Proteomics and metabolomics in renal transplantation-quo vadis?

Transpl Int. 2013 Mar;26(3):225-41. doi: 10.1111/tri.12003. Epub 2012 Nov 21.

Abstract

The improvement of long-term transplant organ and patient survival remains a critical challenge following kidney transplantation. Proteomics and biochemical profiling (metabolomics) may allow for the detection of early changes in cell signal transduction regulation and biochemistry with high sensitivity and specificity. Hence, these analytical strategies hold the promise to detect and monitor disease processes and drug effects before histopathological and pathophysiological changes occur. In addition, they will identify enriched populations and enable individualized drug therapy. However, proteomics and metabolomics have not yet lived up to such high expectations. Renal transplant patients are highly complex, making it difficult to establish cause-effect relationships between surrogate markers and disease processes. Appropriate study design, adequate sample handling, storage and processing, quality and reproducibility of bioanalytical multi-analyte assays, data analysis and interpretation, mechanistic verification, and clinical qualification (=establishment of sensitivity and specificity in adequately powered prospective clinical trials) are important factors for the success of molecular marker discovery and development in renal transplantation. However, a newly developed and appropriately qualified molecular marker can only be successful if it is realistic that it can be implemented in a clinical setting. The development of combinatorial markers with supporting software tools is an attractive goal.

Publication types

  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Biomarkers / analysis*
  • Graft Rejection
  • Graft Survival
  • Humans
  • Kidney Transplantation / methods*
  • Kidney Transplantation / mortality*
  • Metabolomics / methods
  • Metabolomics / statistics & numerical data*
  • Monitoring, Physiologic / methods
  • Needs Assessment
  • Prognosis
  • Proteomics / methods
  • Proteomics / statistics & numerical data*
  • Reproducibility of Results
  • Survival Analysis

Substances

  • Biomarkers