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Abstract: FR-OR30

The Transcriptomic Landscape of the Arteriovenous Fistula: The Postoperative Genetic Signature of Maturation Failure

Session Information

Category: Dialysis

  • 703 Dialysis: Vascular Access

Authors

  • Vazquez-Padron, Roberto I., University of Miami School of Medicine, Miami, Florida, United States
  • Rojas, Miguel G., University of Miami School of Medicine, Miami, Florida, United States
  • Tabbara, Marwan, University of Miami School of Medicine, Miami, Florida, United States
  • Challa, Akshara Sree, University of Miami School of Medicine, Miami, Florida, United States
  • Duque, Juan Camilo, University of Miami School of Medicine, Miami, Florida, United States
  • Salman, Loay H., Albany Medical College, Albany, New York, United States
  • Martinez, Laisel, University of Miami School of Medicine, Miami, Florida, United States
Background

The molecular mechanisms contributing to arteriovenous fistulas (AVF) maturation or failure remain elusive, in part due to the scarcity of human postoperative biological data that may guide mechanistic and translational studies. The brachiobasilic AVF created in two stages overcomes this limitation and allows to collect vascular tissues representative of both outcomes at the time of transposition.

Methods

In this study, we compared the transcriptomic profiles of 40 postoperative AVF samples (20 matured and 20 failed) collected 4-6 weeks after access creation by bulk RNA sequencing.

Results

We identified 156 differentially expressed genes (DEG) between both outcomes (log2FoldChange>1, FDR<0.05), including 101 protein-coding genes downregulated with failure and 11 protein-coding genes with higher expression in this group compared to AVF that matured. Gene set enrichment analysis (GSEA) indicated a suppression of responses to stress/stimuli and signal transduction pathways in AVF that failed. The main downregulated players include G protein-coupled receptors, metalloproteinases, and immunoregulatory chemokines. In contrast, upregulated transcripts in AVF that failed include a urea cell-surface transporter, a serotonin biosynthesis enzyme, and various extracellular matrix and cell adhesion proteins. A supervised machine learning algorithm (XGBoost) was applied to gene expression normalized counts to identify the best discerning features of AVF failure. The highest contributors to the decision tree by total gain were IL-10 and GPR183 (each downregulated 2.6 folds in AVF that failed and with an FDR significance level of 2x10-5). The area under the curve (AUC) in the logistic regression models for each of these genes is >90%.

Conclusion

In conclusion, this study identified for the first time a postoperative molecular fingerprint of AVF failure. These findings may allow us to pinpoint venous remodeling deficiencies that are responsible for this outcome and potentially correct them using targeted therapies.

Funding

  • NIDDK Support