Methionine Deprivation Induces a Targetable Vulnerability in Triple-Negative Breast Cancer Cells by Enhancing TRAIL Receptor-2 Expression.

PURPOSE: Many neoplasms are vulnerable to methionine deficiency by mechanisms that are poorly understood. Because gene profiling studies have revealed that methionine depletion increases TNF-related apoptosis-inducing ligand receptor-2 (TRAIL-R2) mRNA, we postulated that methionine stress sensitizes breast cancer cells to proapoptotic TRAIL-R2 agonists. EXPERIMENTAL DESIGN: Human triple (ER/PR/HER2)-negative breast carcinoma cell lines were cultured in control or methionine-free media. The effects of methionine depletion on TRAIL receptor expression and sensitivity to chemotherapy or a humanized agonistic TRAIL-R2 monoclonal antibody (lexatumumab) were determined. The melanoma-associated antigen MAGED2 was silenced to delineate its functional role in sensitizing TNBC cells to methionine stress. An orthotopic TNBC model was utilized to evaluate the effects of dietary methionine deficiency, lexatumumab, or the combination. RESULTS: Methionine depletion sensitized TNBC cells to lexatumumab-induced caspase activation and apoptosis by increasing TRAIL-R2 mRNA and cell surface expression. MCF-10A cells transformed by oncogenic H-Ras, but not untransformed cells, and matrix-detached TNBC cells were highly sensitive to the combination of lexatumumab and methionine depletion. Proteomics analyses revealed that MAGED2, which has been reported to reduce TRAIL-R2 expression, was suppressed by methionine stress. Silencing MAGED2 recapitulated features of methionine deprivation, including enhanced mRNA and cell surface expression of TRAIL receptors and increased sensitivity to TRAIL receptor agonists. Dietary methionine deprivation enhanced the antitumor effects of lexatumumab in an orthotopic metastatic TNBC model. CONCLUSIONS: Methionine depletion exposes a targetable defect in TNBC cells by increasing TRAIL-R2 expression. Our findings provide the foundation for a clinical trial combining dietary methionine restriction and TRAIL-R2 agonists. Clin Cancer Res; 21(12); 2780-91. ©2015 AACR.

In silico proteome-wide amino aCid and elemental composition (PACE) analysis of expression proteomics data provides a fingerprint of dominant metabolic processes.

Proteome-wide Amino aCid and Elemental composition (PACE) analysis is a novel and informative way of interrogating the proteome. The PACE approach consists of in silico decomposition of proteins detected and quantified in a proteomics experiment into 20 amino acids and five elements (C, H, N, O and S), with protein abundances converted to relative abundances of amino acids and elements. The method is robust and very sensitive; it provides statistically reliable differentiation between very similar proteomes. In addition, PACE provides novel insights into proteome-wide metabolic processes, occurring, e.g., during cell starvation. For instance, both Escherichia coli and Synechocystis down-regulate sulfur-rich proteins upon sulfur deprivation, but E. coli preferentially down-regulates cysteine-rich proteins while Synechocystis mainly down-regulates methionine-rich proteins. Due to its relative simplicity, flexibility, generality and wide applicability, PACE analysis has the potential of becoming a standard analytical tool in proteomics.

N-terminal peptide sequence repetition influences the kinetics of backbone fragmentation: a manifestation of the Jahn-Teller effect?

Analysis of large (>10,000 entries) databases consisting of high-resolution tandem mass spectra of peptide dications revealed with high statistical significance (P < 1[Symbol: see text]10(-3)) that peptides with non-identical first two N-terminal amino acids undergo cleavages of the second peptide bond at higher rates than repetitive sequences composed of the same amino acids (i.e., in general AB- and BA- bonds cleave more often than AA- and BB- bonds). This effect seems to depend upon the collisional energy, being stronger at lower energies. The phenomenon is likely to indicate the presence of the diketopiperazine structure for at least some b2 (+) ions. When consisting of two identical amino acids, these species should form through intermediates that have a symmetric geometry and, thus, must be subject to the Jahn-Teller effect that reduces the stability of such systems.

Employment of complementary dissociation techniques for body fluid characterization and biomarker discovery.

Proteomic analysis of biological fluids has become the de facto method for biomarker discovery over the past half decade. Mass spectrometry, in particular, has emerged as the premier technology to perform such analysis. This shift in the prevailing choice of analytical method is primarily due to the rapid evolution of mass spectrometry technology, with advances in acquisition speed, increased resolving power and mass accuracy, and the development of novel fragmentation methods. The benefits of using one of these new fragmentation methods, electron-transfer dissociation, as a complement to the traditional dissociation technique (i.e., collision-activated dissociation) have been thoroughly illustrated. Detailed here is a method for proteomic analysis of a readily obtainable and often investigated biological fluid, blood plasma, which takes advantage of these complementary dissociation techniques and employs the most recent advances in mass spectrometry technology.

Carbonyl charge solvation patterns may relate to fragmentation classes in collision-activated dissociation.

Here, we investigate the hypothesis that the origin of Class I fragmentation in tryptic peptide dications corresponding to the cleavage of the first two amino acids from the N-terminus is due to a dominant charge solvation pattern. Molecular dynamics simulations (MDS) of model A(n)R dications confirmed the existence of a persistent solvation of the protonated N-terminus on the second backbone carbonyl. Additionally, MDS predicted a new distinct fragmentation class corresponding to the loss of two amino acids from the C-terminus. This prediction was confirmed experimentally at very low excitation levels. The pattern produced by electron transfer dissociation of the same dications gave markedly decreased cleavage frequencies at the second peptide bond, which, within the non-local fragmentation mechanism, supports the preferential charge solvation on the second carbonyl. Taken together, these results confirm the role of a charge solvation pattern in the origin of fragmentation classes.

Radical a-ions in electron capture dissociation: on the origin of species.

Radical a* ions appear in electron capture dissociation mass spectra sporadically, but sometimes with high intensity. Mechanistically, radical a ions are hypothesized to arise due to thermodynamically disadvantaged charge solvation on the backbone nitrogen (instead of carbonyl), which upon neutralization produces a hypervalent group instantly fragmenting into a radical b* and conventional y' ion. The former species is unstable and, after releasing a CO molecule, decays to an a* ion. Here we validate this scenario by direct observation of the complementarity of a* and y' ions by interrogation of an ECD MS/MS database of >10,000 doubly and >5,000 triply charged tryptic peptides. Intriguingly, the most abundant a*/y' pairs are found to come from the cleavage of the same backbone link as the most abundant c' and z* complementary ions. This result gives strong support to the "local" N-Cα bond cleavage mechanism, in which the dissociation occurs at the site of charge solvation. However, a second strong peak is observed in the c'/z* fragment distribution four residues away from the a*/y' cleavage, which supports the indirect N-Cα bond cleavage mechanism. The size distribution of a ions from doubly (but not triply!) charged precursors shows deficit of a3 ions, and possibly a6 ions.

Sequence scrambling in shotgun proteomics is negligible.

Analysis of 15,897 low-energy (CAD) and 10,878 higher-energy (HCD) collisional dissociation mass spectra of doubly protonated tryptic peptides taken with high resolution revealed that the rate of sequence scrambling due to b-ion cyclization is negligible (<1%) and can be safely ignored as a possible source of erroneous sequence assignment in shotgun proteomics. On the other hand, there is significant presence of normal (non-scrambled) internal fragments in HCD, which should be taken into account by MS/MS search engines.

Drug target identification from protein dynamics using quantitative pathway analysis.

Dynamic proteomics promises to greatly facilitate identification of target proteins for drug molecules. Cohen et al. [Science, 2008, 322 (5907), 1511-1516] illustrated this potential, with the responses of 812 fluorescently tagged proteins to camptothecin administration monitored over 48 h. Directly from this data, one can restrict the list of candidate targets to 52 proteins. However, this approach has numerous limitations: equipment, labor (tagging and analyzing ≥1 colony/protein), and data analysis (aggregating individual cell data into population-relevant data sets). Furthermore, analytical success requires both explicit knowledge of drug target time-course evolution and, most importantly, monitoring of the target, itself. To address these issues, we developed a quantitative pathway analysis (qPA) technique, which employs well-annotated signaling pathways and elucidates putative drug targets and other molecules of interest. qPA, using more general assumptions and only 3 out of 144 available time points, identified the single known camptothecin target, TOPI, among only a handful of putative targets. Importantly, identification was possible without containing TOPI within the input data. These results demonstrate the potential of qPA in drug target discovery and highlight the importance of systems biology approaches for analysis of proteomics data.

Are the majority of a(2)-ions cyclic?

Detailed analysis of >18 400 high-mass accuracy tandem mass spectra resulting from higher energy collisional dissociation yields further evidence of the cyclic nature of a(2)-ions.

Calibration function for the Orbitrap FTMS accounting for the space charge effect.

Ion storage in an electrostatic trap has been implemented with the introduction of the Orbitrap Fourier transform mass spectrometer (FTMS), which demonstrates performance similar to high-field ion cyclotron resonance MS. High mass spectral characteristics resulted in rapid acceptance of the Orbitrap FTMS for Life Sciences applications. The basics of Orbitrap operation are well documented; however, like in any ion trap MS technology, its performance is limited by interactions between the ion clouds. These interactions result in ion cloud couplings, systematic errors in measured masses, interference between ion clouds of different size yet with close m/z ratios, etc. In this work, we have characterized the space-charge effect on the measured frequency for the Orbitrap FTMS, looking for the possibility to achieve sub-ppm levels of mass measurement accuracy (MMA) for peptides in a wide range of total ion population. As a result of this characterization, we proposed an m/z calibration law for the Orbitrap FTMS that accounts for the total ion population present in the trap during a data acquisition event. Using this law, we were able to achieve a zero-space charge MMA limit of 80 ppb for the commercial Orbitrap FTMS system and sub-ppm level of MMA over a wide range of total ion populations with the automatic gain control values varying from 10 to 10(7).

Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease.

Because of its availability, ease of collection, and correlation with physiology and pathology, urine is an attractive source for clinical proteomics/peptidomics. However, the lack of comparable data sets from large cohorts has greatly hindered the development of clinical proteomics. Here, we report the establishment of a reproducible, high resolution method for peptidome analysis of naturally occurring human urinary peptides and proteins, ranging from 800 to 17,000 Da, using samples from 3,600 individuals analyzed by capillary electrophoresis coupled to MS. All processed data were deposited in an Structured Query Language (SQL) database. This database currently contains 5,010 relevant unique urinary peptides that serve as a pool of potential classifiers for diagnosis and monitoring of various diseases. As an example, by using this source of information, we were able to define urinary peptide biomarkers for chronic kidney diseases, allowing diagnosis of these diseases with high accuracy. Application of the chronic kidney disease-specific biomarker set to an independent test cohort in the subsequent replication phase resulted in 85.5% sensitivity and 100% specificity. These results indicate the potential usefulness of capillary electrophoresis coupled to MS for clinical applications in the analysis of naturally occurring urinary peptides.

The effect of interfering ions on search algorithm performance for electron-transfer dissociation data.

Collision-activated dissociation and electron-transfer dissociation (ETD) each produce spectra containing unique features. Though several database search algorithms (e.g. SEQUEST, MASCOT, and Open Mass Spectrometry Search Algorithm) have been modified to search ETD data, this consists chiefly of the ability to search for c- and z(*)-ions; additional ETD-specific features are often unaccounted for and may hinder identification. Removal of these features via spectral processing increased total search sensitivity by approximately 20% for both human and yeast data sets; unique peptide identifications increased by approximately 17% for the yeast data sets and approximately 16% for the human data set.

Identification and validation of urinary biomarkers for differential diagnosis and evaluation of therapeutic intervention in anti-neutrophil cytoplasmic antibody-associated vasculitis.

Renal activity and smoldering disease is difficult to assess in anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) because of renal scarring. Even repeated biopsies suffer from sampling errors in this focal disease especially in patients with chronic renal insufficiency. We applied capillary electrophoresis coupled to mass spectrometry toward urine samples from patients with active renal AAV to identify and validate urinary biomarkers that enable differential diagnosis of disease and assessment of disease activity. The data were compared with healthy individuals, patients with other renal and non-renal diseases, and patients with AAV in remission. 113 potential biomarkers were identified that differed significantly between active renal AAV and healthy individuals and patients with other chronic renal diseases. Of these, 58 could be sequenced. Sensitivity and specificity of models based on 18 sequenced biomarkers were validated using blinded urine samples of 40 patients with different renal diseases. Discrimination of AAV from other renal diseases in blinded samples was possible with 90% sensitivity and 86.7-90% specificity depending on the model. 10 patients with active AAV were followed for 6 months after initiation of treatment. Immunosuppressive therapy led to a change of the proteome toward "remission." 47 biomarkers could be sequenced that underwent significant changes during therapy together with regression of clinical symptoms, normalization of C-reactive protein, and improvement of renal function. Proteomics analysis with capillary electrophoresis-MS represents a promising tool for fast identification of patients with active AAV, indication of renal relapses, and monitoring for ongoing active renal disease and remission without renal biopsy.

Adapting mass spectrometry-based platforms for clinical proteomics applications: The capillary electrophoresis coupled mass spectrometry paradigm.

Single biomarker detection is common in clinical laboratories due to the currently available method spectrum. For various diseases, however, no specific single biomarker could be identified. A strategy to overcome this diagnostic void is to shift from single analyte detection to multiplexed biomarker profiling. Mass spectrometric methods were employed for biomarker discovery in body fluids. The enormous complexity of biofluidic proteome compartments implies upstream fractionation. For this reason, mass spectrometry (MS) was coupled to two-dimensional gel electrophoresis, liquid chromatography, surface-enhanced laser desorption/ionization, or capillary electrophoresis (CE). Differences in performance and operating characteristics make them differentially suited for routine laboratory applications. Progress in the field of clinical proteomics relies not only on the use of an adequate technological platform, but also on a fast and efficient proteomic workflow including standardized sample preparation, proteomic data processing, statistical validation of biomarker selection, and sample classification. Based on CE-MS analysis, we describe how proteomic technology can be implemented in a clinical laboratory environment. In the last part of this review, we give an overview of CE-MS-based clinical studies and present information on identity and biological significance of the identified peptide biomarkers providing evidence of disease-induced changes in proteolytic processing and posttranslational modification.

Post-acquisition ETD spectral processing for increased peptide identifications.

Tandem mass spectra (MS/MS) produced using electron transfer dissociation (ETD) differ from those derived from collision-activated dissociation (CAD) in several important ways. Foremost, the predominant fragment ion series are different: c- and z(*)-type ions are favored in ETD spectra while b- and y-type ions comprise the bulk of the fragments in CAD spectra. Additionally, ETD spectra possess charge-reduced precursors and unique neutral losses. Most database search algorithms were designed to analyze CAD spectra, and have only recently been adapted to accommodate c- and z(*)-type ions; therefore, inclusion of these additional spectral features can hinder identification, leading to lower confidence scores and decreased sensitivity. Because of this, it is important to pre-process spectral data before submission to a database search to remove those features that cause complications. Here, we demonstrate the effects of removing these features on the number of unique peptide identifications at a 1% false discovery rate (FDR) using the open mass spectrometry search algorithm (OMSSA). When analyzing two biologic replicates of a yeast protein extract in three total analyses, the number of unique identifications with a approximately 1% FDR increased from 4611 to 5931 upon spectral pre-processing--an increase of approximately 28.6%. We outline the most effective pre-processing methods, and provide free software containing these algorithms.

A new probabilistic database search algorithm for ETD spectra.

Peptide characterization using electron transfer dissociation (ETD) is an important analytical tool for protein identification. The fragmentation observed in ETD spectra is complementary to that seen when using the traditional dissociation method, collision activated dissociation (CAD). Applications of ETD enhance the scope and complexity of the peptides that can be studied by mass spectrometry-based methods. For example, ETD is shown to be particularly useful for the study of post-translationally modified peptides. To take advantage of the power provided by ETD, it is important to have an ETD-specific database search engine, an integral tool of mass spectrometry-based analytical proteomics. In this paper, we report on our development of a database search engine using ETD spectra and protein sequence databases to identify peptides. The search engine is based on the probabilistic modeling of shared peaks count and shared peaks intensity between the spectra and the peptide sequences. The shared peaks count accounts for the cumulative variations from amino acid sequences, while shared peaks intensity models the variations between the candidate sequence and product ion intensities. To demonstrate the utility of this algorithm for searching real-world data, we present the results of applications of this model to two high-throughput data sets. Both data sets were obtained from yeast whole cell lysates. The first data set was obtained from a sample digested by Lys-C, and the second data set was obtained by a digestion using trypsin. We searched the data sets against a combined forward and reversed yeast protein database to estimate false discovery rates. We compare the search results from the new methods with the results from a search engine often employed for ETD spectra, OMSSA. Our findings show that overall the new model performs comparably to OMSSA for low false discovery rates. At the same time, we demonstrate that there are substantial differences with OMSSA for results on subsets of data. Therefore, we conclude the new model can be considered as being complementary to previously developed models.

The human urinary proteome reveals high similarity between kidney aging and chronic kidney disease.

Aging induces morphological changes of the kidney and reduces renal function. We analyzed the low molecular weight urinary proteome of 324 healthy individuals from 2-73 years of age to gain insight on human renal aging. We observed age-related modification of secretion of 325 out of over 5000 urinary peptides. The majority of these changes were associated with renal development before and during puberty, while 49 peptides were related to aging in adults. We therefore focussed the remainder of the study on these 49 peptides. The majority of these 49 peptides were also markers of chronic kidney disease, suggesting high similarity between aging and chronic kidney disease. Blinded evaluation of samples from healthy volunteers and diabetic nephropathy patients confirmed both the correlation of biomarkers with aging and with renal disease. Identification of a number of these aging-related peptides led us to hypothesize that reduced proteolytic activity is involved in human renal aging. Finally, among the 324 supposedly healthy individuals, some had urinary aging-related peptide excretion patterns typical of an individual significantly older than their actual age. In conclusion, these aging-related biomarkers may allow noninvasive detection of renal lesions in healthy persons and show high resemblance between human aging and chronic kidney disease. This similarity has to be taken into account when searching for biomarkers of renal disease.

Mass spectrometric analysis of body fluids for biomarker discovery.

Hinging on the concept that extracellular proteins and polypeptides will provide information on the physiological state of specific organs, or even entire organisms, proteomic analysis of biological fluids for biomarker discovery has seen rapid expansion in recent years. Although multiple studies have had success using mass spectrometric analytical techniques for determination of proteins within a sample, inspection of naturally occurring species has been difficult, with most analyses using bottom-up methodology. We have applied a new fragmentation method, electron transfer dissociation (ETD), to this problem. We have previously illustrated the benefits to spectral quality and total identifications when using a combination of the complementary fragmentation techniques, ETD, and collision-activated dissociation, for analysis of naturally occurring proteins and polypeptides within biological fluids.

Urinary proteomics in diabetes and CKD.

Urinary biomarkers for diabetes, diabetic nephropathy, and nondiabetic proteinuric renal diseases were sought. For 305 individuals, biomarkers were defined and validated in blinded data sets using high-resolution capillary electrophoresis coupled with electrospray-ionization mass spectrometry. A panel of 40 biomarkers distinguished patients with diabetes from healthy individuals with 89% sensitivity and 91% specificity. Among patients with diabetes, 102 urinary biomarkers differed significantly between patients with normoalbuminuria and nephropathy, and a model that included 65 of these correctly identified diabetic nephropathy with 97% sensitivity and specificity. Furthermore, this panel of biomarkers identified patients who had microalbuminuria and diabetes and progressed toward overt diabetic nephropathy over 3 yr. Differentiation between diabetic nephropathy and other chronic renal diseases reached 81% sensitivity and 91% specificity. Many of the biomarkers were fragments of collagen type I, and quantities were reduced in patients with diabetes or diabetic nephropathy. In conclusion, this study shows that analysis of the urinary proteome may allow early detection of diabetic nephropathy and may provide prognostic information.