Application of systems biology principles to biomarker discovery in transplantation:
the urinary exosomal proteome.
Trairak Pisitkun2*, Maria T. Gandolfo1*, Samarjit Das1, Mark A. Knepper2, Serena M. Bagnasco1
1
Department of Pathology, Johns Hopkins University, Baltimore, MD;
2
Epithelial Systems Biology Laboratory, NHLBI, NIH, Bethesda, MD.
*These authors contributed equally and are listed as co-first authors
Objectives: An important question in studies to discover urinary biomarkers with mass
spectrometric techniques is how best to analyze data to find the most promising potential
biomarkers for advancing to large-scale validation studies.
We apply a "systems biology-based" approach to large-scale protein mass spectrometry data
from analysis of urinary exosomes from renal transplant patients to select for sets of
biomarkers, reasoning that exosome proteins may reflect changes occurring in the tissue better
than proteins in soluble urine. The objective is to find subpopulations of urinary exosomal
proteins that will discriminate between immunological rejection and tubular injury, and eventually
could classify the rejection as either cell-mediated or antibody-mediated.
Methods: Mass spectrometric analyses using Liquid Chromatography-Mass Spectrometry (LCMS/MS) were performed in urinary exosome samples collected prospectively from kidney
transplant recipients just before “for cause” or “protocol” renal graft biopsy.
Transplant recipients were divided in four groups based on the renal biopsy diagnosis:
Negative/Non specic findings (N); Tubular Injury (TI); Cell Mediated Rejection (CMR); Antibody
Mediated Rejection (AMR). Exosome proteins extraction was performed using previously
described protocols (Gonzales P.A., Pisitkun T., et al. (2009). JASN 20(2): 363-379.).
Samples pooled from transplant recipients in each group were analyzed by LC-MS/MS for
identification of exosome proteins, and determination of their relative abundance in individual
groups.
Results: A total of 1989 urine exosomal proteins were identified among the four groups after
filtering to obtain a false positive rate of <2% at the peptide level (target-decoy).
More than 90% of exosomal proteins were classified as non-secretory or “intrinsic to
exosomes”. Subsets of these proteins were uniquely found in only one of the four groups:
17% in TI, 17% in CMR, 8% in AMR, and 3% in N.
These protein lists were analyzed computationally to identify Gene Ontology – Biological
Process and KEGG Pathway terms that are selectively and statistically significantly associated
with each of the pathologic groups.
Among the most informative of the Biological Process terms for each group were:
“sodium ion transport” for TI; “immune response” for all R; “epithelial cell differentiation” for
CMR; and “acute inflammatory response” for AMR.
We used normalized spectral counting of the proteins in the respective biological-process lists to
identify matrices of potential biomarker pairs that are predicted to discriminate between tubular
injury and rejection, and between CMR and AMR types of rejection.
Conclusions: Here, we introduce a "systems biology-based" approach to detect biological
processes over-represented in protein identification lists, from which to select protein candidates
with the highest estimated discriminating potential for further validation studies. This study lays
out a plan for moving these biomarker pairs to validation studies and implementation for clinical
use, and illustrates a novel strategy that could be applied to investigation of biomarkers in other
renal diseases.
Emails: pisitkut@nhlbi.nih.gov mt.gandolfo@tiscali.it knep@helix.nih.gov sdas11@jhmi.edu
sbagnas1@jhmi.edu