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When analysing phosphoproteins via Western blot, why is total protein level of the target protein recommended as an internal loading control?

When analysing phosphoproteins via Western blot, why is total protein level of the target protein recommended as an internal loading control?


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I am analysing the expression of a protein kinase via Western blots, and it is a phosphoprotein. I have labelled my membrane with antibodies against the phosphorylated form of the protein (using phospho-specific antibodies) and also for total protein (using pan-specific antibodies).

I have read that when studying phosphoproteins, you should normalise the phosphorylated protein signal to the signal for total protein. In this journal paper, it states that the Journal of Biological Chemistry recommends that “signals obtained using antibodies specific for phosphorylated epitopes should be normalized to the total protein level of the target protein”.

I have seen in other journal papers analysing the expression of a phosphoprotein via Western blots that they also normalise the phosphoprotein signal to total protein signal (of the target protein).

However, I am not sure why the total protein level of the target protein can be used as an internal loading control?

I have read in this handbook that internal loading controls are endogenous reference protein(s) that are present in all samples at a stable level and unaffected by the experimental conditions.

I want to compare the expression of my phosphoprotein between samples of different experimental conditions. I know that normally total protein stains such as Ponceau S or housekeeping proteins are used as an internal loading control and for normalisation.

But why is it recommended that you should normalise to total target protein (when looking at phosphoproteins), because one does not know whether the expression of total target protein will be stable between different experimental conditions? Any insights are appreciated.


It is because you don't know how your treatments affect the expression of the total specific protein; perhaps it down- or up-regulates the amount of protein being made, but the relative proportion of phosphorylation doesn't change. For example:

Imagine you have 4 samples from different treatments of which you have loaded an equal amount of total protein (lets say 50 ug total, note that this is too much to load on a WB for accurate quantitation; usually you want less than 10 ug). You run a western blot and find the following numbers (completely made up BTW) for the specific protein. In this scenario, 1 is untreated control.

  1. 10 ug
  2. 20 ug
  3. 10 ug
  4. 20 ug

You then try the phospho-specific antibody and get the following

  1. 1 ug
  2. 2 ug
  3. 3 ug
  4. 1 ug

What does that tell us about the 4 treatments?

Well, it tells us that 2 is up-regulating the total protein, but the relative proportion of phosphorylation is the same. In 3 phosphorylated protein is up-regulated (3x) but not total protein and in 4 total protein is up-regulated but phosphorylated is down-regulated (0.5x).


References

Scopes RK (1994). Protein purification (New York, NY: Springer New York).

Taylor SC et al. (2013). A defined methodology for reliable quantification of western blot data. Mol Biotechnol 55, 217–226.

Walsh T (2006). Post-translational modification of proteins: expanding nature’s inventory (Roberts and Company Publishers).

Yadav G and Liu N (2014). Trends in protein separation and analysis — the advance of stain-free technology. bioradiations.com/trends-in-protein-separation-and-analysis-the-advance-of-stain-free-technology/, accessed November 11, 2018.

Bio-Rad is a trademark of Bio-Rad Laboratories, Inc. in certain jurisdictions. All trademarks used herein are the property of their respective owner.


Abstract

Background

Phosphorylation by protein kinases is a fundamental molecular process involved in the regulation of signaling activities in living organisms. Understanding this complex network of phosphorylation, especially phosphoproteins, is a necessary step for grasping the basis of cellular pathophysiology. Studying brain intracellular signaling is a particularly complex task due to the heterogeneous complex nature of the brain tissue, which consists of many embedded structures.

New method

Overcoming this degree of complexity requires a technology with a high throughput and economical in the amount of biological material used, so that a large number of signaling pathways may be analyzed in a large number of samples. We have turned to Alpha (Amplified Luminescent Proximity Homogeneous Assay) technology.

Comparison with existing method

Western blot is certainly the most commonly used method to measure the phosphorylation state of proteins. Even though Western blot is an accurate and reliable method for analyzing modifications of proteins, it is a time-consuming and large amounts of samples are required. Those two parameters are critical when the goal of the research is to comprehend multi-signaling proteic events so as to analyze several targets from small brain areas.

Result

Here we demonstrate that Alpha technology is particularly suitable for studying brain signaling pathways by allowing rapid, sensitive, reproducible and semi-quantitative detection of phosphoproteins from individual mouse brain tissue homogenates and from cell fractionation and synaptosomal preparations of mouse hippocampus.

Conclusion

Alpha technology represents a major experimental step forward in unraveling the brain phosphoprotein-related molecular mechanisms involved in brain-related disorders.


Results

Inhibition of PP1 and PP2A by ellagitannins

The ellagitannins ( Figure 1 ) assayed on the activity of PP1c or PP2Ac in this study are similar to PGG in composition, but they differ structurally in two important aspects: (i) all of them include hexahydroxydiphenoyl unit linked in various positions, except for pedunculagin which includes two of these linkages (ii) tellimagrandin I, pedunculagin, and praecoxin B have free glycosidic hydroxyls, while GHG has an unmodified hydroxyl at position 3 of the glucopyranose ring. These molecules exerted distinct inhibitory influence on native PP1c and PP2Ac. Figure 2(A,B) illustrate the concentration dependent inhibitory effectiveness of ellagitannins on native PP1c and PP2Ac purified from rabbit skeletal muscle, respectively. Table 1 shows the IC50 values determined for PP1c and PP2Ac, which indicate that PP1c is more sensitive to inhibition by these ellagitannins than PP2Ac. This is also reflected in the PP1/PP2A selectivity ratio ( Table 1 ) of 1:98, 1:82, and 1:81 for tellimagrandin I, mahtabin A, and praecoxin B, respectively, the three most potent ellagitannin inhibitors. We established that the purified, recombinant isoforms of PP1cα and PP1cδ (rPP1cα and rPP1cδ) were inhibited by tellimagrandin I ( Figure 2(C) ) with IC50 values of 0.23 ±𠂐.05 µM and 0.13 ±𠂐.02 µM, respectively, which are similar to that of the native PP1c. Figure 2(D) shows that tellimagrandin I inhibits myosin phosphatase holoenzyme consisting of the FLAG-MYPT1-PP1cδ complex with similar potency (IC50=0.11 ±𠂐.02 M) to that of the isolated PP1c catalytic subunits. Inhibition of the phosphatase activity by ellagitannins in HeLa cell lysate was also assessed. First, we determined the activity of PP1 and PP2A in the lysates using I2 to inhibit PP1, and low concentration of OA (2 nM) to suppress PP2A specifically, in order to determine the contribution of PP1 and PP2A to the phosphatase activity of lysates ( Figure 2(E) ). These data suggest that PP1 (�%) and PP2A (�%) activities distribute in the HeLa lysate at approximately equal ratio. We found that although the inhibitory effectiveness of ellagitannins on the phosphatase activity of HeLa lysate followed the same order as with the purified enzymes, the IC50 values were at least one order of magnitude higher ( Table 1 ). These higher IC50 values might be due to that holoenzymes of both PP1 and PP2A were assayed in the lysate simultaneously with their different sensitivities. As the myosin phosphatase holoenzyme appears to be similarly sensitive to inhibition by tellimagrandin I as PP1c ( Figure 2(D) ) the lower sensitivity to inhibition in the lysates may not be due to interaction of the catalytic subunits with regulatory proteins. However, the lower sensitivity of PP2A may be involved as after suppression of PP1 activity by I2 in the lysate the phosphatase activity due to PP2A was inhibited by tellimagrandin I slightly even at the highest concentration applied ( Figure 2(F) ). On the other hand, the quite “sticky” nature of polyphenols allows binding to several proteins in the lysate decreasing the effective concentration of the polyphenols for inhibition as previously discussed 12 .

The effect of ellagitannins on the activity of PP1 and PP2A. Ellagitannins (0.1� µM) were preincubated with the phosphatase samples for 10 min and the phosphatase activity was determined in triplicates with 1 µM 32 P-MLC20 substrate as described in “Materials and methods” section. Phosphatase activity was determined in the presence of tellimagrandin I (●) mahtabin A (Δ) praecoxin B (■) GHG (^) pedunculagin (▼) with PP1c (A), PP2Ac (B) or HeLa lysate (F). The effect of tellimagrandin I on the activity of rPP1cα (^) and rPP1cδ (●) (C), or FLAG-MYPT1-PP1cδ (▼) (D), or on the phosphatase activity of HeLa lysate in the presence of I2 (□) (F). The effect of I2 and OA on the phosphatase activity of HeLa lysate (E). Phosphatase activity in the absence of ellagitannins, I2 or OA was taken as 100%. Data are means ± SD (n =𠂓𠄵).

Table 1.

IC50 values for the inhibition of PP1 and PP2A by ellagitannins.

 IC50 (µM) PP1c/PP2Ac
EllagitanninsPP1cPP2AcHeLa lysate selectivity
tellimagrandin 10.20 ±𠂐.0219.52 ±𠂘.604.46 ±𠂐.951:97
mahtabin A0.41 ±𠂐.1833.77 ±𠂘.446.19 ±𠂐.371:82
praecoxin B0.79 ±𠂐.1163.86 ±𠂕.526.97 ±𠂑.121:80
GHG1.41 ±𠂐.30𾄀12.97 ±𠂓.30n.d.
pedunculagin2.47 ±𠂐.22 * 𾄀33.90 ±𠂔.54n.d.

The IC50 values were derived from the data of Figure 2(A,B,E) and given as means ± SD.

Interaction of polyphenols with PP1c

Experiments with tellimagrandin I and rPP1cα or rPP1δ were carried out to assess their interaction with the application of different binding techniques ( Figure 3) . 1 H NMR spectra of tellimagrandin I recorded in the absence ( Figure 3(A) , bottom spectrum) and in the presence of 10 µM rPP1cα ( Figure 3(A) , middle spectrum) show remarkable differences. The significant broadening of 1 H resonance signals of tellimagrandin I observed in the presence of rPP1cα ( Figure 3(A) , middle spectrum) is a strong indication of the chemical exchange between the free and PP1c-bound form of tellimagrandin I. One-dimensional (1D) 1 H NMR saturation transfer difference (STD) experiment provided further evidence and structural details on the formation of tellimagrandin I-rPP1c complexes. The STD NMR spectrum ( Figure 3(A) , top spectrum) identifies the aromatic ring hydrogens in close contacts with PP1c suggesting that hydrophobic interactions may dominate the binding of tellimagrandin I to PP1c. Based on the similarity of the interaction of PP1c and tellimagrandin I to that of PGG 12 as well as on the potent inhibitory influence of tellimagrandin I on PP1c, it may be concluded that tellimagrandin I might also occupy part of the hydrophobic substrate binding groove on the surface of PP1c.

Interaction of PP1c with tellimagrandin I and with other polyphenols. (A): NMR spectra of tellimagrandin I in the absence (lower panel) and in the presence of rPP1cα (middle panel) and the corresponding STD-NMR spectrum (upper panel). (B): Interaction of rPP1cα with tellimagrandin I as revealed by ITC: ΔH =𠂘.038 ±𠂐.275 kcal/mol S =𠂐.0036 kcal/mol/K N =𠂑.16 ±𠂐.03 Ka=4.68 ±𠂑.02x10 6 M − 1 . (C): Interaction of rPP1cδ with tellimagrandin I as revealed by SPR: Kd=0.31 µM. Representative sensorgrams of two independent experiments are shown. (D): Affinities of rPP1cα to tellimagrandin I (^), PGG (●) and EGCG (▼) were measured using MST. The changes in either, the intrinsic fluorescence of unlabelled rPP1cα upon binding to tellimagrandin I and PGG, or changes in fluorescent signal from rPP1cα extrinsically labelled with NT647 dye upon binding with EGCG, were determined at a range of concentration of polyphenols, and the fractions bound are presented. Data points are means ± SD (n =𠂓).

PP1c-tellimagrandin I interaction was also assessed by isothermal titration calorimetry (ITC) and surface plasmon resonance based (SPR) binding techniques ( Figure 3(B,C) ) and the dissociation constants (Kd) determined were 0.23 µM and 0.31 µM, respectively. These Kd values were in good agreement with the IC50 value (0.20 µM) determined for inhibition of native PP1c by tellimagrandin I ( Table 1 ). We also applied microscale thermophoresis (MST) based technique to compare binding affinity of different polyphenols (tellimagrandin I, PGG, and EGCG) to PP1c. MST determined Kd values for the interaction of PP1c with tellimagrandin I, PGG, and EGCG were 1.78 ±𠂐.59 µM, 3.08 ±𠂐.35 µM and 8.2 ±𠂔.3 µM, respectively. The affinities of the polyphenols to PP1c follow the same order as their inhibitory potencies reflected in the IC50 values ( Table 1 ) 12 .

Effect of polyphenols on cellular systems

Previous results provided data for the effects of tellimagrandin I on cells and revealed that differentiation of human leukemic K562 cells 26 and gap junctional communication of HeLa cells were influenced by this polyphenol 27 . However, no attempts were made to relate these effects to changes in phosphatase activities. We tested first the effect of tellimagrandin I and mahtabin A on the survival and on the phosphatase activity of HeLa cells. Tellimagrandin I suppressed the viability of HeLa cells dramatically in 5� µM concentration range after 24 h incubation ( Figure 4(A) ). Tellimagrandin I partially reduced the phosphatase activity of HeLa cells (after 1 h incubation) in a concentration dependent manner and the effective concentration range was 10� µM ( Figure 4(B) ). These data may suggest that phosphatase inhibition by tellimagrandin I contributes to the initiation of cell death of HeLa cells, however, the different time courses of the viability and phosphatase assays as well as the lack of revealing more detailed molecular background make this conclusion elusive. Quite surprisingly mahtabin A, which is similar in structure to tellimagrandin I and it is also among the potent ellagitannin inhibitors of PP1 ( Table 1 ), was without effect on either the survival or the phosphatase activity of HeLa cells ( Figure 4(A,B) ).

Distinct influence of ellagitannins on the survival and phosphatase activity of HeLa cells. The effect of tellimagrandin I and mahtabin A was assayed on the survival (A) and the phosphatase activity (B) of HeLa cells in 0� µM concentration range as detailed in Materials and methods. The cell survival and the phosphatase activity in the absence of ellagitannins were taken as 100%. Data represent means ± SD (n =𠂓). ANOVA *p < .05, **p < .01, ***p < .001, n.s.: not significant.

To further elucidate the possible physiological influence of tellimagrandin I and mahtabin A we studied their effect on the exocytosis of mouse cortical synaptosomes that is an ex vivo preparation with intact membranes and serves as a model for studies of exocytosis/neurotransmitter release 25 . In our previous report we described that synaptosome exocytosis is inversely correlated with the increased phosphorylation of synaptosomal-associated protein of 25 kDa (SNAP-25) at Thr138 (SNAP-25 Thr138 ) in a RhoA-associated protein kinase/myosin phosphatase (PP1-type) mediated manner 24 . First, we tested how incubation of synaptosomes with tellimagrandin I ( Figure 5(A) ) or mahtabin A ( Figure 5(B) ) influenced the phosphorylation level of SNAP-25 Thr138 . Tellimagrandin had no any effect while mahtabin A increased the amount of phosphorylated SNAP-25 Thr138 (SNAP-25 pThr138 ) in a concentration dependent manner at a range of 1� µM. Second, the effect of these polyphenols on KCl induced synaptosomal exocytosis was probed and the results revealed that mahtabin A suppressed exocytosis measured as a decrease in FM 2� fluorescence ( Figure 5(D) ), whereas tellimagrandin I was without any influence ( Figure 5(C) ). Finally the effect of tellimagrandin I and mahtabin A was compared to tautomycetin (TMC), a well-defined PP1 specific inhibitor in synaptosomes 25 . Figure 5(E,F) demonstrate that TMC and mahtabin A increased the amount of SNAP-25 pThr138 in the same manner while tellimagrandin I had no influence. These data support the conclusions that the two ellagitannins, in spite of their similar structures and phosphatase inhibitory features, mediate the physiological responses of cells as well as membrane-surrounded ex vivo preparations in diverse manners.

Effect of tellimagrandin I and mahtabin A on the phosphorylation of SNAP-25 Thr138 and on the exocytosis of mouse synaptosomes. The influence of tellimagrandin I (A and C) and mahtabin A (B and D) on the phosphorylation of SNAP-25 Thr138 (A, B), and on the exocytosis of synaptosomes (C, D). Comparison of the effect of tellimagrandin I, mahtabin A and TMC on the phosphorylation level of SNAP-25 Thr138 . E: Representative Western blot with anti- SNAP-25 pThr138 specific antibody. F: Relative changes in the amount of SNAP-25 pThr138 determined by densitometry of the Western blots normalised on the bands obtained for the loading controls. Data are means ± SD (n =𠂓). ANOVA *p < .05, **p < .01, ***p < .001, n.s.: not significant.


Results

Screening of the chemical library

Taking into account the spatial conformation of the binding site for NGF in TrkA, compounds with molecular dimensions fitting in the domain-5 pouch 5 were selected from a chemical library based on a bicyclic three-dimensional scaffold, prepared from commercial sources in which derivatives are differently decorated with the side chains of proteinogenic amino acids and are compatible with solid phase peptide synthesis. 11, 12 Over hundred fifty compounds were initially screened by selecting active molecules for the ability to sustain survival, assessed by the MTT(thiazolyl blue tetrazolium bromide) test, of PC12 cells cultured in the absence of serum, using rhNGF as internal standard. A great variability existed in the performance of the active compounds, likely related to structural and spatial differences in the scaffold and its decoration. The structure of four compounds which repeatedly showed NGF-mimetic activity with a dose-dependent effect in this assay is shown in (Figures 1a and b Supplementary Table 1 Chemical name of active compounds – Supplementary Information).

NGF mimetic activity of selected compounds. (a) PC12 cells were cultured for 72 h in serum-free medium in the presence or absence of the indicated concentrations of compounds n. 11 ( ○ ), 13 ( ▵ ), 46 ( ▽ ), 60 ( ◊ ), or 4 nM hrNGF as positive control. Cell survival was measured as MTT incorporation of triplicate cultures and expressed as percentage of the survival activity induced by hrNGF. Results of four different experiments (mean±S.E.) are reported. (b) Structural formula of the compounds n. 11, 13, 46, 60. Compound n. 11 was named MT2. (c) PC3 cells were cultured for 48 h in the presence or absence of the indicated concentrations of compounds n. ( ○ ), 13 ( ▵ ), 46 ( ▽ ), 60 ( ◊ ) or 4 nM hrNGF as positive control. Data are expressed as stimulation index ( 3 H-TdR incorporation in stimulated cultures/ 3 H-TdR incorporation in unstimulated cultures). Stimulation index obtained with 4 nM of hrNGF was 2.3±0.18 (mean±S.E.). Results of three different experiments (mean±S.E.) are reported. (d) Effect of MT2 on apoptosis induced by serum starvation. PC12 cells were cultured in serum-free medium in the presence or absence of different concentrations of MT2 or 4 nM hrNGF as positive control. Cells were washed, stained with FITC Annexin V/PI and the percentage of Annexin + PI − cells recorded by cytofluorimetry. Statistical analysis, performed by Student’s t-test shows significant differences (P at least <0.01) between untreated versus treated (any concentration) starved cultures

Next, as NGF promotes proliferation of the prostatic carcinoma cell line PC3, which does not express p75 NTR , we chose this assay to assess whether the active compounds selectively interact with TrkA, rather than p75 NTR or heterodimeric complex. Figure 1c reports the proliferation indexes induced by the four compounds, which were able to induce a vigorous growth, at times even stronger than that elicited by NGF. Thus, transmission of biologic signal presumably relied solely on their interaction with TrkA chain.

At the end of the selection procedure, we chose the molecules endowed with the highest NGF-like activity and addressed them to further studies. Henceforth, the functional analysis of one representative molecule, named MT2, will be reported. As serum deprivation typically triggers the intrinsic pathway of apoptosis, to explain the survival-promoting activity induced by NGF mimetics in serum-deprived PC12 cells, we wanted to study specifically whether the apoptotic process could be downregulated by MT2 and chose an early event to measure its activity, the surface exposure of phosphatidyl-serine in PC12. Figure 1d shows that the compound was able to markedly affect the apoptosis that takes place upon serum starvation, in a dose-dependent fashion and at levels even higher than those attained by optimal concentrations of human recombinant (hr) NGF.

Interaction with TrkA receptor

Based on the above evidence of a selective interaction with TrkA, we set up initial binding studies with 125 I-NGF on PC12 cells and tested cold MT2 for its ability to displace the binding of fixed amounts of iodinated cytokine. Figure 2a shows the results of a representative experiment, which indicated an affinity of MT2 for TrkA in the nanomolar range of concentration.

MT2 interactions with TrkA. (a) Displacement of 125 I-hrNGF bound to PC12 cells by MT2. PC12 cells were incubated with 0.1 nM 125 I-hrNGF in the presence or absence of different concentrations of MT2. Specific cell bound radioactivity was calculated and the results analyzed by Origin software. Results of one representative experiment out of three performed are shown. (b and c) Binding of 3 H-MT2 to TrkA NIH-3T3. NIH-3T3, stably transfected with full-length human TrkA, were incubated in triplicate with different concentrations of 3 H-MT2, in the presence or absence of excess cold MT2 (b) or 4 nM cold hrNGF (c). Specific cell bound radioactivity was calculated and the results analyzed by the Origin software (one-site binding assay). No specific binding was recorded on mock-transfected NIH-3T3 cells (not shown). (d) Internalization of 3 H-MT2. TrkA-NIH-3T3 or mock-transfected cells were incubated for 1 h at 4° C with 3 H-MT2 in the presence or absence of excess cold MT2 or hrNGF. Then, cells were washed and brought at 37° C for 1 h. Membrane radioactivity was eluted with 0.1 M glycine buffer, pH 2.8. RBC were incubated for 1 h at 37° C with 3 H-MT2 in the presence or absence of excess cold MT2 or hrNGF. Cells were lysed, and cell-bound radioactivity recorded. Data are expressed as mean bound radioactivity±S.E. of triplicate cultures. Results of one representative experiments out of three performed are shown. (e) MT2 interaction with the ECD fraction of human recombinant TrkA. One microgram purified TrkA ECD was incubated in triplicate with different concentrations of 3 H-MT2, in the presence or absence of excess cold MT2. The mixture was absorbed on filter papers and, after washing, the radioactivity recorded. Data were analyzed by Origin software. Results from one representative experiments out of three performed are shown

This analysis, albeit suggestive, could not demonstrate conclusively that the NGF mimetic compound actually bound to TrkA. We therefore transfected plasmids coding human full-length TrkA chain into TrkA − NIH-3T3 cells and obtained stable transfectants. Such cells were then used in binding experiments with MT2, labeled by introducing a 3 H-methyl moiety as the final step of the synthetic procedure. Analysis of binding data at 4° C revealed Kd values of the MT2/TrkA interaction ranging between 50 and 100 nM (Figure 2b) no specific binding was evident on mock-transfected-NIH 3T3 cells (data not shown). As expected, cold recombinant human NGF efficiently displaced binding of the tritiated compound (Figure 2c). In further experiments, after incubation at 4° C, reactants were brought at 37° C to observe ligand internalization, which indeed occurred in TrkA + NIH-3T3 cells, but not in mock-transfected cells (Figure 2d) again, 3 H-MT2 internalization was blocked by rhNGF. Consistently, no internalization of labeled MT2 was observed in human erythocytes (RBC) kept at 37° C for 1 h (Figure 2d).

We finally wanted to obtain direct, unequivocal evidence of MT2–TrkA interaction, performing cell-free binding experiments with labeled MT2 and the extracellular portion of the receptor chain expressed in recombinant form. Repeated experiments yielded results completely coherent with those provided by the cell-based approach, as the specific binding observed displayed superimposable affinity, with Kd values ranging between ∼ 20 and 80 nM (Figure 2e).

Thus, MT2 molecule was able to bind TrkA in a specific manner, undergoing subsequent receptor-mediated internalization, antagonized by the native neurotrophin, and failed to passively diffuse through cellular membranes. The affinity of the reaction nicely correlated with the survival activity of the compound, which was therefore likely related to its interaction with TrkA receptor chain.

Molecular models and binding hypothesis

Domain-5 of TrkA contains amino-acid residues directly involved in the contact between NGF and TrkA. 8 Thus, we asked whether MT2 interacts with any of those TrkA residues and chose to conduct molecular modeling studies, in which docking was performed as a global energy optimization by means of the biased probability Monte Carlo stochastic procedure. 13 It turned out that MT2 bound TrkA’s fifth domain at a shallow hydrophobic cavity in the region close to the membrane. The pocket is present in both the subunits but only one monomer was in contact with the molecule. As shown in Figure 3, MT2 established three key interactions with the protein: (i) one of the benzyl moieties formed a π-stacking interaction with Phe327, almost completely overlapping to the side chain of NGF Arg103, (ii) the other benzyl ring formed hydrophobic interactions with the methyl group of Thr352 and with the side chain of Val354, fitting the region that in the co-crystal is occupied by Ile31 of NGF, and (iii) the carbonyl oxygen of the methyl ester chain formed a hydrogen bond with Thr325. The evidence that the compound likely interacts with residues engaged by NGF indicates partly shared contact regions between the latter and its mimetics that may reveal functionally meaningful.

Binding sites of MT2 in TrkA molecule. In silico prediction of amino-acid residues involved. The predicted bound conformation of MT2 at the binding site of TrkA is shown. The surface of the pocket is reported explicitly with the structure of TrkA in gray ribbons as background. The amino acids of TrkA that establish direct interaction with MT2 are displayed in ball and stick representation. MT2 is displayed in ball and stick with carbon atoms colored dark yellow. As a term of comparison, Arg103 and Ile31 of NGF are reported in ball and stick with the carbon atoms colored orange. The H-bond between Thr325 of TrkA and MT2 is highlighted with green dots. The van der Waals volume of part of Thr352 and Val354 side chains is highlighted with a black dotted line

We therefore checked whether the interaction of NGF mimetics with TrkA involves the amino-acid residues predicted by the docking analysis reported above. We transfected NIH-3T3 cells with T352A or F327A mutants of the full-length TrkA-coding construct, selected stable transfectants, and used them to perform binding experiments with tritiated MT2, as described above. Consistent with the docking analysis, the results showed virtual absence of a saturation curve (data not shown), confirming that those residues were critical for MT2 binding to TrkA.

Induction of biochemical events in NGF-sensitive cells

The first demonstrable event upon NGF binding to TrkA is autophosphorylation of the receptor. 1 To ascertain whether such event is induced by MT2, we prepared western blots, developed with anti-phosphotyrosine (PY) antibodies, of TrkA immunoprecipitates from PC12 cells exposed to NGF or MT2 (Figure 4a). It is evident that the compound was able to induce autophosphorylation, but at lower levels compared with NGF, a finding raising the question of whether a full-blown pattern of receptor activation is actually triggered by the NGF mimetic compound.

NGF mimetic activities expressed by MT2. (a) Effect of MT2 on TrkA autophosphorylation. PC12 cells were incubated with 10 μM MT2 or 4 nM hrNGF. Cell lysates were immunoprecipitated with a-TrkA or with control IgG antibodies, blotted on nitrocellulose filter, and stained with anti-PY antibodies. (b) Effect of MT2 on VGF production by PC12 cells. PC12 cells, stimulated with 10 μM MT2 or 4 nM hrNGF, were lysed, blotted, and stained with anti-VGF antibodies. Results of one representative experiment out of three performed are shown in a and b. (c–e) MT2 induces an NGF-like growth arrest in PC12 cells. Cells were exposed to MT2 (5–20 μM) and the number of neuronal nuclei stained with a specific neuronal marker (NeuN) was assessed after 7 days. (c and d) phase contrast microscope analysis of PC12 cells exposed to MT2 10 μM for 3 (c) or 7 days (d). At day 3, MT2- exposed PC12 cells appear to be aggregated into clumps with shorter and limited numbers of neuritic processes, compared with the corresponding NGF-differentiated (2 nM) samples (c). All these events appear more prominent at day 7, where most of MT2-treated cells are aggregated to form larger clusters, with neuritic processes significantly shorter than those observed in the corresponding NGF-treated cells (d) (e) number of NeuN+ cells after 7 days of culture (n=6). Ten micromolar MT2 results to be the highest active concentration. (fi) NGF-like neuronal differentiating activity in DRG neurons: (f) Neuronal nuclei were assessed following stimulation for 4 days with MT2 (10 μM) and hrNGF (2 nM). MAP2 positive neuritic processes are stained in red (scale bar: 25 μm). (g) Neuronal nuclei assessed following 4 days of stimulation with MT2 (5–20 μM) and hrNGF (2 nM). (h-i) The number of branching points (h) and Neurite extension (i) in cells stimulated with 2 nM NGF or 10 μM MT2 were recorded after the indicated times. Images were analyzed using the Olympus inverted microscope (Olympus Corporation, Tokyo, Japan) or NIH ImageJ (NIH, Bethesda, MD, USA). Neurites were viewed with a × 20 objective, images were projected onto a video monitor, and neurite lengths were traced with a digitizing tablet while being viewed on the monitor. Ten DRG neurons were analyzed for each slide and the experiments were repeated at least three times. Statistical significance was determined with independent t-test and one-way ANOVA. For distribution, the data were presented as mean S.E.M.

Upon exposure to the neurotrophin, NGF-sensitive cells express sets of genes that bring about the relevant cell type-specific response in PC12 cells, one of the most reproducible event is expression of VGF gene, which is either de novo expressed or strongly upregulated by NGF signaling. 14 Treatment of PC12 cells with MT2 clearly induced definite increases in the amount of VGF, albeit less pronounced than those elicited by NGF (Figure 4b). This finding indicates that significant differences may exist in the spectrum of signals generated by TrkA upon triggering by different ligands.

As the latter point was deemed critical for elucidating the functional properties of NGF mimetics, the compound was tested in another typical NGF-dependent assay: the induction of mitotic arrest and neurite outgrowth in PC12 cells. Figure 4c shows that MT2 treatment of PC12 cells for 3 days consistently induced morphology modifications, as cells tended to aggregate into clumps and to extend neuritic processes, that appeared, however, shorter and in limited numbers compared with NGF. Furthermore, while cultures exposed to NGF for 7 days reached an almost complete neurite-like morphology and aggregated to form small clumps, cells treated with MT2 appeared less differentiated and formed larger aggregates (Figure 4d). However, after 7 days, MT2 blocked cell growth at levels comparable with those sustained by saturating amounts of hrNGF (Figure 4e). Consistently, when embryonic dorsal root ganglia (DRG) cells were studied, MT2 induced an NGF-like neurite growth-promoting activity after 4 days (Figures 4fi) even if neuritic arborization and fasciculation appeared to be decreased in MT exposed neurons. Comparable results were observed in cultures of superior cervical ganglia cells (data not shown). Together, these results suggest that the limited, compared with NGF, interface between NGF mimetics and TrkA molecule translates into a somewhat restricted array of downstream signals departing from the receptor.

To clarify the biochemical bases of such functional divarication, we studied in closer detail the phosphorylation pattern of the intracytosolic tail of TrkA chain upon exposure to NGF versus MT2, using monospecific antibodies for the tyrosines 490, 674/675, and 785, known to be critical for the transmission of neurotrophin signal. 1, 8 To this purpose, we used either PC12 cells or the wtTrkA-NIH-3T3 stable transfectants, obtaining superimposable results. Figure 5a shows that MT2 added to PC12 cells invariably caused phosphorylation of Tyr490, and that Tyr674/675 and Tyr785 underwent minimal activation, while all of them were clearly phosporylated upon exposure to hrNGF. The latter findings indicate that some biologic responses to NGF mimetics are expressed at a lower level, compared with NGF, and drove us to investigate further the biochemical pathways originating from the activation of Tyr490.

Biochemical pathways induced by MT2 in PC12 cells. (a) TrkA phosphorylation. Serum-starved PC12 cells were stimulated with 10 μM MT2, or 4 nM hrNGF as positive control, for 15 min. Cells lysates were blotted with antibodies to P-Y490, P-Y674/675, P-Y785 and with anti-actin as loading control. Membranes were stripped and stained with anti-total TrkA IgG. The relative histograms represent the data of densitometric analysis and are expressed as the ratio between phosphoprotein (P) and total protein (T) of five different experiments (mean±S.E.). Statistical analysis was performed by paired Student’s t-test. (b) Kinases phosphorylation. Serum-starved PC12 cells were stimulated with 10 μM MT2, or 4 nM hrNGF, for 30 min. Cell lysates were blotted with rabbit anti-P-ERK, anti-P-Akt, anti-P-p38 MAPK, anti-P-JNK. Then, membranes were stripped and stained with antibodies to the respective total protein and with anti-actin antibodies as loading control. The relative histograms represent the data of densitometric analysis and are expressed as the ratio between phosphoprotein (P) and total protein (T) of five different experiments (mean±S.E.). Statistical analysis was performed by paired Student’s t-test. (c) Phosphatase. Serum-starved PC12 cells were stimulated with 10 μM MT2, or 4 nM hrNGF as positive control, for 30 min. Cell lysates were blotted with rabbit anti-MKP1 IgG and anti-actin antibodies as loading control. The relative histogram represents the data of densitometric analysis and is expressed as the ratio between MKP1 expression and actin of five different experiments (mean±S.E.). Statistical analysis was performed by paired Student’s t-test

It is known that upon phosphorylation of such residue, via sequential involvement of Shc and Ras, the MAP kinase cascade is activated, a pathway central to the death/survival divide in a disparate number of cell types, which comprises kinases often displaying opposing activities. 15 As NGF has a conspicuous role in the homeostasis of such proteins, 16, 17 we studied the phosphorylation status of extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinases (JNK), p38 mitogen-activated protein kinase (MAPK) proteins, and of Akt, another kinase involved in the regulation of cell survival, 18 in serum-starved PC12 cells exposed to hrNGF or to MT2. Figure 5b shows that indeed p42/44 ERK and Akt became strongly activated upon addition of either stimulus. Conversely, MT2 – or hrNGF as control – was able to induce a marked dephosphorylation of p38 MAPK in PC12 cells, from sustained levels down to negligible amounts, while JNK activation was reduced, but not at significance levels (Figure 5b).

To better understand the profound modulation of p38 MAPK activation elicited by MT2, we tested whether MKP-1, a phosphatase highly specific for p38 MAPK and also for JNK, 17, 19 could have a role in this setting, as previously observed. 17 Figure 5c shows that exposure of serum-deprived PC12 cells to MT2 or hrNGF caused marked increase in MKP-1 protein levels, as early as 30 min after stimulation. Taken together, this set of experiments is coherent with the observed capacity of MT2 to sustain cell survival in cultures undergoing metabolic derangement.

To confirm the interaction of MT2 with TrkA residues involved in NGF binding and to rule out the involvement of other acceptors, for example, other protein kinase receptors, in the response to MT2, similar studies were repeated on the wild type (WT), F327A, and T352A TrkA-NIH-3T3 stable transfectants. Figure 6a reports a western blot analysis showing induction of ERK 1/2 phosphorylation by MT2, while Figure 6b reports dephosphorylation of p38 MAPK in the same cell cultures. In both instances the observed activity was largely comparable with that induced by NGF and was almost completely abolished by addition of K252a, a classic inhibitor of TrkA catalytic function. As expected, if the T352A- or F327A-TrkA/NIH-3T3 mutants were used to study MT2-induced ERK 1/2 and p38 MAPK activation status, the latter was influenced by either mutation, as assessed by western blot analysis (Figures 6a and b). We also wanted to use a quantitative approach to measure the levels of activated p38 MAPK proteins. The results of this analysis were totally consistent with the western blot approach (Supplementary Figure 1). Thus, at least a substantial part of the biologic activity exerted by MT2 is elicited upon interaction with two of the TrkA amino-acid residues involved in binding the native neurotrophin.

Biochemical pathways induced by MT2 in WT TrkA-NIH-3T3 or mutants. NIH-3T3 cells, stably transfected with full-length WT human TrkA, T352A, or F327A TrkA mutants, were cultured in serum-free medium and incubated with 10 μM MT2 or 4 nM hrNGF, in the presence or absence of K252a. Cell lysates were blotted with rabbit anti-P-ERK (a) and anti-P-p38 MAPK (b). Then, membranes were stripped and stained with antibodies to the respective total protein. The relative histograms represent the densitometric analysis. Data are expressed as ratio between phosphoprotein (P) and total protein (T) of three different experiments (mean±S.E.). Statistical analysis was performed by paired Student’s t-test

Activity of NGF mimetic on organ cultures of rat hippocampus

Based on the above findings showing a clear-cut activity of MT2 in several in vitro systems, we wanted to assess its ability to rescue NGF deficit in a recently characterized rat hippocampal neuronal model, in which NGF deficit is strictly connected to the activation of the amyloidogenic pathway. 7, 20 In this neuronal in vitro model, accumulation of both amyloid precursor protein (APP) and the 28-kDa active form of presenilin 1 (PS1) (endowed with α-secretase activity) is observed as early as 30 min after administration of neutralizing anti-NGF antibodies the simultaneous addition of NGF to the cultures prevents such biochemical modifications. We therefore tested whether MT2 was able to mimic the native neurotrophin in preventing amyloidogenesis and neuronal death. Figure 7a shows that MT2 induced dose-dependent TrkA phosphorylation in such cultures. Moreover, it could indeed abolish the dramatic enhancement in APP generation, at levels comparable with those attained by the addition of NGF. An even stronger activity was evident when generation of full-length PS1 or of its 28-kDa enzymatically active form was investigated, as the amounts of both proteins were profoundly modulated in cultures treated with MT2 (Figures 7b and c) in occasional experiments, it acted more than hrNGF did in parallel cultures. Consistent with the inhibition of amyloidogenic pathway, neurons were strongly protected from death in a dose-dependent fashion (Figures 7d and e). Thus, the activity of NGF mimetics was confirmed also in a highly representative model of human pathology, a finding which warrants their exhaustive evaluation in in vivo settings.

MT2 protects neurons from Aβ amyloid-mediated death in NGF-deficient neurons. (a and b) 3–4 days cultured hippocampal neurons were exposed to 2 nM NGF or to MT2 (5–30 μM) and the highest active concentration of MT2 evaluated as induction of TrkA phosphorylation by western blot analysis with anti-phosphorylated (P) and anti total (T) Trk-A antibodies or by counting the number of NeuN stained nuclei (b). (c and d) hippocampal neurons were deprived of NGF by exposure to anti-NGF antibodies (30 μg/ml) for 24 h and incubated in the presence or absence of 10 μM MT2 or 2 nM hrNGF. Cells were lysed and western blot analysis performed with antibodies against APP full-length (c) and PS1 28 kDa N-terminal fragment (d). (e and f) Hippocampal neurons were deprived of NGF by exposure to anti-NGF antibodies (30 μg/ml) for 24 h and then treated with concentrations of MT2 ranging from 5 to 30 μM or 2 nM hrNGF. The percentage of alive neurons was obtained by counting the number of intact nuclei (d) or alternatively by counting the number of condensed nuclei (e). Ctrl, neurons not exposed to NGF or MT2 NGF, neurons exposed for 48 h to NGF MT2, neurons exposed for 48 h to MT2 (10 μM) Dep, neurons deprived of NGF for 24 hours Dep+NGF, neurons washed with NGF-free media and immediately exposed to NGF containing media Dep+MT2 neurons washed with NGF-free media and immediately exposed to MT2-containing media. Statistical analysis was performed by Newman–Keuls test (N=4), P<0.05. Consistent with the inhibition of the amyloidogenic pathway, neurons were completely protected from death


Human Urine Proteomics: Analytical Techniques and Clinical Applications in Renal Diseases

Urine has been in the center of attention among scientists of clinical proteomics in the past decade, because it is valuable source of proteins and peptides with a relative stable composition and easy to collect in large and repeated quantities with a noninvasive procedure. In this review, we discuss technical aspects of urinary proteomics in detail, including sample preparation, proteomic technologies, and their advantage and disadvantages. Several recent experiments are presented which applied urinary proteome for biomarker discovery in renal diseases including diabetic nephropathy, immunoglobulin A (IgA) nephropathy, focal segmental glomerulosclerosis, lupus nephritis, membranous nephropathy, and acute kidney injury. In addition, several available databases in urinary proteomics are also briefly introduced.

1. Introduction

Clinical samples such as tissues and bio fluids (e.g., serum, plasma, urine, and saliva) are undoubtedly valuable sources in biomarker discovery studies for diagnostic proposes. The protein content complexity is the first issue in handling and analysis of such samples. As urine, which contains approximately 2000 proteins [1, 2] and is a less complex sample than plasma which contain more than 10,000 core proteins [3], could be collected in a noninvasive and unrestricted way, it is a preferable resource for investigation of a broad spectrum of diseases. Moreover, the protein composition and fragmentation of urine are relatively stable in comparison with other biofluids such as plasma or serum which are prone to proteolytic degradation during and after sampling [4].

As urine is the consequence of blood filtration and also contained all secreted proteins from tubules and kidney specific cells [5–10], it thus has been studied for investigating the pathological process of systemic as well as renal diseases [11]. Serum proteins are filtered based on their sizes and charges at the glomeruli [12] while reabsorption of abundant serum proteins such as albumin, immunoglobulin light chain, transferrin, vitamin D binding protein, myoglobin, and receptor-associated protein occurs in proximal renal tubules mainly by endocytic receptors, megalin, and cubilin [13–16]. Thus, protein concentration in normal donor urine is very low (less than 100 mg/L when urine output is 1.5 L/day), and normal protein excretion is less than 150 mg/day. This is about a factor 1000 less compared with other body fluids such as plasma. Excretion of more than 150 mg/day protein is defined as proteinuria and is indicative of glomerular or reabsorption dysfunction.

Urine proteomics studies account for several barriers such as low concentration of total protein, high concentration of salts and other ingredients that hinder protein separation [17], and high dynamics of changes in urine composition between different seasons of the material collection. Due to differences in sensitivity and availability of various proteomic techniques, considerable efforts have been made to find a suitable method to analyze the expression of specific urine marker proteins [18].

Urine proteomics is a powerful platform to identify urinary excreted proteins and peptides in different stages of disease or therapy and to determine their quantity, functions, and interactions [19]. Therefore, mechanism of the disease and novel therapeutic targets could be suggested by proteomic approaches. In this review we focus on technical aspects of analyzing urine proteome application of this platform in clinic and also several urine proteome databases are reviewed.

2. Normal Human Urinary Proteome

Normal human urine contains a significant amount of peptides and protein. In 2004 nearly 1400 protein spots were separated by using two-dimensional gel electrophoresis (2DE) and 150 distinct proteins from 420 spots were identified by mass spectrometry [8]. This number of identified urinary proteins increased significantly to around 1534 in 2006 by combining one-dimensional gel electrophoresis and reverse phase liquid chromatography coupled to mass-spectrometry [1]. To date, over 2000 proteins in total are estimated in normal human urine of which 1823 proteins were identified by Marimuthu et al. in 2011 [20]. Identified proteins by Marimuthu et al. were

300 greater than Adachi’s report and hence could serve as a comprehensive reference list for future studies. Some of the urinary proteins have greater experimental molecular weights than the theoretically possible from amino acid sequence alone, indicating the presence of posttranslational modifications. 225 N-glycoproteins [10] and 31 phosphoproteins [21] were identified in normal human urinary proteome. Normal human urine contains exosomes, small vesicles with diameters less than 100 nm that are secreted from renal epithelial cells.

Exosomes contain a number of disease related proteins. There are 1132 proteins in urinary exosomes including 14 phosphoproteins [22]. It is known that the urinary proteome differs between healthy individuals, particularly between men and women. In addition to the interindividual differences, the urinary proteome from the same individual varies at different time points due to the effect of exercise, diet, lifestyle, and other factors. The urinary proteome changes significantly over time. Urine collected in the morning contained more proteins than in the afternoon and evening. In addition, the interday proteome variation was observed to be greater than the variation at different time points in the same day [23].

3. Urine Preparation

Optimization of sample preparation is a necessary first step for urinary proteome analysis and sample desalting that is, using precipitation or dialysis is often required. Several protocols that are used to isolate urinary proteins were employed using precipitation, lyophilisation, ultracentrifugation, and centrifugal filtration. Acetone precipitates more acidic and hydrophilic proteins. Ultracentrifugation fractionates more basic, hydrophobic, and membrane proteins. Organic solvents (90% ethanol and 10% acetic acid) precipitate urinary proteins in the highest recovery rate. The ACN-precipitated urine sample produced the greatest number of spots on a two-dimensional (2D) gel, whereas the acetic-precipitated sample yielded the smallest number of spots [24]. The dialysis of urine proteins and concentration by lyophilisation without fractionation significantly improve reproducibility and resolution and are probably able to reflect total urinary proteins on 2D gels. In addition, the removal of albumin from the urine helps to identify the low abundant proteins [25]. Two main variables have been analyzed in urine preparation methods: quantity (protein recovery yield) and quality (2D spot patterns or proteomic profiles). The conclusions reached are that there is no single perfect protocol that can be used to examine the entire urinary proteome since each method has both advantages and disadvantages in comparison with the others. A combination of several sample preparation methods is required to obtain the greatest amount of quantitative and qualitative information [24].

4. Urine Proteome Enrichment

Due to the fact that disease specific biomarkers are likely in the low abundant fraction of proteome which are masked by high abundant proteins, enrichment strategies may increase the chance of capturing these potential biomarkers.

Depletion of high abundant proteins using chromatographic based enrichment methods or commercial kits specialized for urine samples are available strategies for enrichment.

Several high abundant proteins (e.g., albumin, transferrin, haptoglobin, immunoglobulin G, immunoglobulin A, and alpha-1 antitrypsin) could be removed using antibody-based affinity depletion approaches (e.g., multiple affinity removal system (MARS)) that are able to be coupled with either 2DE or LC-MS analysis [26, 27]. Enrichment of low abundant and depletion of high abundant proteins could be combined in peptide ligand library approach. In this method a diverse library of peptides immobilized on a solid-phase chromatographic matrix interact with the specific recognition site of proteins. Captured proteins, therefore, are included in the analysis procedure while other proteins without suitable interaction are washed out and excluded. Saturation of peptides for high abundant proteins leads to decrease in their concentration in the final eluent. In addition, concentrated low abundant proteins on their specific ligands result in decreasing the dynamic range of proteins in the specimen [28]. The success of this method depends on using high amounts of initial material otherwise, the enriched profile would be similar to the nonenriched original profile [29]. Chromatography techniques such as ion-exchange, size-exclusion, and affinity chromatography are widely used for enrichment or depletion purposes however, they are also applicable for fractionation and separation prior to MS (see Section 5.2).

Charge-charge interaction between proteins of the sample and charged column is the base of capturing subproteome in ion-exchange chromatography. In anion exchange chromatography, negatively charged proteins or peptides interact with positively charged column and gradually eluted off the column by a mobile phase with gradient of salt solution. Majority of bound proteins with negative charge are eluted at the highest ionic strength [30]. Lu et al. enriched low-abundance proteins in urine specimen using this technique and observed improved number of identification in 2DE map of enriched samples in comparison with the map of nonenriched samples [30].

In contrast to anion exchange, charge of stationary phase in strong cation exchange chromatography (SCX) becomes negative in aqueous solution and therefore interacts with strongly basic analytes. To elute the analytes, column is then washed with a solvent with pH gradient. The most cationic proteins/peptides would elute off the column at higher pH. Therefore, this technique has been introduced as a suitable method for phosphoproteome enrichment [31]. Thongboonkerd et al. showed application of SCX for enrichment of the basic/cationic urinary proteome [32].

Commercial protein depletion kits are currently available that some of them have been optimized for proteomic studies on urine samples (e.g., UPCK urine protein enrichment/concentration kit). According to the literature using these depletion kits could improve the number of low-abundance proteins identified in urine up to 2.5-fold [33].

Selection of the enrichment strategy depends on the aim and specific study requirements.

5. Techniques for Urinary Proteomic Studies

Common techniques for urinary proteome analysis are two-dimensional gel electrophoresis followed by mass spectrometry (2DE-MS), liquid chromatography coupled to mass spectrometry (LC-MS), surface enhanced laser desorption/ionization coupled to mass spectrometry (SELDI-TOF), and capillary electrophoresis coupled to mass spectrometry (CE-MS) and protein microarrays [34].

5.1. DE-MS

Two-dimensional gel electrophoresis is a robust and widely used method for protein separation. In this technique proteins are separated based on their isoelectric point and molecular mass, visualized, and semiquantified by staining [25]. Advantages of 2DE include ability to detect relatively large molecules, estimate molecular weight and pI of proteins, and investigate posttranslational modifications [35]. Common limitations of 2DE technique are requirements for high protein amount, lack of automation, loss of extremely acidic (pH < 3) or basic proteins (pH > 10), lack of detection for large (Mr > 150 kD) and small (Mr < 10 kD) proteins and hydrophobic proteins, and lack of reproducibility, low-throughput capacity, and narrow dynamic range [36, 37]. A few of these limitations have been granted by some technical improvements. Development of two-dimensional difference gel electrophoresis (2D-DIGE) ameliorated problem of low-throughput capacity improve the quantification accuracy and statistical confidence by use of internal standard and pooling samples labeled with different fluorescent dyes (Cy2, Cy3, and Cy5) [38]. Blue native technique was developed in order to specify investigations on hydrophobic proteins, protein complexes, and protein-protein interactions on the gel matrix [39]. Blue native technique separates proteins based on their molecular weights on two dimensions. The reproducibility problem has been minimized using precast gels with multirun gel tanks which are capable of running several electrophoretic gels simultaneously. Furthermore, staining gels with different fluorescent dyes postelectrophoretically increase the sensitivity of detection spots. In case of study the urinary proteins by 2DE, desalting by chromatographic columns or filters is recommended to obtain well separated spots on the gel. Despite improvement in 2DE technique, lack of automation and time consuming steps (approximately 3 days for obtaining a 2DE pattern) lead to decrease inclination of researchers for applying this technique in their recent experiments.

5.2. LC-MS

Liquid chromatography (LC) is a high resolution separation method that employs one or more inherent characteristics of a protein, its mass, isoelectric point, hydrophobicity, or biospecificity [40]. This method separates large amounts of analytes (protein/peptides) on HPLC column or small amount of analytes (peptides) on a capillary LC column with high sensitivity and can be automated [41]. LC/MS can identify low-abundance and hydrophobic proteins not seen by 2DE and thus is considered a complementary method for 2DE in proteomics 2D liquid phase fractionation and can be used for in-depth analysis of body fluids such as urine [42]. A combination of gel and different steps of gel-free techniques (i.e., sequential separation using different matrices in two or more independent steps) is used for a better separation and referred to as Mud PIT. Strong cation exchange column (SCX) is a good choice for separate urinary peptides before injection to reverse phase columns coupled to MS. The retention times of many urinary compounds including organic acids and bile salts overlap with peptides in reverse phase LC which can be removed using SCX column [43]. Recently, weak anion exchange columns are used for fractionation and enrichment of low abundant proteins excreted in the urine prior to 2DE [44]. This combination method has potential for identification of low abundant biomarkers but still has challenges for identification of isoforms and PTMs. Affinity chromatography columns before LC-MS runs are the other alternative methods for capturing subproteomes from the urine such as glycoproteome and phosphoproteome [22, 45]. LC technique can be coupled with diverse types of mass spectrometry instruments that affect the accuracy and confidence of identification and quantification.

The high resolution instruments such as the Fourier transform ion cyclotron resonance (FTICR) and orbitrap or hybrid and tribrid instruments such as Q-exactive (hybrid of quadrupole and orbitrap) and orbitrap Fusion (tribrid of quadrupole, orbitrap, and linear ion trap) may couple to LC for clinical sample analysis including urine. Kalantari et al. and Adachi et al. analyzed urine samples from IgA nephropathic patients and normal human urine, respectively, using LC coupled with high resolution MS instruments [1, 46]. Proteins separated by LC could be quantified with the labeling (e.g., stable isotope affinity tag and isobaric tags) and label-free techniques [41]. Quantification of proteins or peptides in large scale is possible only by gel-free MS based methods which is considered an advantage for these techniques. As LC-MS is time-consuming and sensitive towards interfering compounds (e.g., salts) and precipitation of analytes on column materials, it is not applicable yet for routine clinical diagnostic tests [48].

5.3. SELDI-TOF

Surface-enhanced laser desorption/ionization time of flight (SELDI-TOF) technology uses protein-chip arrays with different properties on the surface (hydrophilic or hydrophobic materials, cationic or anionic matrices, lectin, or antibody affinity reagents) coupled with a TOF mass spectrometer [36]. Proteins or peptides of interest are bound to the active surface depending on the surface property while unwanted unbound proteins are washed away with an appropriate solvent or buffer [36, 49]. It is a widely used method in clinical proteomics that can detect different protein expression patterns of body fluid and tissue specimens between patients and healthy subjects and thus is a powerful tool for biomarker discovery [50]. SELDI-TOF is a high throughput and easy to use technique that can be automated. In addition, a low sample volume (<10 μL) without prior concentration or precipitation of proteins is required [42, 51]. However, it has some limitations such as difficulties in standardization since the proteome profiles generated by SELDI are influenced by many factors such as the type of surface coating, pH, salt condition, and protein concentration. Other limitations of SELDI are lack of reproducibility, restriction to selected proteins, low-resolution mass spectrometer, and lack of a sequence based identification of the resolved peaks [48, 52, 53].

5.4. CE-MS

Capillary electrophoresis coupled to MS represents ideal analytical technique for different omics approaches that provides high resolution protein separation based on differential migration through a buffer-filled capillary column in an electrical field (300 to 500 V/cm) [54, 55]. CE can be coupled either with MALDI (off-line) or ESI (on-line). While data analysis of CE-MALDI is more sight forward, signal suppression and variability results from matrix effect as well as loss of resolution are considered challenges in this method and thus coupling with ESI is preferred [56]. Though CE is capable of being coupled with any type of MS instrument, ESI-FT-ICR is the most applicable one for identification of proteins and peptides disease biomarkers in urine [47, 57]. CE-MS offers several advantages including fast and high efficient separation, selectivity, sensitivity, low cost, absence of buffer gradients, capability of fast reconditioning with NaOH, and insensitivity towards precipitating proteins peptides, lipids, and other compounds that often interfere with LC-separation [51, 58].

This method is especially applicable for analysis of the low molecular weight (<20 kDa) molecules. Its disadvantages are limited capacity to separate high molecular weight proteins (>20 kDa) and low-abundance proteins, lack of reproducibility and robustness, and small sample loading capacity [53, 55].

5.5. Protein Microarrays

Protein microarrays (or protein chips) are miniaturized solid-phase ligand-binding assay systems using immobilized antibodies or antigens on a support surface, generally a slide or membrane [59]. According to different features, such as content, surface, or detection system, there are many types of protein microarrays [60]. Main advantages of protein microarrays include high-throughput sensitivity and discovery of low molecular weight markers make them an ideal approach for urinary proteomics. However, microarrays have several limitations such as requirement for a highly specific probe for each analyte, low density coverage that allows detection of only a few proteins, variable specificity, and lack of detection of posttranslational modifications [25, 61].

6. Advances in Urine Proteomic Research

Over the last two decades, advances in proteomic tools have been impressive. “Microfluidic chip CE (MC-CE) device,” a recent analyzing tool for biomarker discovery, is one of the precious advances that enabled profiling of urinary markers with adjustable on-chip sample dilution [62]. This device is specially suitable for detection of urine anionic biomarkers and has high separation efficiency and sensitivity in analyte detection. It is composed of two units: sample dilution and on-chip CE separation [63]. In this device sequence dilution and separation are applicable by magnetic fluid activated valves. Recent studies on urine samples using microfluidic devices have been reviewed by Lin et al. [64].

Filter aided sample preparation (FASP), a method of detergent depletion, is one of the effective recent technique in shotgun proteomic analysis [65]. This method benefits from the advantages of both in-gel and in-solution digestion and has high protein coverage [66].

Low volume of sample is required and feasibility of digesting protein mixtures directly on the filter membrane is the most important advantages of this method.

96-well based parallel FASP is a robust method in urine proteomics which is cost-effective, repeatable, and efficient. Wiśniewski et al. and Yu et al. [65, 66] have applied this technique successfully for analyzing urine samples.

Advances in quantification methods provide more information from peptides and proteins in biological samples including urine. In label-free quantification the main goal is to develop the software with capability of normalization and reduced errors originated from instrument and loading samples. “Quanti” is a right example of recently developed software which is capable of relatively accurate label-free quantification of proteins with correction of responses to instrumental fluctuation [67]. This software has been applied in several studies on urine proteomics [46, 68–71]. MaxLFQ is also newly developed label-free software that can handle very large experiments, uses delayed normalization which makes it compatible with different separation procedures, and extracts the maximum ratio information from peptide signals [72]. Other label-free software programs and advantages and limitations of this technique have been reviewed elsewhere [73, 74].

Labeling techniques for quantification seems to be less improved during recent years in competition with label-free quantification techniques. However, isobaric tags for relative and absolute quantification (iTRAQ) as a versatile labeling technique which developed early in 2000s decade are still popular and frequently used specially in urine proteomics studies [75].

7. The Use of Urine in Clinical Proteomics

A major challenge in clinical proteomics is the identification of reliable biomarkers that help early diagnosis of disease and contribute to the development of personalized medicine. As 70% of urinary proteins stem from kidney and urinary tract and 30% originate from the other organs that are secreted into the blood circulation, study of the urinary proteomics could be useful in better understanding of pathophysiological mechanisms and the discovery novel biomarkers and therapeutic targets of kidney and nonkidney diseases [76]. Anderson et al. reported the identification of several proteins as urinary biomarkers in 1979. The explanation of disease-specific biomarkers in the urine is complicated by significant changes in urinary proteome during the day under the influence of some factors such as exercise, variations in the diet, and circadian rhythms. Simply comparing the proteome urine of patients with a particular disease with healthy individuals to distinguish between different diseases with similar symptoms is inadequate. Since the variability of conditions and irrelevant differences between healthy people and target diseased group are inevitably large and uncontrolled, comparison of one or more symptomatic similar diseases but different in etiology and severity simultaneously as controls is recommended for biomarker discovery. Thus, careful experimental design is the key to success in biomarker studies. Another issue is the correct matching of case and control groups. For instance, since some diseases such as cancer and chronic kidney disease usually occur in middle-aged and elderly people, urine samples from young volunteers are not appropriate controls for studying these diseases. Urine proteomics, if observing the careful study design, is the promising platform for identification of early detection and noninvasive biomarkers in the new era of modern medicine.

Despite large number of studies published in the past decades using proteomic tools in biomarker discovery field, still no reliable, specific, sensitive biomarker is available. A general shortcoming of biomarker research is lack of reproducibility and effect of unknown factors originates from dark side of disease pathogenesis which we do not have enough knowledge about and therefore could not be controlled. This concern will be fade in the future by advances in preparation methods, separation procedure, and extended databases. In addition, following a comprehensive workflow in clinical proteomics from initial discovery to translation into a clinically useful assay would help a biomarker to be meaningful and applicable in the clinic. Mischak et al. defined six steps for this workflow: (I) initial identification and verification, (II) evaluation the results by a knowledgeable independent panel of experts, (III) evaluation in suitable biobank samples or newly collected samples, (IV) evaluation in clinical trial, (V) implementation in clinical practice, and (VI) proving the cost-effectiveness of validated biomarker [77].

Another important point that should be considered in the context of biomarker implementation in clinics is the use of a panel of biomarkers instead of a single biomarker. A panel of biomarkers can lead to increase sensitivity and accuracy of assays.

7.1. Urinary Biomarkers in Renal Disease
7.1.1. Diabetic Nephropathy

Diabetic nephropathy (DN) is a serious complication of diabetes with a complex etiology that involves up to 40% of diabetic patients [39, 43]. DN is diagnosed by excretion of 30–300 mg protein in the urine per 24 hours (microalbuminuria) which can progress to macroalbuminuria (>300 mg/24 h) and/or changes in serum creatinine indicating decline in the glomerular filtration rate. The histological presentations of DN are loss of podocytes, thickness in glomerular basement membrane, proliferation of mesangial cells, and tubule interstitial fibrosis [44]. The current diagnostic tool for DN is biopsy that is invasive and not recommended for all suspicious cases due to its consequences. Urinary diagnostic biomarkers are promising noninvasive diagnostic molecules complementary to biopsy that will enter into the clinic in the near future. These diagnostic molecules can either promote the accurate diagnosis or decision for a more effective treatment.

Microalbuminuria (MA) is important for early diagnosis of DN and is the best predictor of DN available in the clinic [46]. However, several studies have shown that only a subset of patients with MA progress to proteinuria [47, 56]. Moreover, many individuals with type 1 diabetes have already experienced early renal function decline before or accidental with the onset of MA. So, it seems that MA may be an inadequate early diagnostic biomarker of DN (however, it may be appropriate for diagnosis of advanced DN) and discovery of new sensitive and specific markers by proteomic techniques is necessary [76]. In addition, some of the suggested biomarkers for DN have been achieved on samples from patients whose disease was diagnosed by clinical criteria and not confirmed by biopsy which is highlighted necessity of more accurate studies on biomarker discovery for DN. The study of Soldatos and Cooper [44] was a well-designed pioneer pilot study for profiling the urine proteome of DN patients using SELDI-TOF for discovery of early diagnostic markers. They could identify a 12-peak urine protein signature in type 2 diabetic patients compared with healthy controls with 93% sensitivity and 86% specificity. Interestingly, patients in their study were normalbuminuric (albumin-to-creatinine ratio < 30 mg/g) without renal dysfunction. In a case-control study Rossing using high-resolution two-dimensional gel electrophoresis separation and protein identifications by MALDI-TOF-MS and LC-MS/MS analysis [43] reported correlation between level of a set of proteins, in particular Tamm-Horse fall urinary glycoprotein (THP) and zinc-α-2 glycoprotein (ZA2G), and the state of diabetic progressing in diabetic patients type 1. Jim et al. using ELISA indicated that nephrinuria is detectable in all of type 2 diabetic patients with macroalbuminuria and microalbuminuria and thus nephrine has potential to be a new early biomarker of DN [78]. Capillary electrophoresis-coupled mass spectrometry was used to profile urine proteome of DN patients in a longitudinal study. Altered content of collagen fragments was suggested as a potential early detection biomarker which occurs 3–5 years before onset of macroalbuminuria [79].

The most recent report of protein biomarkers for the stages of DN using iTRAQ suggested a diminished excretion of pancreatic amylase and deoxyribonuclease I [80]. Some of the novel urinary biomarkers for DN reporting in the last two years are tabulated in Table 1.

Protein biomarkersUp/downregulationCohortTechniqueType of biomarkerReference
Pancreatic amylase deoxyribonuclease IHealthy controls (

) and T2DM with microalbuminuria (

) and T2DM with macroalbuminuria (

As biopsy is not performed in all patients suffering from diabetes (due to its invasiveness), DN is diagnosed based on clinical manifestations in some cases and, therefore, treated based on available guideline for DN. Nevertheless, interestingly, kidney injury in some of diabetic patients would not be a result of diabetic nephropathy and other kidney diseases such as membranous nephropathy or focal segmental glomerulosclerosis might also occur in diabetic patients which could not be diagnosed only by clinical manifestations. Therefore, biomarkers sensitive and specific for DN are undoubtedly needed as a noninvasive alternative of kidney biopsy.

7.1.2. IgA Nephropathy

IgA nephropathy (IgAN) is the most common type of glomerulonephritis worldwide that is characterized at biopsy by histological features including mesangial immunodeposits of IgA1 (by immune fluorescence) in association with C3 and IgG or IgM or both [81, 82]. The clinical presentation of IgAN is variable and could be a spectrum of clinical features from asymptomatic hematuria to rapidly progressive glomerulonephritis. Urinary excretion of relevant proteins such as immunoglobulin, cytokines, and complement factor suggest a promising new possibility in the search for noninvasive diagnostic markers in patients with IgAN.

SELDI-TOF as one of the powerful techniques in urinary proteomics was used by some research groups for biomarker discovery of IgAN. Rocchetti et al. analyzed urine proteome of 49 IgA nephropathy patients, 42 CKD (chronic kidney disease) patients, and 40 healthy individuals using this technique followed by MALDI-TOF for identification of differentially proteins and reported Perlecan laminin G-like 3 peptide and Igκ light chains as indicator of disease activity [83].

Julian et al. applied CE-MS for detection of urinary polypeptide biomarkers differentiated patients with IgA nephropathy from other renal diseases including diabetic nephropathy, lupus nephritis, hypertensive renal disease, acute vasculitis with nephritis, and amyloidosis and from subjects with a functional renal allograft [84]. Some of these markers were identified by top-down MS/MS approach (e.g., alpha-1-antitrypsin, collagen type III alpha-1 chain, and uromodulin). In addition, they defined a panel of biomarkers named “Renal Damage Pattern” by comparing pattern obtained from healthy controls and several renal diseases. These markers then correlated with IgA nephropathy pattern using SDS-PAGE/western blotting.

Zhao et al. for first time used SILAC-labeled mouse serum as internal standard for human and urine proteome analysis by IEF-LC-MS/MS. They compared urine from IgAN patients treated and untreated and reported fifty-three peptides that were different between two groups. The authors could find novel candidates like ApoA1 and insulin-like growth factor-binding protein 7 (IGFBP7) for IgAN by this quantitation strategy [85]. Kalantari et al. in a pilot study on urine samples from 13 patients with IgA attempted to find a correlation between proteome data and pathological presentation used for classification of IgAN in biopsy through a high resolution mass spectrometry [46]. They performed two independent proteomic procedures (nano-LC-MS/MS and GeLC-MS/MS) and reported a panel of candidates as prognostic markers including afamin, leucine-rich alpha-2-glycoprotein, ceruloplasmin, alpha-1-microgolbulin, hemopexin, apolipoprotein A-I, complement C3, vitamin D-binding protein, beta-2-microglobulin, and retinol-binding protein 4. The recent study on IgAN with a cohort of 30 patients and 30 controls suggested 18 differential urinary proteins separated and identified by IEF/LC-MS/MS [86]. Most of these reported candidates were complement components, coagulation factors, and extracellular and intracellular matrix and transmembrane. Table 2 shows some of the recent (last 3 years) reported urinary markers for IgAN.

Protein biomarkersUp/downregulationCohortTechniqueType of biomarkerReference
GP2, vasorin, and EGF Healthy controls (
7.1.3. Focal Segmental Glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is a glomerular podocytopathy characterized by massive proteinuria as clinical manifestation and glomerular scaring as histological feature [87, 88]. hypoalbuminemia, hypercholesterolemia, and peripheral edema are associated with proteinuria and considered also clinical manifestations [87]. It is a complex disease categorized as primary (

20%) based on the etiology. Diffuse foot process effacement of podocytes is mostly associated with primary form detected by electron microscopy [88]. Increasing frequency of FSGS in the past 20 years with 50% ESRD rate during 5–8 years from the time of diagnosis in resistant patients to therapy [88, 89] indicates the need of biomarkers for early diagnosis and prediction of responsiveness. Molecular biomarkers are helpful not only in diagnosis but even in understanding the pathogenesis and disease mechanism.

Shui et al. performed a classic proteomic study on urinary biomarkers FSGS mouse model [90]. Serial urine samples was collected on days 0, 4, 7, 11, 15, and 20 and analyzed by two-dimensional electrophoresis followed by MALDI-TOF-MS. They identified 37 proteins changed during the disease course and confirmed few of them via western blot. Collagen IV fragment, glutathione S-transferase, and E-cadherin were suggested as candidates for disease progression. The other candidates for early diagnosis identified in their study are tabulated in Table 3.

Protein biomarkersUp/downregulationCohortTechniqueType of biomarkerReference
Collagen IV, cadherinAD-treated mice (

) for test and relapsing group (

), steroid sensitive nephrotic syndrome (

Wang et al. also studied biomarkers to distinguish between adriamycin nephropathy and Thy1.1 glomerulonephritis in the rat model [10]. Urine proteome was precipitated by acetone and, after protein concentration determination, equal urine proteome from each five sample pooled in four groups and subjected to lectin enrichment. LC-MS/MS analysis on eluted glycoproteome followed by quantification using spectral counting approach resulted in 46 differential proteins including Ig lambda-2 chain C region, protein YIPF3, hemopexin precursor, and 35 other proteins with different direction of changes and serum albumin precursor, isoform 1 of serotransferrin precursor, alpha-1-antiproteinase precursor, T-kininogen 1 precursor, trefoil factor 3 precursor, superoxide dismutase, and urinary protein 1 precursor with similar direction of changes.

SELDI technique was applied by Woroniecki et al. to identify different pattern in urine proteome of healthy control and steroid resistant (SRNS) and steroid sensitive (SSNS) patients with nephrotic syndrome such as MCD and FSGS [91]. Predictive models were constructed by supervised algorithm to differentiate between control and diseased (combination of SSNS and SRNS) and between SSNS and SRNS. Generated model for responsiveness prediction had 100% accuracy and resulted in a differential protein of mass of 4,144 daltons with significant changes as the most important classifier.

Zhao et al. recently performed a study on urine biomarkers which reflect dynamic changes during disease course, analyzed by UPLC coupled with triple-TOF-MS, quantified by label-free quantification, and confirmed by western blot [92]. They examined six stages in ADR-induced rats. Relative abundance of twelve proteins showed an overall increasing trend while nine proteins shared an overall decreasing trend. Fetuin-B and B2-microglobulin changed at the early stage. These markers were further investigated to find human orthologues. Confirmed suggested candidates are shown in Table 3.

A few studies with proteomic approaches have been performed recently for identification biomarkers specially in the urine samples of FSGS patients. In several studies FSGS subjects were not the main target and were considered the control disease. Additional longitudinal studies are required to determine more valuable noninvasive biomarkers for FSGS in the context of early detection, treatment response, and prognosis.

7.1.4. Lupus Nephritis

Lupus nephritis (LN), a common consequence of systemic lupus erythematous (SLE), is associated with significant mortality and morbidity. LN is classified to six classes based on histologic presentations as well as two score indices: activity and chronicity. Currently, renal biopsy beside clinical presentations is used for diagnosis of LN. The challenging part is the treatment which differs totally based on the class and activity and chronicity indices. Therefore, careful diagnosis is critical for treatment. Moreover, current markers for LN such as proteinuria, serum creatinine level, creatinine clearance, complement levels, anti-dsDNA, and antinuclear antibodies are not sensitive or specific enough and diagnosis based on biopsy is sometimes impossible to perform invasively. Therefore, there is a need for some biomarkers which correlate with renal activity to be used for diagnosis of the disease class, early detection before renal failure, and prediction of the prognosis and response. Urinary biomarkers detected by proteomic tools are appropriate candidates to fulfill these issues.

Urinary VCAM-1, CXCL16, P-selectin, and TNFR-1 are diagnostic candidates detected and validated by ELISA [93, 94]. Urinary monocyte chemotactic protein (MCP-1) and TWEAK level have been suggested as an indicator of disease activity also using ELISA [95, 96].

Detection of transferrin, ceruloplasmin, α1-acid-glycoprotein (AGP), lipocalin-type prostaglandin D-synthetase (LPDGS), albumin, and albumin-related fragments in the urine of pediatric lupus nephritis patients using MALDI-TOF is one of the outstanding studies performed by Suzuki et al. [94]. Important points of this report were correlation of these candidates with disease activity and increase in level of some of them (such as transferrin, AGP, and L-PDGS) before clinical presentations of disease flare. Other candidates which were reported as predictor marker of flare were reported later by Lee et al. using SELDI-TOF [97]. They detected increased excretion of hepcidin 20 and albumin fragment (N-terminal region), four months before the flare, and decrease of excretion of hepcidin 25 at the flare time. This technique was also used by Mosley et al. for discrimination between patients with active and inactive forms of lupus nephritis [98]. The proteins with masses 3340 and 3980 were differentially excreted between these two forms which were not further identified.

A classical 2D electrophoresis followed by MALDI-TOF by Oates et al. showed a list of candidates for diagnosis of the class and chronicity of which highest sensitivity belonged to a-1 acid glycoprotein [99].

7.1.5. Membranous Nephropathy

Membranous nephropathy (MN) is categorized as one of the most common causes of primary glomerular diseases especially in adults [100]. MN is a kidney-specific autoimmune disease in which circulating autoantibodies (such as IgG4) bind to intrinsic antigen on glomerular podocytes (i.e., M-type phospholipase A2 receptor 1in primary form of MN), form immune complex, and deposit in subepithelial area of the basement membrane [101]. Therefore, thickening of glomerular basement membrane due to immune complex depositions is a histologic hallmark under the light microscopy [102]. As other renal diseases, the main reason for scientists’ interest in discovering urinary biomarkers for MN is invasiveness of kidney biopsy and its potential risk of serious complications.

A classic proteomic study on urine samples collected from animal model of passive Heymann nephritis (PHN), which mimics human membranous nephropathy, using 2D-PAGE and SYPRO Ruby staining followed by MALDI-TOF-MS showed a panel of potential biomarkers [103]. Serial urine samples in 6 different time points after the injection with anti-Fx1A were collected. Signaling pathways, glomerular trafficking, and controlling the glomerular permeability altered significantly in the disease course. Despite its good design, lack of enough number of technical replicate could be considered a weak point for their study.

Recent study on urine microvesicles obtained from idiopathic membranous nephropathy (iMN), focal segmental glomerulosclerosis (FSGS) patients, and healthy controls via iTRAQ labeling system revealed twofold increases in lysosome membrane protein-2 (LIMP-2) excretion in iMN group [104]. The pooled iTRAQ8plex samples were fractionated using strong cation exchange (SCX) and reverse phase (RP) columns and analyzed with LTQ orbitrap mass spectrometer. Glomerular expression of LIMP-2 was further investigated using immunofluorescence staining which was consistent with proteomic results.

Apart from importance of biomarkers in prompt diagnosis of this disease because of high rate of ESRD in MN patients (

40%) [105], identification of biomarkers predictive of responsiveness to treatment is also critical. To our knowledge, no investigation using routine proteomic tools (i.e., 2DE or LC coupled to mass spectrometry) is still available however, Irazabal et al. examined the relationship between a panel of known biomarkers with responsiveness of MN patients to rituximab with western blot and ELISA [106]. They suggested urinary IgG (mg/24 h) as a significant predictor for proteinuria changes at first year of therapy while fractional excretion of IgG, urinary alpha 1 microglobulin (Uα1M) (mg/24 h), and urinary retinol binding protein (URBP) (μg/24 h) were predictor of the response at 12 months but not at 24 months.

Applying high-throughput proteomic tools in identification of the predictive biomarkers for MN is a promising approach which needs more effort. Some of the studies on urine proteins aiming at biomarker discovery are tabulated in Table 4.

), minimal change disease (MCD) (

7.1.6. Acute Kidney Injury

Acute kidney injury (AKI) is a complicated condition encompassing wide range of clinical manifestations (e.g., elevated serum creatinine and anuric renal failure) [107]. This disorder has high rate of mortality and morbidity which mostly affected critically ill patients (e.g., patients admitted to ICU) and associated with a sudden fail in renal function [108]. The markers that are currently used in clinic such as serum creatinine and decline in GFR are detected late after kidney injury. Therefore, lack of early markers leading to delay in initiating effective therapy, high risk of mortality, and high risk of ESRD explain the urgent need for AKI biomarkers that could be detected early and noninvasively. Furthermore, identification biomarkers may aid to understanding the mechanism underneath this enigmatic condition. Numbers of studies have been performed using urine proteomics that are reviewed as follows (Table 5).

) from ICU patients as training set, AKI (

) from ICU patients, and AKI (

Nguyen et al. performed a proteomic study on urine samples of sixty patients undergoing cardiopulmonary bypass (CPB) and investigated acute renal injury in this cohort [109]. Urine protein profile of these patients obtained by SELDI-TOF-MS and biomarkers with m/z of 28.5, 43, and 66 kDa were suggested for prediction of AKI at 2 h following CPB with sensitivity and specificity of 100%. This study had promising results and highlighted the potential application of SELDI-TOF in screening patients with AKI risk. Further investigation by this group with proteomic approaches revealed aprotinin as a predictor of AKI, 2 h after initiation of CPB with sensitivity of 92% and specificity of 96% [110]. Metzger et al. performed CE-MS analysis to identify peptides predictive of AKI [111]. They suggested 20 urinary polypeptides specific for AKI and validated them in two independent, blinded ICU and cross-sectional HSCT patient cohorts. In addition, their suggested markers could detect AKI accurately 5 days before the rise of serum creatinine. The data showed overrepresentation of peptides of albumin, α-1-antitrypsin, and β-2-microglobulin and underrepresentation of fibrinogen α and collagens 1α(I) and 1α(III) fragments with accuracy of 91%.

A comprehensive study in terms of study design was performed by Aregger et al. on patients undergoing elective cardiopulmonary bypass [112]. They analyzed protein profile in spot urine samples from thirty-six patients before and after CPB and investigated AKI (according to RIFLE criteria) using 2D-DIGE and MALDI-TOF-MS. Regulated proteins in comparison between patients before and after CPB were inflammation-associated or tubular dysfunction-associated proteins while modified urinary albumin, zinc-alpha-2-glycoprotein (ZAG), and a fragment of adrenomedullin-binding protein were associated with AKI. An independent cohort of 23 patients with and 45 patients without acute kidney injury was further examined for ZAG (as it was nonmodified and nonfragmented) using western blot and ELISA. The positive points of their study were validation in an independent cohort by two methods (WB and ELISA) and definition of comprehensive exclusion criteria including great amount of proteinuria and microhematuria, CKD patients with low GFR, use of radiocontrast media or nonsteroid anti-inflammatory drugs (NSAIDs), and need for urgent surgery.

Further investigation of urinary biomarkers was performed later, on the larger cohort using similar method by this research group. Sixty-four critically ill patients of whom 52 had AKI were analyzed by 2D-DIGE for separation followed by LC-ESI-MS/MS for identification of differential spots [113]. In this study, α-1 microglobulin, α-1 antitrypsin, apolipoprotein D, calreticulin, cathepsin D, CD59, insulin-like growth factor-binding protein 7 (IGFBP-7), and neutrophil gelatinase-associated lipocalin (NGAL) were suggested as candidate markers. IGFBP-7 and NGAL were selected for further validation using ELISA in an independent verification group of 28 patients with and 12 control patients without AKI. IGFBP-7 had better accuracy for prediction of renal outcome in their cohort.

In the most recent study, Bell et al. have investigated the association of nonrenal factors with elevated biomarker levels in AKI using ELISA [114]. They reported NGAL and cystatin C, two famous biomarkers of AKI, as well as urinary [TIMP-2]

[IGFBP7] as poor predictors which could not predict AKI within 12 to 48 hours and might be affected by factors other than AKI. This finding indicates the need for more study of AKI predictive biomarkers despite large number of studies performed so far and the need for more practical and precise study design.

8. Specific Urinary Proteome Database

Identification and characterization of candidate biomarkers which were carefully extracted from bunch of complex and confusing data using appropriate statistical methods are a critical step in a typical study for biomarker discovery. Identification of the most robust urinary protein markers is enhanced by means of databases specific for urine and kidney proteins.

Currently there are a few available databases specific for human urine proteome. A number of urine databases are based on identified proteins derived from tryptic peptides of which MAPU [115] and Sys-BodyFluid [116] are more stated. The other useful urine specific databases are tabulated in Table 6.

Max Planck Institute has provided a proteome database entitled MAPU which consists of different sources such as tear, urine, seminal fluid, and tissue from Homo sapiens and Mus musculus [115]. In the urine part MAPU contains information about 1543 proteins which were separated and fractionated using one-dimensional SDS-PAGE and reverse phase HPLC and analyzed with the LTQ-FT and LTQ-Orbitrap at p.p.m. accuracy after both in-gel and in-solution digestion. It worth to note that approximately half of these deposited proteins are membrane proteins according to gene ontology (GO) analysis.

Sys-BodyFluid is a comprehensive proteome database which is composed of 11 body fluid proteomes including urine [116]. The data deposited in this database come from 50 peer-review publications of different laboratories across the world. Information and annotation of proteins are description, gene ontology, domain information, protein sequence, and involved pathway.

Mosaiques diagnostics database is known as peptidome urinary database including 13027 urine samples taken from both diseased and healthy subjects that is obtained by analytical platform CE-MS [117]. Another part of the urinary Mosaiques database is biomarker sequence information which was obtained for 953 peptides that are deriving from 116 different proteins [115, 117].

Human urinary proteomic fingerprint database (UPdb) was established in 2013, using urine samples from 200 individuals analyzed by SELDI-MS on several chip surfaces (SEND, HP50, NP20, Q10, CM10, and IMAC30). The database lists 2490 unique peaks/ion species from 1172 nonredundant SELDI analyses as well as 1384 included peaks from external studies using CE-MS, MALDI, and CE-MALDI hybrids. The database provides information relating to the MS environment, subfractionation methods, chromatography setups, studied diseases, identified biomarker, statistical information, and identified proteins.

9. Conclusion

Urine is a valuable source of molecules which is capable of being diagnostic markers specially for renal diseases. The strength of urine in comparison to plasma and tissue samples is the noninvasive collection procedure and less complex protein content. The complications in biopsy based diagnosis (i.e., invasiveness, dependence of diagnosis on pathologist tact and observation, limitations due to infection, hypertension, and kidney size) make urinary biomarkers a safe reliable complement alternative way for diagnosis beside traditional biopsy. In addition, lack of limitation in amount of specimen at the time of collection and relatively stable content of peptides and proteins because of complete proteolytic process by endogenous proteases during the storage in the bladder make urine an ideal specimen for biomarker researches. As urinary proteins are mainly originating from kidney tissue (

70%) (remaining 30% derived from plasma), therefore urine is the most appropriate sample for biomarker discovery in renal disease, urogenital track, and vascular system.

However, molecular biomarkers have not become practical in clinics yet, and extensive attempts have been devoted to validate these molecular markers. Proteomic techniques beside advanced statistical analysis and bioinformatics knowledge are versatile tools in urinary biomarker discovery. It is expected that advances in analytical tools and software programs as well as accurate study design in the near future will improve sensitivity and specificity of available biomarkers.

Abbreviations

IgA:Immunoglobulin A
2D:Two-dimensional
2DE-MS:Two-dimensional gel electrophoresis followed by mass spectrometry
LC-MS:Liquid chromatography coupled to mass spectrometry
CE-MS:Capillary electrophoresis coupled to mass spectrometry
2D-DIGE:Two-dimensional difference gel electrophoresis
LC:Liquid chromatography
SCX:Strong cation exchange column
FTICR:Fourier transform ion cyclotron resonance
SELDI-TOF:Surface-enhanced laser desorption/ionization time of flight
DN:Diabetic nephropathy
MA:Microalbuminuria
THP:Tamm-Horse fall urinary glycoprotein
ZA2G:Zinc-α-2 glycoprotein
IgAN:IgA nephropathy
IGFBP7:Insulin-like growth factor-binding protein 7
TEC:Tubular epithelial cell
LN:Lupus nephritis
SLE:Systemic lupus erythematous
MCP-1:Monocyte chemotactic protein
AGP:α1-acid-glycoprotein
LPDGS:Lipocalin-type prostaglandin D-synthetase
CVD:Cardiovascular disease
CAD:Coronary artery disease
HF:Heart failure
NCD:Noncardiac dyspnea patients
CHF:Chronic heart failure
AHF:Acute heart failure
IGFBP2:Insulin-like growth factor binding protein 2
NF-κB:Nuclear factor

Disclosure

The authors namely A. Jafari, R. Moradpoor, E. Ghasemi, and E. Khalkhal are Ph.D. students in applied proteomics. S. Kalantari is assistant professor in chronic kidney disease research center at Shahid Beheshti University of Medical Sciences in Tehran.

Conflict of Interests

There is no competing interests regarding the publication of this paper.

Authors’ Contribution

R. Moradpoor, A. Jafari, E. Ghasemi, and E. Khalkhal contributed equally to writing this paper and researching the data. S. Kalantari contributed to conceive, design, writing, and edition of the paper. All authors read and approved the final paper.

Acknowledgment

The authors are grateful to Chronic Kidney Disease Research Center for help and support.

References

  1. J. Adachi, C. Kumar, Y. Zhang, J. V. Olsen, and M. Mann, “The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins,” Genome Biology, vol. 7, article R80, 2006. View at: Publisher Site | Google Scholar
  2. H. Husi, N. Stephens, A. Cronshaw et al., “Proteomic analysis of urinary upper gastrointestinal cancer markers,” PROTEOMICS𠅌linical Applications, vol. 5, no. 5-6, pp. 289–299, 2011. View at: Publisher Site | Google Scholar
  3. V. C. Wasinger, M. Zeng, and Y. Yau, “Current status and advances in quantitative proteomic mass spectrometry,” International Journal of Proteomics, vol. 2013, Article ID 180605, 12 pages, 2013. View at: Publisher Site | Google Scholar
  4. D. M. Good, V. Thongboonkerd, J. Novak et al., “Body fluid proteomics for biomarker discovery: lessons from the past hold the key to success in the future,” Journal of Proteome Research, vol. 6, no. 12, pp. 4549–4555, 2007. View at: Publisher Site | Google Scholar
  5. S. Cui, P. J. Verroust, S. K. Moestrup, and E. I. Christensen, “Megalin/gp330 mediates uptake of albumin in renal proximal tubule,” American Journal of Physiology—Renal Fluid and Electrolyte Physiology, vol. 271, no. 4, pp. F900–F907, 1996. View at: Google Scholar
  6. T. Pisitkun, R.-F. Shen, and M. A. Knepper, “Identification and proteomic profiling of exosomes in human urine,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 36, pp. 13368–13373, 2004. View at: Publisher Site | Google Scholar
  7. A. Castagna, D. Cecconi, L. Sennels et al., “Exploring the hidden human urinary proteome via ligand library beads,” Journal of Proteome Research, vol. 4, no. 6, pp. 1917–1930, 2005. View at: Publisher Site | Google Scholar
  8. R. Pieper, C. L. Gatlin, A. M. McGrath et al., “Characterization of the human urinary proteome: a method for high-resolution display of urinary proteins on two-dimensional electrophoresis gels with a yield of nearly 1400 distinct protein spots,” Proteomics, vol. 4, no. 4, pp. 1159–1174, 2004. View at: Publisher Site | Google Scholar
  9. W. Sun, F. Li, S. Wu et al., “Human urine proteome analysis by three separation approaches,” Proteomics, vol. 5, no. 18, pp. 4994–5001, 2005. View at: Publisher Site | Google Scholar
  10. L. Wang, F. Li, W. Sun et al., “Concanavalin A-captured glycoproteins in healthy human urine,” Molecular and Cellular Proteomics, vol. 5, no. 3, pp. 560–562, 2006. View at: Publisher Site | Google Scholar
  11. M. Ożgo, W. F. Skrzypczak, A. Herosimczyk, and A. Mazur, “Proteomika a fizjologia i patofizjologia nerek,” MedWet, vol. 63, pp. 1146–1150, 2007. View at: Google Scholar
  12. B. Haraldsson and J. Sörensson, “Why do we not all have proteinuria? An update of our current understanding of the glomerular barrier,” News in Physiological Sciences, vol. 19, no. 1, pp. 7–10, 2004. View at: Publisher Site | Google Scholar
  13. A. B. Maunsbach, “Absorption of I125-labeled homologous albumin by rat kidney proximal tubule cells. A study of microperfused single proximal tubules by electron microscopic autoradiography and histochemistry, 1966,” Journal of the American Society of Nephrology, vol. 8, no. 2, pp. 323–351, 1997. View at: Google Scholar
  14. M. J. Burne, T. M. Osicka, and W. D. Comper, “Fractional clearance of high molecular weight proteins in conscious rats using a continuous infusion method,” Kidney International, vol. 55, no. 1, pp. 261–270, 1999. View at: Publisher Site | Google Scholar
  15. V. Batuman, P. J. Verroust, G. L. Navar et al., “Myeloma light chains are ligands for cubilin (gp280),” American Journal of Physiology—Renal Physiology, vol. 275, no. 2, pp. F246–F254, 1998. View at: Google Scholar
  16. E. I. Christensen and J. Gburek, “Protein reabsorption in renal proximal tubule𠅏unction and dysfunction in kidney pathophysiology,” Pediatric Nephrology, vol. 19, no. 7, pp. 714–721, 2004. View at: Publisher Site | Google Scholar
  17. M. Afkarian, M. Bhasin, S. T. Dillon et al., “Optimizing a proteomics platform for urine biomarker discovery,” Molecular and Cellular Proteomics, vol. 9, no. 10, pp. 2195–2204, 2010. View at: Publisher Site | Google Scholar
  18. J. Peng and S. P. Gygi, “Proteomics: the move to mixtures,” Journal of Mass Spectrometry, vol. 36, no. 10, pp. 1083–1091, 2001. View at: Publisher Site | Google Scholar
  19. V. Thongboonkerd, “Proteomics in nephrology: current status and future directions,” American Journal of Nephrology, vol. 24, no. 3, pp. 360–378, 2004. View at: Publisher Site | Google Scholar
  20. A. Marimuthu, R. N. O'Meally, R. Chaerkady et al., “A comprehensive map of the human urinary proteome,” Journal of Proteome Research, vol. 10, no. 6, pp. 2734–2743, 2011. View at: Publisher Site | Google Scholar
  21. Q.-R. Li, K.-X. Fan, R.-X. Li et al., “A comprehensive and nonprefractionation on the protein level approach for the human urinary proteome: touching phosphorylation in urine,” Rapid Communications in Mass Spectrometry, vol. 24, no. 6, pp. 823–832, 2010. View at: Publisher Site | Google Scholar
  22. P. A. Gonzales, T. Pisitkun, J. D. Hoffert et al., “Large-scale proteomics and phosphoproteomics of urinary exosomes,” Journal of the American Society of Nephrology, vol. 20, no. 2, pp. 363–379, 2009. View at: Publisher Site | Google Scholar
  23. A. Khan and N. H. Packer, “Simple urinary sample preparation for proteomic analysis,” Journal of Proteome Research, vol. 5, no. 10, pp. 2824–2838, 2006. View at: Publisher Site | Google Scholar
  24. V. Thongboonkerd, S. Chutipongtanate, and R. Kanlaya, “Systematic evaluation of sample preparation methods for gel-based human urinary proteomics: quantity, quality, and variability,” Journal of Proteome Research, vol. 5, no. 1, pp. 183–191, 2006. View at: Publisher Site | Google Scholar
  25. G. Gopalan, V. S. Rao, and V. V. Kakar, “An overview of urinary proteomics applications in human diseases,” International Journal of High Throughput Screening, vol. 1, pp. 183–192, 2010. View at: Google Scholar
  26. H.-Y. Tang, L. A. Beer, and D. W. Speicher, “In-depth analysis of a plasma or serum proteome using a 4D protein profiling method,” Methods in Molecular Biology, vol. 728, pp. 47–67, 2011. View at: Publisher Site | Google Scholar
  27. N. S. Vasudev, R. E. Ferguson, D. A. Cairns, A. J. Stanley, P. J. Selby, and R. E. Banks, “Serum biomarker discovery in renal cancer using 2-DE and prefractionation by immunodepletion and isoelectric focusing increasing coverage or more of the same?” Proteomics, vol. 8, no. 23-24, pp. 5074–5085, 2008. View at: Publisher Site | Google Scholar
  28. C.-L. Chen, T.-S. Lin, C.-H. Tsai et al., “Identification of potential bladder cancer markers in urine by abundant-protein depletion coupled with quantitative proteomics,” Journal of Proteomics, vol. 85, pp. 28–43, 2013. View at: Publisher Site | Google Scholar
  29. S. Filip, K. Vougas, J. Zoidakis et al., “Comparison of depletion strategies for the enrichment of low-abundance proteins in urine,” PLoS ONE, vol. 10, no. 7, Article ID e0133773, 2015. View at: Publisher Site | Google Scholar
  30. C.-M. Lu, Y.-J. Wu, C.-C. Chen et al., “Identification of low-abundance proteins via fractionation of the urine proteome with weak anion exchange chromatography,” Proteome Science, vol. 9, article 17, 2011. View at: Publisher Site | Google Scholar
  31. S. A. Beausoleil, M. Jedrychowski, D. Schwartz et al., “Large-scale characterization of HeLa cell nuclear phosphoproteins,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 33, pp. 12130–12135, 2004. View at: Publisher Site | Google Scholar
  32. V. Thongboonkerd, T. Semangoen, and S. Chutipongtanate, “Enrichment of the basic/cationic urinary proteome using ion exchange chromatography and batch adsorption,” Journal of Proteome Research, vol. 6, no. 3, pp. 1209–1214, 2007. View at: Publisher Site | Google Scholar
  33. M. M. Kushnir, P. Mrozinski, A. L. Rockwood, and D. K. Crockett, “A depletion strategy for improved detection of human proteins from urine,” Journal of Biomolecular Techniques, vol. 20, no. 2, pp. 101–108, 2009. View at: Google Scholar
  34. V. Thongboonkerd, “Current status of renal and urinary proteomics: ready for routine clinical application,” Nephrology Dialysis Transplantation, vol. 25, no. 1, pp. 11–16, 2010. View at: Publisher Site | Google Scholar
  35. S. Decramer, A. G. de Peredo, B. Breuil et al., “Urine in clinical proteomics,” Molecular and Cellular Proteomics, vol. 7, no. 10, pp. 1850–1862, 2008. View at: Publisher Site | Google Scholar
  36. J. Jia and L. Zhang, “Advance in proteomics research and application,” Journal of Animal and Veterinary Advances, vol. 11, no. 20, pp. 3812–3817, 2012. View at: Publisher Site | Google Scholar
  37. H. Dihazi, “The urinary proteomics: a tool to discover new and potent biomarkers for kidney damage,” Journal of the International Federation of Clinical Chemistry and Laboratory Medicine, vol. 20, no. 1, pp. 82–91, 2009. View at: Google Scholar
  38. J. M. González-Buitrago, L. Ferreira, and I. Lorenzo, “Urinary proteomics,” Clinica Chimica Acta, vol. 375, no. 1-2, pp. 49–56, 2007. View at: Publisher Site | Google Scholar
  39. M. M. Camacho-Carvcajal, B. Wollscheid, R. Aebersold, V. Steimle, and W. W. A. Schamel, “Two-dimensional Blue Native/SDS gel electrophoresiss of multi-protein complexes from whole cellular lysates: a proteomics approach,” Molecular and Cellular Proteomics, vol. 3, no. 2, pp. 176–182, 2004. View at: Publisher Site | Google Scholar
  40. I. Neverova and J. E. Van Eyk, “Role of chromatographic techniques in proteomic analysis,” Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, vol. 815, no. 1-2, pp. 51–63, 2005. View at: Publisher Site | Google Scholar
  41. T. Niwa, “Biomarker discovery for kidney diseases by mass spectrometry,” Journal of Chromatography B, vol. 870, no. 2, pp. 148–153, 2008. View at: Publisher Site | Google Scholar
  42. M. G. Janech, J. R. Raymond, and J. M. Arthur, “Proteomics in renal research,” The American Journal of Physiology—Renal Physiology, vol. 292, no. 2, pp. F501–F512, 2007. View at: Publisher Site | Google Scholar
  43. P. Rossing, “The changing epidemiology of diabetic microangiopathy in type 1 diabetes,” Diabetologia, vol. 48, no. 8, pp. 1439–1444, 2005. View at: Publisher Site | Google Scholar
  44. G. Soldatos and M. E. Cooper, “Diabetic nephropathy: important pathophysiologic mechanisms,” Diabetes Research and Clinical Practice, vol. 82, no. 1, pp. S75–S79, 2008. View at: Publisher Site | Google Scholar
  45. A. Soggiu, C. Piras, L. Bonizzi, H. A. Hussein, S. Pisanu, and P. Roncada, “A discovery-phase urine proteomics investigation in type 1 diabetes,” Acta Diabetologica, vol. 49, no. 6, pp. 453–464, 2012. View at: Publisher Site | Google Scholar
  46. S. Kalantari, D. Rutishauser, S. Samavat et al., “Urinary prognostic biomarkers and classification of IgA nephropathy by high resolution mass spectrometry coupled with liquid chromatography,” PLoS ONE, vol. 8, no. 12, Article ID e80830, 2013. View at: Publisher Site | Google Scholar
  47. B. Xin, D. Pu, S. X. Xiong, and G. H. Wang, “On-line separation and detection of peptides by capillary electrophoresis/electrospray FT-ICR-MS,” Chinese Chemical Letters, vol. 14, pp. 191–194, 2003. View at: Google Scholar
  48. D. Fliser, J. Novak, V. Thongboonkerd et al., “Advances in urinary proteome analysis and biomarker discovery,” Journal of the American Society of Nephrology, vol. 18, no. 4, pp. 1057–1071, 2007. View at: Publisher Site | Google Scholar
  49. Z. Liu, Z. Yuan, and Q. Zhao, “SELDI-TOF-MS proteomic profiling of serum, urine, and amniotic fluid in neural tube defects,” PLoS ONE, vol. 9, no. 7, Article ID e103276, 2014. View at: Publisher Site | Google Scholar
  50. J. Siwy, A. Vlahou, L. U. Zimmerli, P. Zürbig, and E. Schiffer, “Clinical proteomics: current techniques and potential applications in the elderly,” Maturitas, vol. 68, no. 3, pp. 233–244, 2011. View at: Publisher Site | Google Scholar
  51. H. Mischak, E. Schiffer, P. Zürbig, M. Dakna, and J. Metzger, “Urinary proteome analysis using capillary electrophoresis coupled to mass spectrometry: a powerful tool in clinical diagnosis, prognosis and therapy evaluation,” Journal of Medical Biochemistry, vol. 28, no. 4, pp. 223–234, 2009. View at: Publisher Site | Google Scholar
  52. G. A. Müller, C. A. Müller, and H. Dihazi, “Clinical proteomics—on the long way from bench to bedside?” Nephrology Dialysis Transplantation, vol. 22, no. 5, pp. 1297–1300, 2007. View at: Publisher Site | Google Scholar
  53. A. Albalat, H. Mischak, and W. Mullen, “Urine proteomics in clinical applications: technologies, principal considerations and clinical implementation,” Prilozi, vol. 32, pp. 44–45, 2011. View at: Google Scholar
  54. M. M. Nilsen, K.-E. Uleberg, E. A. M. Janssen, J. P. A. Baak, O. K. Andersen, and A. Hjelle, “From SELDI-TOF MS to protein identification by on-chip elution,” Journal of Proteomics, vol. 74, no. 12, pp. 2995–2998, 2011. View at: Publisher Site | Google Scholar
  55. C. Ibá༞z, C. Simó, V. Garc໚-Ca༚s, A. Cifuentes, and M. Castro-Puyana, “Metabolomics, peptidomics and proteomics applications of capillary electrophoresis-mass spectrometry in Foodomics: a review,” Analytica Chimica Acta, vol. 802, pp. 1–13, 2013. View at: Publisher Site | Google Scholar
  56. H. Mischak, J. J. Coon, J. Novak, E. M. Weissinger, J. P. Schanstra, and A. F. Dominiczak, “Capillary electrophoresis-mass spectrometry as a powerful tool in biomarker discovery and clinical diagnosis: an update of recent developments,” Mass Spectrometry Reviews, vol. 28, no. 5, pp. 703–724, 2009. View at: Publisher Site | Google Scholar
  57. J. Wu, Y.-D. Chen, and W. Gu, “Urinary proteomics as a novel tool for biomarker discovery in kidney diseases,” Journal of Zhejiang University: Science B, vol. 11, no. 4, pp. 227–237, 2010. View at: Publisher Site | Google Scholar
  58. C. Simó, A. Cifuentes, and V. Kašička, “Capillary electrophoresis-mass spectrometry for peptide analysis: target-based approaches and proteomics/ peptidomics strategies,” Methods in Molecular Biology, vol. 984, pp. 139–151, 2013. View at: Publisher Site | Google Scholar
  59. S. Hu, J. A. Loo, and D. T. Wong, “Human body fluid proteome analysis,” Proteomics, vol. 6, no. 23, pp. 6326–6353, 2006. View at: Publisher Site | Google Scholar
  60. P. Dໞz, N. Dasilva, M. González-González et al., “Data analysis strategies for protein microarrays,” Microarrays, vol. 1, pp. 64–83, 2012. View at: Google Scholar
  61. R. Chen and M. Snyder, “Yeast proteomics and protein microarrays,” Journal of Proteomics, vol. 73, no. 11, pp. 2147–2157, 2010. View at: Publisher Site | Google Scholar
  62. W. Ruige and Y. S. Fung, “Microfluidic chip-capillary electrophoresis device for the determination of urinary metabolites and proteins,” Bioanalysis, vol. 7, pp. 907–922, 2015. View at: Google Scholar
  63. W. P. Guo, Z. B. Rong, Y. H. Li, Y. S. Fung, G. Q. Gao, and Z. M. Cai, “Microfluidic chip capillary electrophoresis coupled with electrochemiluminescence for enantioseparation of racemic drugs using central composite design optimization,” Electrophoresis, vol. 34, no. 20-21, pp. 2962–2969, 2013. View at: Publisher Site | Google Scholar
  64. C.-C. Lin, C.-C. Tseng, T.-K. Chuang, D.-S. Lee, and G.-B. Lee, “Urine analysis in microfluidic devices,” Analyst, vol. 136, no. 13, pp. 2669–2688, 2011. View at: Publisher Site | Google Scholar
  65. J. R. Wiśniewski, A. Zougman, N. Nagaraj, and M. Mann, “Universal sample preparation method for proteome analysis,” Nature Methods, vol. 6, no. 5, pp. 359–362, 2009. View at: Publisher Site | Google Scholar
  66. Y. Yu, M.-J. Suh, P. Sikorski, K. Kwon, K. E. Nelson, and R. Pieper, “Urine sample preparation in 96-well filter plates for quantitative clinical proteomics,” Analytical Chemistry, vol. 86, no. 11, pp. 5470–5477, 2014. View at: Publisher Site | Google Scholar
  67. Y. Lyutvinskiy, H. Yang, D. Rutishauser, and R. A. Zubarev, “In silico instrumental response correction improves precision of label-free proteomics and accuracy of proteomics-based predictive models,” Molecular and Cellular Proteomics, vol. 12, no. 8, pp. 2324–2331, 2013. View at: Publisher Site | Google Scholar
  68. S. Samavat, S. Kalantari, M. Nafar et al., “Diagnostic urinary proteome profile for immunoglobulin a nephropathy,” Iranian Journal of Kidney Diseases, vol. 9, pp. 239–248, 2015. View at: Google Scholar
  69. S. Kalantari, M. Nafar, D. Rutishauser et al., “Predictive urinary biomarkers for steroid-resistant and steroid-sensitive focal segmental glomerulosclerosis using high resolution mass spectrometry and multivariate statistical analysis,” BMC Nephrology, vol. 15, no. 1, article 141, 2014. View at: Publisher Site | Google Scholar
  70. S. Kalantari, M. Nafar, S. Samavat, M. Rezaei-Tavirani, D. Rutishauser, and R. Zubarev, “Urinary prognostic biomarkers in patients with focal segmental glomerulosclerosis,” Nephro-Urology Monthly, vol. 6, no. 2, Article ID e16806, 2014. View at: Publisher Site | Google Scholar
  71. M. Nafar, S. Kalantari, S. Samavat, M. Rezaei-Tavirani, D. Rutishuser, and R. A. Zubarev, “The novel diagnostic biomarkers for focal segmental glomerulosclerosis,” International Journal of Nephrology, vol. 2014, Article ID 574261, 10 pages, 2014. View at: Publisher Site | Google Scholar
  72. J. Cox, M. Y. Hein, C. A. Luber, I. Paron, N. Nagaraj, and M. Mann, “Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ,” Molecular and Cellular Proteomics, vol. 13, no. 9, pp. 2513–2526, 2014. View at: Publisher Site | Google Scholar
  73. K. A. Neilson, N. A. Ali, S. Muralidharan et al., “Less label, more free: approaches in label-free quantitative mass spectrometry,” Proteomics, vol. 11, no. 4, pp. 535–553, 2011. View at: Publisher Site | Google Scholar
  74. S. Nahnsen, C. Bielow, K. Reinert, and O. Kohlbacher, “Tools for label-free peptide quantification,” Molecular and Cellular Proteomics, vol. 12, no. 3, pp. 549–556, 2013. View at: Publisher Site | Google Scholar
  75. L. Su, R. Zhou, C. Liu et al., “Urinary proteomics analysis for sepsis biomarkers with iTRAQ labeling and two-dimensional liquid chromatography-tandem mass spectrometry,” Journal of Trauma and Acute Care Surgery, vol. 74, no. 3, pp. 940–945, 2013. View at: Publisher Site | Google Scholar
  76. M. T. Davis, C. S. Spahr, M. D. McGinley et al., “Towards defining the urinary proteome using liquid chromatography-tandem mass spectrometry II. Limitations of complex mixture analyses,” Proteomics, vol. 1, no. 1, pp. 108–117, 2001. View at: Publisher Site | Google Scholar
  77. H. Mischak, J. P. A. Ioannidis, A. Argiles et al., “Implementation of proteomic biomarkers: making it work,” European Journal of Clinical Investigation, vol. 42, no. 9, pp. 1027–1036, 2012. View at: Publisher Site | Google Scholar
  78. B. Jim, M. Ghanta, A. Qipo et al., “Dysregulated nephrin in diabetic nephropathy of type 2 diabetes: a cross sectional study,” PLoS ONE, vol. 7, no. 5, Article ID e36041, 2012. View at: Publisher Site | Google Scholar
  79. P. Zürbig, G. Jerums, P. Hovind et al., “Urinary proteomics for early diagnosis in diabetic nephropathy,” Diabetes, vol. 61, no. 12, pp. 3304–3313, 2012. View at: Publisher Site | Google Scholar
  80. A. Lewandowicz, M. Bakun, R. Kohutnicki et al., “Changes in urine proteome accompanying diabetic nephropathy progression,” Polskie Archiwum Medycyny Wewnetrznej, vol. 125, pp. 27–38, 2015. View at: Google Scholar
  81. J. Barratt and J. Feehally, “IgA nephropathy,” Journal of the American Society of Nephrology, vol. 16, no. 7, pp. 2088–2097, 2005. View at: Publisher Site | Google Scholar
  82. B. A. Julian, S. Wittke, M. Haubitz et al., “Urinary biomarkers of IgA nephropathy and other IgA-associated renal diseases,” World Journal of Urology, vol. 25, no. 5, pp. 467–476, 2007. View at: Publisher Site | Google Scholar
  83. M. T. Rocchetti, M. Papale, A. M. d'Apollo et al., “Association of urinary laminin G-like 3 and free K light chains with disease activity and histological injury in IgA nephropathy,” Clinical Journal of the American Society of Nephrology, vol. 8, no. 7, pp. 1115–1125, 2013. View at: Publisher Site | Google Scholar
  84. B. A. Julian, S. Wittke, J. Novak et al., “Electrophoretic methods for analysis of urinary polypeptides in lgA-associated renal diseases,” Electrophoresis, vol. 28, no. 23, pp. 4469–4483, 2007. View at: Publisher Site | Google Scholar
  85. S. Zhao, R. Li, X. Cai et al., “The application of SILAC mouse in human body fluid proteomics analysis reveals protein patterns associated with IgA nephropathy,” Evidence-Based Complementary and Alternative Medicine, vol. 2013, Article ID 275390, 10 pages, 2013. View at: Publisher Site | Google Scholar
  86. K. Mucha, M. Bakun, R. Jaźwiec et al., “Complement components, proteolysis-related, and cell communication-related proteins detected in urine proteomics are associated with IgA nephropathy,” Polskie Archiwum Medycyny Wewnetrznej, vol. 124, no. 7-8, pp. 380–386, 2014. View at: Google Scholar
  87. V. D. D'Agati, F. J. Kaskel, and R. J. Falk, “Focal segmental glomerulosclerosis,” The New England Journal of Medicine, vol. 365, no. 25, pp. 2398–2411, 2011. View at: Publisher Site | Google Scholar
  88. B. Bose and D. Cattran, “Glomerular diseases: FSGS,” Clinical Journal of the American Society of Nephrology, vol. 9, no. 3, pp. 626–632, 2014. View at: Publisher Site | Google Scholar
  89. C. Kitiyakara, J. B. Kopp, and P. Eggers, “Trends in the epidemiology of focal segmental glomerulosclerosis,” Seminars in Nephrology, vol. 23, no. 2, pp. 172–182, 2003. View at: Publisher Site | Google Scholar
  90. H.-A. Shui, T.-H. Huang, S.-M. Ka, P.-H. Chen, Y.-F. Lin, and A. Chen, “Urinary proteome and potential biomarkers associated with serial pathogenesis steps of focal segmental glomerulosclerosis,” Nephrology Dialysis Transplantation, vol. 23, no. 1, pp. 176–185, 2008. View at: Publisher Site | Google Scholar
  91. R. P. Woroniecki, T. N. Orlova, N. Mendelev et al., “Urinary proteome of steroid-sensitive and steroid-resistant idiopathic nephrotic syndrome of childhood,” American Journal of Nephrology, vol. 26, no. 3, pp. 258–267, 2006. View at: Publisher Site | Google Scholar
  92. M. Zhao, M. Li, X. Li, C. Shao, J. Yin, and Y. Gao, “Dynamic changes of urinary proteins in a focal segmental glomerulosclerosis rat model,” Proteome Science, vol. 12, article 42, 2014. View at: Publisher Site | Google Scholar
  93. T. Wu, C. Xie, H. W. Wang et al., “Elevated urinary VCAM-1, P-selectin, soluble TNF receptor-1, and CXC chemokine ligand 16 in multiple murine lupus strains and human lupus nephritis,” Journal of Immunology, vol. 179, no. 10, pp. 7166–7175, 2007. View at: Publisher Site | Google Scholar
  94. M. Suzuki, K. Wiers, E. B. Brooks et al., “Initial validation of a novel protein biomarker panel for active pediatric lupus nephritis,” Pediatric Research, vol. 65, no. 5, pp. 530–536, 2009. View at: Publisher Site | Google Scholar
  95. R. F. Rosa, K. Takei, N. C. Araújo, S. M. A. Loduca, J. C. M. Szajubok, and W. H. Chahade, “Monocyte chemoattractant-1 as a urinary biomarker for the diagnosis of activity of lupus nephritis in Brazilian patients,” The Journal of Rheumatology, vol. 39, no. 10, pp. 1948–1954, 2012. View at: Publisher Site | Google Scholar
  96. Z. Xuejing, T. Jiazhen, L. Jun, X. Xiangqing, Y. Shuguang, and L. Fuyou, “Urinary TWEAK level as a marker of lupus nephritis activity in 46 cases,” Journal of Biomedicine and Biotechnology, vol. 2012, Article ID 359647, 7 pages, 2012. View at: Publisher Site | Google Scholar
  97. P. Lee, H. Peng, T. Gelbart, L. Wang, and E. Beutler, “Regulation of hepcidin transcription by interleukin-1 and interleukin-6,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 6, pp. 1906–1910, 2005. View at: Publisher Site | Google Scholar
  98. K. Mosley, F. W. K. Tam, R. J. Edwards, J. Crozier, C. D. Pusey, and L. Lightstone, “Urinary proteomic profiles distinguish between active and inactive lupus nephritis,” Rheumatology, vol. 45, no. 12, pp. 1497–1504, 2006. View at: Publisher Site | Google Scholar
  99. J. C. Oates, S. Varghese, A. M. Bland et al., “Prediction of urinary protein markers in lupus nephritis,” Kidney International, vol. 68, no. 6, pp. 2588–2592, 2005. View at: Publisher Site | Google Scholar
  100. W. Sui, R. Zhang, J. Chen et al., “Comparative proteomic analysis of membranous nephropathy biopsy tissues using quantitative proteomics,” Experimental and Therapeutic Medicine, vol. 9, no. 3, pp. 805–810, 2015. View at: Publisher Site | Google Scholar
  101. L. H. Beck Jr. and D. J. Salant, “Membranous nephropathy: forms models to man,” Journal of Clinical Investigation, vol. 124, no. 6, pp. 2307–2314, 2014. View at: Publisher Site | Google Scholar
  102. L. H. Beck Jr. and D. J. Salant, “Membranous nephropathy: recent travels and new roads ahead,” Kidney International, vol. 77, no. 9, pp. 765–770, 2010. View at: Publisher Site | Google Scholar
  103. H. H.-Y. Ngai, W.-H. Sit, P.-P. Jiang, R.-J. Xu, J. M.-F. Wan, and V. Thongboonkerd, “Serial changes in urinary proteome profile of membranous nephropathy: implications for pathophysiology and biomarker discovery,” Journal of Proteome Research, vol. 5, no. 11, pp. 3038–3047, 2006. View at: Publisher Site | Google Scholar
  104. I. M. Rood, M. L. Merchant, D. W. Wilkey et al., “Increased expression of lysosome membrane protein 2 in glomeruli of patients with idiopathic membranous nephropathy,” Proteomics, vol. 15, no. 21, pp. 3722–3730, 2015. View at: Publisher Site | Google Scholar
  105. P. Ruggenenti, C. Chiurchiu, V. Brusegan et al., “Rituximab in idiopathic membranous nephropathy: a one-year prospective study,” Journal of the American Society of Nephrology, vol. 14, no. 7, pp. 1851–1857, 2003. View at: Publisher Site | Google Scholar
  106. M. V. Irazabal, A. Eirin, J. Lieske et al., “Low-and high-molecular-weight urinary proteins as predictors of response to rituximab in patients with membranous nephropathy: a prospective study,” Nephrology Dialysis Transplantation, vol. 28, no. 1, pp. 137–146, 2013. View at: Publisher Site | Google Scholar
  107. R. L. Mehta, J. A. Kellum, S. V. Shah et al., “Acute kidney injury network: report of an initiative to improve outcomes in acute kidney injury,” Critical Care, vol. 11, article R31, 2007. View at: Publisher Site | Google Scholar
  108. S. Herget-Rosenthal, J. Metzger, A. Albalat, V. Bitsika, and H. Mischak, “Proteomic biomarkers for the early detection of acute kidney injury,” Prilozi, vol. 33, pp. 27–48, 2012. View at: Google Scholar
  109. M. T. Nguyen, G. F. Ross, C. L. Dent, and P. Devarajan, “Early prediction of acute renal injury using urinary proteomics,” American Journal of Nephrology, vol. 25, no. 4, pp. 318–326, 2005. View at: Publisher Site | Google Scholar
  110. M. T. Nguyen, C. L. Dent, G. F. Ross et al., “Urinary aprotinin as a predictor of acute kidney injury after cardiac surgery in children receiving aprotinin therapy,” Pediatric Nephrology, vol. 23, no. 8, pp. 1317–1326, 2008. View at: Publisher Site | Google Scholar
  111. J. Metzger, T. Kirsch, E. Schiffer et al., “Urinary excretion of twenty peptides forms an early and accurate diagnostic pattern of acute kidney injury,” Kidney International, vol. 78, no. 12, pp. 1252–1262, 2010. View at: Publisher Site | Google Scholar
  112. F. Aregger, C. Pilop, D. E. Uehlinger et al., “Urinary proteomics before and after extracorporeal circulation in patients with and without acute kidney injury,” Journal of Thoracic and Cardiovascular Surgery, vol. 139, no. 3, pp. 692–700, 2010. View at: Publisher Site | Google Scholar
  113. F. Aregger, D. E. Uehlinger, J. Witowski et al., “Identification of IGFBP-7 by urinary proteomics as a novel prognostic marker in early acute kidney injury,” Kidney International, vol. 85, no. 4, pp. 909–919, 2014. View at: Publisher Site | Google Scholar
  114. M. Bell, A. Larsson, P. Venge, R. Bellomo, and J. Mårtensson, “Assessment of cell-cycle arrest biomarkers to predict early and delayed acute kidney injury,” Disease Markers, vol. 2015, Article ID 158658, 9 pages, 2015. View at: Publisher Site | Google Scholar
  115. Y. Zhang, Y. Zhang, J. Adachi et al., “MAPU: max-planck unified database of organellar, cellular, tissue and body fluid proteomes,” Nucleic Acids Research, vol. 35, supplement 1, pp. D771–D779, 2007. View at: Publisher Site | Google Scholar
  116. S.-J. Li, M. Peng, H. Li et al., “Sys-BodyFluid: a systematical database for human body fluid proteome research,” Nucleic Acids Research, vol. 37, supplement 1, pp. D907–D912, 2009. View at: Publisher Site | Google Scholar
  117. J. Siwy, W. Mullen, I. Golovko, J. Franke, and P. Zürbig, “Human urinary peptide database for multiple disease biomarker discovery,” Proteomics𠅌linical Applications, vol. 5, no. 5-6, pp. 367–374, 2011. View at: Publisher Site | Google Scholar
  118. T. M. Shiju, V. Mohan, M. Balasubramanyam, and P. Viswanathan, “Soluble CD36 in plasma and urine: a plausible prognostic marker for diabetic nephropathy,” Journal of Diabetes and its Complications, vol. 29, pp. 400–406, 2015. View at: Publisher Site | Google Scholar
  119. J. Ma, X. Chen, J.-S. Li et al., “Upregulation of podocyte-secreted angiopoietin-like-4 in diabetic nephropathy,” Endocrine, vol. 49, no. 2, pp. 373–384, 2014. View at: Publisher Site | Google Scholar
  120. A. Caseiro, A. Barros, R. Ferreira et al., “Pursuing type 1 diabetes mellitus and related complications through urinary proteomics,” Translational Research, vol. 163, no. 3, pp. 188–199, 2014. View at: Publisher Site | Google Scholar
  121. I. Zubiri, M. Posada-Ayala, A. Sanz-Maroto et al., “Diabetic nephropathy induces changes in the proteome of human urinary exosomes as revealed by label-free comparative analysis,” Journal of Proteomics, vol. 96, pp. 92–102, 2014. View at: Publisher Site | Google Scholar
  122. K. Inoue, J. Wada, J. Eguchi et al., “Urinary Fetuin-A is a novel marker for diabetic nephropathy in type 2 diabetes identified by lectin microarray,” PLoS ONE, vol. 8, no. 10, Article ID e77118, 2013. View at: Publisher Site | Google Scholar
  123. B. Surin, E. Sachon, J.-P. Rougier et al., “LG3 fragment of endorepellin is a possible biomarker of severity in IgA nephropathy,” Proteomics, vol. 13, no. 1, pp. 142–152, 2013. View at: Publisher Site | Google Scholar
  124. J. Lopez-Hellin, C. Cantarell, L. Jimeno et al., “A form of apolipoprotein A-I is found specifically in relapses of focal segmental glomerulosclerosis following transplantation,” American Journal of Transplantation, vol. 13, no. 2, pp. 493–500, 2013. View at: Publisher Site | Google Scholar
  125. N. Piyaphanee, Q. Ma, O. Kremen et al., “Discovery and initial validation of α1-B glycoprotein fragmentation as a differential urinary biomarker in pediatric steroid-resistant nephrotic syndrome,” Proteomics𠅌linical Applications, vol. 5, no. 5-6, pp. 334–342, 2011. View at: Publisher Site | Google Scholar
  126. E. O. Honkanen, A.-M. Teppo, and C. Grönhagen-Riska, “Decreased urinary excretion of vascular endothelial growth factor in idiopathic membranous glomerulonephritis,” Kidney International, vol. 57, no. 6, pp. 2343–2349, 2000. View at: Publisher Site | Google Scholar
  127. A. J. W. Branten, P. W. du Buf-Vereijken, I. S. Klasen et al., “Urinary excretion of beta2-microglobulin and IgG predict prognosis in idiopathic membranous nephropathy: a validation study,” Journal of the American Society of Nephrology, vol. 16, no. 1, pp. 169–174, 2005. View at: Publisher Site | Google Scholar
  128. T. Nakatsue, H. Koike, G. D. Han et al., “Nephrin and podocin dissociate at the onset of proteinuria in experimental membranous nephropathy,” Kidney International, vol. 67, no. 6, pp. 2239–2253, 2005. View at: Publisher Site | Google Scholar
  129. C. R. Parikh, J. Mishra, H. Thiessen-Philbrook et al., “Urinary IL-18 is an early predictive biomarker of acute kidney injury after cardiac surgery,” Kidney International, vol. 70, no. 1, pp. 199–203, 2006. View at: Publisher Site | Google Scholar
  130. W. K. Han, G. Wagener, Y. Zhu, S. Wang, and H. T. Lee, “Urinary biomarkers in the early detection of acute kidney injury after cardiac surgery,” Clinical Journal of the American Society of Nephrology, vol. 4, no. 5, pp. 873–882, 2009. View at: Publisher Site | Google Scholar
  131. J. Mishra, C. Dent, R. Tarabishi et al., “Neutrophil gelatinase-associated lipocalin (NGAL) as a biomarker for acute renal injury after cardiac surgery,” The Lancet, vol. 365, no. 9466, pp. 1231–1238, 2005. View at: Publisher Site | Google Scholar
  132. V. S. Vaidya, V. Ramirez, T. Ichimura, N. A. Bobadilla, and J. V. Bonventre, “Urinary kidney injury molecule-1: a sensitive quantitative biomarker for early detection of kidney tubular injury,” The American Journal of Physiology—Renal Physiology, vol. 290, no. 2, pp. F517–F529, 2006. View at: Publisher Site | Google Scholar
  133. G. Ramesh, C. D. Krawczeski, J. G. Woo, Y. Wang, and P. Devarajan, “Urinary netrin-1 is an early predictive biomarker of acute kidney injury after cardiac surgery,” Clinical Journal of the American Society of Nephrology, vol. 5, no. 3, pp. 395–401, 2010. View at: Publisher Site | Google Scholar
  134. P. Devarajan, C. D. Krawczeski, M. T. Nguyen, T. Kathman, Z. Wang, and C. R. Parikh, “Proteomic identification of early biomarkers of acute kidney injury after cardiac surgery in children,” American Journal of Kidney Diseases, vol. 56, no. 4, pp. 632–642, 2010. View at: Publisher Site | Google Scholar
  135. L. E. Morales-Buenrostro, O. I. Salas-Nolasco, J. Barrera-Chimal et al., “Hsp72 is a novel biomarker to predict acute kidney injury in critically ill patients,” PLoS ONE, vol. 9, no. 10, Article ID e109407, 2014. View at: Publisher Site | Google Scholar

Copyright

Copyright © 2015 Shiva Kalantari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Materials and methods

Adenoviral constructs

JR Nevins, Department of Genetics, Duke University, North Carolina kindly provided the Ras 61L and RAS N17 adenoviruses (adv). The adv dl309 was a generous gift of Dr Adrian, Department of Virology, Medizinische Hochschule Hannover, Germany. The adv β-gal construct has been described before (Bradham et al., 1998).

Adenovirus preparation

To generate high titer viral stocks, 2×10 8 293 packaging cells at 90% confluence were infected at a multiplicity of infection (MOI) of 5–10 Pfu per cell. The infected cells were cultured for 3–5 days until a strong cytopathic effect could be observed and about 50% of these cells were detached. The cells were then collected by centrifugation and viral particles were released by four cycles of freezing in liquid nitrogen and rapid thawing at 37°C. For further purification the virus preparation was subjected to a twofold CsCl2 banding. CsCl2 banding and determination of infectivity by viral plaquing were performed according to protocols previously described (Becker et al., 1994) Endotoxin contaminations were monitored by the LAL-test kit (Chromogenix) following the protocol provided by the manufacturer. All virus preparations used for infection experiments and luciferase assays were LPS free. Virus preparations were stored at −20°C in 25% glycerol, 10 m M Tris/HCl, pH 7.4, and 1 m M MgCl2, 140 m M NaCl.

Cell culture, transfection experiments and luciferase assays

HepG2 cells were cultured in DMEM supplemented with 10% FCS. DNA transfection was performed using a modified calcium phosphate precipitation method with an overnight incubation at 3% CO2 and 35°C as described previously (Niehof et al., 1997). HepG2 were grown on 60 mm dishes to approximately 50% confluence when used for transfection experiments. By adding pBSK + DNA (Stratagene, USA) to 6 μg the total amount of DNA was kept constant in each transfection experiment. All transfections contained 0.2 μg of the β-galactosidase reporter pRSVβGal as an internal standard and 3 μg of the luciferase reporter gene construct. After transfection the cells were washed twice with phosphate-buffered saline (PBS) and incubated in DMEM supplemented with 10% fetal bovine serum. After 4 h the cells were infected with adv Ras 61L, adv Ras N17 or adv dl309 at a multiplicity of infection of 100 for 24 h.

To measure luciferase activity cells were washed twice with phosphate-buffered saline (PBS) and lysed by adding 350 μl extraction buffer (25 m M Tris-H3PO4, pH 7.8, 2 m M EDTA, 10% (v/v) glycerol, 1% (v/v) Triton X-100 and 2 m M DTT) for 10 min. The lysates were cleared by centrifugation. Fifty μl of the supernatant were assayed by adding 300 μl measuring buffer (25 m M glycylglycine, 15 m M MgSO4 and 5 m M ATP). The light emission was measured in duplicate for 10 s in a Lumat LB 9501 (Berthold, Bad Wildbad, Germany) by injecting 100 μl 250 μ M luciferin (Niehof et al., 1997). Each experiment was performed in duplicate and repeated at least three times. Luciferase activity was normalized against β-galactosidase activity. The data show the specific luciferase activity and represent the average of three independent experiments.

For in-vitro infection experiments HepG2 cells were grown on 100 mm dishes to approximately 80% confluence and then infected with the different viral constructs at an MOl of 100 for 24 h. Cells were then washed twice in PBS, harvested and lysed in a NP-40 lysis buffer (150 m M NaCl, 10 m M Tris/HCl, pH 7.4, 1 m M EDTA, 1% NP-40) to gain whole cell protein extracts.

Reporter gene constructs

The deletion reporter gene constructs of the Cyclin E promoter were kindly provided by P Jansen-Dürr, Innsbruck, Austria (Botz et al., 1996).

GST-cyclin E fusion construct

The cyclin E cDNA was cloned by nested-PCR from liver mRNA by using the primers: sense CCG AAT TCT GCC AAG GGA GAG AGA CTC GAC G and antisense GGG GTC GAC TTA GCA GCT TCT GGA GCA CTC AG (mouse Cyclin E: accession number No. X 75888 Damjanov et al., 1994) into the cloning vector Topo-TA (Invitrogen). The PCR product was verified by sequencing. The Cyclin E cDNA insert was prepared by using the restriction sites EcoRI and Sall and cloned in frame into the vector pGEX-KG Glutagen TM plasmid. The resulting GST-cyclin E construct was used for protein expression in E. coli after induction with 0.5 m M IPTG. Quantification of the fusion protein was performed by Coomassie blue staining. The specificity of the protein was verified by Western blot analysis using an anti-Cyclin E antibody.

Determination of transaminases

After a short spin at 200 g, plasma was recovered and stored at −80°C until it was used for the determination of aminotransferases. ALT and AST activities in plasma were determined by an automated enzyme assay as described before (Trautwein et al., 1998).

Two-thirds hepatectomy and preparation of nuclear extracts

Eight-week-old male NMRI mice with a weight between 20 to 25 g were obtained from the animal facility of the Medizinische Hochschule Hannover. The animals were maintained on a 12-h dark and 12-h light schedule. Twenty-four hours before surgery 1.6×10 9 Pfu of the respective virus were injected per tail vein in 250 μl of the carrier solution (10 m M Tris/HCl, pH 7.4, 1 m M MgCl2, 140 m M NaCl).

Using increasing amounts of the β-gal virus the efficacy of hepatocyte infection and liver toxicity was tested. A dose of 1.6×10 9 Pfu β-gal adenovirus (as tittered by plaques assay) infected at least 80% of the hepatocytes and did not result in a significant increase in transaminases after 24 h. The reliability of the infection was controlled by X-gal-stained liver sections of adv β-gal infected livers. Transaminases were determined at each point of time after hepatectomy. For the HGF-experiments we used recombinant human HGF (Pepro-Tech, Inc.- USA, #100-39) dissolved in carrier solution in concentrations as indicated in Figure 7.

Twelve hours before the surgery, food was withdrawn from the animals. Surgery was performed between 08.00 and 10.00 h. Animals were anesthetized by intraperitoneal injection of a combination of rompun/ketamine. After a small subxyphoid incision, two-thirds hepatectomy was performed as described by Higgins and Anderson (1931). Following surgery a suture closed the abdominal cavum. Sham surgery was performed exactly as indicated for two-thirds hepatectomy, except that the liver was only manipulated and not resected.

For each time point indicated five mice were used in parallel. Blood was drawn from the animals at each time point. The remaining livers were removed, pooled, rinsed in ice cold PBS and part of the livers were frozen immediately in liquid nitrogen or tissue-tek (Sakura Europe, Netherlands). The remaining liver was used to prepare nuclear extracts as described before (Trautwein et al., 1998). All the steps were performed at 4°C.

SDS-polyacrylamide-gel electrophoresis and Western blot analysis

Nuclear extracts were separated on a 10% SDS-polyacrylamide gel and blotted onto a nitrocellulose membrane (Schleicher and Schuell) in 1% SDS, 20% Methanol, 400 m M glycine, 50 m M Tris-HCL, pH 8.3, at 4°C for 2 h at 200 mA as described previously (Trautwein et al., 1999). Western blot analysis was performed as described before (Trautwein et al., 1999). For primary antibody incubation, membranes were probed with anti-ERK2 (Santa Cruz, USA, #Sc-153) anti-Cyclin A (Santa Cruz, USA, #Sc-751), anti-Cyclin E (Santa Cruz, USA, #Sc-481) or anti-Cyclin D1/2 (Upstate Biotechnology, USA, #05-362), anti-Phospho-p42/44 MAP kinase (Thr 202, Tyr 204) (New England Biolabs, #9101S), anti-Phospho-c-Jun, (Cell Signaling Technology, USA, #9261S) and anti-α-Tubulin (Santa Cruz, USA, #Sc-5546). As a secondary antibody, peroxidase-conjugated Donkey anti-rabbit IgG (Jackson Immuno Research Laboratories, Inc. USA, #711-035-152) was used. The antigen-antibody complexes were visualized using the ECL detection system as recommended by the manufacturer (Amersham, Braunschweig, Germany). Western blot analysis was performed for each protein of interest at least three times.

Gel retardation assays

Gel shift experiments were performed as described before (Trautwein et al., 1999). Liver nuclear extracts were incubated with a 32P-labeled consensus E2F site in 1×binding buffer (25 m M HEPES, pH 7.6, 5 m M MgCl2, 34 m M KCL, 2 m M DTT, 0.2 m M PMSF, 1 mg/ml poly (dI-dC), 2 mg/ml bovine serum albumin). 3.5 μg of liver nuclear extracts were incubated for 30 min on ice. Free DNA and DNA-protein complexes were resolved on a 4% TBE-polyacrylamide gel. For competition experiments, unlabeled (cold) consensus E2F oligonucleotide was added to the binding reaction.

BrdU labeling

For in vivo labeling, 30 μg/g mice of 5-bromo-2′-deoxyuridine (BrdU, Amersham, Braunschweig, Germany) were injected i.p. 2 h before sacrificing. Liver tissue was frozen immediately in liquid nitrogen. To detect labeled nuclei, cryosections were prepared (5 μm thick). The tissue was fixed in ice cold acetone/methanol and stained according to the Amersham cell proliferation kit manual (Amersham, Braunschweig, Germany).

Northern blot analysis

Northern blot analysis was performed according to standard procedures (Michalopoulos and DeFrances, 1997). The cyclin E and GAPDH cDNA probes were labeled with (α-P 32 ) ATP according to the Random priming protocol (Boehringer, Mannheim, Germany). The hybridization procedure was performed as described previously (Trautwein et al., 1999).

In vitro kinase assays

ERK kinase assays

ERK activity was assessed essentially as described before (Bradham et al., 1998). Immunoprecipitation was performed with liver nuclear extracts using an anti-ERK 2 antibody and protein A agarose beads (Santa Cruz, C14 cross-reactive with ERK 1). The kinase reaction was performed in 50 μl kinase buffer (20 m M HEPES (pH 7.5), 20 m M MgCl2, 20 m M Glycerolphosphat, 2 m M DTT) with 7.5 μg MBP and 5 μCi (γ-P 32 ) ATP. The proteins were fractionated using 12.5% SDS-polyacrylamide gel and visualized/quantitated using phospho imager analysis. Coomassie staining was used to demonstrate equal protein loading.

CDK2 kinase assay

CDK2 activity was determined similarly as described for the ERK kinase assay. Immunoprecipitation was performed with 1 μl anti-CDK2 (Santa Cruz, USA, #Sc-163) in 15 μg of liver nuclear extracts for 0.5 h on ice. Forty μl protein A agarose beads were added for 1 h. After two washes in RIPA buffer, CDK2 activity was determined by incubating 3 μg Histone H1 and 7 μCi (γ-ATP 32 ) ATP in kinase buffer. The kinase reaction was stopped after 30 min with 3×SDS sample buffer and the proteins were separated using a 12.5% SDS-polyacrylamide gel. Visualization and quantification was performed by phospho Imager analysis.

Cyclin E phosphorylation assay

In the kinase reaction 5 μg of GST-Cyclin E bound to glutathione agarose were used as substrate. The fusion protein was incubated with 25 μg nuclear extracts in 30 μl kinase buffer containing 5 μCi γ-ATP 32 for 30 min at 30°C. The kinase reaction was washed twice with HBB-buffer (20 m M HEPES (pH 7.5), 50 m M NaCl, 0.1 m M EDTA, 2.5 m M MgCl2, 0.1% Triton X-100, 20 m M Glycerolphosphat, 1 m M DTT) and 3×SDS sample buffer was added. The proteins were separated using 12.5% SDS-polyacrylamide gel. Visualization and quantification was performed by phospho Imager analysis.


Background

Importance of the field

The personalized management of diseases has and is being extended and this implies the prescription of specific therapeutics best suited for the individual patient and his/her type of illness. With the combination of different proteomic strategies this can be improved, and this would imply the coupling of proteomic and clinical research.

Areas covered in this review

Clinical and Proteomic research can be carried out in a complementary manner in order to advance and innovate therapies and diagnostics. We also point out the importance of immunology diseases including cancer, especially those which are directly connected to phosphorylated protein kinases and the way in which to isolate and methodologically analyse phosphoproteins-phosphopeptides, with their advantages and disadvantages, when using proteomic tools.

What the reader will gain

An overview of various types of different proteomic strategy-combinations personalized for specific diseases. The principles of phosphoproteomic techniques with examples are also presented in a simple manner. In addition, important mass spectrometry clues will be detailed in order to identify and correctly assign a phosphate group in a phosphorylated protein.

Take home message

A high number of proteomic-combination-approaches are available for clinical research. It is always necessary to test different proteomic tools in order to raise a greater level of efficiency for your clinical proteomic study, especially those related to phosphorylated proteins which are poorly expressed as some kinases. Nowadays it is essential that clinicians and proteomic experts work together in order to improve the therapies and drug candidates development.

We aim to detail the current and most useful techniques with research examples to isolate and carry out clinical phosphoproteomic studies which may be helpful for immunology and cancer research. Different phosphopeptide enrichment and quantitative techniques need to be combined to achieve good phosphopeptide recovery and good up- and-down phospho-regulation protein studies.

(A) Phosphorylation's role in immune disorders and cancer

Phosphorylation and de-phosphorylation at serine, threonine and tyrosine residues are one of the most common mechanisms of activation and/or inactivation signalling pathways. A variety of cellular processes including cellular growth, proliferation, cell cycle control, cytoskeletal mobility and receptor regulation [1] are controlled by this type of modification. Phosphorylation leads to allosteric modifications that may result in conformational changes sufficient to cause activation or inactivation of various proteins and associated altered functioning. It is our hypothesis that identification of phosphoproteins associated with the various stages of different immunological disorders, including cancer, may provide information on the development of the pathology. In addition the mechanism of tumorigenesis gives us insight into the development of diagnostic and therapeutic procedures.

The mitogen activated protein kinase (MAPK) pathways are known to be deregulated in many human malignancies [2–5]. With relation to malignancy, the best studies MAPKs are the extracellular signal regulated protein kinases (ERK). ERKs phosphorylate cytoplasmic targets migrate to the nucleus where they can activate transcription factors involved in cellular proliferation. Eractic signalling in the MAPK/ERK pathways has been described in prostate, breast and colon cancers in vitro as well as in vivo models [6–9]. In cervical cancer, furthermore, a study has described decreased activation of ERK1/2 in invasive cervical carcinoma [10]. Annexin A1, which is a calcium dependent phospholipid binding protein that has been linked to membrane trafficking through exocytosis and endocytosis [11], is a second relevant example. Other studies have evaluated the role of annexin A1 in the modulation of the MAPK/ERK [12]. In fact, many members of the Annexin family are known to undergo alternative splicing yielding a number of isoforms. The resulting variant forms may have different functions and binding capacity in comparison to the native forms [13]. The DNA-Protein Kinase catalytic subunit (DNA-PKcs) - another relevant example - a macromolecule found to be involved in the repair of double stranded DNA fractures through activation of p53, found to be expressed in cancer specimens in its tyrosine phosphorylated and cleaved form [14]. In contrast, in normal specimens DNA-PKcs existed in its whole, full length in non-phosphorylated form. This study was aimed at identifying differential expression and modification of proteins that could suggest erratic pathways which could serve as novel targets for developing new therapies in the treatment of cervical cancer and help in monitoring disease recurrence or progression. The general principles of signalling pathways are illustrated (Figure 1) and also, an example of the structure of a relevant phosphorylated protein kinase (Figure 2).

Signalling pathways: general principles. Followed by communication of the signal to different cellular compartments are signal processing and amplification by plasma membrane proximal events. The activation of multiple signal cascades by receptors, different protein post-translational modifications (PTMs), crosstalk between signalling pathways and feedback loops to ensure optimal signalling output are involved in this process. The binding of receptor Tyr kinases (RTKs) to their cognate ligands at the cell surface results in receptor dimerization and autophosphorylation. Phosphorylated Tyr residues subsequently serve as docking sites to recruit signalling mediators, such as growth factor receptor-bound protein 2 (GRB2). Multiple signalling cascades such as the phosphoinositide-3 kinase (PI3K)-AKT, Ras-Raf- extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase (MAPK), and signal transducer and activator of transcription (STAT) pathways are activated by the assembly of these signalling complexes. Casitas B-lineage lymphoma (CBL)-mediated ubiquitylation of RTKs controls their endocytosis and the duration of receptor signalling. In addition, binding of tumour necrosis factor-α (TNFα) to its receptor, TNFR1, induces trimerization of the receptor and recruitment of the adaptor protein TNFR1-associated death domain (TRADD) This functions as a hub to assemble a multiprotein signalling complex containing TNFR-associated factor 2 (TRAF2), receptor interacting Ser/Thr protein kinase 1 (RIPK1) and nuclear factor-κB (NF-κB) essential modulator (NEMO). The result is the activation of different signalling networks, such as the ERK MAPK, p38 MAPK and NF-κB pathways. Proteins in the MAPK signalling pathways are activated by both RTKs and TNFα, which allows cells to integrate multiple signals. [Dotted lines indicate indirect activation of signalling pathways or translocation of proteins into the nucleus. IκB, inhibitor of NF-κB IKK, inhibitor of NF-κB kinase JNK1, Jun N-terminal kinase 1 MEK, MAPK ERK kinase mTOR, mammalian target of rapamycin p70S6K, p70 ribosomal S6 kinase-α RSK, ribosomal protein S6 kinase-α].

Example of a phosphorylated protein kinase. The location of phosphorylated Ser-279 in the protein structure of human MAP kinase p38beta (p38B) is shown in this figure. A model for phosphorylated serine was located in the structural position of residue Ser-279 in the 3D crystallographic coordinates of p38B (Protein Data Bank code: 3GC8). Position of the ATP binding site is indicated. Plot was generated using PyMOL (DeLano Scientific, San Carlos, CA). The p38 pathway is one of the mitogen-activated protein kinase (MAPK) signalling cascades along with the extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) pathways. Similar to other MAPK pathways, the p38 signalling cascade involves sequential activation of MAPK kinases (MAP3Ks) and MAPK kinases (MKKs) including MKK3, MKK4, and MKK6, which directly activate p38 through phosphorylation in a cell-type- and stimulus-dependent manner. Once activated, p38 MAPKs phosphorylate serine/threonine residues on their substrates, such as transcription factors, cell cycle regulators as well as protein kinases. By the p38 signalling pathway cells can interpret a wide range of external signals, such as inflammation, hyperosmorality, UV radiation and oxidative stress and they respond appropriately by generating an excessive abundance of different biological effects.

On the other hand, the CDC25 family of proteins consists of dual specificity phosphatases which regulate cell cycle transitions, and they are key targets for the checkpoint machinery to maintain genome stability during DNA damage. Three isoforms of CDC25 have been identified in mammalian cells: CDC25A, CDC25B, and CDC25C. CDC25A and CDC25B over-expression has been reported in many types of human cancers, but these are insufficient to cause cancer, and the mechanism responsible for CDC25 over-expression is unclear [15, 16]. The study of dose-response effects of the anti-cancer drug rapamycin on the phosphoproteomics level has identified hundreds of novel rapamycin-targeted cellular proteins and their phosphorylation sites. This information has enabled us to identify CDC25B as the key enzyme in mediating rapamycin induced oncogenic AKT activation. It is important to point out that we can demonstrate that phosphoproteomic profiling of a certain therapeutic agent does not only identify potential drug target(s) to improve the efficiency of that therapeutic approach in disease treatment, but it can also provide cellular information about possible beneficial and adverse side effects of a specific disease therapy when treating patients [17].

In addition, primary immunodeficiencies (PID) are "nature's experiments" which have allowed, not only the elucidation of many signalling pathways, but also their function an clinical relevance. Bruton's tyrosine kinase, is an interesting example: (Btk member of the Tec family of kinases) [18, 19], important in B-lymphocyte development, differentiation, and signalling. Btk is predominantly expressed in B lymphocytes and monocytes but not in plasma cells [20, 21]. Btk expression in the B-cell lineage is also developmentally regulated, with bone marrow derived hematopoietic stem cells, common lymphoid progenitor cells, developing B and myeloid lineages showing the highest levels, whereas the remaining mature cells prior to activation have reduced cellular Btk. What remains to be established is the physiological significance of Btk expression in other cell types as B lymphocytes are the only cells known to be affected in X-linked agammaglobulinemia (XLA). Mutations in the Btk gene lead to XLA in humans and X-linked immunodeficiency (Xid) in mice. Activation of Btk triggers a cascade of signalling-events that culminates in the generation of calcium mobilization and fluxes, cytoskeletal rearrangements, and transcriptional regulation involving nuclear factor-κB (NF-κB) and nuclear factor of activated T cells (NFAT). In B cells, NF-κB was shown to bind to the Btk promoter and induce transcription, whereas the B-cell receptor dependent NF-κB signalling pathway requires functional Btk. In addition, Btk activation is strictly regulated by a plethora of other signalling proteins including protein kinase C (PKC), Sab/SH3BP5, and caveolin-1. Additionally, the prolyl isomerase Pin1 negatively regulates Btk by decreasing tyrosine phosphorylation and uniform state levels of Btk [22]. It is of great interest that PKC and Pin1, both of which are negative regulators of Btk, bind to the pleckstrin homology domain of Btk. For this purpose, novel mutations in the pleckstrin homology are under research, in order to design selective and novel drugs [23]. Common variable immunodeficiency (CVID) is a PID disease. CVID is the result of intrinsic deficits affecting immunologic functions. Moreover lymphomas and neoplams are found to be related to CVID. CVID is heterogeneous, can be present early or late in life, and it is associated with specific comorbidities [24, 25]. Efforts to subcategorize CVID to predict outcomes and comorbid-condition, both clinically and based on immunologic phenotypes, are ongoing [26]. B cell-activating factor of the TNF family receptor [27], transmembrane activator, calcium modulator, cyclophilin ligand interactor (TACI) [28–30], and certain HLA haplotypes [31, 32] have been identified as potential gene candidates for susceptibility to CVID. Inducible costimulator [33, 34], CD81 [35], CD19 [36, 37] and CD20 [38] harbour disease-causing mutations that presently explain only a small percentage of cases [39]. Recently, a genome-wide association work [40] has identified diverse causes of common variable immunodeficiency providing new mechanistic insights into immunopathogenesis based on these unique genetic variations. A highly significant number of subjects with duplications in ORC4L, a gene previously associated with B-cell lymphoproliferative disorders was observed. All these new insights could be susceptible to phosphoproteomic analysis in order to clarify the clues of the different pathologies [41].

(B) Analytical techniques used in phosphoproteomics

B.1. Preparation of samples

The key to any successful analysis is good sample preparation phosphorylated proteins are quite stable, chemically, but there are highly susceptible to enzymatic modification. We emphasize the importance of phosphorylation of protein kinases due to the fact that they modulate many immunology diseases and they are usually poorly expressed. Moreover, the human genome contains around 500 kinases and over 100 phosphatases [42], so that when tissues or cells are lysed and extracted, it is highly probable that further enzymatic reactions will occur. Samples have to be prepared (a) quickly, (b) generally be snap-frozen and (c) treated with phosphatase inhibitors to avoid modification of phosphopeptides during sample work-up [43, 44]. Phosphopeptides generally constitute a small portion of the peptides in a given protein sample, making them difficult to detect by MS their enrichment helps to overcome this problem. It is important (d) to avoid salts and detergents, which can decrease the recovery of phosphopeptides and/or interfere with subsequent analysis [45].

B.2. Enrichment of phosphoprotein and phosphopeptide

The aim in many focuses, including the study of immune disorders, is to generate a global view of serine, threonine and tyrosine phosphorylation within the sample, concentrating specifically on the selected subset of phosphopeptides. Since the detection of phosphopeptides by MS is often hindered by suppression effects, many different strategies have been established: for the removal of unphosphorylated peptides: (I) immunoprecipitation by antibodies, (II) pre-fraction systems such as ionic chromatographic exchange (SCX/SAX), calcium phosphate precipitation and hydrophilic interaction chromatography (HILIC) (III) metal affinity chromatography i.e. IMAC, TiO2, ZrO2, and (IV) reverse phase chromatography (RP). (V) Immunoprecipitation of phosphotyrosine coupled or not to polyacrylamide gels, is still much more frequent [43] than immunoprecipitation using phospho- serine or threonine antibodies. This is because affinity chromatography such as IMAC or titanium dioxide has higher a capacity for phosphoserine and phosphothreonine peptide binding.

• Antibody purification and Polyacrylamide gels

Affinity purification, a method for purifying proteins, can be used together with SDS-PAGE or alone. Antibodies raised against a protein can be used to immunoprecipitate the protein and search for phosphorylation sites. Immunoprecipitation permits the isolation of a protein under a variety of biological conditions to assess changes in phosphorylation on that protein. In the same way, antibodies raised against a specific phosphosite on a protein can be used for immunoprecipitation. Assessment of other phosphosites on a protein is possible when one phosphosite is known (the epitope of the antibody) under this scenario. However, care must be taken when a protein is phosphorylated at multiple serines as certain phosphorylation events could be mutually exclusive and be obliterated during subsequent analysis. Phosphospecific antibodies can be used to determine the proteins that bind to a phosphoprotein (protein-phosphoprotein interactions) using phosphosite-specific immunoprecipitation followed by analysis of the binding partners. Furthermore, antibodies specific for phosphotyrosines, not affected by the surrounding amino acids, have been successfully used to immunoprecipitate the "phosphotyrosineome" of cells. Since phosphoserine and phosphothreonine are much more abundant in cells and these antibodies seem to have less specificity, phosphoproteome-wide experiments are much more complicated [46].

Moreover, phospho-specific antibodies against a consensus sequence-motif for a specific kinase-motif (for example SXR, where × is any amino acid) can also be used to immunopurify all proteins that contain this motif. This form of phosphorylation has been enriched by the use of antiphosphotyrosine antibodies. It is an interesting strategy as phosphotyrosine is far less common than phosphoserine or threonine the antibodies generally have a higher specificity and tyrosine kinases play a prominent role in human cancer. The isolated proteins are enzymatically cleaved with trypsin and analysed by MS or the phosphopeptides can be further enriched for analysis by MS. Tyrosine phosphorylated proteins are enriched by these methods to levels sufficient for detection and sequencing by MS [47–52]. Antiphosphoserine and antiphosphothreonine antibodies have been also generated [43] but have not been widely used due to their low specificity.

We would like to mention the scientific study of Kemna and co-workers (2007) [53], who used immunocapture, and tandem MS to identify and characterize hepcidin in serum and urine. In addition to diagnostic application, they investigated analytical reproducibility and biological and preanalytical variation for both serum and urine sample fluids. Samples were obtained from healthy controls and patients with documented iron-deficiency anaemia, inflammation-induced anaemia, thalassemia major, and hereditary hemochromatosis. This important proteomic technique showed that hepcidin-20, -22, and -25 isoforms are present in urine. Hepcidin-25 in serum had the same amino acid sequence as hepcidin-25 in urine, whereas hepcidin-22 was not detected in serum. In this work, Kemna and co-workers (2007) also observed that urine hepcidin is more affected by multiple freeze-thaw cycles and storage conditions, but less influenced by diurnal variation, than serum hepcidin.

Barbey and co-workers (2009) [54] also produced another interesting scientific work where they described the results of a proteomic analysis based on SDS-PAGE, immunoblot and mass spectrometry, aimed at the identification of secreted proteins that are differently expressed at 30°C versus 37°C and at mid-exponential versus early-stationary growth phase and antigenic proteins from Rhodococcus equi ATCC 33701. A total of 48 proteins were identified irrespective of growth conditions. Cholesterol oxidase ChoE appears to be the major secretory protein. Four proteins, in addition, revealed high homologies with the mycolyl transferases of the Ag85 complex from Mycobacterium tuberculosis. 24 proteins are transported by a signal peptide-dependent pathway according to the prediction of the sequence analysis. Moreover, five antigenic proteins of R. equi were identified by immunoblot, including a novel, strongly immunoreactive protein of unknown function. In conclusion, the elucidation of the secretome of R. equi identified several proteins with different biological functions and a new candidate developing vaccines against R. equi infection in horses.

Radio-labelling polyacrylamide gels (P32 & 2DE) and 2D phosphopeptide mapping P32 labelling has long been used, on the other hand, for the analysis of immuno-precipitated and gel-separated signalling complexes and for quantify cation of differentially phosphorylated proteins by two-dimensional gel electrophoresis (2DE) of total cell lysates. The latter technique uses differential labelling of cells with P32 and P33 in a control and experimental group respectively. The samples are combined, and then separated by 2DE before the gels are exposed twice to radio-sensitive film. Comparison of these two exposures will reveal spots that are specifically phosphorylated under the experimental conditions tested [55, 56]. It measures the incorporation of the label but not the phosphorylation level, although interesting studies can be carried out to study different isoforms of the same protein [57]. Two-dimensional (2D) phosphopeptide mapping by electrophoresis is another useful technique combined with thin-layer chromatography of peptides derived by proteolysis of a phosphoprotein. The number of spots detected indicates the number of sites of phosphorylation, but it is not easy to determine the position of the phosphorylation sites. Nevertheless, analysis of temporal and positional changes in a protein phosphorylation pattern under different physiological conditions [58] is permitted by this technique. Immunoprecipitation with specific antibodies against phosphopeptides can be used to immunoprecipitate phosphoproteins from the cell lysates [59].

• Immobilised metal ion affinity chromatography (IMAC)

IMAC [60] is an enrichment technique that makes use of metal ions to capture and enrich negatively charged phosphopeptides prior to mass spectrometric analysis [61–66]. Simple and complex samples containing phosphopeptides and non-phosphorylated peptides are dissolved in an acidic solution to stimulate the electrostatic interactions between the negatively charged peptides, mainly phosphopeptides, and the metal ions [64]. The phosphopeptides are eluted from the stationary phase using alkaline buffers. It is also possible to bind peptides containing the acidic amino acid residues glutamic acid and aspartic acid to the metal ions. Ficarro and co-workers [67] bypassed this problem with IMAC (Fe 3+ ) by converting acidic amino acid residues to methyl esters. They were able to purify and sequence hundreds of phosphopeptides from yeast, although there was a strong tendency towards phosphoproteins highly expressed within the cell.

Collins and co-workers (2008) [68] analyzed the mouse forebrain cytosolic phosphoproteome using sequential (protein and peptide) IMAC purifications, enzymatic dephosphorylation, and targeted tandem mass spectrometry analysis strategies (MS clues will be detailed later) which we consider a relevant biological study. To summarize, Collins et al., (2008) [68] with the use of complementary phosphoenrichment and LCMS/MS strategies, 512 phosphorylation sites on 540 nonredundant phosphopeptides from 162 cytosolic phosphoproteins were characterized. Analysis of protein domains and amino acid sequence composition of this data set of cytosolic phosphoproteins revealed that it is significantly enriched in intrinsic sequence disorder, which enrichment is associated with both cellular location and phosphorylation status. The majority of phosphorylation sites found by MS were located outside structural protein domains (97%) They were mostly located in regions of intrinsic sequence disorder (86%). 368 phosphorylation sites were located in long regions of disorder (over 40 amino acids long), and 94% of proteins contained at least one such long region of disorder. In addition, it was found that 58 phosphorylation sites in this data set occur in 14-3-3 binding consensus motifs linear motifs that are associated with unstructured regions in proteins. These results demonstrate that in this data set protein phosphorylation is distinctively depleted in protein domains and distinctively enriched in disordered protein sequences and that enrichment of intrinsic sequence disorder may be a common feature of phosphoproteomes. This goes to support the hypothesis that disordered regions in proteins allow kinases, phosphatases, and phosphorylation-dependent binding proteins to gain access to target sequences to regulate local protein conformation and activity.

• Titanium dioxide metal-based chromatography (TiO2)

TiO2 is also capable of binding negatively charged phosphate groups from aqueous solutions [65, 69, 70]. TiO2, like IMAC, experiences the problem of binding acidic non-phosphorylated peptides (negatively charged peptides). Heck and co-workers [65] observed a number of non-phosphorylated peptides in their analysis and recommended esterification of the acidic residues prior to the MS analysis. Larsen et al. [45, 71, 41] used 2,5-dihydroxybenzoic acid (DHB) with TiO2 and achieved higher specificity and yield compared to IMAC (Fe 3+ ) for the selective enrichment of phosphorylated peptides from model proteins. It was also demonstrated that by the use of glycolic acid in the loading buffer, more phosphopeptides are bound to the metal ions and more phosphopeptides can be eluted by using ammonium hydroxide as the eluent. TiO2 binds multi-phosphorylated peptides in a strong way, thus their elution is difficult. However, this is a very effective method for the isolation of singly phosphorylated peptides [72].

The research work of Craft and co-workers (2007) [73] is an interesting example of the application of TiO2 technique coupled to other proteomic tools. Amphiphysin I (amphI) is dephosphorylated by calcineurin during nerve terminal depolarization and synaptic vesicle endocytosis (SVE). Some amphI phosphorylation sites (phosphosites) have been identified with in vitro studies or phosphoproteomics screens. A multifaceted strategy including 32P tracking to identify all in vivo amphI phosphosites and determine their relative abundance and potential relevance to SVE was used. AmphI was extracted from 32P-labeled synaptosomes phosphopeptides were isolated from proteolytic digests using TiO2 chromatography, and mass spectrometry revealed 13 sites: serines 250, 252, 262, 268, 272, 276, 285, 293, 496, 514, 539, and 626 and Thr-310. These were distributed into two clusters around the proline-rich domain and the C-terminal Src homology 3 domain. Hierarchical phosphorylation of Ser- 262 preceded phosphorylation of Ser-268, -272, -276, and -285. Off-line HPLC (High-performance liquid chromatography or high-pressure liquid chromatography separation and two-dimensional tryptic mapping of 32P-labeled amphI revealed that Thr-310, Ser-293, Ser-285, Ser-272, Ser-276, and Ser-268 contained the highest 32P incorporation and were the most stimulus-sensitive. Individually Thr-310 and Ser-293 were the most abundant phosphosites, incorporating 16 and 23% of the 32P. The multiple phosphopeptides containing Ser-268, Ser-276, Ser-272, and Ser-285 had 27% of the 32P. Evidence for a role for at least one proline-directed protein kinase and one non-proline-directed kinase was obtained. Four phosphosites predicted for non-prolinedirected kinases, Ser-626, -250, -252, and -539, contained low amounts of 32P and were not depolarization-responsive. At least one alternatively spliced amphI isoform was identified in synaptosomes as being constitutively phosphorylated because it did not incorporate 32P during the 1-h labeling period. Multiple phosphosites from amphIco- migrating synaptosomal proteins were also identified, including SGIP (Src homology 3 domain growth factor receptor-bound 2 (Grb2)-like (endophilin)-interacting protein 1), AAK1, eps15R, MAP6, α/β-adducin, and HCN1. Their results revealed two sets of amphI phosphosites that are either dynamically turning over or constitutively phosphorylated in nerve terminals and they improve the understanding of the role of individual amphI sites or phosphosite clusters in synaptic SVE.

• IMAC (SIMAC) Sequential elution

Sequential elution from IMAC is useful for purifying, detecting and characterising phosphorylated peptides from complex biological samples [72]. It makes use of the observation that mono-phosphorylated peptides tend to elute from IMAC (Fe 3+ ) under acidic conditions whereas multi-phosphorylated peptides elute at high basic pH. TiO2 is used to capture and purify the unbound mono-phosphorylated peptides in the combined IMAC flowthrough and washings. SIMAC has been used successfully in the study of human stem cells (

300 μg) with more than 300 phosphopeptides, including the identification of mono and multiply phosphorylated peptides [74].

This technology was developed by Tine E. Thingholm and co-workers (2007) [74]. They reported a simple and rapid strategy, SIMAC (sequential elution from IMAC), using stem cells as a sample to be studied, for sequential separation of monophosphorylated peptides and multiply phosphorylated peptides from highly complex biological samples. This research study, allowed individual analysis of different pools of phosphorylated peptides using mass spectrometric parameters differentially optimized due to their unique properties. They compared the phosphoproteome identified from 120 μg of human mesenchymal stem cells using SIMAC and an optimized titanium dioxide chromatographic method. More than double the total number of identified phosphorylation sites was obtained with SIMAC, primarily from a 3-fold increase in recovery of multiply phosphorylated peptides.

• Zirconium dioxide (ZrO2)

The utility of ZrO2 for phosphopeptide isolation prior to mass spectrometric analysis has been demonstrated [75]. When compared with TiO2 using is α and ß casein as protein models, ZrO2 was capable of isolating singly phosphorylated peptides more selectively than TiO2. An interesting research study was carried out by Houjiang Zhou et al (2007) [76] where the high specificity of this approach was also demonstrated by the isolation of phosphopeptides from the digests of model phosphoproteins. The strong affinity of ZrO2 nanoparticles to phosphopeptides enables the specific enrichment of phosphopeptides from a complex peptide mixture in which the abundance of phosphopeptides is two orders of magnitude lower than that of nonphosphopeptides. ZrO2 nanoparticles were further applied to selectively isolate phosphopeptides from the tryptic digestion of mouse liver lysate for phosphoproteome analysis by nanoliter LC MS/MS (nano-LC-MS/MS) and MS/MS/MS. Manual validation, using a series of rigid criteria, identified a total of 248 defining phosphorylation sites and 140 phosphorylated peptides. Therefore, ZrO2 has been successfully used in the large-scale characterisation of phosphoproteins from mouse liver samples (

1 mg) [76]. A total of 248 phosphorylation sites and 140 phosphorylated peptides were identified in this study.

• Calcium phosphate precipitation

This is a strategy providing a useful pre-fractionation step to simplify and enrich phosphopeptides from complex samples. Zhang and co workers [77] have demonstrated that phosphopeptide precipitation by calcium phosphate combined with a two step IMAC (Fe 3+ ) procedure resulted in the observation of an increased number of phosphopeptides. This method consists of precipitating phosphopeptides by adding 0.5 M NaHPO4 and 2 M NH3OH to the peptide-mixture followed by 2 M CaCl2. The sample is vortexed and centrifuged, and, subsequently, the supernatant is removed before washing the pellet with 80 mM CaCl2. The washed pellet is dissolved in 5% of formic acid and the resulting peptide mixture is desalted through reversed phase chromatography before isolating the phosphopeptides by IMAC (Fe 3+ ).

Zhang and co workers [77] point out that even with very complex biological samples such as the total enzymatic digest of rice embryo proteins, high enrichment of the phosphopeptides can be achieved with minimal contamination with non-phosphopeptides. In addition, it could be possible to reduce the complexity of the samples by successive IMAC enrichments using a limited amount of IMAC material at each step. This technique demonstrates that serial phosphopeptide enrichment initiated by a precipitation step improves the selectivity of phosphopeptide enrichment and allows identification of more phosphopeptides. In addition, Zhang and co workers say that further analyses to examine the rice phosphoproteome in detail are now underway. Moreover, it can be applied for clinical phosphoproteomics clinical research.

• Strong cation and anion exchange (SCX and SAX)

The principle of SCX/SAX phosphopeptide enrichment is based on the negative charged phosphate group (PO 4- ) of the phosphopeptides. In cation exchange chromatography, a positively charged analyte is attracted to a negatively charged solid-support whilst in anion exchange chromatography negatively charged molecules are attracted to a positively charged solid-support. SAX has previously been successfully combined with IMAC [66] and has resulted in greater recovery and identification by MS of mono-phosphorylated peptides originating from membrane proteins. In a similar way, SCX has been combined with IMAC (Fe 3+ ) and MS analysis, allowing the identification of thousands of phosphorylated residues from complex biological samples [78]. Moreover, Gruhler and co-workers [78] demonstrated that use of the SCX/IMAC combination is consistent with their previous study where strong anion exchange chromatography/IMAC was used. Thus, either strong anion exchange chromatography (SAX) or SCX can be used to reduce the sample complexity prior to IMAC enrichment of phosphopeptides in large scale phosphoproteomics.

As practical issues, Nuhse et al., 2003 [66], investigated and presented a scheme for two-dimensional peptide separation using SAX chromatography prior to IMAC (Fe 3+ ) in order to decrease the complexity of IMAC-purified phosphopeptides, obtaining a wide coverage of monophosphorylated peptides. Nuhse and co-workers did, in fact, obtain a high yield in identifying phosphopeptides from membrane proteins. SCX has also been successfully used coupled to IMAC (Fe 3+ ) and MS analysis allowing the identification of thousands of phosphorylated residues from biological complex samples [78, 79]. Gruhler and co-workers showed that performing SCX at low pH (2.7-3.0), phosphorylated peptides are separated from nonphosphorylated species according to the charge difference associated with the negatively charged phosphate group. Therefore, net charged peptides (+1) were collected in the first fractions of the SCX prefraction step containing mainly single phosphorylated peptides. These first fractions were then loaded onto IMAC (Fe 3+ ) micro tips in order to recover a large number of phosphopeptides from biologically complex samples.

• Hydrophilic interaction chromatography (HILIC)

Hydrophilic interaction chromatography (HILIC) is a less commonly used method for peptide fractionation despite the fact that it is often used to fractionate small metabolites. HILIC is commonly described as partition chromatography or liquid/liquid extraction system between the mobile and stationary phase. A water-poor layer of mobile phase versus a water-rich layer on the surface of the polar stationary phase is formed. Thus, a distribution of the analytes between these two layers will occur. In addition, HILIC includes weak electrostatic mechanisms as well as hydrogen donor interactions between neutral polar molecules under high organic elution conditions. This distinguishes HILIC from ion exchange chromatography - the main principle for HILIC separation is based on the compound's polarity and degree of salvation. More polar compounds will have stronger interaction with the stationary aqueous layer than less polar compounds - resulting in a stronger retention. In addition, HILIC shows a very good separation and peak shape for critical compounds like adenosine and its phosphate derivatives.

It is of interest to note that Alburquerque and co-workers (2008) [80] carried out a study related to the separation of unphosphorylated peptides using SCX, HILIC, and RP-HPLC, indicating that a better orthogonal separation could occur between HILIC and RP-HPLC for unphosphorylated peptides. The observed orthogonal separation between HILIC and RP-HPLC is probably a reflection of their different mechanisms of separation. Although RP-HPLC depends on interaction with the hydrophobic amino acid side chains, HILIC depends on interaction with those hydrophilic and possibly charged amino acid residues via hydrogen bonding and ionic interactions. Moreover, because phosphopeptides are generally hydrophilic and charged, one would expect that phosphopeptides should interact more strongly with HILIC than do unphosphorylated peptides. Thus, it should be possible to separate phosphopeptides using HILIC.

Dean E. McNulty and Roland S. Annan (2008) [81] reported the use of hydrophilic interaction chromatography (HILIC) as part of a multidimensional chromatography strategy for proteomics. Analysis of tryptic digests from HeLa cells yielded numbers of protein identifications comparable to those obtained using strong cation exchange. They also demonstrate that HILIC represents a significant advance in phosphoproteomics analysis. In fact, they exploited the strong hydrophilicity of the phosphate group to selectively enrich and fractionate phosphopeptides based on their increased retention under HILIC conditions. In addition, in this study IMAC enrichment of phosphopeptides from HILIC fractions showed more than 99% selectivity. This was achieved without the use of derivatization or chemical modifiers. In a 300 μg equivalent of HeLa cell lysate over 1000 unique phosphorylation sites were identified. More than 700 novel sites were added to the HeLa phosphoproteome.

• Reverse phase chromatography

All the phosphorylated proteins and phosphopeptides isolations can be coupled to reverse phase chromatography. Subsequently, most phosphoprotein-phosphopeptide analyses are performed nowadays by MS. As the MS technique is sensitive to contaminants such as salts, it is necessary to clean the samples prior to analysis, generally by reversed phase chromatography combining POROs R3 with C18 Disks and also graphite powder [82–84]. Poros R3, C18 Disks and graphite powder are materials containing long hydrocarbon chains, proven to be effective for the desalting and cleaning of very hydrophilic peptides, including phosphopeptides [71, 85]. In 1999, Gobom and co-workers [82] introduced a micro column purification method in which a chromatographic resin was packed in the tip of a small constricted GELoader tip, creating a micro-column. With GELoader tips packed with R3, C18 or graphite material, contaminants like salts can be separated from the phosphopeptides using a chromatographic approach. In fact, using RP chromatography, molecules such as proteins, peptides and nucleic acids are separated according to their hydrophobicity. In addition to the removal of salts, these techniques also facilitate a concentration of the sample by the use of a low elution volume. This is an additional improvement for the sensitivity and quality of the subsequent mass spectrometric analysis. RP chromatography is usually coupled to all the phosphoproteins and phosphopeptides enrichment-methods previously described.

• Current methodologies for the detection of phosphorylated proteins - Advantages and limitations

There are several analytical techniques for the analysis of phosphorylation, i.e., Edman sequencing and 32 P-phosphopeptide mapping for localization of phosphorylation sites however, these methods do not allow high-throughput analysis or imply very high- labour operations [86], whereas with the use of Mass Spectrometry (MS) high-throughput analysis of phosphorylated protein residues can be developed [67, 78]. On the other hand, phosphospecific antibodies are routinely used to immunoprecipitate and therefore enrich in phosphorylated proteins from complex mixtures [87], but, currently, no commercial antibodies are available which are suitable for enriching all proteins that are phosphorylated, and thus, these proteins must be purified or enriched from complex mixtures using alternative methods [88]. By carrying out in-gel or in-solution trypsin digestion of protein complex mixtures, the resulted phosphopeptides and non-phosphopeptides can be loaded into different metal ion chromatographies (i.e. Immobilized Metal ion Affinity Chromatography IMAC (Fe 3+ ), and Titanium Dioxide TiO2 [71] in order to enrich in phosphopeptides. The enriched solution can also be submitted into different reverse-phase chromatographies (i.e. Graphite powder [89], POROS R3 [88] in order to clean and desalt those phosphopeptides previously eluted. In addition these kinds of chromatographies will reduce the suppression of phosphorylated peptides in the mass spectra.

Using IMAC (Fe 3+ ) and also (TiO2) [71] and (ZrO2) [1], the negatively charged phosphopeptides are purified by their affinity to positively charged metal ions, but some of these methods experience the problem of binding acidic, non-phosphorylated peptides. Ficarro and co-workers [67] bypased this problem on IMAC (Fe 3+ ) by converting acidic peptides to methyl esters but increased the spectra complexity and requiring lyophilization of the sample, which causes adsorptive losses of phosphopeptides in particular [90]. Ficarro et al., were able to sequence hundreds of phosphopeptides from yeast, including Slt2p kinase, but the level of phosphorylated residues identified from kinases were low compared to those from phosphoproteins highly expressed within the cell. Recently, TiO2 chromatography using 2,5-dihydroxybenzoic acid (DHB) was introduced as a promising strategy by Larsen et al., [71]. TiO2/DHB resulted in a higher specificity and yield as compared to IMAC (Fe 3+ ) for the selective enrichment of phosphorylated peptides from model proteins (i.e. lactoglobulin bovine, casein bovine). Moreover, SIMAC has been developed in order to get a higher efficiency than IMAC and TiO2 for the isolation of as many phosphopeptides as possible [74].

The fact that mainly phosphopeptides from highly expressed proteins within cells can be purified, while those from phosphorylated proteins with low level expression (i. e. kinases) do not bind so well to those resins, constitutes another important limitation concerning phosphoenrichment methods This is due to the low proportion of this kind of protein, or, on the other hand, their available amount binds to metal ions although it is not sufficient to be detected by MS. The combination of SCX with IMAC (Fe 3+ ) has been proven on yeast, resulting in a huge number of phosphorylated residues identified (over 700 including Fus3p kinase) [78]. Although more than 100 signalling proteins and functional phosphorylation sites, including receptors, kinases and transcription factors, were identified, it was clear that only a fraction of the phosphoproteome was revealed [78]. In addition, recent combinations of HILIC with IMAC (Fe 3+ ) have been proven in clinical studies (HeLa samples), with the result of the identification of a large number of phosphorylated residues (1000) [81].

Improvement in methodologies to enrich for phosphorylated residues from kinases is clearly necessary. However, this is not straightforward for several reasons: (a) The low abundance of those signalling molecules within cells, (b) The stress/stimulation time-duration, as only a small fraction of phosphorylated kinases are available at any given time as a result of a stimulus. The time adaptation over signalling pathways is also a relevant and fast factor for kinases phosphorylation [91].

• Summary - phosphoprotein and phosphopeptide enrichments based on electrostatic interactions

The most common techniques for enrichment for individual and/or global phosphorylation are IMAC and Titanium Dioxide (TiO2) [45], which are based on the high affinity of positively charged metal ions. However, conversion of carboxylate groups to esters effectively eliminates nonspecific retention of non-phosphorylated peptides, although this constitutes a drawback due to increased complexity in the subsequent MS analysis.

During the last five years, titanium dioxide (TiO2) has emerged as the most common of the metal oxide affinity chromatography (MOAC) based phosphopeptide enrichment methods. This technique offers increased capacity compared to IMAC resins in order to bind and elute mono-phosphorylated peptides. TiO2 exploits the same principle as IMAC, and is similarly prone to nonspecific retention of acidic nonphosphorylated peptides. However, when loading peptides in 2, 5-dihydroxybenzoic acid (DHB) [71], glycolic and phthalic acids, nonspecific binding to TiO2 is reduced, thereby improving phosphopeptide enrichment without a chemical modification of the sample. In addition, TiO2 is often considered to be interchangeable with IMAC. It works on similar levels of sample amounts (e.g., micrograms of protein) for the identification of phospho-sites by MS analysis. Recently, SIMAC [72, 74] appeared as a phosphopeptide enrichment tool which exploits the properties of IMAC coupled to TiO2, making it possible to carry out more refined studies. Another phosphopeptide enrichment prior to mass spectrometric analysis is ZrO2 [75] and its principle is based on metal affinity chromatography like IMAC and TiO2. ZrO2 permits the isolation of single phosphorylated peptides in a more selective manner than TiO2. It has, in fact, been successfully used in the large-scale characterization of phosphoproteins [66, 78–80]. Furthermore, strategies which consist of fractionating and subsequently enriching phosphopeptides on a proteome wide scale are based on strong cation/anion exchange (SCX and SAX) chromatography and HILIC interaction chromatography. Calcium phosphate precipitation is also a useful pre-fractionation step to simplify and enrich phosphopeptides from complex samples which can be coupled to IMAC [77].

B.3. Phosphopeptides isolated by Proteomic techniques - MS analysis

Phosphorylation on serine and threonine residues are labile and conventional fragmentation CID (Collision Induced Dissociation) typically results in the partial neutral loss of phosphoric acid (H3PO4, 98/z) in MS2 mode, due to the gas phase β-elimination of the phosphor-ester bond. Therefore, dehydroalanine and dehydroaminobutyric acid are generated. When peptide ions are fragmented by CID, series of y- and b- ions are formed [92, 93]. By correlating mass difference between peaks in the y-ion series or between peaks in the b- ion series with amino acid residue masses the peptide sequence is obtained. The CID fragmentation occurs on the peptide backbone, and only limited sequence information is obtained. This event can also compromise the identification of phosphorylation sites. In relation to phosphotyrosine residues, partial neutral loss is also observed (HPO3, 80/z) in MS2 mode, but the phosphate group on tyrosine residues is more stable than on serine and threonine residues. In addition, the phospho-finger-print characteristic of phosphotyrosine, is the phosphotyrosine immonium ion (

216 Da), this being a positive indicator for the presence of a peptide phosphorylated on tyrosine [94, 95]. The ion originating from neutral loss of phosphoric acid (H3PO4) can be selected for further fragmentation by MS3 mode. After neutral loss fragmentation, the selected ion is automatically selected for further fragmentation. This makes it possible to add extra energy for the fragmentation of peptide backbone. However, the MS3 mode requires that the phosphorylation on serine and threonine residues are labile and conventional fragmentation CID (Collision Induced Dissociation) typically results in the partial neutral loss of phosphoric acid (H3PO4, 98/z) in MS2 mode, due to the gas phase β-elimination of the phosphor-ester bond. Therefore, dehydroalanine and dehydroaminobutyric acid are generated. When peptide ions are fragmented by CID, series of y- and b- ions are formed [92, 93]. The peptide sequence is obtained by correlating mass difference between peaks in the y-ion series or between peaks in the b-ion series with amino acid residue masses. The CID fragmentation occurs on the peptide backbone, and only limited sequence information is obtained. This event can also compromise the identification of phosphorylation sites. In relation to phosphotyrosine residues, partial neutral loss is also observed (HPO3, 80/z) in MS2 mode, but the phosphate group on tyrosine residues is more stable than on serine and threonine residues. In addition, the phospho-finger-print characteristic of phosphotyrosine, is the phosphotyrosine immonium ion (

216 Da), which is a positive indicator for the presence of a peptide phosphorylated on tyrosine [94, 95]. The ion originating from neutral loss of phosphoric acid (H3PO4) can be selected for further fragmentation by MS3 mode. The selected ion, after neutral loss fragmentation, is automatically selected for further fragmentation. This makes it possible to add extra energy for the fragmentation of peptide backbone. However, the MS3 mode requires that the selected ion is abundant in order to observe the fragmented ions. A pseudo-MS3 development is MultiStage Activation (MSA) [96], which was implemented on quadrupole-IT and linear IT-orbitrap. In MSA, the fragmentation of the precursor ion occurs simultaneously with the fragmentation of the ion originating from the neutral loss. The MS2 and MS3 mass-data are then combined in a hybrid spectrum, resulting in improved sequence information and also in an improvement of reliance for the phosphorylation site assignment. Alternative fragmentations to CID are ECD (electron capture Dissociation) and ETD (Electron transfer dissociation). By ECD, radical peptide ions are obtained when multiplycharged peptide ions are rationed with low-energy thermalelectrons. In addition, this fragmentation occurs in the peptide between the backbone amide and the alpha carbon, generating c- and z-ions [97]. An advantage of ECD is that it only occurs on the peptide backbone, and labile phosphate groups remain intact on the resulting c- and z- fragment ions, thus enabling the identification of the specific phosphorylation sites. Therefore, it is extremely useful for the analysis of multiply-phosphorylated peptides. A disadvantage of ECD is its selectivity for disulfide bonds, due to the high radical affinity of the bond [98, 99]. The main drawback of ECD is that it is solely used in the Fourier transform-Ion Cyclotron Resonance (FT-ICR) instruments due to the requirement of a static magnetic field for the thermal electrons, meaning high costs and high specialization. c- and z- ions are also generated by ETD. This fragmentation was actually developed in order to carry out ECD-like dissociation experiments, in a Quadrupole Linear Ion Trap [96, 100]. ETD is a chemical process in which reaction with fluoranthene radical anions disrupts the peptide backbone at regular intervals. ETD preserves the intact information about labile modifications, which are not observed directly when using CID. For instance, phosphate groups are good leaving groups, which mean that they are easily lost in the excitation process. However, by using ETD one can directly observe fragments that contain the intact phosphopeptides. The drawback of ETD is that it is less sensitive compared to CID, because of lower ionization efficiency. As a result, we recommend using CID to start with, and would recommend switching to ETD in case you are not able to determine the phosphorylation site.

(C) Quantitative proteomic methodologies used in clinical research examples of relevant phosphorylated proteins studied

For phosphopeptides proteins containing amino acids with one or more of the stable isotopes of 2 H, 13 C, 15 N or 18 O can be used as internal standards by addition, at an early stage of the analysis, of a complex protein sample. There are two approaches for introducing a stable isotope into proteins or peptides: metabolic labelling using whole cells grown in culture (e.g. SILAC) or chemical labelling (e.g. iTRAQ, ICAT). Since protein phosphorylation is very dynamic and constantly changing throughout the life of a cell, measuring the changes in phosphorylation is critical for understanding the biology of a phosphorylation event, We restrict the discussion here to four MS based quantitation strategies which have direct utility towards measuring changes in protein phosphorylation extensively: SILAC, iTRAQ, AQUA and MRM. Other chemical labelling techniques which rely on stable isotope incorporation using e.g. 18 O labelled water during trypsin digestions and stable isotope incorporation ICAT can also be considered to contain relevant information, but will not be described here. In addition, we will also include the explanation and examples of 2-D Fluorescence Difference Gel Electrophoresis (2D_DIGE) quantification methodology, which nowadays also provides interesting research studies.

C.1. Stable Isotope Labelling with Amino acid in cell Culture (SILAC)

Stable isotope labelling by amino acids in cell culture (SILAC) is a quantitative method based on in vivo labelling of proteins in cell cultures with amino acids that contain stable isotopes (non radioactive, e.g. 2 H, 13 C and 15 N) [57, 101]. In its simplest form, two separated cell cultures are grown in a pair-wise fashion for example, culture A might be yeast cells grown under "normal" conditions (light conditions) while culture B might be yeast cells grown in the presence of a stress condition. The growth conditions of the cells are identical (except for the presence of the stress stimuli), but the growth media of culture B has an essential amino acid (one not synthesized by the cell) replaced with an isotopically "heavy" form of that amino acid (e.g. 13 C6-arginine). A number of cell lines have been used for SILAC experiments, and the growth and morphology of the cells have not been affected by the isotopically labelled amino acid [78, 101, 102].

After approximately five rounds of doubling, cellular proteins are essentially 100% labelled with the selected amino acid. After culturing, the light and heavy cell populations are combined (1:1) into one pool and the proteins are isolated. The protein pool is then digested with a protease, typically trypsin, to form a peptide pool that is analyzed by MS. Each peptide analyzed will be present in two forms: the light and the heavy form. They are distinguishable based on the mass difference due to the heavy isotope incorporation in the selected amino acid. The SILAC method is compatible with the above mentioned enrichment of phosphoproteins/phosphopeptides including the immunoprecipitation of a target protein [103]. One of the first research studies which carryied out this technology was provided by Gruhler and co-workers (2005) [78]. In this study, more than 700 phosphopeptides from Sacharomyces cerevisiae were identified, 139 were differentially regulated at least 2-fold in response to mating pheromone. Components belonging to the mitogen-activated protein kinase signalling pathway and to downstream processes including transcriptional regulation, the establishment of polarized growth, and the regulation of the cell cycle were among these regulated proteins.

C.2. Isobaric Tag for Relative and Absolute (iTRAQ)

The second method for the global quantification of proteins and protein modifications is an in vitro chemical labelling procedure called iTRAQ. The iTRAQ reagent consists of two to eight isobaric tags that can be used to label two to eight separate protein samples. The iTRAQ tags contain three regions: a peptide reactive region, a reporter region, and a balance region [104]. The peptide reactive region of the tag consists of an NHS ester and is designed to react with the N-termini and lysines of peptides after protease digestions. In the case of 4-plex iTRAQ, the four reporter groups appear in the tandem mass spectrum at m/z 114, 115, 116, and 117. The attached balance groups are designed to make the total mass of the balance and reporter group 145 Da for each tag, which results in balance groups of 31 Da, 30 Da, 29 Da, and 28 Da, respectively. Protein samples for quantification are separately isolated and digested proteolytically, and each sample is chemically labelled with one of the iTRAQ reagents. After labelling, the samples are combined and subsequently analyzed by MS. Identical peptides from each sample will have identical masses as the iTRAQ reagents are isobaric The iTRAQ reagent labels phosphopeptides to the same degree as nonphosphorylated peptides and it does not affect the stability of phosphopeptides. Enrichment strategies, such as IMAC [44, 105] or immunoprecipitation with anti-phosphotyrosine antibodies [44], have been used to remove non-phosphorylated peptides to focus the analysis on site-specific phosphorylation. Since iTRAQ is an in vitro labelling procedure it can also be applied to clinical samples such as tumour tissues and fluids (e.g. serum, urine, blood). iTRAQ has been described as a very powerful method for the quantification of phosphorylation on a proteomic scale. As a relevant example we mention that Boja and co-workers (2009) [106] successfully monitored phosphorylation sites of mitochondrial proteins including adenine nucleotide translocase, malate dehydrogenase and mitochondrial creatine kinase, etc. Among them, four proteins exhibited phosphorylation changes with these physiological stimuli: (a) BCKDH-E1α subunit increased phosphorylation at Ser337 with DCA and de-energization (b) apoptosis-inducing factor phosphorylation was elevated at Ser345 with calcium (c) ATP synthase F1 complex α subunit and (d) mitofilin dephosphorylated at Ser65 and Ser264 upon de-energization. This screening validated the iTRAQ/HCD technology as a method for functional quantitation of mitochondrial protein phosphorylation as well as providing insights into the regulation of mitochondria via phosphorylation.

C.3. Absolute Quantitation (AQUA)

The AQUA strategy provides an absolute quantification of a protein of interest [107]. In the AQUA method, a peptide from the protein of interest is constructed synthetically containing stable isotopes, and the isotopically labelled synthetic peptide is called AQUA peptide. The synthetic peptides can be synthesized with modifications such as phosphorylation to allow for the direct, quantitative analysis of post-translationally modified proteins. The stable isotopes are incorporated into the AQUA peptide by using isotopically "heavy" amino acids during the synthesis process of the interesting peptide (native peptide). In this way, the synthetic peptide has a mass increase of e.g. 10 Daltons, due to the incorporation of a 13C6 and 15N4-arginine into the synthetic peptide, compared to the native peptide. Although the mass difference between the native and the synthetic peptide allows the mass spectrometer to differentiate between the two forms, both forms have the same chemical properties, resulting in the same chromatographic retention, ionization efficiency, and fragmentation distribution. In AQUA experiments, a known amount of the isotopically labelled peptide is added to a protein mixture, which is proteolytically digested, and later analyzed by MS. Since the native peptide and its synthetic counterpart have the same chemical properties, the MS signal from the quantified synthetic peptide can be compared to the signal of the native peptide. The absolute quantification of the peptide to be determined [108] is thus finally permitted. Multiple AQUA peptides can be used to quantify multiple proteins in a single experiment.

Ziwei Yu and co-workers (2007) [109] using AQUA as a novel system of in situ quantitative protein expression analysis, studied the protein expression levels of phosphorylated Akt (p-Akt). Activation of Akt in tumours is mediated via several mechanisms, including activation of cell membrane receptor tyrosine kinases such as EGFR and loss of phosphatase PTEN with dephosphorylation of phosphoinositol triphosphate. Ziwei and co-workers (2007) discovered that Akt activation in oropharyngeal squamous cell carcinoma (OSCC) is associated with adverse patient outcome, indicating that Akt is a promising molecular target in oropharyngeal squamous cell carcinoma.

C.4. Multiple Reaction Monitoring (MRM)

MRM is a very sensitive method for detecting phosphorylated peptides on a hybrid triple quadrupole linear ion trap mass spectrometer (qTRAP). This method requires that the sequence of the protein be known in order to calculate precursor and fragment ion values, which can be used to trigger dependent ion scans in a qTRAP instrument [110, 111]. This technique can also be used to perform a precursor ion and neutral loss scan, to identify unknown phosphopeptides from a complex mixture, and is a powerful method for the identification and quantification of post-translational modifications in proteins. MRM has recently been used by White and co-workers [112, 113] to identify and quantify tyrosine phosphorylated kinases for hundreds of nodes within a signalling network and across multiple experimental conditions. Moreover, White and co-workers [112, 113] applied iTRAQ combined with MRM for phospho quantitative analysis of signalling networks, identifying and quantifying 222 tyrosine phosphorylated peptides, obtaining an extremely high percentage of signalling nodes covered. They defined the mechanisms by which EGFRvIII protein alters cell physiology, as it is one of the most commonly mutated proteins in GBM and has been linked to radiation and chemotherapeutic resistance. They performed a phosphoproteomic analysis of EGFRvIII signalling networks in GBM cells. The results of this study provided important insights into the biology of this mutated receptor, including oncogene dose effects and differential utilization of signalling pathways. In addition, clustering of the phosphoproteomic data set revealed a previously undescribed crosstalk between EGFRvIII and the c-Met receptor. Treatment of the cells with a combination using both EGFR and c-Met kinase inhibitors dramatically decreased cell viability in vitro.

C.5. 2-D Fluorescence Difference Gel Electrophoresis (DIGE)

In DiGE, proteins extracted from a control extract are labelled with one CyDye (Cy3 or Cy5 conjugated), and proteins isolated from a test extract labelled with the other colour of CyDye fluorophore, which are size and charge matched. These labelled protein extracts are mixed and co-resolved (often with the addition of an internal standard, which can be labelled with Cy2) on large-format two-dimensional gels for analysis of expression changes in the resulting pattern of spots ('spot maps') [114]. In comparison with two-dimensional gel electrophoresis, DiGE offers the advantage that multiple samples could be compared on a single gel ('multiplexing'), and made it possible to stain control and test samples with different fluorescent dyes prior to electrophoresis. This advance alleviated issues of gel-to-gel comparison and decreased the number of gels required. The capability to include an internal standard, composed of an equal fraction of all the samples in an experiment, also improved intergel matching and facilitated normalization of matched spots in replicate samples on multiple gels. The use of CyDyes to label proteins, in place of non-fluorescent post-stains, can give a large enhancement of sensitivity for protein detection [115] and constitutes the crucial advantage of the DiGE approach for biomaterial applications. This enables analysis of even very scarce protein samples, including small areas of laser-microdissected tissue [116, 117]. Two-dimensional difference gel electrophoresis (2D-DIGE) with novel ultra high sensitive fluorescent dyes (CyDye DIGE Fluor saturation dye) enables the efficient protein expression profiling of laser-microdissected tissue samples. The combined use of laser microdissection allows accurate proteomic profiling of specific cells including tumour tissues [118].

As an example, differential protein analysis was performed using 2-dimensional differential in-gel electrophoresis (2D-DIGE) by Yefei Rong and co-workers (2010) [119]. They found that 16 protein spots were differently expressed between the two mixtures (a comparison was made with serum samples from five individuals with pancreatic cancer and five individuals without cancer). Yefei Rong and co-workers [119] demonstrated that eight proteins from these fluids were up-regulated and 8 were down-regulated in cancer. Mass spectrometry and database searching allowed the identification of the proteins corresponding to the gel spots. Up-regulation of mannose-binding lectin 2 and myosin light chain kinase 2, which had not previously been implicated in pancreatic cancer, were observed. In an independent series of serum samples from 16 patients with pancreatic cancer and 16 non-cancerbearing controls, increased levels of mannose-binding lectin 2 and myosin light chain kinase 2 were confirmed by western blot.

Moreover, Nagano (2010) [120] has recently developed the technology named "antibody proteomics technology". This technology can screen for biomarker proteins by isolating antibodies against each candidate in a rapid and comprehensive manner. He applied "antibody proteomics technology" to breast cancer-related biomarker discovery and evaluated the utility of this novel technology. Cell extracts derived from breast tumour cells (SKBR3) and normal cells were analyzed by two-dimensional differential gel electrophoresis (2D-DIGE) in order to identify proteins over-expressed in the tumour cells. Candidate proteins were extracted from the gel pieces, immobilized onto a nitrocellulose membrane using a dot blot apparatus and then used as target antigens in scFv-phage enrichment and selection. scFvs binding to 21 different over-expressed proteins in tumor cells were successfully isolated within several weeks following this in vitro phage selection procedure. The expression profiles of the identified proteins were then determined by tissue microarray analysis using the scFv-phages. Consequently, three breast tumour-specific proteins were identified. His data demonstrated the utility of an antibody proteomics system for discovering and validating tumour-related proteins in pharmaceutical proteomics. Currently, he and other related groups are analyzing the functions of these proteins in order to be able to confirm and use them as diagnostic markers or therapeutic targets.

(D) Phosphorylated proteins related to different diseases and the benefits of proteomic strategies in such clinical studies

In a recent study Steen et al. examined the role of phosphorylation in the dynamics of the anaphase promoting complex (APC) [121]. Some drugs that bind to microtubules and block mitosis are ineffective in cancer treatment others show inexplicable focal efficacy. For example, the vinca alkaloids are useful for treating lymphoma, neuroblastoma and nephroblastomas, whereas taxol is useful for advanced breast cancer and ovarian cancer. It is not known why these drugs are not all equally effective, nor why they have different therapeutic value against different cancers. The authors observed distinct phosphorylation states of the APC in response to different antimitotic drugs and suggest they may explain some of these differences. They also propose it is possible that cells from different tissues, or cells harbouring different mutations, or cells under different physiological stresses, such as hypoxia, may differ in their response to spindle poisons and would thus reflect those differences in different sites of phosphorylation. Differences in spindle checkpoint phosphorylation may reveal new features of the mitotic state. The categorisation of drugs, the discrimination of the response of tumours to drugs and the identification of new means of checkpoint control may be facilitated by the ability to characterise drug candidates based on the spectrum of APC phosphorylations The authors further suggest that the results of the study indicate that the term mitotic arrest is a misnomer: arrest is a dynamic state in which some cells enter apoptosis and other cells revert to interphase. The ability to observe biochemical events during arrest could be very important for understanding antiproliferative treatments. The exploration of the dynamics of phosphorylation, however, makes great demands on the accuracy of quantitation. Most mass spectrometric based quantitative approaches, including stable isotope labelling with amino acids in cell culture (SILAC) and isobaric tag for relative and absolute quantitation (iTRAQ), give relative data, meaning that one state of phosphorylation is determined relative to another phosphorylation state [122–124] these data can help to establish the kinetics of a pathway. The method used in this work offers a significant advance over earlier techniques. It allowed the measurement of specific quantitative changes in APC phosphorylation in cells arrested in nocodazole for varying periods. If these dynamics can be correlated with the process by which the arrested state is resolved, we may be provided with new tools to understand the mitotic process and to find more effective drug targets in cancer.

The long-held belief in the cancer research community that a precise molecular understanding of cancer can result in cancer therapy is validated by the development of drugs for specific biological pathways with increased specificity and reduced toxicity. The development of Herceptin, a monoclonal antibody against the HER2 receptor for breast cancer therapy is one of the most successful recent examples of cancer-specific drugs. HER2 is an important target in cancer because its overexpression increases tumour cell proliferation, vessel formation and invasiveness, and predicts poor prognosis. Wolf-Yadlin and other scientists [111, 112], [121–124] have used phosphoproteomics and MS to investigate the role of phosphorylation in the effects of HER2 overexpression on EGF- and HRG-mediated signalling of erbB receptors. Identification was possible of specific combinations of phosphorylation sites that correlate with cell proliferation and migration and that potentially represent targets for therapeutic intervention. Unfortunately, owing to sensitivity limitations, only 68 out of 322 phosphorylation sites could be analysed kinetically, so the study does not provide a comprehensive analysis of the multitude of effects produced by HER2 overexpression. It does, however, mark an important breakthrough in the characterisation of the erbB receptor signalling network in tumours and illustrates the importance of understanding protein phosphorylation.

A central role is played by mitochondria in energy metabolism and cellular survival, and consequently mitochondrial dysfunction is associated with a number of human pathologies. Moreover, mitochondrial dysfunction is linked to insulin resistance in humans with obesity and type 2 diabetes. Recently, Zhao and co-workers (2011) [125], studied the phosphoproteome of the mitochondria isolated from human skeletal muscle. Zhao and co-workers revealed extensive phosphorylation of inner membrane protein complexes and enzymes combining titanium dioxide (TiO2) protocols with reverse phase chromatography coupled to MS analysis. 155 distinct phosphorylation sites in 77 mitochondrial phosphoproteins, including 116 phosphoserine, 23 phosphothreonine, and 16 phosphotyrosine residues were identified. Phosphorylation sites in mitochondrial proteins involved in amino acid degradation, importers and transporters, calcium homeostasis, and apoptosis were also assigned. Furthermore, many of these mitochondrial phosphoproteins are substrates for protein kinase A, protein kinase C, casein kinase II and DNA-dependent protein kinase. The high number of phosphotyrosine residues suggests that tyrosine phosphorylation has an important role in mitochondrial signalling. Many of the mitochondrial phosphoproteins are involved oxidative phosphorylation, tricarboxylic acid cycle, and lipid metabolism, i.e. processes proposed to be involved in insulin resistance. It is well known that mitochondria dysfunction is centrally involved in a number of human pathologies, such as type 2 diabetes, parkinson's disease and cancer [126]. In this study, the most prevalent form of cellular protein post-translational modifications (PTMs), reversible phosphorylation [127–134][135-139], emerges as a central mechanism in the regulation of mitochondrial functions [130, 131]. The steadily increasing numbers of reported mitochondrial kinases, phosphatases and phosphoproteins also imply the important role of protein phosphorylation in different mitochondrial processes [132–134]. The prototypical proteomics pipe-line useful for clinical research is illustrated (Figure 3).

A prototypical proteomics pipe-line useful for clinical research. Depending on the application, different samples processed and fed into the proteomics pipeline yield different results. The pipeline's several steps are listed in the different panels: (1) proteolytic digest, (2) the separation and ionization of peptides, (3) their analysis by MS, (4) fragmentation of selected peptides and analysis of the resulting MS/MS spectra and, (5) (6) data-computer analysis, which includes identification and quantification of proteins from several detected peptides.

(E) Observations and Future Needs

Cancer and immune disorders are still among the leading causes of death worldwide. Therefore, there is still a need for the identification of useful biomarkers and the improvement of the understanding of the development of these diseases. The immune system is readily affected by the existence of cancer in the body, even at a preclinical stage, and these studies should be expanded and extended in the future to answer the numerous questions concerning (a) the roles of immune cells in cancer surveillance (b) the characteristics of inflammation in association with cancer development, (c) the effects of environment/lifestyle factors on the immune system, and (d) the interaction between aging diseases. The importance of protein kinase-regulated signal transduction pathways in immunology disorders and cancer has led to the development of drugs that inhibit protein kinases at the apex or intermediary levels of these pathways. Protein phosphorylation assignment studies of these signalling pathways will provide important insights into the operation and connectivity of these pathways that will facilitate the identification of the best targets for cancer therapies and immunology diseases (e.g. the identification of a phosphate group on a specific serine, threonine or tyrosine by phosphoenrichments combined with MS). Phosphoproteomic analysis of individual tumours, blood, sera, tissues will also help match targeted therapeutic drugs to the appropriate patients. It is now generally accepted that no single proteomic method is comprehensive, but combinations of different enrichment methods produce distinct overlapping phosphopeptide datasets to enhance the overall results in phosphoproteome analysis. During the last decade, phosphopeptide sequencing by mass spectrometry has seen tremendous advances. For example, MS/MS product ion scanning, multistage activation and precursor ion scanning are effective methods for identifying serine (Ser), threonine (Thr) and tyrosine (Tyr) phosphorylated peptides.

The current phosphoproteomic goals imply the identification of phosphoproteins, mapping of phosphorylation sites, quantitation of phosphorylation under different conditions, and the determination of the stoichiometry of the phosphorylation. In addition, knowing when a protein is phosphorylated, which kinase/s is-are involved, and how each phosphorylation fits into the signalling network, are also important challenges for researchers in order to understand the significance of different biological events. The new MS technologies are fundamental for cataloguing all this information, and it is heading towards the collection of accurate data on phosphopeptides on a global scale. In addition, the possible difficulties to get sufficient amount of specific phosphorylated proteins of specific low abundant protein-kinases in vivo which might limit the usability of the phosphoproteome analysis, must be pointed out.

Finally, it is important to state that to develop clinical proteomic applications using the identified proteins and phosphoproteins, collaboration between research scientists, clinicians and diagnostic companies, and proteomic experts is essential, particularly in the early phases of the biomarker development projects. The proteomics modalities currently available have the potential to lead to the development of clinical applications, and channelling the wealth of the information produced towards concrete and specific clinical purposes is urgent.


DISCUSSION

In the present study, TAA associated protein expression in patients with BAV and TAV was identified using 2D-DIGE together with multivariate statistics. Moreover, the analysis of protein spots was combined with microarray mRNA and exon expression analysis. Our results collectively demonstrated that TAA formation in patients with BAV has clearly diverging expression fingerprints compared with the patients with TAV, at all three levels of gene, exon and protein analysis. This study provides further support to our previously performed mRNA expression analyses including all expressed genes in the human genome (2).

2D-DIGE proteomic results were validated using an independent, peptide based proteomic method, LC-MS/MS and 57 and 71% of the identified proteins were validated in TAV and BAV respectively. It is important to notice that 2D-DIGE analysis results in a number of protein spots that have been identified as the same protein, but modified potentially with post translational modifications. LC-MS/MS analysis on the other hand, identifies a number of protein isoforms that have at least one unique peptide included in their sequence. The 2D-DIGE statistical analysis was performed on 302 protein spots for which the statistics were summarized in 43 proteins in Table I , whereas LC-MS/MS statistical analysis was performed on 35 proteins (a subset of the 43 proteins from 2D-DIGE experiments because eight proteins were not identified with LC-MS/MS) ( Table II ). This discrepancy between the two methods nevertheless, the percentage of validated proteins turned out to be very high which clearly strengthens the results of the 2D-DIGE analysis ( Table II ).

Literature search of the identified proteins indicated that the function of differentially expressed proteins in medial degeneration of aorta in patients with TAV was dominated by inflammatory processes. On the other hand, the corresponding function in patients with BAV was most probably because of the impaired repair capacity in these patients. The functional repertoire of all the identified proteins is summarized in Table III . Two proteins found to be associated with BAV were the plasma protein transthyretin (TTR) and the highly conserved plasma glycoprotein, Serum Amyloid P component (SAP/APCS). The function of these two proteins in the aortopathy of BAV patients is not clear. However, TTR protein expression was reduced in dilated aorta of BAV patients as compared with BAV patients with nondilated aorta whereas APCS protein expression was increased. Hence, both proteins may function in the repair processes of the damaged vessels in BAV because the association of increased TTR with facilitated wound healing (13) and inhibition of fibroblasts differentiation by elevated level of APCS in wound healing has been reported (14). TTR is also associated with senile systemic amyloidosis (SSA), in which wild-type TTR forms amyloid deposits in various tissues, and is an age-related nonhereditary systemic amyloidosis affecting mainly cardiac functions in elderly (Reviewed in (15)). The role of TTR in amyloidosis is complex and although TTR itself has been associated with fibril formation in amyloidosis, it is also capable of acting as a protease cleaving amyloid beta peptide thereby exerting a protective role in the pathology of Alzheimer′s disease AD (16). Reductions of TTR concentration in cerebrospinal fluid of AD patients (17) and hippocampus of AD mouse model (18) was shown to be negatively correlated with disease severity. Similar to TTR, APCS is also universally found associated with amyloid depositions independently of the protein origin (19). The possibility of BAV dilatation being related to proteinopathies and the function of these two amyloidosis-related proteins in dilatation of BAV patients deserves further clarification. Furthermore, the expression of three myosin light chains, MYL6, MYL9, and MYL12B have significantly increased in dilatation of BAV whereas only MYL6 expression is also increased in TAV dilated patients. Myosin light chains are phosphorylated and thereby promote actomyosin contraction which generates pulling force between the adjacent cells and widening of the intercellular gap (20). This may imply that the process of vascular barrier breakdown is activated more significantly in BAV dilatation. This is further supported by an increased albumin expression in dilated BAV.

The only protein where one of the protein spots (isoforms) was up-regulated in dilated BAV aorta and the other protein spot (isoform) down regulated in dilated TAV is filamin binding LIM protein (FBLIM1/migfilin). FBLIM1 has several important interactions that makes it a very important molecular switch for cell-ECM and cell-cell interactions. It localizes to both cell-extracellular matrix and in endothelial cells adherens junctions via its C-terminal end LIM binding domain. However, its recruitment to ECM is via interaction with another protein, fermitin family member 2 (FERMT2/MIG2) that is not required for the FBLIM1 localization to adherens junctions. The N-terminal part of the protein binds filamin through which it regulates the cytoskeleton (Reviewed in (21)). Furthermore, it acts as a molecular switch, disconnecting filamin from integrin for regulating integrin activation and dynamics of extracellular matrix-actin linkage (22). It has been proposed that because the C-terminal domain is required for both localization of FBLIM1 to cell-ECM adhesions as well as cell-cell junctions there will be a competition between the two activities of FBLIM1 and this will dictate the relative distribution of FBLIM1 between the two pathways (Reviewed in (21)). Interestingly, we have observed a higher expression of FREMT2/MIG2 mRNA in BAV relative to TAV irrespective of the dilatation state (Maleki et al., (23)), implying that the balance between the two cellular pathways may have changed in BAV dilatation relative to TAV. This observation may be very important with respect to the differences between the dilatation in BAV and TAV and may separate the two events mechanistically. Another relevant observation in this regard is that the distribution of FBLIM1 is barely detectable in normal smooth muscle cells (SMC), but abundant in neoplastic transformed SMC, implying that its expression in SMC is related to the pathological changes (24).

Up-regulation of transglutaminase 2 (TGM2) was only detected in TAV patients. This is interesting because transforming growth factor β (TGFβ) is known to up-regulate TGM2 (25). Previous data have indicated differences in TGFβ signaling pathway between BAV and TAV patients (4, 26). Furthermore, vimentin (VIM), one of the components of intermediate filament, is a major substrate for transglutaminases (27). Hence, medial degeneration in TAV patients may be the result of vimentin crosslinking by TGM2, as has been proposed for arterial remodeling (27). Moreover, most of the proteins specifically associated with dilatation in TAV i.e. FGG, VIM, MFAP4, and HSPA1L have also been cited in association with either inflammatory diseases or inflammatory processes ( Table I , Table III ) which is in line with our previous observation of an increased medial inflammation in TAV but not in BAV patients (2). We propose that medial degeneration in TAV may be the result of a direct attack on the medial layer by pro-inflammatory molecules, whereas in BAV it may be secondary to the higher permeability and diminished capacity of vascular endothelium for regeneration. One of the factors which may contribute to higher permeability of endothelial layer can be the constant exposure of BAV aorta to abnormal hemodynamics as has been demonstrated (28, 29). In support of this argument, we have recently shown that in BAV aorta, there is an angiostatic profile of gene expression, containing several EC-specific-genes, even in nondilated aortas (23).

Although both mRNA expression and protein expression could separate patients with dilated aorta from patients with nondilated aorta, it was clear that there were very few differentially expressed genes showing the same pattern of differential expression at protein level (Supplemental Table S8, Table I and IV). This is not unexpected and, in accordance with our data, other studies have shown that correlation between mRNA expression and protein expression is overall low (30). An explanation to this may be that individual half-lives of proteins are influenced by several post-translational factors which will influence the mRNA-protein expression correlation. The life time of a protein is dependent on a number of different processes, e.g. protein stability, post-translational processing (phosphorylation, and ubiqutination), as well as localization of the protein (31) (30), processes that can lead to discrepancy observed in the expression at transcript and protein levels. Furthermore, as shown from our analyses, there were several spots that could be detected representing the same protein. The different spots probably represent different post-translational modifications that will influence the comparison between mRNA level, representing the total gene expression, with the expression of a subset of the protein. Another possibility could be that the different spots represent different splice forms of the protein. We have previously analyzed differential splicing in the TGFβ signaling pathway in the same cohort (4). From this analysis it was clear that differential splicing is a common phenomena but the study also showed that the expression of the differentially spliced mRNAs were low compared with the wild-type transcript. Therefore, it is unlikely that the protein spots that could be detected by the 2D gel analyses represent different protein isoforms as a result of differential splicing. This is further supported by the lack of clear association between the presence of splicing and the number of detected protein spots.

Several previous studies have performed proteomic studies to identify protein expression associated with TAA. In only one of the studies, TAV and BAV patients were analyzed separately (32). However, in that study, only patients with dilated aorta tissues were included. By analyzing 16 patients in total, the authors showed that heat chock protein 27 was significantly lower in dilated aorta of BAV patients compared with dilated aorta of TAV patients. In the present study, heat chock protein 27 was significantly higher in dilatation in both TAV and BAV samples ( Table I ), whereas there is no significant difference observed between TAV and BAV in neither dilated nor nondilated samples (data not shown). Furthermore, a study of aneurysm in human ascending aorta showed that ENO1 variant (fragment) had significantly higher expression whereas VIM shows lower expression in aneurysm tissues compared with controls (33). In the present study, ENO1 protein expression was higher in dilated aorta in both TAV and BAV patients ( Table I ) thus showing similar pattern as the study performed by Schachner et al. VIM, however, was higher in dilated samples only in TAV, but not in BAV in the present study ( Table I ).

Because nondilated samples were obtained from patients with TAV and BAV undergoing valve repair it is important to evaluate their status as control samples. We have previously analyzed the global mRNA expression profiles of these samples together with control samples taken from heart transplantation donors. Based on total mRNA expression, the transplant control samples clustered close to nondilated aorta media tissue samples in a PCA thereby showing that the nondilated tissue samples have similar mRNA expression profile as the transplant control samples (2). The divergent mRNA expression and protein fingerprints further strengthen the notion that dilated samples from patients with BAV need to be compared with nondilated aorta from patients with BAV.

In summary, the present study suggests that the dilatation and medial degeneration evolves by two different mechanisms in patients with BAV and TAV. In particular, the importance of components of the wound healing machinery and inflammation for the observed differences in aortopathy between patients with BAV and TAV is highlighted.