定量蛋白质组学无标记定量方法的研究进展

依靠质谱技术的蛋白质组学快速发展,寻求速度快、重复性好以及准确度高的定量方法是该领域的一项艰巨任务,定量蛋白质组学分支领域应运而生．其中,无标记定量方法以其样品制备简单、耗材费用低廉以及结果数据分析便捷等优点渐露锋芒．无标记定量方法通常分为信号强度法和谱图计数法两大类．本文在这两种无标记定量方法计算原理的基础上,针对各种常用的无标记定量方法及最新进展做一个较为全面的介绍,并将详细讨论两类方法的异同点,以及目前蛋白质组学中无标记定量方法所面临的主要挑战,希望能为这一领域的研究人员在选择无标记定量方法时提供一个合理的参考．     

Clinical Proteomics Identifies Urinary CD14 as a Potential Biomarker for Diagnosis of Stable Coronary Artery Disease

Inflammation plays a key role in coronary artery disease (CAD) and other manifestations of atherosclerosis. Recently, urinary proteins were found to be useful markers for reflecting inflammation status of different organs. To identify potential biomarker for diagnosis of CAD, we performed one-dimensional SDS-gel electrophoresis followed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Among the proteins differentially expressed in urine samples, monocyte antigen CD14 was found to be consistently expressed in higher amounts in the CAD patients as compared to normal controls. Using enzyme-linked immunosorbent assays to analyze the concentrations of CD14 in urine and serum, we confirmed that urinary CD14 levels were significantly higher in patients (n = 73) with multi-vessel and single vessel CAD than in normal control (n = 35) (P < 0.001). Logistic regression analysis further showed that urinary CD14 concentration level is associated with severity or number of diseased vessels and SYNTAX score after adjustment for potential confounders. Concomitantly, the proportion of CD14+ monocytes was significantly increased in CAD patients (59.7 ± 3.6%) as compared with healthy controls (14.9 ± 2.1%) (P < 0.001), implicating that a high level of urinary CD14 may be potentially involved in mechanism(s) leading to CAD pathogenesis. By performing shotgun proteomics, we further revealed that CD14-associated inflammatory response networks may play an essential role in CAD. In conclusion, the current study has demonstrated that release of CD14 in urine coupled with more CD14+ monocytes in CAD patients is significantly correlated with severity of CAD, pointing to the potential application of urinary CD14 as a novel noninvasive biomarker for large-scale diagnostic screening of susceptible CAD patients.     

Quantitative Proteomics Analysis Identifies the Potential Mechanism Underlying Yellow-Green Leave Mutant in Wheat

Enhancing photosynthesis efficiency is considered as one of the most crucial targets during wheat breeding. However, the molecular basis underlying high photosynthesis efficiency is not well understood up to now. In this study, we investigated the protein expression profile of wheat Jimai5265yg mutant, which is a yellow-green mutant with chlorophylls b deficiency but high photosynthesis efficiency. Though TMT-labeling quantitative proteomics analysis, a total of 72 differential expressed proteins (DEPs) were obtained between the mutant and wild type (WT). GO analysis found that they significantly enriched in thylakoid membrane, pigment binding, magnesium chelatase activity and response to light intensity. KEGG analysis showed that they involved in photosynthesis-antenna protein as well as porphyrin and chlorophyll metabolism. Finally, 118 RNA editing events were found between mutant and WT genotype. The A to C editing in the 3-UTR of TraesCS6D02G401500 lead to its high expression in mutant through removing the inhibition of tae-miR9781, which might have vital role in regulating the yellow-green mutant. This study provided some useful clues about the molecular basis of Jimai5265yg mutant as well as chlorophylls metabolism in wheat.     

Analysis of a Urinary Biomarker Panel for Obstructive Nephropathy and Clinical Outcomes

Objectives

To follow up renal function changes in patients with obstructive nephropathy and to evaluate the predictive value of biomarker panel in renal prognosis.

Methods

A total of 108 patients with obstructive nephropathy were enrolled in the study; 90 patients completed the follow-up. At multiple time points before and after obstruction resolution, urinary samples were prospectively collected in patients with obstructive nephropathy; the levels of urinary kidney injury molecule-1 (uKIM-1), liver-type fatty acid-binding protein (uL-FABP), and neutrophil gelatinase associated lipocalin (uNGAL) were determined by enzyme-linked immunosorbent assay (ELISA). After 1 year of follow-up, the predictive values of biomarker panel for determining the prognosis of obstructive nephropathy were evaluated.

Results

uKIM-1 (r = 0.823), uL-FABP (r = 0.670), and uNGAL (r = 0.720) levels were positively correlated with the serum creatinine level (all P<0.01). The levels of uKIM-1, uL-FABP, and uNGAL were higher in the renal function deterioration group than in the renal function stable group. Cox regression analysis revealed that the 72-h postoperative uKIM-1 level and the preoperative and 72-h postoperative uL-FABP levels were all risk factors for renal function deterioration (all P<0.01). The area under the curve of Receiver Operating Characteristic(ROC-AUCs) of 72-h postoperative uKIM-1, preoperative uL-FABP, and 72-h postoperative uL-FABP were 0.786, 0.911, and 0.875, respectively. When the combined preoperative uKIM-1, uL-FABP, and uNGAL levels or combined 72-h postoperative uKIM-1, uL-FABP, and uNGAL levels were considered, the accuracy of prediction for renal prognosis was markedly increased, with an ROC-AUC of 0.967 or 0.964, respectively. Kaplan-Meier survival curve analysis demonstrated that a 72-h postoperative uKIM-1>96.69 pg/mg creatinine (Cr), a preoperative uL-FABP>154.62 ng/mg Cr, and a 72-h postoperative uL-FABP>99.86 ng/mg Cr were all positively correlated with poor prognosis (all P<0.01).

Conclusion

Biomarker panel may be used as a marker for early screening of patients with obstructive nephropathy and for determining poor prognosis.     

A New Player in the Spermiogenesis Pathway of Caenorhabditis elegans

Precise timing of sperm activation ensures the greatest likelihood of fertilization. Precision in Caenorhabditis elegans sperm activation is ensured by external signaling, which induces the spherical spermatid to reorganize and extend a pseudopod for motility. Spermatid activation, also called spermiogenesis, is prevented from occurring prematurely by the activity of SPE-6 and perhaps other proteins, termed “the brake model.” Here, we identify the spe-47 gene from the hc198 mutation that causes premature spermiogenesis. The mutation was isolated in a suppressor screen of spe-27(it132ts), which normally renders worms sterile, due to defective transduction of the activation signal. In a spe-27(+) background, spe-47(hc198) causes a temperature-sensitive reduction of fertility, and in addition to premature spermiogenesis, many mutant sperm fail to activate altogether. The hc198 mutation is semidominant, inducing a more severe loss of fertility than do null alleles generated by CRISPR-associated protein 9 (Cas9) technology. The hc198 mutation affects an major sperm protein (MSP) domain, altering a conserved amino acid residue in a β-strand that mediates MSP–MSP dimerization. Both N- and C-terminal SPE-47 reporters associate with the forming fibrous body (FB)-membranous organelle, a specialized sperm organelle that packages MSP and other components during spermatogenesis. Once the FB is fully formed, the SPE-47 reporters dissociate and disappear. SPE-47 reporter localization is not altered by either the hc198 mutation or a C-terminal truncation deleting the MSP domain. The disappearance of SPE-47 reporters prior to the formation of spermatids requires a reevaluation of the brake model for prevention of premature spermatid activation.     

Quantitative Early Auxin Root Proteomics Identifies GAUT10, a Galacturonosyltransferase,as a Novel Regulator of Root Meristem Maintenance

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Highlights

• •Auxin responsive proteins in Arabidopsis roots were identified from 3,514 detected proteins.
• •All six auxin receptors are stable in response to hormone via novel MRM assays.
• •The >100 differentially expressed proteins exhibit dynamic and transient responses to auxin.
• •Phenotypic screening of the top responsive proteins uncovered several novel root mutants.

     

Quantitative Chemical Proteomics Identifies Novel Targets of the Anti-cancer Multi-kinase Inhibitor E-3810

Novel drugs are designed against specific molecular targets, but almost unavoidably they bind non-targets, which can cause additional biological effects that may result in increased activity or, more frequently, undesired toxicity. Chemical proteomics is an ideal approach for the systematic identification of drug targets and off-targets, allowing unbiased screening of candidate interactors in their natural context (tissue or cell extracts).E-3810 is a novel multi-kinase inhibitor currently in clinical trials for its anti-angiogenic and anti-tumor activity. In biochemical assays, E-3810 targets primarily vascular endothelial growth factor and fibroblast growth factor receptors. Interestingly, E-3810 appears to inhibit the growth of tumor cells with low to undetectable levels of these proteins in vitro, suggesting that additional relevant targets exist. We applied chemical proteomics to screen for E-3810 targets by immobilizing the drug on a resin and exploiting stable isotope labeling by amino acids in cell culture to design experiments that allowed the detection of novel interactors and the quantification of their dissociation constant (K_{d imm}) for the immobilized drug. In addition to the known target FGFR2 and PDGFRα, which has been described as a secondary E-3810 target based on in vitro assays, we identified six novel candidate kinase targets (DDR2, YES, LYN, CARDIAK, EPHA2, and CSBP). These kinases were validated in a biochemical assay and—in the case of the cell-surface receptor DDR2, for which activating mutations have been recently discovered in lung cancer—cellular assays.Taken together, the success of our strategy—which integrates large-scale target identification and quality-controlled target affinity measurements using quantitative mass spectrometry—in identifying novel E-3810 targets further supports the use of chemical proteomics to dissect the mechanism of action of novel drugs.The “target deconvolution” process, namely, the identification and characterization of proteins bound by a drug of interest (1), is a crucial step in drug development that allows definition of the compound selectivity and the early detection of potential side effects. Target deconvolution can be achieved by means of systematic in vitro biochemical assays measuring the ability of the drug to interact with candidate binders and, if they are enzymes, interfere with their activity. An alternative approach is chemical proteomics (chemoproteomics), which combines affinity chromatography and proteomic techniques (2, 3). Up-to-date chemical proteomics essentially consists of three main steps: (i) drug immobilization on a solid phase; (ii) drug affinity chromatography to capture drug targets in complex protein mixtures, such as cell or tissue lysates; and (iii) mass spectrometry (MS)-based¹ identification of the proteins retained by the immobilized drug (4–6).In chemical proteomics, the affinity chromatography step is typically performed under mild conditions, to allow the identification of all possible natural binders. The drawback of using mild, non-denaturing conditions is the significant number of proteins nonspecifically binding to the solid phase, which, once identified via MS, can be difficult to discern from genuine drug targets. The relatively high number of such nonspecific binders has limited the widespread use of this strategy.More recently, the development and implementation of quantitative strategies in proteomics based on the use of differentially stable isotopes to label proteomes from distinct functional states, together with significant technological and instrumental developments in the MS field concerning sensitivity and throughput, have largely allowed this limitation to be overcome. One of the most popular labeling techniques is stable isotope labeling by amino acids in cell culture (SILAC) (7). In SILAC, dividing cells are cultured in media supplemented with amino acids containing stable isotopic variants of carbon (¹²C/¹³C), nitrogen (¹⁴N/¹⁵N), or hydrogen (¹H/²H), which are incorporated into newly synthesized proteins during cell division. When extensive labeling (>98%) of cells is achieved upon the appropriate number of replications, light and heavy cells are differentially treated (e.g. exposed to drug versus vehicle), mixed in equal proportion, and subjected to proteomics analysis by means of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Peptides from the two functional states can be distinguished by their specific delta mass values, and their intensity ratio in MS spectra is directly proportional to the relative abundance of the corresponding proteins in the initial protein extract. Robust analysis of SILAC data is possible with dedicated software, such as MaxQuant (8). The application of SILAC strategies to interactomic studies is an efficient means of discerning specific from background binders (9). When applied to chemical proteomics, quantitative proteomics is crucial, as it offers quality filters to discern genuine drug interactors from proteins binding to the solid phase, with the use of different experimental setups (4, 5).In this study, we successfully coupled SILAC with chemical proteomics to carry out an unbiased screening of protein interactors of the anti-cancer drug E-3810, currently in Phase II clinical trials. E-3810 is a novel multi-kinase inhibitor, a class of targeted drug that comprises different molecules currently used in clinical practice (e.g. imatinib, dasatinib, sunitinib, sorafenib) (10). E-3810 exhibits both anti-tumor and anti-angiogenic properties (11). In preclinical studies, E-3810 showed broad anti-tumor activity in vivo, when used as monotherapy in a variety of human xenografts, or in conjunction with conventional chemotherapy (11, 12).Cellular vascular endothelial growth factor receptors (VEGFRs) and fibroblast growth factor receptors (FGFRs) are the principal targets of E-3810, as previously demonstrated by in vitro kinase assays, which showed that E-3810 inhibited VEGFR-1, -2, and -3 and FGFR-1 and -2 in the nanomolar range (11). Studies performed on several kinase inhibitors demonstrated that these molecules can elicit pleiotropic effects not easily explained by the sole inhibition of their known targets (13, 14). These effects are in most cases due to an inhibitory activity of the drug on additional kinase targets not tested in vitro that may lead to synergistic anti-cancer effects or undesirable toxicity. This could also be the case for E-3810, which was shown to inhibit in vitro additional kinase targets with high affinity, and which is able to inhibit the growth of tumor cells expressing low to undetectable levels of VEGFRs/FGFRs, suggesting that its spectrum of target inhibition has not been fully explored (11).We thus established a SILAC-based chemical proteomic platform composed of a set of affinity chromatography experiments using E-3810 immobilized on agarose resin and incubated with SILAC-labeled extract from the ovarian cancer cell line A2780. We identified proteins interacting with the resin via MS and took advantage of SILAC-based protein quantitation to discern genuine from background binders and derive quantitative information about the specific interactions. Our findings demonstrate that additional targets of E-3810 exist and that these targets may contribute to the anticancer effect of E-3810.     

蛋白质组研究新前沿：定量蛋白质组学

在过去几年里,蛋白质组研究取得了令人鼓舞的进展,2DE-MS途径的自动化,多维色谱整合串联质谱的使用,弥补了一些用双向凝胶电泳分离蛋白质的技术缺陷;从稳定同位素标记到ICAT战略的提出,为准确定量在细胞或组织中发挥重要调节功能的低丰度蛋白质提供了一个较为理想的方法。同时,蛋白质芯片技术的不断发展,也极大的丰富了定量蛋白质组学的研究。就定量蛋白质组学及其相关技术研究进展作一简要综述。     

In-Cell Intrabody Selection from a Diverse Human Library Identifies C12orf4 Protein as a New Player in Rodent Mast Cell Degranulation

The high specificity of antibodies for their antigen allows a fine discrimination of target conformations and post-translational modifications, making antibodies the first choice tool to interrogate the proteome. We describe here an approach based on a large-scale intracellular expression and selection of antibody fragments in eukaryotic cells, so-called intrabodies, and the subsequent identification of their natural target within living cell. Starting from a phenotypic trait, this integrated system allows the identification of new therapeutic targets together with their companion inhibitory intrabody. We applied this system in a model of allergy and inflammation. We first cloned a large and highly diverse intrabody library both in a plasmid and a retroviral eukaryotic expression vector. After transfection in the RBL-2H3 rat basophilic leukemia cell line, we performed seven rounds of selection to isolate cells displaying a defect in FcεRI-induced degranulation. We used high throughput sequencing to identify intrabody sequences enriched during the course of selection. Only one intrabody was common to both plasmid and retroviral selections, and was used to capture and identify its target from cell extracts. Mass spectrometry analysis identified protein RGD1311164 (C12orf4), with no previously described function. Our data demonstrate that RGD1311164 is a cytoplasmic protein implicated in the early signaling events following FcεRI-induced cell activation. This work illustrates the strength of the intrabody-based in-cell selection, which allowed the identification of a new player in mast cell activation together with its specific inhibitor intrabody.     

Peptide-level Robust Ridge Regression Improves Estimation,Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics

Peptide intensities from mass spectra are increasingly used for relative quantitation of proteins in complex samples. However, numerous issues inherent to the mass spectrometry workflow turn quantitative proteomic data analysis into a crucial challenge. We and others have shown that modeling at the peptide level outperforms classical summarization-based approaches, which typically also discard a lot of proteins at the data preprocessing step. Peptide-based linear regression models, however, still suffer from unbalanced datasets due to missing peptide intensities, outlying peptide intensities and overfitting. Here, we further improve upon peptide-based models by three modular extensions: ridge regression, improved variance estimation by borrowing information across proteins with empirical Bayes and M-estimation with Huber weights. We illustrate our method on the CPTAC spike-in study and on a study comparing wild-type and ArgP knock-out Francisella tularensis proteomes. We show that the fold change estimates of our robust approach are more precise and more accurate than those from state-of-the-art summarization-based methods and peptide-based regression models, which leads to an improved sensitivity and specificity. We also demonstrate that ionization competition effects come already into play at very low spike-in concentrations and confirm that analyses with peptide-based regression methods on peptide intensity values aggregated by charge state and modification status (e.g. MaxQuant''s peptides.txt file) are slightly superior to analyses on raw peptide intensity values (e.g. MaxQuant''s evidence.txt file).High-throughput LC-MS-based proteomic workflows are widely used to quantify differential protein abundance between samples. Relative protein quantification can be achieved by stable isotope labeling workflows such as metabolic (1, 2) and postmetabolic labeling (3–6). These types of experiments generally avoid run-to-run differences in the measured peptide (and thus protein) content by pooling and analyzing differentially labeled samples in a single run. Label-free quantitative (LFQ)¹ workflows become increasingly popular as the often expensive and time-consuming labeling protocols are omitted. Moreover, LFQ proteomics allows for more flexibility in comparing samples and tends to cover a larger area of the proteome at a higher dynamic range (7, 8). Nevertheless, the nature of the LFQ protocol makes shotgun proteomic data analysis a challenging task. Missing values are omnipresent in proteomic data generated by data-dependent acquisition workflows, for instance because of low-abundant peptides that are not always fragmented in complex peptide mixtures and a limited number of modifications and mutations that can be accounted for in the feature search. Moreover, the overall abundance of a peptide is determined by the surroundings of its corresponding cleavage sites as these influence protease cleavage efficiency (9). Similarly, some peptides are more easily ionized than others (10). These issues not only lead to missing peptides, but also increase variability in individual peptide intensities. The discrete nature of MS1 sampling following continuous elution of peptides from the LC column leads to increased variability in peptide quantifications. Finally, competition for ionization and co-elution of other peptides with similar m/z values may cause biased quantifications (11). However, note that in this respect, using data-independent acquisition (DIA), all peptide ions (or all peptide ions within a certain m/z range, depending on the method used) are fragmented simultaneously, resulting in multiplexed MS/MS spectra (12, 13). Hence, issues of missing fragment spectra are less a problem with DIA, however, some of its challenges lie in deconvoluting MS/MS spectra and mapping their features to their corresponding peptides (14).Standard data analysis pipelines for DDA-LFQ proteomics can be divided into two groups: spectral counting techniques, which are based on counting the number of peptide features as a proxy for protein abundance (15), and intensity-based methods that quantify peptide features by measuring their corresponding spectral intensities or areas under the peaks in either MS or MS/MS spectra. Spectral counting is intuitive and easy to perform, but, the determination of differences in peptide and thus protein levels is not as precise as intensity-based methods, especially when analyzing rather small differences (16). More fundamentally, spectral counting ignores a large part of the information that is available in high-precision mass spectra. Further, dynamic exclusion during LC-MS/MS analysis, meant to increase the overall number of peptides that are analyzed, can worsen the linear dynamic range of these methods (17). Also, any changes in the MS/MS sampling conditions will prevent comparisons between runs. Intensity-based methods are more sensitive than spectral counting (18). Among intensity-based methods, quantification on the MS-level is somewhat more accurate than summarizing the MS/MS-level feature intensities (19). Therefore, we further focus on improving data analysis methods for MS-level quantification.Typical intensity-based workflows summarize peptide intensities to protein intensities before assessing differences in protein abundances (20). Peptide-based linear regression models estimate protein fold changes directly from peptide intensities and outperform summarization-based methods by reducing bias and generating more correct precision estimates (21, 22). However, peptide-based linear regression models suffer from overfitting due to extreme observations and the unbalanced nature of proteomics data; i.e. different peptides and a different number of peptides are typically identified in each sample. We illustrate this using the CPTAC spike-in data set where 48 human UPS1 proteins were spiked at five different concentrations in a 60 ng protein/μl yeast lysate. Thus, when comparing different spike-in concentrations, only the human proteins should be flagged as differentially abundant (DA), whereas the yeast proteins should not be flagged as DA (null proteins). Fig. 1 illustrates the structure of missing data in label-free shotgun proteomics experiments using a representative DA UPS1 protein from the CPTAC spike-in study: missing peptides in the lowest spike-in condition tend to have rather low log₂ intensity values in higher spike-in conditions compared to peptides that were not missing in both conditions, which supports the fact that the missing value problem in label-free shotgun proteomic data is largely intensity-dependent (23).Open in a separate windowFig. 1.Missing peptides are often low abundant. The boxplots show the log₂ intensity distributions for each of the 33 identified peptides corresponding to the human UPS1 protein cytoplasmic Histidyl-tRNA synthetase ({"type":"entrez-protein","attrs":{"text":"P12081","term_id":"135123","term_text":"P12081"}}P12081) from the CPTAC dataset in conditions 6A (spike-in concentration 0.25 fmol UPS1 protein/μl) and 6B (spike-in concentration 0.74 fmol UPS1 protein/μl). Vertical dotted lines indicate peptides present in both conditions. Note, that most peptides that were not detected in condition 6A exhibit low log₂ intensity values in condition 6B (colored in red).Fig. 2 shows the quantile normalized log₂ intensity values for the peptides corresponding to the yeast null protein CG121 together with average log₂ intensity estimates for each condition based on protein-level MaxLFQ intensities, as well as estimates derived from a peptide-based linear model. Here, three important remarks can be made:

• (1) CG121 is a yeast background protein, for which the true concentration is thus equal in all conditions, which appears to be monitored as such by MaxLFQ, except in conditions 6B and 6E (for the latter, no estimate is available). The LM estimate, however, is more reliable but seems to suffer from overfitting.
• (2) A lot of shotgun proteomic datasets are very sparse, causing a large sample-to-sample variability. Constructing a linear model based on a limited number of observations will thus lead to unstable variance estimates. Intuitively, a small sample drawn from a given population might “accidentally” show a very small variance while another small sample from the same population might display a very large variance just by random chance. This effect is clear from the sizes of the boxes. The interquartile range is twice as large in condition 6E compared to condition 6C. This issue leads to false positives since some proteins with very few observations are flagged as DA with very high statistical evidence solely due to their low observed variance (24).
• (3) Two observed features at log₂ intensities 14.0 and 14.3 in condition 6B have a strong influence on the parameter estimate for this condition. Without these extreme observations, the 6B estimate lies closer to the estimates in the other conditions. As missingness is strongly intensity-dependent, these low intensity values could easily become missing values in subsequent experiments. More generally, a strong influence of only one or two peptides on the average protein level intensity estimate for a condition is an unfavorable property.

Open in a separate windowFig. 2.Effect of outliers, variability, and sparsity of peptide intensities on abundance estimations. The figure shows log₂ transformed quantile normalized peptide intensities for the yeast null protein CG121 from the CPTAC data set for spike-in conditions 6A, 6B, 6C, 6D, and 6E. Each color denotes a different condition. Connected crosses: average protein log₂ intensity estimates for each condition are provided for a traditional protein level workflow where the mean of the protein-level MaxLFQ values was calculated (MaxLFQ, blue), the estimates of the peptide-based regression model fitted with ordinary least squares (LM, black) and the estimates of the peptide based ordinary least squares fit after omitting the two lowest observations in condition 6B (LM-extremes, orange). In condition 6E there were not enough data points to provide a MaxLFQ protein-level estimate. Boxes denote the interquartile range (IQR) of the log₂ transformed quantile normalized peptide intensities in each condition with the median indicated as a thick horizontal line inside each box. Whiskers extend to the most extreme data point that lies no more than 1.5 times the IQR from the box. Points lying beyond the whiskers are generally considered as outliers. Note, that the presence of two low-intensity peptide observations in concentration 6B has a strong effect on the estimates for both MaxLFQ and LM.These issues illustrate that state-of-the-art analysis methods experience difficulties in coping with peptide imbalances that are inherent to DDA LFQ proteomics data. We here propose three modular improvements to deal with the problems of overfitting, sample-to-sample variability and outliers:

• (1) Ridge regression, which penalizes the size of the model parameters. Shrinkage estimators can strongly improve reproducibility and overall performance as they have a lower overall mean squared error compared to ordinary least squares estimators (25–27).
• (2) Empirical Bayes variance estimation, which shrinks the individual protein variances toward a common prior variance, hence stabilizing the variance estimation.
• (3) M-estimation with Huber weights, which will make the estimators more robust toward outliers (28).

We illustrate our method on the CPTAC Study 6 spike-in data and a published ArgP knock-out Francisella tularensis proteomics experiment and show that our method provides more stable log₂ FC estimates and a better DA ranking than competing methods.     

Proteomic Differences Between Hepatocellular Carcinoma and Nontumorous Liver Tissue Investigated by a Combined Gel-based and Label-free Quantitative Proteomics Study

Proteomics-based clinical studies have been shown to be promising strategies for the discovery of novel biomarkers of a particular disease. Here, we present a study of hepatocellular carcinoma (HCC) that combines complementary two-dimensional difference in gel electrophoresis (2D-DIGE) and liquid chromatography (LC-MS)-based approaches of quantitative proteomics. In our proteomic experiments, we analyzed a set of 14 samples (7 × HCC versus 7 × nontumorous liver tissue) with both techniques. Thereby we identified 573 proteins that were differentially expressed between the experimental groups. Among these, only 51 differentially expressed proteins were identified irrespective of the applied approach. Using Western blotting and immunohistochemical analysis the regulation patterns of six selected proteins from the study overlap (inorganic pyrophosphatase 1 (PPA1), tumor necrosis factor type 1 receptor-associated protein 1 (TRAP1), betaine-homocysteine S-methyltransferase 1 (BHMT)) were successfully verified within the same sample set. In addition, the up-regulations of selected proteins from the complements of both approaches (major vault protein (MVP), gelsolin (GSN), chloride intracellular channel protein 1 (CLIC1)) were also reproducible. Within a second independent verification set (n = 33) the altered protein expression levels of major vault protein and betaine-homocysteine S-methyltransferase were further confirmed by Western blots quantitatively analyzed via densitometry. For the other candidates slight but nonsignificant trends were detectable in this independent cohort. Based on these results we assume that major vault protein and betaine-homocysteine S-methyltransferase have the potential to act as diagnostic HCC biomarker candidates that are worth to be followed in further validation studies.Hepatocellular carcinoma (HCC)¹ currently is the fifth most common malignancy worldwide with an annual incidence up to 500 per 100,000 individuals depending on the geographic region investigated. Whereas 80% of new cases occur in developing countries, the incidence increases in industrialized nations including Western Europe, Japan, and the United States (1). To manage patients with HCC, tumor markers are very important tools for diagnosis, indicators of disease progression, outcome prediction, and evaluation of treatment efficacy. Several tumor markers have been reported for HCC, including α-fetoprotein (AFP) (2), Lens culinaris agglutinin-reactive fraction of AFP (AFP-L3) (3), and des-γ-carboxyl prothrombin (DCP) (4). However, none of these tumor markers show 100% sensitivity or specificity, which calls for new and better biomarkers.To identify novel biomarkers of HCC, many clinical studies using “omics”-based methods have been reported over the past decade (5–6). In particular, the proteomics-based approach has turned out to be a promising one, offering several quantification techniques to reveal differences in protein expression that are caused by a particular disease. In most studies, the well-established 2D-DIGE technique has been applied for protein quantification followed by identification via mass spectrometry (7–15). Even if the quantification is very accurate and sensitive in this gel-based approach, the relatively high amount of protein sample necessary for protein identification is the major disadvantage of this technique. Several mass-spectrometry-based quantitative studies using labeling-techniques like SILAC (stable isotope labeling by amino acids in cell culture) or iTRAQ (isobaric tags for relative and absolute quantification) have also been carried out for biomarker discovery of HCC (16–18). Here, the concomitant protein quantification and identification in a mass spectrometer allows high-throughput analyses. However, such experiments imply additional labeling reactions (in case of iTRAQ) or are limited to tissue culture systems (in case of SILAC). In the latter case, one can overcome the limitation by using the isotope-labeled proteins obtained from tissue culture as an internal standard added to a corresponding tissue sample. This approach is known as CDIT (culture-derived isotope tags) and was applied in a HCC study, very recently (19). Label-free proteomics approaches based on quantification by ion-intensities or spectral counting offer another possibility for biomarker discovery. These approaches are relatively cheap compared with the labeling approaches, because they do not require any labeling reagents and furthermore they allow for high-throughput and sensitive analyses in a mass spectrometer. A quantitative study of HCC using spectral counting has been reported (20), whereas to our knowledge an ion-intensity-based study has not been performed yet. Apart from these quantification strategies, protein alterations in HCC have been studied by MALDI imaging, as well. Here, the authors could show that based on its proteomic signature, hepatocellular carcinoma can be discriminated with high accuracy from liver metastasis samples or other cancer types (21) as well as liver cirrhosis (22). Based on these results, it could be assumed that MALDI imaging might be a promising alternative to standard histological methods in the future.Here, we report a quantitative proteomic study that combines two different techniques, namely the well-established 2D-DIGE approach and a label-free ion-intensity-based quantification via mass spectrometry and liquid chromatography. To our knowledge this is the first time such a combined study was performed with regard to hepatocellular carcinoma. By comparing the results of both studies, we aim to identify high-confident biomarker candidates of HCC, as gel- and LC-MS-based techniques are complementary. To verify the differential protein expressions detected in our proteomic studies we performed additional immunological verifications for selected proteins within two different sample sets (Fig. 1).Open in a separate windowFig. 1.Schematic representation of the applied workflow.     

Application of Label-free Quantitative Peptidomics for the Identification of Urinary Biomarkers of Kidney Chronic Allograft Dysfunction

The advent of quantitative proteomics opens new opportunities in biomedical and clinical research. Although quantitative proteomics methods based on stable isotope labeling are in general preferred for biomolecular research, biomarker discovery is a case example of a biomedical problem that may be better addressed by using label-free MS techniques. As a proof of concept of this paradigm, we report the use of label-free quantitative LC-MS to profile the urinary peptidome of kidney chronic allograft dysfunction (CAD). The aim was to identify predictive biomarkers that could be used to personalize immunosuppressive therapies for kidney transplant patients. We detected (by LC-M/MS) and quantified (by LC-MS) 6000 polypeptide ions in undigested urine specimens across 39 CAD patients and 32 control individuals. Although unsupervised hierarchical clustering differentiated between the groups when including all the identified peptides, specific peptides derived from uromodulin and kininogen were found to be significantly more abundant in control than in CAD patients and correctly identified the two groups. These peptides are therefore potential biomarkers that might be used for the diagnosis of CAD. In addition, ions at m/z 645.59 and m/z 642.61 were able to differentiate between patients with different forms of CAD with specificities and sensitivities of 90% in a training set and, significantly, of ∼70% in an independent validation set of samples. Interestingly low expression of uromodulin at m/z 638.03 coupled with high expression of m/z 642.61 diagnosed CAD in virtually all cases. Multiple reaction monitoring experiments further validated the results, illustrating the power of our label-free quantitative LC-MS approach for obtaining quantitative profiles of urinary polypeptides in a rapid, comprehensive, and precise fashion and for biomarker discovery.A major goal of clinical proteomics is to identify biomarkers that can aid in the diagnosis and prognosis of different conditions. In their ideal form, these biomarkers will not only assist the clinician in the diagnosis of a disease, but they will also give directions as to which therapy may be more appropriate for each patient, thus contributing to the development of personalized medicine. In this regard, urine represents an ideal, but yet largely unexplored, source of biomarkers because of the presence of large numbers of small peptides in this biological fluid and because it can be obtained non-invasively.However, although proteomics techniques are instrumental for increasing our understanding of molecular cell biology (1) the impact of proteomics in clinical practice has not yet reached initial expectations perhaps because of technological limitations (2, 3). Using hyphenated methods such as novel LC-MS techniques for quantitative proteomics (4, 5) may prove advantageous for the identification and validation of biomarkers (3, 6). This is because LC-MS allows the detection of proteomes with greater depth, dynamic range, and enhanced accuracy of quantization than when using one-dimensional profiling techniques that record all ions in a single mass spectrum, such as MALDI-TOF MS or SELDI-TOF MS (7). On-line LC-ESI-MS is quantitative in nature because the initial LC separation step contributes to reducing the amount of analytes that are simultaneously ionized, thus reducing the possibility of ion suppression, and because ion formation by electrospray ionization is proportional to analyte concentration (8, 9). Initial reports that used LC-MS for the analysis of the urinary proteome provided proof of principle of the use of this technique for the analysis of urinary polypeptides (10–12), and recently, using new generation LC-MS/MS instrumentation, more than 1500 proteins have been detected in urine (13). Nevertheless despite these advances in our understanding of the qualitative composition of the urinary proteome, precise and comprehensive quantification of urinary polypeptides to discover potential biomarkers remains a challenge.The ideal, and more widely used, strategies to derive quantitative information from LC-MS experiments are based on differential stable isotope labeling of proteins or peptides, which are then mixed and quantified relative to each other in single multidimensional LC-LC-MS experiments (14). This technique, however, is not ideal for biomarker discovery because of problems associated with protein derivatization in a clinical setting, because of its limited throughput, and because, although not impossible, isotope labeling techniques make it difficult to compare a large number of specimens; at present labeling reagents can be used for simultaneous comparison of up to eight protein samples (15).Novel analytical strategies for quantitative proteomics that do not require isotope labeling have been reported (4, 5, 16). These techniques can quantify polypeptides with precisions and accuracies comparable to those based on isotope labeling (17). In addition, such label-free quantitative LC-MS approaches can compare an unlimited number of samples, and it is therefore ideal for biomarker discovery as experimental designs normally involve comparing a large number of specimens to statistically validate the results. Thus, label-free quantitative LC-MS would clearly assist in analyzing the full potential of urine clinical samples as a source of disease biomarkers. The aim of the study presented herein was to prove this concept taking chronic allograft dysfunction (CAD)¹ as a paradigm.During the last years, the incidence and prevalence of end stage renal disease has increased worldwide (18). Successful renal transplantation improves the patients'' quality of life and increases survival as compared with long term dialysis treatment (19). However, despite these improvements, a substantial portion of grafts develop progressive dysfunction and fail within a decade even with the use of appropriate doses of immunosuppressive drugs to prevent acute rejection (20). CAD is responsible for more than 50% of graft losses and remains a central clinical challenge. Although patients can return to dialysis after transplant failure, loss of a functioning graft is associated with a 3-fold increase in the risk of death, a substantial decrease in quality of life for those who survive, and a 4-fold increase in healthcare costs (21).CAD is mediated by a combination of immune, ischemic, and inflammatory stimuli, and multiple pathways and mediators lead to cumulative structural damage to all compartments of the transplanted kidney. Sclerosing changes associated with tubulointerstitial injury are mediated by the processes of active fibrogenesis, resulting in epithelial loss and the phenotype of tubular atrophy and chronic interstitial fibrosis (22). Available diagnostic methods include clinical presentation, biochemical parameters, and biopsies. Currently the only non-invasive biomarker of CAD is serum creatinine and glomerular filtration rate (GFR), but neither is particularly sensitive or specific and may not reflect early alterations (20, 22). At present, biopsy allograft is regarded as the gold standard for the diagnosis of CAD allowing its early detection; however, this is a costly procedure that is associated with clinical complications (23).Clinicians are hence faced with a dilemma. On the one hand, protocol biopsies may detect rejection at an earlier subclinical stage and allow prompt initiation of treatment, which may translate into improved long term graft survival (24). On the other hand, this also implies that patients with preserved graft function, i.e. without CAD, undergo this invasive procedure unnecessarily. Therefore, identification of non-invasive biomarkers for the early diagnosis of CAD would be invaluable for alleviating the major health and economic burden that this condition causes to western countries (25).The aim of the present study was to evaluate whether the urinary peptidome, as analyzed by a novel analytical strategy based on label-free quantification of urinary polypeptides by LC-MS, would differentiate between patients with CAD, those showing stable renal transplant (SRT), and a group of living donors. To our knowledge, this represents the first study reporting urine polypeptide signatures and individual biomarkers that group patients according to their underlying renal phenotype and hence represent potential candidates for non-invasive diagnosis of CAD.     

DIA-Based Proteomics Identifies IDH2 as a Targetable Regulator of Acquired Drug Resistance in Chronic Myeloid Leukemia

Drug resistance is a critical obstacle to effective treatment in patients with chronic myeloid leukemia. To understand the underlying resistance mechanisms in response to imatinib mesylate (IMA) and adriamycin (ADR), the parental K562 cells were treated with low doses of IMA or ADR for 2 months to generate derivative cells with mild, intermediate, and severe resistance to the drugs as defined by their increasing resistance index. PulseDIA-based (DIA [data-independent acquisition]) quantitative proteomics was then employed to reveal the proteome changes in these resistant cells. In total, 7082 proteins from 98,232 peptides were identified and quantified from the dataset using four DIA software tools including OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA. Sirtuin signaling pathway was found to be significantly enriched in both ADR-resistant and IMA-resistant K562 cells. In particular, isocitrate dehydrogenase (NADP(+)) 2 was identified as a potential drug target correlated with the drug resistance phenotype, and its inhibition by the antagonist AGI-6780 reversed the acquired resistance in K562 cells to either ADR or IMA. Together, our study has implicated isocitrate dehydrogenase (NADP(+)) 2 as a potential target that can be therapeutically leveraged to alleviate the drug resistance in K562 cells when treated with IMA and ADR.     

Quantitative Proteomics with siRNA Screening Identifies Novel Mechanisms of Trastuzumab Resistance in HER2 Amplified Breast Cancers

HER2 is a receptor tyrosine kinase that is overexpressed in 20% to 30% of human breast cancers and which affects patient prognosis and survival. Treatment of HER2-positive breast cancer with the monoclonal antibody trastuzumab (Herceptin) has improved patient survival, but the development of trastuzumab resistance is a major medical problem. Many of the known mechanisms of trastuzumab resistance cause changes in protein phosphorylation patterns, and therefore quantitative proteomics was used to examine phosphotyrosine signaling networks in trastuzumab-resistant cells. The model system used in this study was two pairs of trastuzumab-sensitive and -resistant breast cancer cell lines. Using stable isotope labeling, phosphotyrosine immunoprecipitations, and online TiO₂ chromatography utilizing a dual trap configuration, ∼1700 proteins were quantified. Comparing quantified proteins between the two cell line pairs showed only a small number of common protein ratio changes, demonstrating heterogeneity in phosphotyrosine signaling networks across different trastuzumab-resistant cancers. Proteins showing significant increases in resistant versus sensitive cells were subjected to a focused siRNA screen to evaluate their functional relevance to trastuzumab resistance. The screen revealed proteins related to the Src kinase pathway, such as CDCP1/Trask, embryonal Fyn substrate, and Paxillin. We also identify several novel proteins that increased trastuzumab sensitivity in resistant cells when targeted by siRNAs, including FAM83A and MAPK1. These proteins may present targets for the development of clinical diagnostics or therapeutic strategies to guide the treatment of HER2+ breast cancer patients who develop trastuzumab resistance.HER2 is a member of the epidermal growth factor receptor (EGFR)/ErbB family of receptor tyrosine kinases. Under normal physiologic conditions, HER2 tyrosine kinase signaling is tightly regulated spatially and temporally by the requirement for it to heterodimerize with a ligand bound family member, such as EGFR, HER3/ErbB3, or HER4/ErbB4 (1). However, in 20% to 30% of human breast cancer cases, HER2 gene amplification is present, resulting in a high level of HER2 protein overexpression and unregulated, constitutive HER2 tyrosine kinase signaling (2, 3). HER2 gene amplified breast cancer, also termed HER2-positive breast cancer, carries a poor prognosis, but the development of the HER2 targeted monoclonal antibody trastuzumab (Herceptin) has significantly improved patient survival (2). Despite the clinical effectiveness of trastuzumab, the development of drug resistance significantly increases the risk of patient death. This poses a major medical problem, as most metastatic HER2-positive breast cancer patients develop trastuzumab resistance over the course of their cancer treatment (4). The treatment approach for HER2+ breast cancer patients after trastuzumab resistance develops is mostly a trial-and-error process that subjects the patient to increased toxicity. Therefore, there is a substantial medical need for strategies to overcome trastuzumab resistance.Multiple trastuzumab-resistance mechanisms have been identified, and they alter signaling networks and protein phosphorylation patterns in either a direct or an indirect manner. These mechanisms can be grouped into three categories. The first category is the activation of a parallel signaling network by other tyrosine kinases. These kinases include the receptor tyrosine kinases, EGFR, IGF1R, Her3, Met, EphA2, and Axl, as well as the erythropoietin-receptor-mediated activation of the cytoplasmic tyrosine kinases Jak2 and Src (5–11). The second category is the activation of downstream signaling proteins. Multiple studies have demonstrated activation of the phosphatidylinositol-3-kinase (PI3K)/AKT pathway in trastuzumab resistance, which occurs either via deletion of the PTEN lipid phosphatase or mutation of the PI3K genes (12, 13). Activation of Src family kinases or overexpression of cyclin E, which increases the cyclin E–cyclin-dependent kinase 2 signaling pathway, has also been reported (14). The third category includes mechanisms that maintain HER2 signaling even in the presence of trastuzumab. The production of a truncated isoform of HER2, p95HER2, which lacks the trastuzumab binding site, causes constitutive HER2 signaling (15, 16). Overexpression of the MUC4 sialomucin complex inhibits trastuzumab binding to HER2 and thereby maintains HER2 signaling (17, 18).Given that multiple trastuzumab-resistance mechanisms alter signaling networks and protein phosphorylation patterns, we reasoned that mapping phosphotyrosine signaling networks using quantitative proteomics would be a highly useful strategy for analyzing known mechanisms and identifying novel mechanisms of trastuzumab resistance. Quantitative proteomics and phosphotyrosine enrichment approaches have been extensively used to study the EGFR signal networks (19–23). We and others have used these approaches to map the HER2 signaling network (22, 24, 25). Multiple other tyrosine kinase signaling networks were analyzed using quantitative proteomics, including Ephrin receptor, EphB2 (26–28), platelet-derived growth factor receptor (PDGFR) (21), insulin receptor (29, 30), and the receptor for hepatocyte growth factor, c-MET (31).The goal of this study is to identify, quantify, and functionally screen proteins that might be involved in trastuzumab resistance. We used two pairs of HER2 gene amplified trastuzumab-sensitive (parental, SkBr3 and BT474) and -resistant (SkBr3^R and BT474^R) human breast cancer cell lines as models for trastuzumab resistance. These cell lines and their trastuzumab-resistant derivatives have been extensively characterized and highly cited in the breast cancer literature (32, 33). Using stable isotope labeling of amino acids in cell culture (SILAC),¹ phosphotyrosine immunoprecipitations, and online TiO₂ chromatography with dual trap configuration, we quantified the changes in phosphotyrosine containing proteins and interactors between trastuzumab-sensitive and -resistant cells. Several of the known trastuzumab-resistance mechanisms were identified, which serves as a positive control and validation of our approach, and large protein ratio changes were measured in proteins that had not been previously connected with trastuzumab resistance. We then performed a focused siRNA screen targeting the proteins with significantly increased protein ratios. This screen functionally tested the role of the identified proteins and identifies which proteins might have the largest effect on reversing trastuzumab resistance.