Urinary cell-free DNA is an informative bio-analyte to measure the dynamics of the urinary microbiome after kidney transplantation.

Introduction:: Urine is frequently used in healthcare to detect and monitor health conditions, including Urinary Tract Infections (UTIs) and organs post-transplantation¹. Within a year of receiving a kidney transplant, 25% of recipients risk losing their kidney to complications arising from UTIs². With advances in microbiome studies, it is found that irregular imbalances in the microbiome are correlated with disease³. In principle, documenting the regular changes in the urinary microbiome could help us prepare for or predict the onset of disease. Urinary cell-free DNA (cfDNA) is a reliable biomarker with utility in allograft monitoring and infection detection^1,4. While urinary cfDNA can be used to study the urine microbiome, urine typically has a low microbial cfDNA biomass, making it prone to DNA contamination during handling⁴.

Materials and Methods:: To study the human urinary microbiome, we developed an assay (SIFT-seq)⁴ to tag and recover cfDNA molecules prior to cfDNA extraction⁴. We tagged DNA molecules via bisulfite conversion of unmethylated cytosines to thymine. We built a bioinformatic tool to estimate the unmethylated cytosine abundance and recover bisulfite-converted DNA molecules, which are, in principle, intrinsic to the sample at the time of reception. This assay was applied to a cohort of 10 kidney transplant recipients. Each patient supplied 7-13 samples between day 4 and day 360 post-kidney transplant to characterize and probe the temporal dynamics of the urinary microbiome post-kidney transplant.

Results, Conclusions, and Discussions:: We find that the urinary microbiome is mostly composed of bacteria. The most abundant microorganisms were Proteobacteria, Bacteroides, and Actinobacteria. Due to the risk viral infections pose to a kidney allograft, we took a particular interest in the urinary virome. We detected viruses usually associated with opportunistic urinary tract infections. Through alpha diversity measurements, we find that the urinary microbiome changes in complexity and evenness over time. Beta diversity measurements showed that samples originating from the same patient were more similar than those from a different patient. In sum, the urinary microbiome is complex, with some volatility, and unique to each patient. Microbiome dysbiosis is often associated with disease onset. Recording regular temporal changes in the urinary microbiome of kidney transplant patients can bring us closer to predicting disease and rethinking antibiotic therapies following kidney transplant.

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References (Optional): : 1. Burnham, P. et al. Urinary cell-free DNA is a versatile analyte for monitoring infections of the urinary tract. Nat. Commun. 9, (2018).
2. Britt, N. S. et al. Effects of recurrent urinary tract infections on graft and patient outcomes after kidney transplantation. Nephrol. Dial. Transplant. 32, 1758–1766 (2017).
3. Perez-Carrasco, V., Soriano-Lerma, A., Soriano, M., Gutiérrez-Fernández, J. & Garcia-Salcedo, J. A. Urinary Microbiome: Yin and Yang of the Urinary Tract. Front. Cell. Infect. Microbiol. 11, (2021).
4. Mzava, O., Djomnang, L. A. et al. A metagenomic DNA sequencing assay that is robust against environmental DNA contamination. Nat. Commun. 13, 4197 (2022).