State of the human proteome in 2013 as viewed through PeptideAtlas: comparing the kidney, urine, and plasma proteomes for the biology- and disease-driven Human Proteome Project

Abstract

The kidney, urine, and plasma proteomes are intimately related: proteins and metabolic waste products are filtered from the plasma by the kidney and excreted via the urine, while kidney proteins may be secreted into the circulation or released into the urine. Shotgun proteomics data sets derived from human kidney, urine, and plasma samples were collated and processed using a uniform software pipeline, and relative protein abundances were estimated by spectral counting. The resulting PeptideAtlas builds yielded 4005, 2491, and 3553 nonredundant proteins at 1% FDR for the kidney, urine, and plasma proteomes, respectively - for kidney and plasma, the largest high-confidence protein sets to date. The same pipeline applied to all available human data yielded a 2013 Human PeptideAtlas build containing 12,644 nonredundant proteins and at least one peptide for each of ∼14,000 Swiss-Prot entries, an increase over 2012 of ∼7.5% of the predicted human proteome. We demonstrate that abundances are correlated between plasma and urine, examine the most abundant urine proteins not derived from either plasma or kidney, and consider the biomarker potential of proteins associated with renal decline. This analysis forms part of the Biology and Disease-driven Human Proteome Project (B/D-HPP) and is a contribution to the Chromosome-centric Human Proteome Project (C-HPP) special issue.

Figure 1

Software analysis pipeline used. See Experimental Procedures for details.

Figure 2

PSM, peptide, and protein counts for each of the three tissue/biofluid-based proteome PeptideAtlas builds.

Figure 3

Nonredundant Swiss-Prot identifiers that were counted as “unseen” or “missing” (had no identified peptides) in our JPR 2013 report. (A) From each of the three KUP atlas builds. Note that some are seen in multiple KUP atlas builds. (B) From KUP, HumanAllPA, by chromosome.

Figure 4

For six identifier sets, the proportion of identifiers with various Swiss-Prot cellular localization keywords. Some identifiers have multiple keywords.

Figure 5

(A) The normalized spectral counts (NSC) for each HKUP atlas, binned on a log scale. (B) The same data are plotted with cumulative protein counts on the X-axis and log(NSC) on the Y-axis to produce familiar dynamic range curves, showing more directly the varying numbers of proteins identified in each atlas.

Figure 6

Thirty-four protein sets were derived from the original three using the redundancy reducing method, NSC comparisons, and set operations described in Experimental Procedures and in Table S4 (Supporting Information). All these sets, along with their GO analyses, can be browsed at www.peptideatlas.org/hupo/hkup.

Figure 7

Nonredundant Swiss-Prot identifier set for KidneyPA intersected with the complete mappings for UrinePA and PlasmaPA. Diagram created using BioVenn

Figure 8

(A) NSC values for proteins shared between two tissue/biofluid-based proteomes are plotted on a log/log scale. (B) Correlation coefficients (r) for (A). Urine/plasma is more strongly correlated than the other two pairs. For urine/plasma, the Pearson correlation is stronger than the Spearman, indicating that the relationship is fairly linear. In contrast, for plasma/kidney, the Spearman correlation is stronger, indicating that the (weak) correlation is monotonic but not linear. Restricting the analysis to small (<40kDa calculated MW) proteins strengthens the Pearson correlation for urine/plasma and weakens it for urine/kidney.