Nessrine Souai

Hemodialysis (HD) is the most widely used renal replacement therapy for patients with end-stage renal disease (ESRD) and is frequently accompanied by long-term complications that impair quality of life, including metabolic and inflammatory disturbances. Growing evidence suggests that these complications may be linked to alterations in the gut microbiota; however, microbial composition and interaction patterns in HD patients remain incompletely characterized. In this exploratory, cross-sectional study, high throughput 16S rRNA gene sequencing was used to profile the fecal microbiota of patients undergoing hemodialysis and of healthy controls. The objective was to characterize associations between hemodialysis and gut microbial composition, ecological network organization, and predicted functional potential. Comparative analyses revealed significant differences in bacterial community structure and microbial networks in the HD cohort. Both gender and dialysis vintage were associated with variation in specific taxa, including increased detection of the Synergistetes phylum, particularly among male patients and those undergoing long-term HD. Associations were also observed between clinical and demographic factors and the relative abundance of several short-chain fatty acid-associated taxa, including members of the Lachnospiraceae and Ruminococcaceae families and the genus Bifidobacterium. Predicted functional potential (PICRUSt2) indicated distinct microbial metabolic profiles in HD patients compared with controls, particularly in pathways related to carbohydrate, nucleotide, and amino acid metabolism, with additional variation according to dialysis vintage. Overall, these findings provide an exploratory characterization of structural, compositional, and predicted functional alterations of the gut microbiota associated with hemodialysis. Although the modest cohort size precludes definitive conclusions, the results support the rationale for larger, longitudinal studies investigating microbiota-derived biomarkers and host–microbiome interactions in ESRD.

There is mounting evidence for the emerging role of gut microbiota (GM) and its metabolites in profoundly impacting allogenic hematopoietic stem cell transplantation (allo-HSCT) and its subsequent complications, mainly infections and graft versus host-disease (GvHD). The present study was performed in order to investigate changes in GM composition and fecal metabolic signature between transplant patients (n = 15) and healthy controls (n = 18). The intestinal microbiota was characterized by NGS and gas chromatography–mass spectrometry was employed to perform untargeted analysis of fecal metabolites. We found lower relative abundances of Actinobacteria, Firmicutes, and Bacteroidetes and a higher abundance of Proteobacteria phylum after allo-HSCT. Particularly, the GvHD microbiota was characterized by a lower relative abundance of the short-chain fatty acid-producing bacteria, namely, the Feacalibacterium, Akkermansia, and Veillonella genera and the Lachnospiraceae family, and an enrichment in multidrug-resistant bacteria belonging to Escherichia, Shigella, and Bacteroides. Moreover, network analysis showed that GvHD was linked to a higher number of positive interactions of Blautia and a significant mutual-exclusion rate of Citrobacter. The fecal metabolome was dominated by lipids in the transplant group when compared with the healthy individuals (p < 0.05). Overall, 76 metabolites were significantly altered within transplant recipients, of which 24 were selected as potential biomarkers. Furthermore, the most notable altered metabolic pathways included the TCA cycle; butanoate, propanoate, and pyruvate metabolisms; steroid biosynthesis; and glycolysis/gluconeogenesis. Specific biomarkers and altered metabolic pathways were correlated to GvHD onset. Our results showed significant shifts in gut microbiota structure and fecal metabolites characterizing allo-HSCT.

Metabolic alteration plays a functional role in kidney allograft complications. Metabolomics is a promising high-throughput approach in nephrology but is still limited by the lack of overlap in metabolite coverage. We performed an untargeted fecal metabolomic analysis of forty stable kidney allograft recipients and twenty non-transplant controls. First, we applied the ultra-high performance liquid chromatography (UHPLC) analysis coupled with the Diod Array detector. The potential biomarkers were then collected and identified by gas chromatography-mass spectrometry (GCMS). In order to allow for complete coverage of the fecal polar and non-polar metabolites, the performance of five organic solvents with increasing polarity was investigated successively. UHPLC analysis revealed that the fecal metabolite profiles following the five extractions were significantly different between controls and kidney allografts. GC-MS analysis showed that the best predictors’ metabolites belonged mainly to long-chain fatty acids, phenolic compounds, and amino acids. Collectively, our results showed the efficiency of our pioneer method to successfully discriminate stable kidney-transplant recipients from controls. These findings suggest that distinct metabolic profiles mainly affect fatty acid biosynthesis and amino acid metabolism. In such a context, the novel insights into metabolomic investigation may be a valuable tool that could provide useful new relevant biomarkers for preventing kidney transplant complications.

Monitoring graft recipients remains dependent on traditional biomarkers and old technologies lacking specificity, sensitivity, or accuracy. Recently, metabolomics is becoming a promising approach that may offer to kidney transplants a more effective and specific monitoring. Furthermore, emerging evidence suggested a fundamental role of gut microbiota as an important determinant of patients’ metabolomes. In the current study, we enrolled forty stable renal allografts recipients compared to twenty healthy individuals. Samples were taken at different time points from patient to patient following transplantation surgery, which varied from 3 months to 22 years post-graft. All patients started the immunosuppression therapy immediately following kidney graft (Day 0). Gas chromatography–mass spectrometry (GC–MS) was employed to perform untargeted analysis of fecal metabolites. Globally, the fecal metabolic signature was significantly different between kidney transplants and the control group. Fecal metabolome was dominated by lipids (sterols and fatty acids) in the stable transplant group compared to the controls (p < 0.05). Overall, 18 metabolites were significantly altered within kidney transplant recipients. Furthermore, the most notable altered metabolic pathways in kidney transplants include ubiquinone and other terpenoid-quinone biosynthesis, tyrosine metabolism, tryptophan biosynthesis, and primary bile acid biosynthesis. Fecal metabolites could effectively distinguish stable transplant recipients from controls, supporting the potential utility of metabolomics in rapid and non-invasive diagnosis to produce relevant biomarkers and to help clinicians in monitoring kidney transplants. Further investigations are needed to clarify the physiological relevance of fecal metabolome and to assess the impact of microbiota modulation.

Breast cancer (BC) is the most common form of cancer among women worldwide. Despite the huge advancements in its treatment, the exact etiology of breast cancer still remains unresolved. There is an increasing interest in the role of the gut microbiome in modulating the anti-cancer therapeutic response. It seems that alteration of the microbiome-derived metabolome potentially promotes carcinogenesis. Taken together, metabolomics has arisen as a fascinating new omics field to screen promising metabolic biomarkers. In this study, fecal metabolite profiling was performed using NMR spectroscopy, to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy (NAC) for breast cancer. Metabolic profiles of feces from patients (n = 8) following chemotherapy treatment cycles were studied. Interestingly, amino acids were found to be upregulated, while lactate and fumaric acid were downregulated in patients under the second and third cycles compared with patients before treatment. Furthermore, short-chain fatty acids (SCFAs) were significantly differentiated between the studied groups. These results strongly suggest that chemotherapy treatment plays a key role in modulating the fecal metabolomic profile of BC patients. In conclusion, we demonstrate the feasibility of identifying specific fecal metabolic profiles reflecting biochemical changes that occur during the chemotherapy treatment. These data give an interesting insight that may complement and improve clinical tools for BC monitoring.

Gaining long-term graft function and patient life quality remain critical challenges following kidney transplantation. Advances in immunology, gnotobiotics, and culture-independent molecular techniques have provided growing insights into the complex relationship of the microbiome and the host. However, little is known about the over time-shift of the gut microbiota in the context of kidney transplantation and its impact on both graft and health stability. Here we aimed to characterize the structure of gut microbiota within stable kidney graft recipients. We enrolled forty kidney transplant patients after at least three months of transplantation and compared them to eighteen healthy controls. The overall microbial community structure of the kidney transplanted group was clearly different from control subjects. We found lower relative abundances of Actinobacteria, Bacteroidetes, and Verrucomicrobia within the patient group and a higher abundance of Proteobacteria compared to the control group. Both richness and Shannon diversity indexes were significantly lower in the kidney graft recipients than in healthy controls. Post-graft period was positively correlated with the relative abundance of the Proteobacteria phylum, especially Escherichia.Shigella genus. Interestingly, only Parabacteroides was found to significantly differentiate patients that were not suffering from lifestyle diseases and those who suffer from post-graft complications. Furthermore, network analysis showed that the occurrence of lifestyle diseases was significantly linked with a higher number of negative interactions of Sutterella and Succinivibrio genera within patients. This study characterizes gut microbiome fluctuation in stable kidney transplant patients after a long post-allograft period. Analysis of fecal microbiota could be useful for nephrologists as a new clinical tool that can improve kidney allograft monitoring and outcomes.