Deep serum discoveries: <scp>SDF</scp>‐1α and <scp>HSA</scp> fragments in myelodysplastic syndromes
Authors: Julia Fröbel, Sonja Hartwig, Sabine Jourdain, Johannes C. Fischer, Christoph Zilkens, Andrea Kündgen, Detlev Suckau, Ulrich Germing, Akos Czibere, Stefan Lehr
Journal: American Journal of Hematology (2015)

To the Editor: Finding a way to diagnose myelodysplastic syndromes (MDS) using proteins of the peripheral blood serum instead of morphologic characteristics of bone marrow (BM) cells would be a large improvement by minimizing invasiveness of the diagnostic procedure and reviewer dependent variability. The key challenge in studying the serum proteome is its extraordinary wide dynamic range of protein concentrations spanning more than 10 orders of magnitude that makes discovery of a potential biomarker almost impossible. This makes pre-fractionation of the serum sample essential to decrease its range of protein concentrations and enrich the low-abundant proteins. We have previously demonstrated that affinity-chromatography-based combinatorial hexapeptide ligand libraries (CPLL) for serum pretreatment are a highly reproducible fractionation method enabling analysis of low abundance proteins in complex mixtures and presented an efficient and reliable workflow for deep serum proteome analysis by combining CPLL with mass spectrometric profiling of low molecular weight proteins and peptides on ProteinChips 1, 2. Here, we used this experimental set-up to analyze the deep serum proteome of 75 patients with MDS and 30 age-matched controls (Table 1). Using 56 sera as learning set (40 MDS and 16 controls), the peak detection and clustering analysis of m/z 2,000–15,000 detected 98 peptides in CM10, 80 in Q10, and 117 in IMAC30 spectra meaning 40% more peaks in an eighth of the m/z range than previous studies 3, 4. Different proteomic profiles were tested as prediction models to distinguish between MDS and controls. Their performance was calculated and the best predictive profile (peptides FC>2.5, P < 0.01) was applied to the independent validation set comprising 49 sera (35 MDS and 14 controls). Needing only one measurement per patient, less predictive peaks and no mix of different ProteinChips we observed better performances (Table 2) and area under the curve (AUC) data (Fig. 1A–C) than previous studies 3, 4. We further used our experimental set-up to classify different MDS subtypes and stages (RCMD/del(5q) = early, RAEBI/II = advanced). Using 11 IMAC30 peptides of the learning set that met our minimal criteria (FC>1.6, P < 0.05), we again acquired good performance data in the validation set with very high accuracy and without false positives (Table 2, Fig. 1D,E). Despite very good predictions of our proteome profiles a more elegant way to diagnose MDS from blood would be an immunoassay against an appropriate serum biomarker being less time-consuming and reaching higher inter-laboratory reproducibility. Using only one chromatographic enrichment step (H50, Bio-Rad Laboratories) before matrix-assisted laser desorption/ionization time-of-flight/time-of-flight (MALDI-TOF/TOF) fragment ion analysis (Ultraflex III, Bruker Daltonics), we identified the two peaks dominating the MDS deep serum proteome (m/z 3,818 and 3,971, >20-times higher than in controls) by SwissProt database comparison directly from the IMAC30 ProteinChip. Peptide m/z 3,971 was sequenced as DAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQC, identified as 34 amino acid long N-terminal fragment of human serum albumin with its C-terminal cysteine (Cys34) sulfonated (HSA[1-34]-SO3H) while m/z 3,818 was identified as the same HSA fragment with Cys34 removed and its C-terminal glutamine amidated (HSA[1-33]-NH2). Both are part of the so-called serum fragmentome containing thousands of proteolytically derived peptides, mostly of high-abundant serum proteins, pointing toward an altered protease activity in MDS, such as previously shown for matrix metalloproteinases, their inhibitors and tryptase 5-7. In line with this are also the unusual modifications at the breakpoints before and after Cys34 as albumin is proposed to counteract oxidative stress by radical scavenging that is mainly attributed to the redox state of its free thiol at Cys34. An irreversible modification to sulfonic acid as in HSA[1-34]-SO3H causes loss of function and protein degradation and is considered a suicide function of proteins involved in antioxidation in several diseases 8. Using median split Kaplan–Meyer survival analyses, we evaluated the prognostic effect of our predictive peptides and found m/z 7,835, previously identified as stromal cell-derived factor-1 (CXCL12/SDF-1α) 9, to show highly significant differences in overall survival (P < 0.001, Fig. 2A). We confirmed this by measuring the SDF-1α concentration in sera of 56 MDS patients and 18 normal donors (Table 1) by Quantikine®ELISA (R&D Systems) revealing significantly decreased levels in MDS (P < 0.05) and significantly prolonged survival of the MDS patients with higher SDF-1α expression (P < 0.05, Fig. 2B). That may be explained by disturbed transforming growth factor-β (TGF-β)/Smad signaling in MDS 10 transcriptionally modulating SDF-1α gene expression and causing down-regulated SDF-1α production of MDS mesenchymal stromal cells of 11, 12. Furthermore, a higher expression of CXCR4, the SDF-1α receptor on hematopoietic cells, was recently identified to be a prognostic factor for overall and progression-free survival in MDS patients 13, which might be an attempt to compensate the reduced SDF-1α levels. ROC analyses of predictive deep serum proteome profiles with AUC used on the validation set distinguishing MDS patients and controls (A–C), different MDS stages (D) as well as MDS del(5q) and other MDS subtypes (E). Kaplan–Meyer plots of m/z 7,835 (A) and SDF-1α (B) categorized by median split show that survival of MDS patients with higher cluster intensities and serum concentrations (black line) is significantly longer than that of patients with lower values (gray line). The authors would like to thank Waltraud Paßlack and Sabine Lensing-Höhn for excellent technical assistance. Julia Fröbel,1,2,3* Sonja Hartwig,1 Sabine Jourdain,4 Johannes C. Fischer,5 Christoph Zilkens,3 Andrea Kündgen,2 Detlev Suckau,4 Ulrich Germing,2 Akos Czibere,2 and Stefan Lehr1 1Department of Clinical Biochemistry and Pathobiochemistry, Leibniz Center for Diabetes Research, Heinrich-Heine-University, 40225 Düsseldorf, Germany; 2Department of Hematology, Oncology and Clinical Immunology, Heinrich-Heine-University, 40225 Düsseldorf, Germany; 3Department of Orthopedic Surgery, Heinrich-Heine-University, 40225 Düsseldorf, Germany; 4Bruker Daltonics, 28359 Bremen, Germany; 5Institute for Transplantation Diagnostics and Cell Therapeutics, Heinrich-Heine-University, 40225 Düsseldorf, Germany