TransplantLines microbiome study
In this study, we collected fecal samples, performed metagenomic sequencing of the extracted DNA and profiled the gut microbiome of 2,553 participants, including 1,370 fecal samples from 415 LTR and 672 RTR from the TransplantLines BioBank and Cohort study 19. The median time since transplantation was 9 years (interquartile range [IQR], 4-18) and 5.5 years (IQR, 1-12) for LTR and RTR, respectively. We also included 1,183 profiled metagenomes from identically processed samples from age-, sex- and BMI-matched healthy DMP controls (Fig. 1)21. To analyze the gut microbiome in end-stage disease, 87 ESLD and 78 ESRD patient samples were collected prior to transplantation. Longitudinal samples (N=361) were collected for the 78 ESRD patients at 3-, 6-, 12- and 24-months post-transplantation and used to characterize the short-term temporal dynamics following renal transplantation. Patient characteristics and immunosuppressive regimens are summarized in Supplementary Table S1, Table S2 and Table S3. After quality control and filtering of the metagenomic sequencing data (see Methods), we retained a total of 384 taxa (8 phyla, 14 class, 20 order, 40 family, 83 genera and 219 species), 351 metabolic pathways, 215 virulence factors and 167 antibiotic resistance genes that exhibited a relative abundance of at least 1% and a prevalence of 10% across samples.
Gut dysbiosis is associated with increased mortality following solid organ transplantation
To characterize the gut microbiome of LTR and RTR following solid organ transplantation, we started by analyzing alpha and beta diversity in comparison to healthy controls. Previous studies have shown that the gut microbiome of both LTR and RTR exhibits dysbiotic characteristics 8,11. We hypothesized that the extent of microbial alterations post transplantation may be associated with an increased recipient mortality, as previously shown for allogeneic hematopoietic stem cell transplantation 17. To investigate this relationship, we first performed a principal component analysis of 328 LTR, 594 RTR and 1,183 healthy controls. In agreement with previous studies, we observed that the gut microbiota of LTR and RTR showed significantly lower diversity (Mann-Whitney U, PRTR=2.0x10-21; U=254,311 and PLTR=3.1x10-9; U=235,071; Fig. 2B) and altered microbial composition (PERMANOVA, PLRT vs. healthy=1x10-4, PRTR vs. healthy=1x10-4; Fig. 2A) compared to healthy controls. To further assess the association of gut dysbiosis with transplant recipient overall patient survival post transplantation, we performed a multivariate survival analysis controlling for recipient age, sex and the years since transplantation (see Methods). Similar to the previous analysis by Peled et al. 17 for allogeneic hematopoietic stem cell transplantation recipients, we first tested the association between microbial diversity and mortality. To do so, we stratified LTR and RTR into a high (Shannon diversity > 2.48; N=160) and low (Shannon diversity < 2.48; N=159) diversity groups based on the median Shannon diversity. In the low diversity group, we observed a significantly increased risk of post-transplant mortality for LTR (hazard ratio [HR], 2.91; 95% confidence interval [CI]: 1.03-8.16; P=0.04; adjusted HR: 2.96; 95% CI: 1.03-8.47; P=0.04). 96% of the recipients survived 3-years post-transplantation in the high diversity compared to 77% in the low diversity group (Fig. 2C). When we considered microbial diversity as a continuous variable, the association between microbial dysbiosis and mortality for LTR was even stronger: for every unit decrease in microbial diversity, the overall mortality risk increased by 45% (adjusted HR: 0.55; 95% CI: 0.37-0.79; P=1.4x10-3; Fig. 2D). In contrast, we did not observe a significant association between gut microbial diversity and transplant recipient overall survival for RTR (adjusted HR: 1.19; 95% CI: 1.16-1.63; P>0.05; Fig. 2F and 2G).
To also allow for testing this association beyond microbial diversity, we quantified the Aitchison distance between the gut microbiota of transplant recipients and healthy controls. Compared to microbial diversity, the Aitchison distance is a measure of dissimilarity in microbial community composition. Here we found that a larger Aitchison distance to healthy controls was associated with an increased likelihood of death for both LTR (HR, 1.68; 95% CI, 1.27-2.23, P=3.3x10-4, adjusted HR, 1.71; 95% CI, 1.27-2.31; P=4.0x10-4; Fig. 2E) and RTR (HR, 1.41; 95% CI, 1.10-1.83, P=9.2x10-3, adjusted HR, 1.69; 95% CI, 1.28-2.23; P=1.7x10-4; Fig. 2H): with every unit increase in dissimilarity, the overall mortality risk increased by 71% for LTR and 69% for RTR. These results show that the likelihood of recipient overall survival decreases with the severity of microbial dysbiosis following liver and renal transplantation.
Post-transplantation gut microbiome is characterized by both taxonomic and metabolic alterations
To better understand the link between recipient post-transplant survival and microbiome alterations, we characterized the microbial species and pathways underpinning this association. To do so, we first modeled the relative abundance of microbial species using linear models, accounting for potential confounders including age, sex, BMI, smoking, and the use of PPIs, laxatives and antibiotics (Supplementary Table 1, 2 and 3, and see Methods). We found 102 species (55%) that were differentially abundant in LTR vs. healthy controls (FDR< 0.10), and 75 species (43%) that were differentially abundant in RTR vs. healthy controls. Of these, 60 differentially abundant species were shared between LTR and RTR, with all but two exhibiting the same direction of the relationship effect (Bifidobacterium adolescentis and Eubacterium rectale were increased in LTR vs. healthy controls but decreased in RTR vs. healthy controls). These results suggest that the gut dysbiosis observed following solid organ transplantation is at least partly underpinned by these 58 species. In agreement with this, Gupta et al. 22 and Gacesa et al. 21 reported several of these species as defining the ‘unhealthy’ gut microbiome. For example, compared to healthy controls, we found that both LTR and RTR suffered a reduction in the relative abundance of three species, Sutterella wadsworthensis, Alistipes senegalensis and Bacteroidales sp., and an increase in the relative abundance of species such as Ruminococcaceae sp., Eggerthella lenta and Anaerotruncus colihominis. Both LTR and RTR also had a significant expansion of Escherichia coli, which has previously been shown to increase the risk for Escherichia bacteriuria and urinary tract infection in RTR (Fig. 3; Supplementary Table 4 and 5) 23.
While these results suggest that the microbiota of transplant recipients changes relative to that of healthy controls, it does not tell us whether this is accompanied by a similar change in microbial metabolism. To test this, we modeled the relative abundance of microbial pathways using the same linear models as described above (see Methods). Here we found 284 pathways (82%) that were differentially abundant in LTR vs. healthy controls (FDR<0.10) and 224 pathways (64%) that were differentially abundant in RTR vs. healthy controls, with 200 pathways shared between LTR and RTR. While these included a variety of metabolic pathways related to amino acid metabolism, fatty acid metabolism, carbohydrate metabolism, nucleotide degradation and synthesis, and fermentation (Supplementary Table 6 and 7), we found evidence that the dysbiotic microbiota of LTR and RTR are accompanied by reduced butyrate production. More specifically, both LTR and RTR showed a reduction of two fermentation pathways (pyruvate fermentation to butanoate and acetyl CoA fermentation to butanoate II (PWY5676) and two flavin synthesis pathways (flavin biosynthesis I and III, both part of the vitamin B2 complex). The consequence of a reduced butyrate production is likely an impedance of the gut microbiome’s anti-inflammatory response 24. Lastly, we also found 11 quinone biosynthesis genes that were significantly increased in both LTR and RTR. While we can only speculate on the mechanisms underlying this observation, quinones are an important form of vitamin K1 which is crucial in hemostasis and bone formation 25 (Supplementary Tables 6 and 7).
Gut microbiome of transplant recipients is enriched in antibiotic resistance genes and virulence factors
Due to the immunosuppressed state, both LTR and RTR frequently require antibiotic therapy, which could select for multi-drug resistant bacteria in the gut 26. Annavajhala et al. 11 have even suggested that multi-drug resistant bacteria could act as a marker of gut dysbiosis. We therefore hypothesized that the gut microbiome of transplant recipients would be enriched with antibiotic resistance genes compared to the gut microbiome of healthy controls. We also investigated the levels of virulence factors in the gut microbiome of transplant recipients compared to healthy controls. Virulence factors are indicative of bacterial pathogenicity 27 and could therefore likely act as another important marker for gut dysbiosis.
We found that antibiotic resistance genes and virulence factors were more common and diverse in LTR and RTR compared to healthy controls (Supplementary Tables 8-11). Because both antibiotic resistance genes and virulence factors were extremely sparse in healthy controls, we could not apply the same linear models that we used for microbial species and pathways. Instead, we performed a logistic regression on presence-absences of antibiotic resistance genes or virulence factors with a prevalence cutoff at 1% across samples (see Methods). In LTR and RTR, 104 (71%) and 109 (67%) antibiotic resistance genes were more common compared to healthy controls, respectively (FDR<0.10). Of these, 93 antibiotic resistance genes were shared between LTR and RTR. For example, compared to healthy controls, both RTR and LTR were enriched with 36 different antibiotic resistance genes that code for efflux proteins, 17 for antibiotic inactivation genes, and 19 for antibiotic target alteration genes (Supplementary Table 8 and 9). Furthermore, both LTR and RTR had increased levels of TolC coding proteins which have the potential to drive antibiotic efflux for multiple classes of antibiotics (FDRLTR=9.3x10-9; FDRRTR=3.8x10-14). These results show that the resistome (all antibiotic resistance genes found in the gut microbiome) of the post-transplantation gut microbiome exhibits a higher richness than that of healthy controls.
Finally, 112 (53%) and 167 (52%) virulence factors were more common in LTR vs. healthy controls and RTR vs. healthy controls, respectively (FDR<0.10), and 85 virulence factors were shared between LTR and RTR. We found that multiple proteins, including adherence (VF0220, VF0222, VF0404) and adherence invasion (VF0221) proteins, and multiple iron uptake proteins (VF0123, VF0136, VF0227, VF0228, VF0229, VF0230 and VF0256), invasion proteins (VF0236, VF0237, VF0239) and type II/III secretion proteins (VF0116, VF0118, VF014, VF0333) were significantly increased in LTR and RTR compared to healthy controls (Supplementary tables 10 and 11). These results show that the dysbiotic gut microbiomes of LTR and RTR are enriched with proteins that can increase the pathogenic potential of the gut microbiota. Overall, the enrichment of both antibiotic resistance genes and virulence factors strongly indicates that the gut microbiome of transplant recipients is characterized by a dysbiotic, unhealthy state.
Phenotypes influencing the gut microbiome of transplant recipients
We next explored which intrinsic and external host factors may explain the observed microbial dysbiosis in LTR and RTR. We conducted Permutational Multivariate Analysis Of Variance (PERMANOVA) tests using 52 different phenotypic variables, including anthropological and clinical markers and medication. In these analyses, we included 328 LTR and 594 RTR samples, but due to the limited overlap of phenotypes between LTR and RTR, the analyses were performed separately for LTR and RTR. Of the 52 different phenotypes, 21 and 24 were statistically significant for LTR and RTR, respectively. These explained a total of 18.8% and 13.7% of the variation in the gut microbiota of LTR and RTR, respectively. The factor that explained most variation (4.2%) was recipient status (LTR, RTR, or healthy control; FDR=4.0x10-04; Fig. 4; Supplementary table 12) followed by the use of mycophenolic acid (1%) and age (0.9%). Age, years since transplantation, occurrence of re-transplantation, and estimated glomerular filtration rate (eGFR), significantly explained variation in the gut microbiota of both LTR and RTR (FDR<0.10). Some phenotypic variables were unique to each transplant group. For example, diarrhea, liver enzymes as measured by alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) and Troponin-T (indicative of ischemic heart injury) significantly explained variation in the microbiota of LTR. In contrast, phenotypic variables important for RTR included anti-hypertensive treatment, C-reactive protein (CRP), diabetes, BMI, fat percentage, body surface area (BSA), and handgrip strength, of which the latter relates to overall muscle status of an individual 28. Biomarkers indicative of chronic heart failure, i.e. amino-terminal pro-B-type natriuretic peptide (NT-proBNP), also explained variation in the gut microbiota of RTR (FDR<0.10). Diabetes and CRP were significantly correlated with increased richness for both antibiotic resistance genes (rdiabetes=0.13, rCRP=0.10, P<0.05) and virulence factors (rDiabetes=0.14, rCRP=0.07, P<0.05). Mychophenlic acid and the use of antibiotics significantly correlated with lower Shannon diversity (rmychophenlic acid=-0.11, rantibiotics=-0.07, P<0.05, respectively). Finally, all immunosuppressive drugs showed significant effects on the gut microbial community in both LTR and RTR (FDR<1.0x10-4).
Immunosuppressive drugs as a driver of gut dysbiosis
Solid organ transplant recipients are in permanent need of immunosuppressive drugs post transplantation to prevent allograft rejection. In the previous section, we found that all different types of immunosuppressive drugs had a significant effect on the gut microbiota and could therefore be an important factor in the observed post-transplantation dysbiosis. To test their effect on the relative abundance of microbial species and metabolic pathways in users compared to non-users, we analyzed the different kinds of immunosuppressive drugs either individually, or in combination. In both analyses, we included the immunosuppressive drugs as fixed effect(s) while controlling for other confounders (see Methods). We focused on immunosuppressive drugs that were used individually or in combination by at least 100 transplant recipients (N=5: prednisolone, mycophenolic acid, azathioprine, cyclosporine, and tacrolimus; Supplementary table 13).
Testing the effect of each kind of immunosuppressive drug individually revealed 56 species (26%) and 157 metabolic pathways (45%) that were differentially abundant (Fig. 5A, FDR<0.10) between users and non-users across all kinds of immunosuppressive drugs. However, each kind of immunosuppressive drug also exhibited a unique pattern of differentially abundant species and pathways (Supplementary table 14 and 15). Prednisolone and mycophenolic acid users had the highest numbers of significantly differentially abundant species and pathways compared to azathioprine, cyclosporine and tacrolimus. This may reflect a power issue, since these two kinds of immunosuppressive drugs were the most commonly used among the transplant recipients. We observed similar effects on the gut microbiomes of prednisolone and mycophenolic acid users, with three species (Akkermansia muciniphila; Bifidobacterium adolescentis and Eubacterium rectale) that are typically involved in the production of short chain fatty acids (SCFAs) were significantly decreased in their relative abundance (FDR<0.10; Fig. 5C) 29. Correspondingly, prednisolone and mycophenolic acid users also exhibited a decrease in the pathways responsible for pyruvate fermentation (PWY108, PWY5100, PWY5676 and PWY6588, FDR<0.10). SCFAs play an important role in the immune system by inducing T regulatory cells in the human intestinal mucosa 29. In tacrolimus-using recipients, we observed a higher relative abundance of Roseburia intestinalis which has been reported as a tacrolimus metabolizer 30.
In the second analysis, we tested the effect of 5 common combinations of immunosuppressive drugs (prednisolone and cyclosporin; prednisolone, mycophenolic acid, and tacrolimus; mycophenolic acid and tacrolimus; prednisolone and tacrolimus; prednisolone and mycophenolic acid; Supplementary table 13). We observed 47 species (21.5%) and 131 metabolic pathways (37.3%) that were differentially abundant (FDR<0.10; Fig 5B) between users and non-users across all 5 combination therapies (Supplementary table 16 and 17). A set of common commensal species—A. muciniphila, R. intestinalis, E. rectale, E. eligens, B. adolescentis, and Butyrivibrio crossotus was significantly altered (FDR<0.10). Consistent with the results from the previous analyses, these bacteria are involved in SCFA biosynthesis pathways 29. In addition, all users exhibited a decrease in the relative abundance of the related SCFA pathways (CENTERM.PKWY and PWY 108; FDR<0.10; Fig. 5D). Lastly, Bacteroides thetaiotaomicron, a commensal bacteria that can become an opportunistic pathogen 31, was significantly increased in all 5 combination therapies. Overall, these results suggest that a complex interaction between immunosuppressive drugs and the gut microbiome contributes to the observed dysbiosis of post-transplantation recipients.
End-stage liver and renal disease are associated with two different dysbiotic community states
End-stage disease represents the pre-transplantation phase, with chronically ill patients waiting to undergo solid organ transplantation. Alterations in the gut microbiome of end-stage disease patients have already been described in both ESLD 6 and ESRD 7 patients. However, these studies had rather small sample sizes and used 16S rRNA marker gene sequencing, which does not allow for a functional characterization of the gut microbiome. We therefore compared the gut microbiome of pre-transplantation samples from 87 ESLD patients and 78 ESRD patients to the gut microbiomes of 1,183 healthy controls. In agreement with previous studies, a principal component analysis revealed that the gut microbiota associated with end-stage disease was markedly distinct from that of healthy controls (PERMANOVA: PESLD vs. healthy=1.0x10-4; PESRD vs. healthy=1.0x10-4), with the largest distance to healthy controls observed for the ESLD patients (Fig. 6A). Correspondingly, ESLD patients also exhibited lower microbial diversity than healthy controls (Supplementary Figure 3, Mann-Whitney, U=74,734, P=1.2x10-12). A sub-analysis of underlying diseases and the severity of disease in relation to diversity did not reveal any strong associations (Supplementary Figure 1). Overall, these results suggest that the gut microbiome of both ESLD and ESRD patients have shifted to an unhealthy microbiota, represented by two different dysbiotic community states (PERMANOVA: PESLD vs ESRD=6.0x10-4; Fig. 6A and 6B). The observation of two distinct community configurations is not surprising given the underlying difference in both physiology and anatomy between ESLD and ESRD. On top of this, ESLD and ESRD patients are on different long-term medication regimes, including therapies for diabetes and hypertension 32,33.
To elucidate which microbial features were likely driving the observed dysbiotic states of ESLD and ESRD, we modeled the relative abundance of each feature using the same linear models approach described earlier. The gut microbiome of ESLD and ESRD patients exhibited markedly different patterns compared to healthy controls, with ESLD patients showing the largest shift away from healthy controls. The gut microbiome of ESLD and ESRD exhibited an altered abundance of 49 species (30%), 206 pathways (59%), 73 antibiotic resistance genes (45%) and 83 bacterial virulence factors (26%), whereas that of ESRD exhibited an altered abundance of 37 species (27%) and 126 pathways (37%), 4 antibiotic resistance genes (3%) and 2 virulence factors (1%) (FDR<0.10; Supplementary Table 18-25). While the total number of significantly differentially abundant features was higher in ESLD patients, there were 21 species that were shared with ESRD patients, including a significant decrease of the generally favorable species Faecalibacterium prausnitzii. Consistent with previous studies 6, ESLD-specific changes included an increase of several species from genera such as Escherichia, Clostridium and Streptococcus, while ESRD-specific alterations included an increase in the abundance of Methanobrevibacter smithii, a methane-producing archaea 34, and Ruminococcus torques which is known to decrease gut barrier integrity 35. Lastly, ESLD and ESRD shared 44 differentially abundant metabolic pathways with similar directionality in their relationships (Supplementary table 18-21). Of these, 18 nucleosides and nucleotides biosynthesis pathways were significantly decreased and 10 amino acids biosynthesis pathways were significantly increased (FDR<0.10; see Supplementary table 18-21). While both ESLD and ESRD showed signs of taxonomic and metabolic dysbiosis, ESLD patients appeared to exhibit especially pronounced metabolic dysbiosis, with 59% of their microbial pathways altered compared to healthy controls (see Supplementary Table 20 for full list).
Temporal development of the gut microbiome following transplantation
After solid organ transplantation, fewer complications may arise if the dysbiotic gut microbiome of end-stage disease is quickly ameliorated and restored. To move the field in this direction, we have to first understand the short-terms dynamics of the gut microbiome directly after transplantation and how it changes relative to the gut microbiome of end-stage disease patients (i.e., pre-transplantation) and healthy controls. To gain such an understanding, we analyzed 361 longitudinal samples from 78 end-stage disease patients who underwent renal transplantation. These RTR were then followed 3-, 6-, 12- and 24-months post transplantation (Fig. 1). Surprisingly, we observed that the gut microbiome 3 to 24 months after renal transplantation occupied the same dysbiotic state as the gut microbiome of end-stage disease (i.e., pre-transplantation; Fig. 6A), and this was the case for both microbial species and pathways. However, for species, we observed that the gut microbiome appeared to move closer to the pre-transplantation state as the time since the transplantation increased (Fig. 6A and 6B). Computation of the Aitchison distance (in 2D Euclidean space) of rclr-transformed relative abundances at pre- and post-transplantation time points, as compared to the healthy controls, revealed that the distance to the healthy gut microbiota was indeed smallest for the pre-transplantation and 24-months post-transplantation samples (Fig. 6A and 6B). Interestingly, we observed the opposite pattern for pathways: the 3- and 24-months post-transplantation samples were the closest and farthest away from healthy controls, respectively. These patterns suggest that, while there is a sharp shift in the microbial community directly following solid organ transplantation, the functional potential of this community changes more gradually, likely in response to the immunosuppressive drugs the transplantation recipients are prescribed. Finally, consistent with extensive gut dysbiosis following solid organ transplantation, we observed a significantly lower Shannon diversity at all post-transplantation time points compared to the pre-transplantation samples (P<0.01; Supplementary figure 3). To confirm whether these short-term changes persist long-term, we analyzed all the microbiome data of recipients who underwent solid organ transplantation. This analysis revealed even as long as 20 years after transplantation the gut microbiome of transplant recipients shows a reduced microbial diversity and altered composition (Supplementary figure 2A and 2B).
We next analyzed which microbial species were responsible for the short-term dynamical patterns described above. In these analyses, we also included RTR for whom the post-transplantation gut microbiome was characterized 3-, 6-, 12-, or 24-months following transplantation (see Methods for more information). These analyses revealed 24 species (11%) that were differentially abundant in the gut microbiota post-transplantation compared to pre-transplantation (FDR<0.10; Supplementary table 26). Plotting log-ratios comparing the rclr-transformed relative abundance of each microbial species at a post-transplantation time point to that at pre-transplantation revealed how these features changed following transplantation compared to pre-transplantation (Fig. 6C). For example, the relative abundance of many species that are generally considered to be commensal, such as A. muciniphila, B. adolescentis and Ruminococcus obeum consistently decreased post-transplantation compared to pre-transplantation (Fig. 6C). Other species such as C. asparagiforme, Bilophila wadsworthia and Coprobacter fastidiosus exhibited increased relative abundances post-transplantation compared to pre-transplantation (Fig. 6C; Supplementary table 26). Overall, these results suggest that the gut microbiota directly following organ transplantation is experiencing additional perturbations that result in an increased state of dysbiosis.
We also found signs of metabolic dysbiosis in the gut microbiome directly following solid organ transplantation. The abundances of 125 metabolic pathways (35.5%) was significantly altered in the gut microbiome post-transplantation compared to pre-transplantation (FDR<0.10; see Supplementary table 27). These pathways could be further classified into 29 metabolic classes, responsible for degradation, biosynthesis and energy metabolism (Fig. 6C). The direction of the relationships post-transplantation compared to pre-transplantation were generally stable, either increasing or decreasing, with few exceptions (Fig. 6C; Fig. S5). Similar to the cross-sectional results, we found evidence for a decreased butyrate production, with the relative abundance of the pathways responsible for pyruvate fermentation to butanoate (FDR<0.10 for 6M, 12M, 24M) and acetyl CoA fermentation to butanoate II (FDR<0.10 for 6M) significantly decrease compared to pre-transplantation. Also similar to the cross-sectional results, we found 25 differentially abundant quinone biosynthesis pathways. Lastly, the number of differentially abundant antibiotic resistance genes (N=16 or 3.7%; Supplementary table 28) and bacterial virulence factors (N=11 or 2.8%; Supplementary table 29) were much smaller in this longitudinal analysis compared to the cross-sectional analysis (3.7% vs 28.3% and 2.8% vs 27.7%, respectively), and this difference is likely the consequence of an increasing cumulative exposure to doses of antibiotics in the years following organ transplantation 36.