1 Mosaiques Diagnostics and Therapeutics AG, Hannover, 3 Department of Nephrology, Medical School of Hannover, Germany and 2 Nephrology Section, Department of Internal Medicine, University Hospital, Gent, Belgium
Correspondence and offprint requests to: Raymond Vanholder, Nephrology Section, Department of Internal Medicine, University Hospital Gent, De Pintelaan 185, B-9000, Gent, Belgium. Email: raymond.vanholder{at}ugent.be
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Abstract |
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Methods. We describe the application of a novel proteomic tool for the identification of a large number of molecules present in ultrafiltrate from uraemic and normal plasma obtained with high- or low-flux membranes. Separation by capillary electrophoresis was coupled on-line to a mass spectrometer, yielding identification of polypeptides based on their molecular weight.
Results. Between 500 and >1000 polypeptides with a molecular weight ranging from 800 to 10 000 Da could be detected in individual samples, and were identified via their mass and their particular migration time in capillary electrophoresis. In ultrafiltrate from uraemic plasma, 1394 polypeptides were detected in the high-flux vs 1046 in the low-flux samples, while in ultrafiltrate from normal plasma, 544 polypeptides vs 490 were found in ultrafiltrate from normal plasma obtained from membranes with comparable cut-off. In addition, polypeptides >5 kDa were virtually only detected in the uraemic ultrafiltrate from the high-flux membrane (n = 28 vs n = 5 with the low-flux membrane). To demonstrate the feasibility of further characterizing the detected molecules, polypeptides present exclusively in uraemic ultrafiltrate were chosen for sequencing analyses. A 950.6 Da polypeptide was identified as a fragment of the salivary proline-rich protein. A 1291.8 Da fragment was derived from -fibrinogen.
Conclusion. The data presented here strongly suggest that the application of proteomic approaches such as capillary electrophoresis and mass spectrometry will result in the identification of many more uraemic solutes than those known at present. This could enable the introduction of more direct elimination strategies, since it is possible to obtain an extended appreciation of the removal capacities of particular dialyser membranes.
Keywords: dialysis; end stage renal disease; mass spectrometry; proteomics; uraemic toxins
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Introduction |
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During the last two decades, progressively more uraemic retention solutes have been identified and characterized with respect to their potential toxicity [1,2]. In a recent publication, the European Uremic Toxin Work Group (EUTox) forwarded an encyclopaedic list containing uraemic compounds described in the literature until the time of publication [3]. This report contained 90 uraemic solutes, but conceivably the information presented was incomplete compared with the number of compounds that are retained in reality.
Due to a lack of suitable technology, the search for uraemic toxins up to now has been biased by the preferential analysis of known solutes that might be of patho-physiological importance. Proteome analysis represents a new and promising analytical tool whereby all peptides present can be registered and potentially identified, offering the possibility to achieve the unbiased identification of markers or solutes. This approach is facilitated by refinements in analytical techniques together with improvements in informatics [4]. Capillary electrophoresis (CE) in on-line combination with an electrospray ionizationtime of flight (ESI-TOF) mass spectrometer (MS) is extremely well suited for the detection of polypeptides in ultrafiltrate or other body fluids [5]. Since the range of sensitivity is currently focused on compounds with a molecular weight >800 Da, this approach is especially suited in the uraemic setting for the study of so-called middle molecules, a group of uraemic compounds that is held responsible for a number of uraemic complications. Conceivably, these middle molecules are removed more efficiently by dialysis with membranes with a large pore size (so-called high-flux membranes) [6], but data on the global yield of molecules through these high-flux membranes in a clinical setting are scarce.
The present preliminary report shows a comparison of solute yield between low- and high-flux dialysis by proteome analysis, and illustrates the new research opportunities opened up by application of proteomics to clinical questions.
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Materials and methods |
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Capillary electrophoresis and mass spectrometry (CEMS)
CEMS combines the high resolution of CE with the mass identification properties of MS to define polypeptides via their mass, charge and the migration time in the CE, thus allowing depiction of all polypeptides present in ultrafitrate or other body fluids within the limit of detection. The method (a schematic drawing is shown in Figure 1) was established to run in a fully automated way without the need for manual operation within a time frame of 45 min [7].
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Generation of data
The software provided by the manufacturers yielded the total ion chromatogram, shown in Figure 2 (upper graphs). Each segment of the chromatogram consists of individual peaks representing mass/charge for particular molecules (see insert). Due to the wealth of data within one single CEMS run, evaluation with the software supplied by the manufacturer is impossible within a reasonable time. Therefore, a specific software was designed to extract the information on the detected polypeptides, based on the original software provided by the MS manufacturer, with modifications to suit the purposes of CEMS (MosaiquesVisu, version 1.0, Biomosaiques Software, Hannover, Germany) [7]. The program uses isotopic distribution and conjugated masses for charge-state determination of polypeptides. In a first electronic analysis, the software identifies all peaks within each single spectrum. This results in >100 000 peaks within a single sample. Since true analytes must appear in at least three successive spectra, signals from individual peptides in consecutive spectra were collected and combined in the next step. This yielded a list of 20005000 CE/MS peaks and these data were deposited as the raw data peak list for individual samples in the database. The peak lists were then converted into a three-dimensional plot, showing mass/charge on the y-axis and the signal amplitude as a colour code (blue to white, ranging from 0 to 10 000 MS counts) plotted against the migration time in the CE (Figure 2, middle graphs, contour plot). Signals that did not fit into certain criteria were removed: (i) signal intensity below the threshold (200 counts); (ii) single charged peaks; and/or (iii) peaks with a width above the threshold (3 min). Next, the peaks representing identical molecules with different charge states were deconvoluted into a single mass. The actual mass of the polypeptides plotted against the migration time gave the protein plot (Figure 2, lower graphs). This resulted in a theoretical CEMS spectrum that now contained the information on mass, migration time and signal intensity for each individual polypeptide, yielding between 500 and >1000 individual polypeptides per sample. To allow comparison and search for conformity and differences between the samples obtained after dialysis either through the high-flux or low- flux membrane, CE migration times were normalized using 20 known polypeptides serving as internal standards, and the signal intensity was normalized to the total ion current (TIC). Polypeptides within different samples were considered identical if the mass deviation was <0.05% and the CE migration time deviation was <5%. Intra- and inter-assay variability were ascertained by repeated analysis of one sample and by analysis of samples obtained at different time points from the same patient under comparable conditions, respectively.
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Results |
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To compare different dialysis modes also, a haemodialysate sample from the same patient was obtained after haemodialysis with a high-flux membrane. Only 542 polypeptides were identified via their mass per charge and the migration time in the CE, as compared with 1394 polypeptides detected in ultrafiltrates from the high-flux membrane. Ultrafiltrates from normal plasma with membranes similar to the high- or low-flux membranes for clinical use yielded only 544 and 490 polypeptides, respectively. This is about one-third of those removed from uraemic plasma under comparable conditions. In addition, the signal intensities of the molecules detected were always considerably higher in the ultrafiltrates obtained from uraemic plasma, as compared with haemodialysate or ultrafiltrate of normal plasma (data not shown).
A direct comparison of identical sections of the unprocessed spectra from the CE-MS runs of ultrafiltrate samples with either low-flux (Figure 4A) or high-flux membranes (Figure 4B) is shown. The increase in signal intensities of the removed polypeptides was 3- to 5-fold higher in the effluent of the high-flux membrane throughout the spectrum (Figure 4).
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To demonstrate the feasibility of sequence identification of the molecules detected with CE-MS analysis in ultrafiltrates from uraemic plasma, tandem MS analyses were performed. The CE run of the ultrafiltrate used for mass identification was spotted onto a MALDI target. Two polypeptides, only present in uraemic ultrafiltrate, within the molecular mass range of 8002500 Da were sequenced and the available protein databases were searched for sequence conformity. In this first set of experiments, two molecules could be identified. One was a 950.6 Da fragment located in the C-terminus of the salivary proline-rich protein (amino acids 323331; PRP1_HUMAN). The other peptide was a 1291.8 Da fragment of -fibrinogen (amino acids 508518; FIBA_HUMAN). The MALDI-MS-MS spectra are shown in Figure 5. The identified sequences are summarized in Table 1.
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Discussion |
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Although a large number of polypeptides in uraemic plasma can be identified with one single run of CEMS via their mass and CE migration time, this number may still be underestimated, due to modifications in the solubility of the analytes during the analytical procedure.
The type of proteome analysis used here represents a new analytical tool, which has evolved after recent progress in separation techniques together with improvements in informatics and statistics [5]. CE in on-line combination with MS appears better suited than HPLCMS and SELDI (surface-enhanced laser desorption ionization) to examine complex biological samples, since this technology offers a much higher resolution, is robust, faster and shows higher reproducibility. The sensitivity towards interfering matrix compounds such as salts or non-volatile buffers is overcome by reversed-phase chromatography prior to analysis. This has resulted in a reproducible and fast adaptation of biological samples for CEMS and a minimum loss of polypeptides during the preparation [4]. Recently, a direct comparison of SELDI with CEMS showed that substantially more biomarkers were detected after CEMS analysis of the same samples [9].
With more classical analytical approaches, the choice of molecules to be studied is limited and hence almost unavoidably leads to a biased search influenced by the researcher's views. With proteome analysis, it becomes possible to analyse all peptides within a given range. Depositing and storing all data in a database allows unravelling of potentially interesting data even years after they have been generated. In this way, the limits created by the application of current methods for the study of the proteome can be overcome at a later stage based on new information and/or new statistical approaches.
Proteome analysis is especially helpful in the recognition of markers and/or causative factors of specific diseases. It has been used for identification of markers of carcinoma, e.g. prostate carcinoma [10].
Although the proteomic approach might be helpful in a range of renal conditions as well, publications displaying the possibilities of this technique in this area have been very scanty as yet, apart from a number of publications in renal cancer research [1113], an analysis of normal urine [14], the search for enhancers of glomerular permeability in focal and segmental glomerulosclerosis [15] and the identification of urinary factors excreted in the context of cyclosporin toxicity [16].
We set out to evaluate the potentials of proteomics for the analysis of ultrafiltrates comparing high- with low-flux membranes, to explore, whether: (i) a substantial number of larger molecules of peptidic nature are retained in uraemia [1]; and (ii) the high-flux membranes have the potential to remove more of these molecules than low-flux membranes. Also in a previous study, dialysate samples obtained after haemodialysis (diffusion) with either high- or low-flux membranes revealed differences between the two types of membranes [4]. In the latter study, however, only dialysate samples were analysed and those were prepared differently for the CEMS analysis, as compared with the present study; ADS columns were not used in the previous study, thus molecules up to 25 000 Da could be detected. The present approach was restricted to a lower molecular weight range, due to the presence of albumin in the ultrafiltrate, which had to be removed with the ADS column prior to analysis. Nonetheless, the yield of polypeptides obtained after CEMS analysis was markedly higher from ultrafiltrate samples studied here: up to 1400 polypeptides were detected in ultrafiltrates with the high-flux membrane, as compared with 611 polypeptides identified in dialysates from the high-flux membrane in the previous study [4]. These data were confirmed in the current study by collecting dialysate from the high-flux membrane of the same patient. Again, CE-MS analysis yielded substantially fewer polypeptides, i.e. approximately one-third of the number of polypeptides recovered after analysis of ultrafiltrate. Thus, for the detection of potential new uraemic toxins, the analysis of ultrafiltrates is more adequate. In addition, in the present study, all samples were obtained from the same patient, who served as his own control, whereas in the previous study different patients were treated with the two membrane types [4].
The data presented here demonstrate that the strategy applied allows the clear distinction between impressive numbers of individual compounds; up to 1400 individual compounds with a molecular weight >800 Da could be recognized. In contrast, our current knowledge refers to only 22 molecules in this range depicted in the literature as being retained in uraemia [3]. Hence our finding demonstrates the enormous potential for proteomics to recognize unknown peptides in uraemia.
As expected, the ultrafiltrate from the high-flux membrane yielded more middle molecules than that from the low-flux membrane. The superiority of high-flux dialysers in removing compounds in this molecular weight range has been demonstrated previously [17,18]. More surprising, though, was the still substantial middle molecule removal with the low-flux membrane. Nevertheless, the threshold for the low-flux membrane decreases dramatically, once the range of 5000 Da is reached, which is close to the cut-off of these membranes (5200 Da for steam-sterilized F10). For most molecular weight ranges, even the smaller ones, the yield in number of molecules was higher in the effluent of the high-flux membrane compared with that obtained from the low-flux membrane. This suggests that molecular configuration and charge also play a role in removal through a given membrane, whereby a larger pore size is an asset to enhance the removal of molecules. Finally, the high-flux membrane may also allow passage of larger concentrations of individual molecules, since an increase in signal intensity was observed throughout the MS-spectra (Figures 2 and 4).
Our findings should be seen in the context of several recent findings suggesting a probable superior clinical impact of high-flux membranes [19,20]. Although in the clinical HEMO study, published recently by Eknoyan and colleagues, no difference in overall survival after dialysis with high- or low-flux membranes had been observed, upon secondary analysis the cardiovascular morbidity and mortality was lower in the patients treated with high-flux membranes. In addition, a number of observational studies also point in the direction of superiority of high-flux membranes. Very probably, among the compounds removed by high-flux and not by low-flux membranes, there might be solutes with patho-physiological importance, e.g. in the development of vascular disease, inflammation or malnutrition.
Likewise, one could imagine that this approach would be of help in the identification of markers and/or causative/protective factors being at play in the development of elements of the uraemic syndrome, such as vascular disease, polyneuropathy, inflammation, anaemia or malnutrition. Once compounds have been separated and their molecular weight identified, their further identification can be achieved by analysis of the original CE run in a second step. Although such an approach is time-consuming compared with the generation of diagnostic patterns with CEMS, it could be especially helpful to elucidate the patho-physiology of certain diseases. Until now, it was possible to identify two molecules only present in uraemic ultrafiltrate. A 950.6 Da peptide was identified as a fragment of salivary proline-rich protein, and another of 1291.8 Da was identified as a fragment of -fibrinogen. One can only speculate about the patho-physiological importance of the peptidic fragments that were found in uraemic ultrafiltrate. It should be stressed that the two structures identified in this study (fragments of salivary proline-rich protein and
-fibrinogen) were chosen randomly and not due to a given structure or patho-physiological behaviour. The only aim was to demonstrate the possibilities of identification following the isolation of compounds by a proteomic approach. Not many functions of salivary proline-rich protein are known, except for an increase of bacterial adhesion/precipitation in saliva, which might have an impact on the development of dental caries. To the best of our knowledge, no systemic effects have been described. The importance of the fragment of
-fibrinogen might be more straightforward, with links to thrombogenesis and inflammation, and hence vascular disease. It should be noted, however, that we are only dealing with fragments, so that the molecules that were identified might have functional effects that are partially or entirely different from those of their mother compounds.
To date, uraemic toxicity has been examined to a large extent in an in vitro setting. The technology presented here is characterized by a high resolution and reproducibility, and thus is well suited for further evaluation of the uraemic condition as well as different types of dialyser membranes under in vivo conditions. As a consequence, this proteomic approach has a high potential to lead to the development of more efficient removal strategies, with structural characteristics based on less empiric principles than today.
In conclusion, the present data demonstrate the broad possibilities of proteome analysis in the identification of markers and of factors causing disease in general, and more specifically in the definition of the uraemic syndrome and comparison of removal strategies on the basis of the description of protein patterns as well as identification of unknown solutes.
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Acknowledgments |
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The European Uraemic Toxin Work Group (EUTox) is a Work Group of the European Society for Artificial Organs (ESAO). Its members are: at University levelA. Argiles, Montpellier, France; P. Brunet, Marseille, France; P. P. De Deyn, Antwerp, Belgium; B. Descamps-Latscha, Paris, France; T. Henle, Dresden, Germany; W. Hörl, Vienna, Austria; S. Huget-Rosenthal, Essen, Germany; A. Jörres, Berlin, Germany; J. Jankowski, Berlin, Germany; Z. Massy, Amiens, France; M. Rodriguez, Cordoba, Spain; G. Spasovski, Skopje, Macedonia; B. Stegmayr, Umea, Sweden; P. Stenvinkel, Huddinge, Sweden; R. Vanholder, Gent, Belgium; A. Wiecek, Katowice, Poland; W. Zidek, Berlin, Germany; C. Zoccali, Reggio di Calabria, Italy; at Industrial levelU. Baurmeister, MAT, Obernburg, Germany; R. Deppisch, Gambro, Hechingen, Germany; R. Guiberteau, Genzyme, Paris, France; H. D. Lemke, Membrana, Obernburg, Germany; A. Mahiout, Nipro Europe, Belgium; B. Lindholm, Baxter Healthcare, Stockholm, Sweden; C. Eeckhout, Roche, Brussels, Belgium; C. Tetta, Fresenius Medical Care, Bad Homburg, Germany; M. C. Van Nes, Amgen, Brussels, Belgium; E. M. Weissinger, Mosaiques Diagnostics and Therapeutics AG, Hannover, Germany.
Conflict of interest statement. None declared.
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References |
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