We are searching data for your request:
Upon completion, a link will appear to access the found materials.
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.
- 10 ug
- 20 ug
- 10 ug
- 20 ug
You then try the phospho-specific antibody and get the following
- 1 ug
- 2 ug
- 3 ug
- 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 | |||
|---|---|---|---|---|
| Ellagitannins | PP1c | PP2Ac | HeLa lysate | selectivity |
| tellimagrandin 1 | 0.20 ±𠂐.02 | 19.52 ±𠂘.60 | 4.46 ±𠂐.95 | 1:97 |
| mahtabin A | 0.41 ±𠂐.18 | 33.77 ±𠂘.44 | 6.19 ±𠂐.37 | 1:82 |
| praecoxin B | 0.79 ±𠂐.11 | 63.86 ±𠂕.52 | 6.97 ±𠂑.12 | 1:80 |
| GHG | 1.41 ±𠂐.30 | | 12.97 ±𠂓.30 | n.d. |
| pedunculagin | 2.47 ±𠂐.22 * | | 33.90 ±𠂔.54 | n.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. (f–i) 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 4f–i) 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.
) 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 NephropathyIgA 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.
|
