An alteration of the gut microbiota composition involving Lactobacillus sp., A. muciniphila and F. prausnitzii is associated with the glycemic status in KT recipients, raising the question of their role in the genesis of NODAT.

50 patients (19 controls without diabetes, 15 who developed New Onset Diabetes After Transplantation, NODAT, and 16 with type 2 diabetes before KT) were included. Before KT, Lactobacillus sp. tended to be less frequently detected in controls than in those who would become diabetic following KT (NODAT) and in initially diabetic patients (60%, 87.5%, and 100%, respectively, p = 0.08). The relative abundance of Faecalibacterium prausnitzii was 30 times lower in initially diabetic patients than in controls (p = 0.002). The relative abundance of F. prausnitzii of NODAT patients was statistically indistinguishable from controls and from diabetic patients. The relative abundance of Lactobacillus sp. increased following KT in NODAT and in initially diabetic patients (20-fold, p = 0.06, and 25-fold, p = 0.02, respectively). In contrast, the proportion of Akkermansia muciniphila decreased following KT in NODAT and in initially diabetic patients (2,500-fold, p = 0.04, and 50,000-fold, p<0.0001, respectively). The proportion of Lactobacillus and A. muciniphila did not change in controls between before and after the transplantation. Consequently, after KT the relative abundance of Lactobacillus sp. was 25 times higher (p = 0.07) and the relative abundance of A. muciniphila was 2,000 times lower (p = 0.002) in diabetics than in controls.

Patients transplanted at our institution provided fecal samples before, and 3–9 months after KT. Fecal bacterial DNA was extracted and 9 bacteria or bacterial groups were quantified by qPCR.

The gut dysbiosis associated with diabetes acquired before or after kidney transplantation (KT) has not been explored.

Funding: JT was supported by an award from “la Fondation du Rein” named “Don de soi, don de vie”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2020 Lecronier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation: Lecronier M, Tashk P, Tamzali Y, Tenaillon O, Denamur E, Barrou B, et al. (2020) Gut microbiota composition alterations are associated with the onset of diabetes in kidney transplant recipients. PLoS ONE 15(1): e0227373. https://doi.org/10.1371/journal.pone.0227373

We decided to investigate whether alterations in the gut microbiota composition observed in diabetic patients in the general population were also associated with diabetes before and/or after KT. For this purpose, we measured the relative abundance of nine bacteria or bacterial groups that have been shown to be associated to metabolic disorders in the feces collected before and after KT in initially diabetic, NODAT and control KTRs.

The possible role of the gut microbiota in the genesis of diabetes before or after KT remains to be explored. However, the interactions between metabolic disorders, the microbiota, and IS drugs are highly complex in the context of KT. Indeed, a dysbiosis and a “leaky gut” have been described in chronic kidney disease patients [ 32 , 33 ], and IS drugs significantly alter microbiota composition [ 34 ].

Metabolic disorders are very common in kidney transplant (KT) recipients (KTRs), both before and after the transplantation. Diabetes is the first cause for end-stage renal disease and the requirement for KTR worldwide, with approximately 40% of diabetics on the waiting list [ 25 ]. In addition, normoglycemic patients before KT are at increased risk of new onset diabetes after transplantation (NODAT; [ 26 ]) which develops in approximately 20% of KTRs in the first year after transplantation [ 27 , 28 ]. This is mainly due to the immunosuppressive (IS) treatment, which may include corticosteroids [ 29 ], cyclosporin, tacrolimus [ 30 ] and sirolimus [ 31 ] which have been shown to induce either insulin resistance or alteration in insulin secretion. In turn, this worsening metabolic syndrome negatively impacts the outcome of KTRs in terms of cardiovascular and renal events [ 26 ].

Microbiota transfer experiments in germ-free mice suggest that dysbiosis is not only associated with, but also responsible for these metabolic disorders [ 19 – 22 ]. Importantly, fecal transplantation from lean humans to metabolically affected obese individuals induced a significant improvement in insulin sensitivity, associated with a modification in gut microbiota composition with an increase in Akkermansia muciniphila [ 23 , 24 ].

Many diseases and disorders, including obesity and diabetes, have been linked to a change in the composition of the gut microbiota called dysbiosis [ 1 ], both in mouse models and in humans. Microbial diversity is dramatically decreased in obese patients with metabolic disorders compared to obese patients without [ 2 – 6 ]. The Firmicutes to Bacteroidetes phyla ratio is increased in obese mice and patients [ 3 , 7 ]. In addition, in obese or diabetic patients, Bifidobacterium and Faecalibacterium prausnitzii [ 8 ] are decreased and Bacteroides and Lactobacilli [ 9 ] are increased [ 10 , 11 ]. Finally, the lower proportion of F. prausnitzii in diabetic patients is restored after weight loss and metabolic improvement either with diet intervention [ 12 ] or after bariatric surgery [ 13 ]. Akkermansia muciniphila has been associated with insulin sensitivity. Indeed, obese mice fed with prebiotic carbohydrates show clinical benefit including weight loss and improved insulin sensitivity [ 14 ]. Furthermore, gavage of obese mice with live or pasteurized Akkermansia muciniphila recapitulated these beneficial effects [ 15 – 17 ]. Finally, we also confirmed that among overweight or obese individuals the proportion of Akkermansia muciniphila was higher in insulin-sensitive patients [ 18 ].

The difference between the means of several groups at two time points (D0 and M3-9) was estimated by two-way ANOVA. When two-way ANOVA showed a significant difference (p<0.05), Turkey’s test with correction for multiple comparisons was used to identify which means in the series were different from one another.

Quantitative data are presented as mean ± standard deviation (normally distributed data) or as median [interquartile range] (variables without a normal distribution). The difference between the means of more than two groups was tested by one-way ANOVA (normally distributed data) or Kruskal-Wallis ANOVA (variables without a normal distribution). When these ANOVA estimates produced a significant difference (p<0.05), Turkey’s test with correction for multiple comparisons was used to identify which means in the series were different from one another. If the groups which were compared contained only paired data (measures in the same patients at two time points), repeated measures ANOVA was used.

The relative amount of a bacterium or a bacterial group in a given sample was inferred with the following formula: where Q Bac is the (log-) relative abundance of the studied bacterium or bacterial group in the sample. The formula is based on the qPCR cycle number (Cq) where the SYBR Green signals exceed the detection threshold: CqEub is the mean Cq obtained with the “Eubacteria” pair of primers (see S1 Table ) which quantifies all bacteria in the sample, and CqBac is the mean Cq obtained with the pair of primers specific of the studied bacterium or bacterial group.

All bacterial quantifications were performed on two independent qPCRs, each containing a duplicate of each sample in the same 96 well plate, with the result calculated as the mean of the 4 measures. Template DNA was thawed only twice, once for each repeat of the qPCR.

All qPCRs were followed by a dissociation curve to check for the amplification of a unique DNA fragment.

After extraction, fecal bacterial DNA was quantified using a Nanodrop ® analyzer and diluted in order to obtain a concentration of 10 ng/μl. qPCRs included the template (50 ng of DNA per reaction), 5 μL of 2X SYBR Green mix (Absolute blue ® qPCR SYBR Green, Thermo scientific ® , including Taq hot start enzyme) and each primer to a final concentration in the mix of 300 nM. Water was added to obtain a final reaction volume of 10 μL. The sequence, and annealing temperature of primer pairs used to quantify each bacterium or bacterial group is shown in S1 Table . qPCRs were carried out in a LightCycler480 (Roche ® ) as follows: one initial activation step of 15 min at 95°C, 40 cycles of 2-step amplifications (95°C for 15” for denaturation, 57–63°C for 1 min for annealing). A bacterium or a bacterial group was considered undetectable in a sample if its quantification cycle (Cq) was ≥ 35.

All these bacteria or bacterial groups were specifically chosen as they have been shown to be associated with metabolic disorders in mice and/or the population of non-transplanted patients.

The fecal microbiota from KTRs was explored at various taxonomic levels through the quantification of nine bacterial groups or species by qPCR: The Firmicutes/Bacteroidetes ratio, Bacteroides-Prevotella group, Lactobacilli, Bifidobacteria, Akkermansia muciniphila, Faecalibacterium prausnitzii, Escherichia coli, Clostridium coccoides, and Clostridium leptum.

Fecal samples were thawed, and 400 μl of the feces and storage medium mix were centrifuged (8,000 rpm, 8 min). The fecal DNA was extracted from the pellet with the QIAamp DNA Stool Mini Kit ® (Qiagen ® ) following the manufacturer’s instructions, with the addition of an initial 1-min bead-beating step on a FastPrep-24 (MP Biomedicals, Solon, OH) on level 5. The quality and quantity of the DNA collected were assessed on a Nanodrop ® analyzer. All DNA samples were kept at -80°C until use.

In all cases, we used swabs (modified Cary-Blair medium FecalSwab ® , Copan ® , Milan, Italy) either to harvest a sample from spontaneously emitted feces or to perform a rectal swabbing (when patients could not provide feces before surgery at D0). Samples were stored at 4°C for a maximum of 2 days before they were frozen at -30°C [ 36 ].

The initial maintenance immunosuppressive regimen in our institution always consists of a combination of prednisone (20 mg QD progressively tapered to 5 mg QD between the 4 rd and the 9 th month), tacrolimus (target trough level 8±2 ng/ml) and mycophenolate mofetil (dose adapted in order to obtain an estimated area under the curve around 30 h x mg/l). We had no clinical indication to change this initial regimen in any of the included patients for the total duration of this study. All patients received cotrimoxazole for pneumocystis prophylaxis during the total duration of this study. Valgancyclovir treatment was prescribed only to patients who presented a CMV infection (systematic CMV qPCR during follow-up) or disease. The proportion of valgancyclovir and antibiotic treatments initiated between D0 and the time of collection of the M3-9 sample was not different between the three groups ( Table 1 ).

The proportion of patients who received basiliximab or anti-lymphocyte polyclonal antibody induction was not different in controls, NODAT and diabetic patients ( Table 1 ).

This study was approved by the local ethic committee (“CPP Ile de France VI”) on 28 March 2013. All patients were orally informed of the research upon arrival in our institution to receive a kidney transplant. They were free to refuse to provide stool samples if they did not want to participate. No written consent was required by local authorities, as the present research did not modify the usual follow-up of transplanted patients in our institution.

Type 2 diabetes and New Onset Diabetes After Transplantation (NODAT) were defined according to the diagnostic criteria of the American Diabetes Association [ 35 ], except that no oral glucose tolerance test was performed.

The only inclusion criterion for this study was that the D0 sample and/or the M3-9 sample needed to contain enough material to allow a DNA extraction of at least 500 ng.

Patients receiving a combined KT (kidney and liver, or kidney and heart transplantations) were excluded because they were treated and followed by different medical teams.

Between September 2013 and December 2014, we prospectively collected feces from all patients undergoing transplantation at our institution. All patients admitted for a kidney transplantation were required to provide a fecal sample upon arrival in the department, before administration of any immunosuppressive drug (“D0 sample”), and 3 months after the KT. Due to organizational reasons (both from the patients’ and the staff’s sides) the second samples were in reality collected 3 to 9 months after KT and the samples are designated as “M3-9 samples” in the manuscript.

Results

Collected fecal samples A total of 73 fecal samples were collected from 50 patients, as we were not able to collect a sample both before and after KT from all patients (Fig 1). The samples collected before KT were all obtained 24 hours before KT, before any immunosuppressive or antibiotic treatment was started. Therefore, these samples are referred to as “D0 samples”. Samples collected after transplantation were collected after a median delay of 3.3 [3.1–7.8] months for controls, 3.1 [2.8–7.7] months for NODAT patients, and 3.3 [3.0–5.5] months for initially diabetic patients (p = 0.65 by Kruskal-Wallis ANOVA). Because of this delay of approximately 3 to 9 months after KT, post-KT samples are referred to as “M3-9 samples”.

Fecal bacteria species associated with NODAT and diabetes prior to KT Before KT, Lactobacillus sp. was detected in 60% of the feces from controls, while it was always detected in the feces of initially diabetic patients. At D0, patients who would develop diabetes after KT (NODAT patients) tended to have an intermediate rate of carriage of Lactobacillus sp. (87.5%, X2 test for the comparison of the three groups: p = 0.08; Fig 3A). When initially diabetic patients were grouped with the patients who would become diabetic after KT (NODAT patients), the proportion of patients in whom Lactobacillus was detected was significantly higher than in controls (93.3% vs. 60%, p = 0.03; Fig 3B). PPT PowerPoint slide

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larger image TIFF original image Download: Fig 3. Quantification of fecal bacteria species before kidney transplantation. A, B: Quantification of Lactobacilli in fecal samples collected before kidney transplantation. The proportion of patients harboring Lactobacilli in their feces before KT tended to be higher in NODAT patients and in initially diabetic patients than in controls (A; p = 0.08 by X2 test). When initially diabetic and NODAT patients were grouped together, they more frequently harbored Lactobacilli in their feces than controls (B; p = 0.03 by X2 test). C: Relative abundance of Faecalibacterium prausnitzii out of the total bacteria in the feces of transplanted patients collected before KT. Initially diabetic patients had a lower proportion of F. prausnitzii than controls. NODAT harbored an intermediate relative abundance of F. prausnitzii in their feces. D: Relative abundance of Clostridium leptum out of the total bacteria in the feces of transplanted patients collected before KT. Bars at the top of graphs indicate significant differences; dotted bars at the top of the graph indicate trends (p≤0.08); A, B: X2 test. C, D: One-way ANOVA. NODAT: New Onset Diabetes After Transplantation. https://doi.org/10.1371/journal.pone.0227373.g003 At the species level, the relative abundance of Faecalibacterium prausnitzii was significantly different in the feces of the three groups of patients at D0 (p = 0.002 by one-way ANOVA, Fig 3C). The relative abundance of F. prausnitzii was significantly lower in the feces of patients who were diabetic before KT compared to controls (difference between the means of Log 10 (F. prausnitzii/Eubacteria) = -1.5, i.e. the relative abundance of F. prausnitzii was 30 times lower in the diabetics than in controls, p = 0.002 by one-way ANOVA with Turkey’s correction for multiple comparisons). Clostridium leptum was also unequally distributed in the three groups of patients (p = 0.02 by one-way ANOVA, Fig 3D). The relative abundance of C. leptum in NODAT patients was one third the proportion in controls (p = 0.05 by one-way ANOVA with Turkey’s correction), and one fifth the proportion in initially diabetic patients (p = 0.02). In contrast, we did not observe any significant differences in the Firmicutes/Bacteroidetes ratio, the presence and the relative abundance of Bifidobacterium sp., Bacteroides-Prevotella group, Akkermansia muciniphila, Escherichia coli, and Clostridium coccoides between the three groups of patients at D0. To summarize, a higher probability of detectable Lactobacillus sp. in the feces and a lower relative abundance of F. prausnitzii characterized the gut microbiota alteration associated with the initial diabetes and (ulterior) NODAT at D0, as compared to the controls.

Fecal bacteria species associated with NODAT and diabetes after KT Three to nine months after KT, at the genus level, Lactobacillus sp. was detected in almost all diabetics (i.e. initially diabetic patients and NODAT patients, 96.6%), while it was detected in fewer controls (78.6%, p = 0.05) in M3-9. Furthermore, the relative abundance of Lactobacillus sp. in the feces of patients from the three groups tended to be different in M3-9 samples (p = 0.07 by one-way ANOVA, Fig 4A). This relative abundance tended to be higher in initially diabetic patients compared to controls (25-fold, p = 0.06 by one-way ANOVA with Turkey’s correction). NODAT patients had an intermediate proportion of Lactobacillus at M3-9. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 4. Quantification of specific fecal bacteria species 3 to 9 months after kidney transplantation. A: Quantification of Lactobacilli in fecal samples collected 3–9 months after kidney transplantation. The relative abundance of Lactobacilli out of the total bacteria was higher in diabetic patients than in controls. NODAT patients had an intermediate proportion of Lactobacilli. B: Relative abundance of Lactobacilli in fecal samples collected at D0 and 3–9 months after kidney transplantation, restricted to carriers for whom a sample at D0 and 3–9 months after kidney transplantation was available (paired samples from the same patients before and after KT). The relative abundance of Lactobacilli tended to increase between D0 and M3-9 in NODAT patients and increased in patients who were diabetics before KT. Finally, the relative abundance of Lactobacilli tended to be higher in diabetic patients than in controls at M3-9. C: Relative abundance of Akkermansia muciniphila in fecal samples collected at D0 and 3–9 months after kidney transplantation. The relative abundance of A. muciniphila out of the total bacteria decreased between D0 and M3-9 in NODAT and diabetic patients. In addition, it was lower in diabetic patients than in controls at M3-9. Finally, NODAT patients had an intermediate relative abundance of A. muciniphila. Box (25th to 75th percentiles) and whiskers (min to max) with the median (line in the middle) and all individual values (rounds) are plotted. Bars at the top of graphs indicate significant differences. Dotted bars indicate trends (p≤0.08). https://doi.org/10.1371/journal.pone.0227373.g004 When we restricted the analysis to the feces of patients who provided a sample both before and 3–9 months after the KT (paired samples from the same individual patients, Fig 4B), the relative abundance of Lactobacilli tended to be higher in diabetic patients than in controls at M3-9 (80-fold, p = 0.07 by Repeated Measures two-way ANOVA with Turkey’s correction). In contrast, the relative abundance of Lactobacilli in D0 samples did not statistically change in the three groups of patients. A. muciniphila was detected in 50% of the controls, and 31% of all diabetic patients (non-significantly different) after transplantation. In M3-9 samples the proportion of A. muciniphila was 2,000 times lower in diabetic patients than in controls (difference of the mean Log 10 (A. muciniphila/Eubacteria) = -3.3, p = 0.002 by two-way ANOVA with Turkey’s correction, Fig 4C). The relative abundance of A. muciniphila in NODAT patients was intermediate. In contrast, the relative abundance of Bifidobacterium, Bacteroides-Prevotella, F. prausnitzii, E. coli, C. leptum and C. coccoides and the Firmicutes/Bacteroidetes ratio were not different between NODAT patients, diabetics and controls in the M3-9 samples. To summarize, a higher percentage of Lactobacillus sp. carriage, a higher relative abundance of Lactobacillus sp. and a lower relative abundance of A. muciniphila in the feces of the carriers characterized the gut microbiota alteration associated with diabetes and NODAT after KT.

Changes in the composition of the gut microbiota between before and after KT In order to evidence a correlation between a change in gut microbiota and the onset of diabetes, we compared the changes in the relative abundance of each bacterial group between D0 and M3-9 in the three metabolic groups of patients. When analyses were restricted to paired (before and after KT) fecal samples, the relative abundance of Lactobacilli increased in NODAT patients and in diabetics while it remained statistically the same in controls between D0 and M3-9 (difference of the mean Log 10 (Lactobacilli/Eubacteria) = 1.3, i.e. a 20-fold difference in relative abundance, p = 0.06, and 1.4, i.e. a 25-fold difference, p = 0.02, and 0.2 p = 0.99 respectively by repeated measures two-way ANOVA with Turkey’s correction, Fig 4B). Similarly, the relative abundance of A. muciniphila decreased in NODAT and in diabetic patients, while it remained statistically the same in controls between D0 and M3-9 (difference of the mean Log 10 (A. muciniphila/Eubacteria) = -3.4, i.e. a 2,500-fold decrease in relative abundance, p = 0.04, -4.7, i.e. a 50,000 decrease, p<0.0001, and -1.5, p = 0.19 respectively by two-way ANOVA with Turkey’s correction, Fig 4C). The relative abundance of Lactobacilli (Log 10 (Lactobacilli/Eubacteria)) in samples from all patients without diabetes at D0 (controls and NODAT) was lower than the relative abundance of Lactobacilli of all patients with diabetes at M3-9 (NODAT and diabetics, -5.0±1.2 vs. -3.4±1.6, p = 0.0009, Fig 5A). PPT PowerPoint slide

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larger image TIFF original image Download: Fig 5. Comparisons of non-diabetic patients at D0 and diabetic patients at M3-9. A: Relative abundance of Lactobacilli in all non-diabetic patients at D0 (controls and NODAT patients) and all diabetic patients at M3-9 (NODAT and diabetics). B: Difference in the relative abundance of Lactobacilli before and after KT in the paired samples of patients without (controls) or with diabetes at M3-9 (NODAT and diabetics). While the relative abundance of Lactobacilli remained stable in controls, it significantly increased in all diabetics at M3-9. C: Relative abundance of A. muciniphila in all non-diabetic patients at D0 (controls and NODAT patients) and all diabetic patients at M3-9 (NODAT and diabetics). Box (25th to 75th percentiles) and whiskers (min to max) with the median (line in the middle) and all individual values (rounds) are plotted. Bars at the top of graphs indicate significant differences. Dotted bars indicate trends (p≤0.08). https://doi.org/10.1371/journal.pone.0227373.g005 When we restricted the analysis to patients who provided samples at D0 and at M3-9, the difference in the relative abundance of Lactobacilli (Log 10 (Lactobacilli/Eubacteria)) between M3-9 and D0 was close to zero in controls and tended to be higher in all diabetic patients (NODAT and diabetics) at M3-9 (0.2±1.3 vs. 1.4±1.0, p = 0.06, Fig 5B). The relative abundance of A. muciniphila (Log 10 (A. muciniphila/Eubacteria)) in samples from all patients without diabetes at D0 (controls and NODAT) was 25,000 higher than the relative abundance of A. muciniphila of all patients with diabetes at M3-9 (NODAT and diabetics, -2.0±0.8 vs. -6.4±1.5, p<0.0001, Fig 5C).