Proteomics: a novel tool to unravel the patho-physiology of uraemia

Eva M. Weissinger1, Thorsten Kaiser1, Natalie Meert2, Rita De Smet2, Michael Walden1, Harald Mischak1,3 and Raymond C. Vanholder2 for the European Uremic Toxin Work Group (EUTox)

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



   Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Background. Uraemic toxicity results in the dysfunction of many organ systems, provoking an increase in morbidity and mortality. To date, only ~90 uraemic retention solutes have been described. To examine unknown uraemic substances thoroughly, the identification of as many compounds as possible in the ultrafiltrate and/or plasma of patients would lead to a less biased definition of the uraemic retention process compared with what is proposed today.

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 {alpha}-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



   Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The uraemic syndrome results in the functional failure of vital organs, attributable to the retention of compounds that under normal conditions are excreted into the urine by the healthy kidneys [1]. ‘Uraemic toxins’ can be defined as ‘uraemic retention solutes’ that modify biological and/or biochemical functions so that they contribute to the uraemic syndrome [1].

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 ionization–time 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.



   Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Samples
Ultrafiltrate was obtained from a patient (male, age 63, primary kidney disease: renal vascular disease) during treatment with two different dialyser membranes [high-flux polysulfone (F70) and low-flux (F10), Fresenius Medical Care, Bad Homburg, Germany]. Samples were collected between the 5th and 10th minute of a dialysis session. Ultrafiltrates were collected at two different time points, 5 months apart on the same day of the week (Wednesday), with a 1 week interval between the two membrane types, after informed consent had been obtained. During sample collection, the dialysate circuit was switched off. On each occasion, 40 ml of ultrafiltrate were collected and, after thorough mixing, stored frozen at –20°C until analysis. In addition, 40 ml of haemodialysate of the same patient, obtained with the high-flux membrane, was also collected 15 min after the start of haemodialysis and treated as described above. As a control, 60-pf plasma were collected on heparin from nine healthy individuals, pooled and filtered through polyethersulfone membranes (Millipore, Bedford, MA) with a cut-off of either 5000 or 50 000 Da. After thawing, all samples were adjusted to pH 10 using ammonia and cleared by centrifugation for 10 min at 13 000 g. To remove albumin and other confounding materials, the supernatants were applied onto LiChrospher RP-18 Alcyl-DIOL-Silica (ADS) columns (Merck, Darmstadt, Germany) in a LiChroCART 25-4 cartridge (Merck) using a Beckman System Gold HPLC System (Beckman Coulter, Fullerton, USA) with a flow rate of 0.8 ml/min. After washing with H2O for 10 min at 0.8 ml/min, the polypeptide fraction was eluted by a step gradient to 80% methanol and 20% H2O. The elution profile was monitored by UV detection at 200 nm. Approximately 6 ml were collected from each sample, frozen and lyophilized in a Christ Speed-Vac RVC 2-18/Alpha 1-2 (Christ, Osterode am Harz, Germany). Shortly before use, samples were resuspended in 20 µl of HPLC-grade water, yielding a 300-fold enrichment of polypeptides present in each sample. The samples were sonicated for 1 min in an ultrasonic bath, centrifuged for 10 min at 13 000 g and injected into the CE.

Capillary electrophoresis and mass spectrometry (CE–MS)
CE–MS 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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Fig. 1. Schematic drawing of the on-line coupling of capillary electrophoresis to the mass spectrometer used to separate and identify proteins and polypeptides in body fluids by their charge and size. After electrophoretic separation, the polypeptides are ionized by the application of high voltage and analysed in the mass spectrometer (ESI–TOF). The combination of the two instruments yields a mass spectrogram of mass per charge plotted against migration time.

 
For capillary electrophoresis, the ‘P/ACE MDQ’ system (Beckman Coulter) equipped with a 90 cm, 75 µm inner diameter bare-fused silica capillary was used. The capillary was rinsed for 3 min with running buffer (30% methanol, 0.5% formic acid, 69.5% water). The samples were injected for 20 s with 1 {psi} pressure. Separation was performed with +30 kV at the injection site and the capillary temperature was set to 35°C. The CE was coupled to an ESI–TOF MS (Perceptive Biosystems, Farmington, CT) with the CE–ESI–TOF sprayer kit (Agilent Technologies, Palo Alto, CA). Sheath-flow contained 30% MeOH and 0.5% formic acid. Between runs, the capillary was rinsed with 1 M NaOH for 5 min at a pressure of 30 {psi}. On-line TOF detection and data acquisition was performed on the ‘Mariner Biospectrometry Workstation’ (Perceptive Biosystems). Spectra were accumulated for 3 s each over a mass-to-charge range (m/z) from 400 to 2500.

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 CE–MS 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 CE–MS (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 2000–5000 ‘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 CE–MS 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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Fig. 2. Comparison of proteomic analysis of ultrafiltrate obtained from high-flux (left) or low-flux (right) polysulfone membranes. The total ion chromatogram (upper graphs) is obtained after CE–MS analysis. The insert shows individual spectra, obtained every 3 s, yielding the total ion chromatogram. These data are converted by application of MosaiquesVisu software to a three-dimensional contour plot (middle graph). Mass per charge is shown on the y-axis against the migration time in minutes on the x-axis, and the signal intensity is colour coded (blue to white; 0–10 000 MS counts). The start of the spectrum is marked by the appearance of highly charged formiates, while the end of the spectrum is marked by the appearance of organic polymers. The positions of these are indicated by arrows in the raw data plots (middle panels) and in the total ion chromatogram (upper graphs). Next, the signal to noise is calculated and the noise removed, thus leaving only signals, and the actual mass is calculated. The resulting individual peak list contains >1000 different molecules defined by their mass and migration time in the CE (bottom graphs). Both the number of individual compounds and the intensity are higher for the high-flux membrane.

 
Sequencing of polypeptides and database search
Tandem MS analyses were performed with a MALDI-TOF–TOF-MS (matrix-assisted laser desorption ionization-TOF; Ultraflex, Bruker Daltonik, Bremen, Germany). A complete CE run was spotted onto the MALDI target (one spot every 15 s) with the matrix solution [5 mg/ml {alpha}-cyano-4-hydroxycinnamic acid (CHC) in 50% acetonitrile and 0.5% formic acid] added as sheath liquid at 1 µl/min. The target was examined subsequently in MS mode for the polypeptides of interest, based on the data for molecular weight from the CE-MS analyses. Two polypeptides within the molecular weight range of 800–2500 Da were sequenced in MS/MS mode, as described [8]. The criteria to choose molecules for sequencing were as follows: (i) the molecules should only be present in uraemic and not in normal biological fluids; and (ii) molecules between 800 and 2500 Da gave the best results with MALDI-TOF-TOF analysis in our hands. The obtained sequences were matched by Mascot-Search to the NCBI-protein database and the SwissProt database.



   Results
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
While the data from the two different membranes appeared quite similar at first sight (compare Figure 2, left and right), several differences became obvious upon closer examination. When analysing the raw data contour plots (Figure 2, middle graphs), the number of spots representing individual molecules at different charge levels was higher in ultrafiltrates obtained with the high-flux membrane. When comparing the different CE-MS runs, the signal intensity was always higher in the effluent of high-flux membranes (Figure 2, upper and middle graphs). The signal intensity is shown as a colour code (0–10 000 MS counts) for both the raw data plot (Figure 2, middle graphs) and the protein plot (Figure 2, lower graphs). In addition, more polypeptides were detectible in ultrafiltrate from thehigh-flux membrane. In ultrafiltrates from uraemic plasma, 1394 different polypeptides were detected with the high-flux membrane, while only 1046 polypeptides were recovered in the ultrafiltrate of the same patient obtained with the low-flux membrane, indicating a more efficient removal of polypeptides by the high-flux membrane (Figure 3). Furthermore, the mass distribution of the removed molecules was different; 28 polypeptides larger than 5 kDa (Figure 3) were detected in the effluent of the high-flux membrane, compared with five in that of the low-flux membrane [ratio NHF/NLF for >5 kDa: 5.6 (see insert of Figure 3)].



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Fig. 3. The number of polypeptides recovered from either high-flux (HF) or low-flux (LF) membranes are compared for different molecular weights in kDa. Recovery each time is higher for the high-flux membrane. The relative yield from the high-flux membrane, indicated as the ratio of number of molecules obtained in the effluent of the high-flux membrane over the same figure obtained in the effluent of the low-flux membrane (NHF/NLF), increases progressively for higher molecular mass (insert).

 
The reproducibility of the data was verified by repeated CE–MS analysis of the same sample and was 95.4% for the high-flux membranes and 97.3% for the low-flux membranes, based on the detection of the polypeptides present within each particular run. When samples from the same patient were collected and analysed within a time interval of 5 months, similar results were obtained. The reproducibility of the data for these samples was 91% for high-flux and 83% for low-flux membranes, respectively.

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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Fig. 4. Quantitative comparison of the crude mass spectra obtained from the high- and low-flux ultrafiltrate. The detailed view from 500–1000 m/z of the summarized unprocessed spectra from the section covering minutes 16–17 of CE migration time is shown. The summarized spectrum for the low-flux membrane is shown in (A), and that for the high-flux membrane is shown in (B). All polypeptides are present with higher signal intensity in the sample from the high-flux dialysate.

 
All polypeptides detected in uraemic ultrafiltrates formed the complete database for these studies for both membranes. This database consisted of 1858 polypeptides; 582 of these were present in either ultrafiltrate, independent of the membrane type, while 812 polypeptides were exclusively eluted with the high-flux membrane and were not detected in ultrafiltrates with the low-flux membrane.

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 800–2500 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 323–331; PRP1_HUMAN). The other peptide was a 1291.8 Da fragment of {alpha}-fibrinogen (amino acids 508–518; FIBA_HUMAN). The MALDI-MS-MS spectra are shown in Figure 5. The identified sequences are summarized in Table 1.



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Fig. 5. The sequence information of two polypeptides, present exclusively in uraemic ultrafiltrate, is shown as MALDI–MS/MS spectra. Sequencing can be achieved using the same CE run. Two polypeptides were identified via their amino acid sequence and matched to available protein databases. The sequences of the detected fragments and the corresponding mother compounds are shown here and in Table 1. Searching of the available protein databases revealed that a 950.6 Da peptide (upper panel) was a fragment of the salivary proline-rich protein, while a 1291.8 Da peptide (lower panel) was a fragment of {alpha}-fibrinogen.

 

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Table 1. The sequence data obtained from the two peptides analysed; the matching parental compounds were found via MASCOTT-Search of SwissProt (see identification)

 


   Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
This study set out to unravel the technical possibilities to recognize as yet unknown retention compounds in a uraemic setting by proteome analysis. The main findings of this analysis are: (i) proteome analysis allows the clear distinction of a large number of individual peptides in ultrafiltrate; (ii) the yield of ‘middle molecules’ registered by this approach exceeds by a factor of 50 the number of molecules presently known in the molecular weight range above 10.8 kDa (~20); (iii) although, as expected, high-flux dialysis removes more middle molecules than low-flux dialysis, even with low-flux dialysis a substantial amount of middle molecules up to a molecular weight of 5000 Da appears in the ultrafiltrate; and (iv) the high-flux membrane removed a larger number of middle molecules at apparently higher concentration, and this superiority is especially marked for the higher molecular weight range.

Although a large number of polypeptides in uraemic plasma can be identified with one single run of CE–MS 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 HPLC–MS 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 CE–MS and a minimum loss of polypeptides during the preparation [4]. Recently, a direct comparison of SELDI with CE–MS showed that substantially more biomarkers were detected after CE–MS 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 [11–13], 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 CE–MS 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 CE–MS 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 CE–MS, 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 {alpha}-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 {alpha}-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 {alpha}-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.



   Acknowledgments
 
The authors thank Meike Hillmann and Frank Hausadel for excellent technical assistance.

The European Uraemic Toxin Work Group (EUTox) is a Work Group of the European Society for Artificial Organs (ESAO). Its members are: at University level—A. 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 level—U. 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.



   References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Received for publication: 14.10.03
Accepted in revised form: 31. 8.04





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