial metabolic function altered by ABX and TAC might play a role in hepatic lipid deposition and glucose disorders.

Discussion

Recent studies showed evidence of the diabetogenic IM induced by ABX and TAC in the pathogenesis of metabolic disorders [²⁷–²⁹]. Interest in the effect of the microbiome and ABX-induced dysbiosis on diabetes during treatment had heightened with the widespread use of TAC. However, whether ABX use could impact the diabetogenic effect of TAC on microbiome and glucose homeostasis remains unclear. Here, we found that ABX significantly increased hepatic lipid accumulation and glucose intolerance in TAC-treated mice. ABX further augmented the TAC-induced shift in gut microbiome, which enhanced metabolic capacities in particular for lipid metabolism. Furthermore, the altered microbiome and microbial function pathways were significantly correlated with hepatic lipid accumulation and glucose tolerance. This study provides clear clues pointing out that the combination of ABX and TAC exacerbated gut microbiota, which directly lead to glucose disorder in mice.

The influence of ABX on IM and glucose tolerance is controversial. The altered microbiota induced by ABX affects physiological and pathophysiological status, e.g., obesity and insulin resistance [¹⁹,³⁰–³²]. In mice fed a high-fat diet, ABX improved glucose homeostasis [³³,³⁴]. However, diabetogenic intestinal microbiomes induced by prolonged ABX accelerate diabetes in NOD mice [⁷,³⁵]. In addition, Livanos et al. reported that ABX-mediated altered microbiome accelerates the development of type 1 diabetes in mice [³⁶]. In humans, oral administration of vancomycin significantly reduces fecal microbial diversity and decreases insulin sensitivity [³⁷]. In transplant patients, the administration of ABX and immunosuppressants is unavoidable to prevent immune rejection and infections related to immune inhibition [³⁸]. Therefore, investigating the effect of combined ABX and TAC on IM is a critical issue to understand the mechanisms underlying transplantation-related diabetes. Before performing human study, we investigated the combined effect of TAC and ABX treatment on IM and its mediated effect on glucose metabolism in a mouse model.

Based on the high-throughput 16S rRNA sequencing analysis, we found that ABX significantly augmented the TAC-induced alterations of microbiotal composition and metabolic capacities in mice. ABX further decreased the relative abundance of Firmicutes and the ratio of Firmicutes to Bacteroidetes and improved glucose tolerance in TAC-treated mice. In both human and animal gut, Firmicutes and Bacteroidetes are the dominant bacteria, accounting for approximately 99% of the whole microbiota, and correlated positively with blood glucose levels [³⁹,⁴⁰]. Thus, the findings indicated that the decreased Firmicutes in TAC- and ABX-treated mice might be crucial in the pathogenesis of glucose tolerance. In addition, we observed a significant inhibition of ABX on TAC-reduced family Lachnospiraceae and its genus Coprococcus. The family Lachnospiraceae is one of the predominant members of Firmicutes in both human and animal gut [⁴¹]. These bacteria produce short-chain fatty acids (SCFAs), which have health-promoting functions, including the production of energy substrates for the colonic epithelium (butyrate) [⁴²,⁴³], the maintenance of host immune homeostasis [⁴⁴,⁴⁵], and promotion of glucose homeostasis [⁴⁶]. Reduction of SCFAs results in deterioration of intestinal integrity and increase of intestinal permeability [⁴⁷,⁴⁸]. The novelty of our study was the suggestion of a link between Firmicutes and the amplified effect of TAC on glucose disorders. Further studies to assess the impact of ABX and TAC on fecal SCFA are needed.

Akkermansia, Verrucomicrobiaceae, and Verrucomicrobia were the genus, family, and phylum that were significantly decreased by TAC in mice. Interestingly, ABX markedly restored the relative abundance of Akkermansia in TAC-treated mice. Moreover, ABX treatment significantly improved insulin secretion in TAC-induced diabetic mice. These findings suggested that Akkermansia might play a key role in insulin secretion. Several previous studies support our findings. Hansen et al. reported that vancomycin propagated Akkermansia muciniphila in NOD mice [⁴⁹]. In addition, Dubourg et al. showed that broad-spectrum ABX treatment increased the proportion of Akkermansia in humans [⁵⁰]. Recently, Shin et al. [⁵¹] reported that the administration of Akkermansia to murines displayed antidiabetic effects. More recently, Hanninen et al. found that Akkermansia muciniphila could induce IM remodeling and control the islet autoimmunity in NOD mice [⁵²]. Further studies are required to assess how the lack of Akkermansia impacts glucose disorders.

To further explore how altered IM by ABX and TAC resulted in glucose disorders, we used PICRUSt to assess the metagenomic profile of IM [²³]. We found that ABX reinforced the effect of TAC on four microbial metabolic pathways related to lipid, cofactor, vitamin, and amino acid metabolism, which were closely correlated with the improvement of hepatic lipid accumulation and glucose tolerance. The disorders of these metabolites, including lipid and amino acids, were observed in the onset and progression of diabetes [⁵³,⁵⁴]. Distinct microbiota and lipid metabolism-related genes were associated with hepatic lipid accumulation. The results strongly suggested the key role of IM in ABX- and TAC-induced glucose disorders.

The current study has several limitations. Although we observed distinct distributions of microbiota and its relationship with glucose metabolism, it remained unclear that how intestinal dysbiosis and its functional changes cause the hepatic lipid metabolic disorders and subsequently glucose disorders after TAC treatment. To date, we have not yet assessed differences in the fecal content of SCFAs and their role in the development of TAC-treated diabetes. As an immunosuppressor, TAC is used by transplant patients, and the currently used mouse models cannot mimic the disease situation in clinical practice. Establishing an ideal animal model is urgently required before initiating a successful translational study.

In summary, this novel study first found that the administration of ABX could amplify the TAC effect on IM profiles and functional categories, which were significantly associated with the hepatic lipid accumulation and glucose tolerance in mice. Our study also provided support for the link between depleted Firmicutes and glucose disorders induced by TAC treatment. The findings might open new avenues for the microbial therapeutic targets in diabetes, in particular for transplant patients receiving treatment of both ABX and TAC. For these patients, manipulation on IM by beneficial microbes, such as Akkermansia muciniphila, might overcome the effect of the diabetogenic microbiome.

References

[1] Valderhaug TG, Hjelmesaeth J, Jenssen T, Roislien J, Leivestad T, Hartmann A. Early posttransplantation hyperglycemia in kidney transplant recipients is associated with overall long-term graft losses. Transplantation 2012; 94(7): 714–720

[2] Valderhaug TG, Hjelmesaeth J, Hartmann A, Roislien J, Bergrem HA, Leivestad T, Line PD, Jenssen T. The association of early post-transplant glucose levels with long-term mortality. Diabetologia 2011; 54(6): 1341–1349

[3] Zaza G, Dalla Gassa A, Felis G, Granata S, Torriani S, Lupo A. Impact of maintenance immunosuppressive therapy on the fecal microbiome of renal transplant recipients: comparison between an everolimus- and a standard tacrolimus-based regimen. PLoS One 2017; 12(5): e0178228

[4] Lankarani KB, Eshraghian A, Nikeghbalian S, Janghorban P, Malek-Hosseini SA. New onset diabetes and impaired fasting glucose after liver transplant: risk analysis and the impact of tacrolimus dose. Exp Clin Transplant 2014; 12(1): 46–51

[5] Ling Q, Xu X, Wang B, Li L, Zheng S. The origin of new-onset diabetes after liver transplantation: liver, islets, or gut? Transplantation 2016; 100(4): 808–813

[6] Bhat M, Pasini E, Copeland J, Angeli M, Husain S, Kumar D, Renner E, Teterina A, Allard J, Guttman DS, Humar A. Impact of immunosuppression on the metagenomic composition of the intestinal microbiome: a systems biology approach to post-transplant diabetes. Sci Rep 2017; 7(1): 10277

[7] Candon S, Perez-Arroyo A, Marquet C, Valette F, Foray AP, Pelletier B, Milani C, Ventura M, Bach JF, Chatenoud L. Antibiotics in early life alter the gut microbiome and increase disease incidence in a spontaneous mouse model of autoimmune insulin-dependent diabetes. PLoS One 2015; 10(5): e0125448

[8] Kawecki D, Pacholczyk M, Lagiewska B, Sawicka-Grzelak A, Durlik M, Mlynarczyk G, Chmura A. Bacterial and fungal infections in the early post-transplantation period after liver transplantation: etiologic agents and their susceptibility. Transplant Proc 2014; 46(8): 2777–2781

[9] Yousuf T, Kramer J, Kopiec A, Jones B, Iskandar J, Ahmad K, Keshmiri H, Dia M. In search for equilibrium: immunosuppression versus opportunistic infection. J Clin Med Res 2016; 8(2): 175–177

[10] Marchesi JR, Adams DH, Fava F, Hermes GD, Hirschfield GM, Hold G, Quraishi MN, Kinross J, Smidt H, Tuohy KM, Thomas LV, Zoetendal EG, Hart A. The gut microbiota and host health: a new clinical frontier. Gut 2016; 65(2): 330–339

[11] Velagapudi VR, Hezaveh R, Reigstad CS, Gopalacharyulu P, Yetukuri L, Islam S, Felin J, Perkins R, Boren J, Oresic M, Backhed F. The gut microbiota modulates host energy and lipid metabolism in mice. J Lipid Res 2010; 51(5): 1101–1112

[12] Nieuwdorp M, Gilijamse PW, Pai N, Kaplan LM. Role of the microbiome in energy regulation and metabolism. Gastroenterology 2014; 146(6): 1525–1533

[13] Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK, Al-Soud WA, Sorensen SJ, Hansen LH, Jakobsen M. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 2010; 5(2): e9085

[14] Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, Peng Y, Zhang D, Jie Z, Wu W, Qin Y, Xue W, Li J, Han L, Lu D, Wu P, Dai Y, Sun X, Li Z, Tang A, Zhong S, Li X, Chen W, Xu R, Wang M, Feng Q, Gong M, Yu J, Zhang Y, Zhang M, Hansen T, Sanchez G, Raes J, Falony G, Okuda S, Almeida M, LeChatelier E, Renault P, Pons N, Batto JM, Zhang Z, Chen H, Yang R, Zheng W, Li S, Yang H, Wang J, Ehrlich SD, Nielsen R, Pedersen O, Kristiansen K, Wang J. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490(7418): 55–60

[15] Tilg H, Moschen AR. Microbiota and diabetes: an evolving relationship. Gut 2014; 63(9): 1513–1521

[16] Wu ZW, Ling ZX, Lu HF, Zuo J, Sheng JF, Zheng SS, Li LJ. Changes of gut bacteria and immune parameters in liver transplant recipients. Hepatobiliary Pancreat Dis Int 2012; 11(1): 40–50

[17] Zhang X, Shen D, Fang Z, Jie Z, Qiu X, Zhang C, Chen Y, Ji L. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS One 2013; 8(8): e71108

[18] Jakobsson HE, Rodriguez-Pineiro AM, Schutte A, Ermund A, Boysen P, Bemark M, Sommer F, Backhed F, Hansson GC, Johansson ME. The composition of the gut microbiota shapes the colon mucus barrier. EMBO Rep 2015; 16(2): 164–177

[19] Cho I, Yamanishi S, Cox L, Methe BA, Zavadil J, Li K, Gao Z, Mahana D, Raju K, Teitler I, Li H, Alekseyenko AV, Blaser MJ. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 2012; 488(7413): 621–626

[20] Boj SF, van Es JH, Huch M, Li VS, Jose A, Hatzis P, Mokry M, Haegebarth A, van den Born M, Chambon P, Voshol P, Dor Y, Cuppen E, Fillat C, Clevers H. Diabetes risk gene and Wnt effector Tcf7l2/TCF4 controls hepatic response to perinatal and adult metabolic demand. Cell 2012; 151(7): 1595–1607

[21] Wang B, Jiang X, Cao M, Ge J, Bao Q, Tang L, Chen Y, Li L. Altered fecal microbiota correlates with liver biochemistry in nonobese patients with non-alcoholic fatty liver disease. Sci Rep 2016; 6(1): 32002

[22] Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010; 7(5): 335–336

[23] Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 2013; 31(9): 814–821

[24] Parks DH, Beiko RG. Identifying biologically relevant differences between metagenomic communities. Bioinformatics 2010; 26(6): 715–721

[25] Lozano I, Van der Werf R, Bietiger W, Seyfritz E, Peronet C, Pinget M, Jeandidier N, Maillard E, Marchioni E, Sigrist S, Dal S. High-fructose and high-fat diet-induced disorders in rats: impact on diabetes risk, hepatic and vascular complications. Nutr Metab (Lond) 2016; 13(1): 15

[26] Bamgbola O. Metabolic consequences of modern immunosuppressive agents in solid organ transplantation. Ther Adv Endocrinol Metab 2016; 7(3): 110–127

[27] Ussar S, Griffin NW, Bezy O, Fujisaka S, Vienberg S, Softic S, Deng L, Bry L, Gordon JI, Kahn CR. Interactions between gut microbiota, host genetics and diet modulate the predisposition to obesity and metabolic syndrome. Cell Metab 2015; 22(3): 516–530

[28] Prokai A, Fekete A, Pasti K, Rusai K, Banki NF, Reusz G, Szabo AJ. The importance of different immunosuppressive regimens in the development of posttransplant diabetes mellitus. Pediatr Diabetes 2012; 13(1): 81–91

[29] Santos L, Rodrigo E, Pinera C, Quintella E, Ruiz JC, Fernandez-Fresnedo G, Palomar R, Gomez-Alamillo C, de Francisco A, Arias M. New-onset diabetes after transplantation: drug-related risk factors. Transplant Proc 2012; 44(9): 2585–2587

[30] Ubeda C, Pamer EG. Antibiotics, microbiota, and immune defense. Trends Immunol 2012; 33(9): 459–466

[31] Million M, Thuny F, Angelakis E, Casalta JP, Giorgi R, Habib G, Raoult D. Lactobacillus reuteri and Escherichia coli in the human gut microbiota may predict weight gain associated with vancomycin treatment. Nutr Diabetes 2013; 3(9): e87

[32] Bailey LC, Forrest CB, Zhang P, Richards TM, Livshits A, DeRusso PA. Association of antibiotics in infancy with early childhood obesity. JAMA Pediatr 2014; 168(11): 1063–1069

[33] Membrez M, Blancher F, Jaquet M, Bibiloni R, Cani PD, Burcelin RG, Corthesy I, Mace K, Chou CJ. Gut microbiota modulation with norfloxacin and ampicillin enhances glucose tolerance in mice. FASEB J 2008; 22(7): 2416–2426

[34] Carvalho BM, Guadagnini D, Tsukumo DM, Schenka AA, Latuf-Filho P, Vassallo J, Dias JC, Kubota LT, Carvalheira JB, Saad MJ. Modulation of gut microbiota by antibiotics improves insulin signalling in high-fat fed mice. Diabetologia 2012; 55(10): 2823–2834

[35] Brown K, Godovannyi A, Ma C, Zhang Y, Ahmadi-Vand Z, Dai C, Gorzelak MA, Chan Y, Chan JM, Lochner A, Dutz JP, Vallance BA, Gibson DL. Prolonged antibiotic treatment induces a diabetogenic intestinal microbiome that accelerates diabetes in NOD mice. ISME J 2016; 10(2): 321–332

[36] Livanos AE, Greiner TU, Vangay P, Pathmasiri W, Stewart D, McRitchie S, Li H, Chung J, Sohn J, Kim S, Gao Z, Barber C, Kim J, Ng S, Rogers AB, Sumner S, Zhang XS, Cadwell K, Knights D, Alekseyenko A, Backhed F, Blaser MJ. Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice. Nat Microbiol 2016; 1(11): 16140

[37] Vrieze A, Out C, Fuentes S, Jonker L, Reuling I, Kootte RS, van Nood E, Holleman F, Knaapen M, Romijn JA, Soeters MR, Blaak EE, Dallinga-Thie GM, Reijnders D, Ackermans MT, Serlie MJ, Knop FK, Holst JJ, van der Ley C, Kema IP, Zoetendal EG, de Vos WM, Hoekstra JB, Stroes ES, Groen AK, Nieuwdorp M. Impact of oral vancomycin on gut microbiota, bile acid metabolism, and insulin sensitivity. J Hepatol 2014; 60(4): 824–831

[38] Romero FA, Razonable RR. Infections in liver transplant recipients. World J Hepatol 2011; 3(4): 83–92

[39] Li M, Wang B, Zhang M, Rantalainen M, Wang S, Zhou H, Zhang Y, Shen J, Pang X, Zhang M, Wei H, Chen Y, Lu H, Zuo J, Su M, Qiu Y, Jia W, Xiao C, Smith LM, Yang S, Holmes E, Tang H, Zhao G, Nicholson JK, Li L, Zhao L. Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci USA 2008; 105(6): 2117–2122

[40] Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature 2006; 444(7122): 1022–1023

[41] Kittelmann S, Seedorf H, Walters WA, Clemente JC, Knight R, Gordon JI, Janssen PH. Simultaneous amplicon sequencing to explore co-occurrence patterns of bacterial, archaeal and eukaryotic microorganisms in rumen microbial communities. PLoS One 2013; 8(2): e47879

[42] Bergman EN. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol Rev 1990; 70(2): 567–590

[43] Wong JM, de Souza R, Kendall CW, Emam A, Jenkins DJ. Colonic health: fermentation and short chain fatty acids. J Clin Gastroenterol 2006; 40(3): 235–243

[44] Bohmig GA, Krieger PM, Saemann MD, Wenhardt C, Pohanka E, Zlabinger GJ. n-butyrate downregulates the stimulatory function of peripheral blood-derived antigen-presenting cells: a potential mechanism for modulating T-cell responses by short-chain fatty acids. Immunology 1997; 92(2): 234–243

[45] Noverr MC, Huffnagle GB. Does the microbiota regulate immune responses outside the gut? Trends Microbiol 2004; 12(12): 562–568

[46] De Vadder F, Kovatcheva-Datchary P, Goncalves D, Vinera J, Zitoun C, Duchampt A, Backhed F, Mithieux G. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell 2014; 156(1-2): 84–96

[47] Peng L, He Z, Chen W, Holzman IR, Lin J. Effects of butyrate on intestinal barrier function in a Caco-2 cell monolayer model of intestinal barrier. Pediatr Res 2007; 61(1): 37–41

[48] Suzuki T, Yoshida S, Hara H. Physiological concentrations of short-chain fatty acids immediately suppress colonic epithelial permeability. Br J Nutr 2008; 100(02): 297–305

[49] Hansen CH, Krych L, Nielsen DS, Vogensen FK, Hansen LH, Sorensen SJ, Buschard K, Hansen AK. Early life treatment with vancomycin propagates Akkermansia muciniphila and reduces diabetes incidence in the NOD mouse. Diabetologia 2012; 55(8): 2285–2294

[50] Dubourg G, Lagier JC, Armougom F, Robert C, Audoly G, Papazian L, Raoult D. High-level colonisation of the human gut by Verrucomicrobia following broad-spectrum antibiotic treatment. Int J Antimicrob Agents 2013; 41(2): 149–155

[51] Shin NR, Lee JC, Lee HY, Kim MS, Whon TW, Lee MS, Bae JW. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut 2014; 63(5): 727–735

[52] Hänninen A, Toivonen R, Poysti S, Belzer C, Plovier H, Ouwerkerk JP, Emani R, Cani PD, De Vos WM. Akkermansia muciniphila induces gut microbiota remodelling and controls islet autoimmunity in NOD mice. Gut 2018; 67(8): 1445–1453

[53] Zhang AH, Qiu S, Xu HY, Sun H, Wang XJ. Metabolomics in diabetes. Clin Chim Acta 2014; 429: 106–110

[54] Menni C, Fauman E, Erte I, Perry JR, Kastenmuller G, Shin SY, Petersen AK, Hyde C, Psatha M, Ward KJ, Yuan W, Milburn M, Palmer CN, Frayling TM, Trimmer J, Bell JT, Gieger C, Mohney RP, Brosnan MJ, Suhre K, Soranzo N, Spector TD. Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes 2013; 62(12): 4270–4276

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