Search

Background: Acute myeloid leukaemia (AML) is a bone marrow malignancy with poor prognosis. One of several treatments for AML is midostaurin combined with intensive chemotherapy (MIC), currently approved for FLT3 mutation-positive (FLT3-MP) AML. However, many patients carrying FLT3 mutations are refractory or experience an early relapse following MIC treatment, and might benefit more from receiving a different treatment. Development of a stratification method that outperforms FLT3 mutational status in predicting MIC response would thus benefit a large number of patients., Methods: We employed mass spectrometry phosphoproteomics to analyse 71 diagnosis samples of 47 patients with FLT3-MP AML who subsequently received MIC. We then used machine learning to identify biomarkers of response to MIC, and validated the resulting predictive model in two independent validation cohorts (n = 20)., Findings: We identified three distinct phosphoproteomic AML subtypes amongst long-term survivors. The subtypes showed similar duration of MIC response, but different modulation of AML-implicated pathways, and exhibited distinct, highly-predictive biomarkers of MIC response. Using these biomarkers, we built a phosphoproteomics-based predictive model of MIC response, which we called MPhos. When applied to two retrospective real-world patient test cohorts (n = 20), MPhos predicted MIC response with 83% sensitivity and 100% specificity (log-rank p < 7∗10 -5 , HR = 0.005 [95% CI: 0-0.31])., Interpretation: In validation, MPhos outperformed the currently-used FLT3-based stratification method. Our findings have the potential to transform clinical decision-making, and highlight the important role that phosphoproteomics is destined to play in precision oncology., Funding: This work was funded by Innovate UK grants (application numbers: 22217 and 10054602) and by Kinomica Ltd., Competing Interests: Declaration of interests WEB—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences, named on a Kinomica patent; LN—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; SFP—is an employee at Kinomica, owns Kinomica share options; AEC—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; NN—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; JAC—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; DNP—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; PMC—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; JK—is an employee at Kinomica, owns Kinomica share options; HRF—no conflict of interest; BP—no conflict of interest; PG—no conflict of interest; AA—no conflict of interest; AJA—received a honorarium for speaking engagements from Astellas; AT—received consultant fees from Kinomica for the role of Programme Director; AW—owns Kinomica share options, funded attendance and travel to conferences; GG—no conflict of interest; MDM—no conflict of interest; JGG—Kinomica co-founder, owns Kinomica share options; DJB—co-founder of Kinomica, owns Kinomica share options, named on Kinomica patents, Kinomica funded attendance and travel to conferences, received honoraria from Kinomica in a consulting role; PRC—co-founder and director of Kinomica, owns Kinomica share options, named on Kinomica patents, Kinomica funded attendance and travel to conferences, received honoraria from Kinomica in a consulting role; ADD—is an employee at Kinomica (CTO), named on Kinomica patents, Kinomica funded attendance and travel to conferences owns Kinomica share options., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies., (© 2023 The Author(s).)

Acute myeloid leukaemia (AML) patients harbouring certain chromosome abnormalities have particularly adverse prognosis. For these patients, targeted therapies have not yet made a significant clinical impact. To understand the molecular landscape of poor prognosis AML we profiled 74 patients from two different centres (in UK and Finland) at the proteomic, phosphoproteomic and drug response phenotypic levels. These data were complemented with transcriptomics analysis for 39 cases. Data integration highlighted a phosphoproteomics signature that define two biologically distinct groups of KMT2A rearranged leukaemia, which we term MLLGA and MLLGB. MLLGA presented increased DOT1L phosphorylation, HOXA gene expression, CDK1 activity and phosphorylation of proteins involved in RNA metabolism, replication and DNA damage when compared to MLLGB and no KMT2A rearranged samples. MLLGA was particularly sensitive to 15 compounds including genotoxic drugs and inhibitors of mitotic kinases and inosine-5-monosphosphate dehydrogenase (IMPDH) relative to other cases. Intermediate-risk KMT2A-MLLT3 cases were mainly represented in a third group closer to MLLGA than to MLLGB. The expression of IMPDH2 and multiple nucleolar proteins was higher in MLLGA and correlated with the response to IMPDH inhibition in KMT2A rearranged leukaemia, suggesting a role of the nucleolar activity in sensitivity to treatment. In summary, our multilayer molecular profiling of AML with poor prognosis and KMT2A-MLLT3 karyotypes identified a phosphoproteomics signature that defines two biologically and phenotypically distinct groups of KMT2A rearranged leukaemia. These data provide a rationale for the potential development of specific therapies for AML patients characterised by the MLLGA phosphoproteomics signature identified in this study., (© 2022. The Author(s).)

The identification of biomarkers for companion diagnostics is revolutionizing the development of treatments tailored to individual patients in different disease areas including cancer. Precision medicine is most frequently based on the detection of genomic markers that correlate with the efficacy of selected targeted therapies. However, since nongenetic mechanisms also contribute to disease biology, there is a considerable interest of using proteomic techniques as additional source of biomarkers to personalize therapies. In this chapter, we describe label-free mass spectrometry methods for proteomic and phosphoproteomic analysis compatible with routine analysis of clinical samples. We also outline bioinformatic pipelines based on statistical learning that use these proteomics datasets as input to quantify kinase activities and predict drug responses in cancer cells., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Antipsychotic drugs remain the current standard for schizophrenia treatment. Although they directly recognize the orthosteric binding site of numerous monoaminergic G protein-coupled receptors (GPCRs), these drugs, and particularly second-generation antipsychotics such as clozapine, all have in common a very high affinity for the serotonin 5-HT 2A receptor (5-HT 2A R). Using classical pharmacology and targeted signaling pathway assays, previous findings suggest that clozapine and other atypical antipsychotics behave principally as 5-HT 2A R neutral antagonists and/or inverse agonists. However, more recent findings showed that antipsychotics may also behave as pathway-specific agonists. Reversible phosphorylation is a common element in multiple signaling networks. Combining a quantitative phosphoproteomic method with signaling network analysis, we tested the effect of clozapine treatment on the overall level of protein phosphorylation and signal transduction cascades in vitro in mammalian cell lines induced to express either the human 5-HT 2A R or the H452Y variant of the gene encoding the 5-HT 2A R receptor. This naturally occurring variation within the 5-HT 2A R gene was selected because it has been repeatedly associated with schizophrenia patients who do not respond to clozapine treatment. Our data show that short time exposure (5 or 10 min) to clozapine (10 -5  M) led to phosphorylation of numerous signaling components of pathways involved in processes such as endocytosis, ErbB signaling, insulin signaling or estrogen signaling. Cells induced to express the H452Y variant showed a different basal phosphoproteome, with increases in the phosphorylation of mTOR signaling components as a translationally relevant example. However, the effect of clozapine on the functional landscape of the phosphoproteome was significantly reduced in cells expressing the 5-HT 2A R-H452Y construct. Together, these findings suggest that clozapine behaves as an agonist inducing phosphorylation of numerous pathways downstream of the 5-HT 2A R, and that the single nucleotide polymorphism encoding 5-HT 2A R-H452Y affects these clozapine-induced phosphorylation-dependent signaling networks., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Cell survival is regulated by a signaling network driven by the activity of protein kinases; however, determining the contribution that each kinase in the network makes to such regulation remains challenging. Here, we report a computational approach that uses mass spectrometry-based phosphoproteomics data to rank protein kinases based on their contribution to cell regulation. We found that the scores returned by this algorithm, which we have termed kinase activity ranking using phosphoproteomics data (KARP), were a quantitative measure of the contribution that individual kinases make to the signaling output. Application of KARP to the analysis of eight hematological cell lines revealed that cyclin-dependent kinase (CDK) 1/2, casein kinase (CK) 2, extracellular signal-related kinase (ERK), and p21-activated kinase (PAK) were the most frequently highly ranked kinases in these cell models. The patterns of kinase activation were cell-line specific yet showed a significant association with cell viability as a function of kinase inhibitor treatment. Thus, our study exemplifies KARP as an untargeted approach to empirically and systematically identify regulatory kinases within signaling networks., (© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.)

Cells integrate extracellular signals into appropriate responses through a complex network of biochemical reactions driven by the activity of protein and lipid kinases, among other proteins. In order to understand this complexity, new approaches, both experimental and computational, have recently been developed with the aim to identify regulatory kinases and infer their activation status in the context of their signalling network. Here, we review such approaches with particular focus on those based on phosphoproteomics. Integration of kinase activity measurements inferred from phosphoproteomics data with other 'omics' datasets is starting to be used to identify regulatory nodes in biochemical networks. These methodologies may in the future be used to identify patient-specific targets and thus advance personalised cancer medicine., (© 2017 Federation of European Biochemical Societies.)

The extracellular matrix (ECM) is a complex meshwork of insoluble fibrillar proteins and signaling factors interacting together to provide architectural and instructional cues to the surrounding cells. Alterations in ECM organization or composition and excessive ECM deposition have been observed in diseases such as fibrosis, cardiovascular diseases, and cancer. We provide here optimized protocols to solubilize ECM proteins from normal or tumor tissues, digest the proteins into peptides, analyze ECM peptides by mass spectrometry, and interpret the mass spectrometric data. In addition, we present here two novel R-script-based web tools allowing rapid annotation and relative quantification of ECM proteins, peptides, and intensity/abundance in mass spectrometric data output files. We illustrate this protocol with ECMs obtained from two pairs of tissues, which differ in ECM content and cellularity: triple-negative breast cancer and adjacent mammary tissue, and omental metastasis from high-grade serous ovarian cancer and normal omentum. The complete proteomics data set generated in this study has been deposited to the public repository ProteomeXchange with the data set identifier: PXD005554.

Signaling pathways driven by protein and lipid kinases are altered in most human diseases. Therefore, pharmacological inhibitors of cell signaling are one of the most intensively pursued therapeutic approaches for the treatment of diseases such as cancer, neurodegeneration, and metabolic syndromes. Phosphoproteomics is a technique that measures the products of kinase activities and, with the appropriate bioinformatics techniques, the methodology can also provide measures of kinase pathway activation and network circuitry. Hence, due to recent technological advantages, LC-MS-based quantitative phosphoproteomics provides relevant information for the design and implementation of kinase inhibitor based therapies. Here, we review how phosphoproteome profiling is being used in translational research as a means to identify drug targets and biomarkers for personalizing therapies based on kinase inhibitors., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Phosphoproteomics is a powerful platform for the unbiased profiling of kinase-driven signaling pathways. Quantitation of phosphorylation can be performed by means of either labeling or label-free mass spectrometry (MS) methods. Because of their simplicity and universality, label-free methodology is gaining acceptance and popularity in molecular biology research. Analytical workflows for label-free quantification of phosphorylation, however, need to overcome several hurdles for the technique to be accurate and precise. These include the use of biochemical extraction procedures that efficiently and reproducibly isolate phosphopeptides from complex peptide matrices and an analytical strategy that can cope with missing MS/MS phosphopeptide spectra in a subset of the samples being compared. Testing the accuracy of the developed workflows is an essential prerequisite in the analysis of small molecules by MS, and this is achieved by constructing calibration curves to demonstrate linearity of quantification for each analyte. This level of analytical rigor is rarely shown in large-scale quantification of proteins using either label-based or label-free techniques. In this chapter we show an approach to test linearity of quantification of each phosphopeptide quantified by liquid chromatography (LC)-MS without the need to synthesize standards or label proteins. We further describe the appropriate sample handling techniques required for the reproducible recovery of phosphopeptides and explore the essential algorithmic features that enable the handling of missing MS/MS spectra and thus make label-free data suitable for such analyses. The combined technology described in this chapter expands the applicability of phosphoproteomics to questions not previously tractable with other methodologies.

Proteomic approaches are being increasingly applied to study multiple facets of healthy and diseased processes. In particular, the application of global proteome profiling in the field of oncology is already starting to shape the diagnostic, prognostic, monitoring, and therapeutic approaches. At the heart of such approaches lies a quest for clinically relevant biomarkers, particularly arising from global analyses of body fluids, as, in major part, they represent easily accessible and noninvasive matrices. A detailed protocol of one of the popular approaches for global proteome profiling, SDS-PAGE-liquid chromatography-tandem mass spectrometry or GeLC-MS/MS, and its application for biomarker discovery in urine is provided here.

Mass spectrometry is widely used to probe the proteome and its modifications in an untargeted manner, with unrivalled coverage. Applied to phosphoproteomics, it has tremendous potential to interrogate phospho-signalling and its therapeutic implications. However, this task is complicated by issues of undersampling of the phosphoproteome and challenges stemming from its high-content but low-sample-throughput nature. Hence, methods using such data to reconstruct signalling networks have been limited to restricted data sets and insights (for example, groups of kinases likely to be active in a sample). We propose a new method to handle high-content discovery phosphoproteomics data on perturbation by putting it in the context of kinase/phosphatase-substrate knowledge, from which we derive and train logic models. We show, on a data set obtained through perturbations of cancer cells with small-molecule inhibitors, that this method can study the targets and effects of kinase inhibitors, and reconcile insights obtained from multiple data sets, a common issue with these data.

The tumor microenvironment plays key roles in cancer biology, but its impact on the regulation of signaling pathway activity in cancer cells has not been systemically investigated. We designed an analytical strategy that allows differential analysis of signaling between cancer and stromal cells present in tumor xenografts. We used this approach to investigate how in vivo growth conditions and PI3K inhibitors regulate pathway activities in both cancer and stromal cell populations. We found that, despite inducing more modest changes in protein expression, in vivo growing conditions extensively rewired protein kinase networks in cancer cells. As a result, different sets of phosphorylation sites were modulated by PI3K inhibitors in cancer cells growing in tumors relative to when these cells were in culture. The p110δ PI3K-selective compound CAL-101 (Idelalisib) did not inhibit markers of PI3K activity in cancer or stromal cells; however, unexpectedly, it induced phosphorylation on SQ motifs in both subpopulations of tumor cells in vivo but not in vitro. Thus, the interaction between cancer cells and the stroma modulated the ability of PI3K inhibitors to induce the activation of apoptosis in solid tumors. Our study provides proof-of-principle of a proteomics workflow for measuring signaling specifically in cancer and stromal cells and for investigating how cancer biochemistry is modulated in vivo., (© 2014 by The American Society for Biochemistry and Molecular Biology, Inc.)

Protein kinase signaling is fundamental to cell homeostasis and is deregulated in all cancers but varies between patients. Understanding the mechanisms underlying this heterogeneity is critical for personalized targeted therapies. Here, we used a recently established LC-MS/MS platform to profile protein phosphorylation in acute myeloid leukemia cell lines with different sensitivities to kinase inhibitors. The compounds used in this study were originally developed to target Janus kinase, phosphatidylinositol 3-kinase, and MEK. After further validation of the technique, we identified several phosphorylation sites that were inhibited by these compounds but whose intensities did not always correlate with growth inhibition sensitivity. In contrast, several hundred phosphorylation sites that correlated with sensitivity/resistance were not in general inhibited by the compounds. These results indicate that markers of pathway activity may not always be reliable indicators of sensitivity of cancer cells to inhibitors that target such pathways, because the activity of parallel kinases can contribute to resistance. By mining our data we identified protein kinase C isoforms as one of such parallel pathways being more active in resistant cells. Consistent with the view that several parallel kinase pathways were contributing to resistance, inhibitors that target protein kinase C, MEK, and Janus kinase potentiated each other in arresting the proliferation of multidrug-resistant cells. Untargeted/unbiased approaches, such as the one described here, to quantify the activity of the intended target kinase pathway in concert with the activities of parallel kinase pathways will be invaluable to personalize therapies based on kinase inhibitors.

Protein regulation by reversible phosphorylation is fundamental in nature, and large-scale phosphoproteomic analyses are becoming routine in proteomics laboratories. These analyses utilise phosphopeptide separation and enrichment techniques linked to LC-MS/MS. Herein, we report that IMAC and TiO(2) also enrich for non-phosphorylated modified peptides such as acetylated, deamidated and carbamylated peptides. Urea and digestion conditions commonly used in phosphoproteomic workflows are the likely sources of the induced modifications (deamidation and carbamylation) and can easily modify phosphopeptides. Including these variable modifications in database searches increased the total number of identified phosphopeptides by 15%. We also show that strong cation exchange fractionation provides poor resolution of phosphopeptides and actually enriches these alternatively modified peptides. By switching to reverse-phase chromatography, we show a significant improvement in the number of identified phosphopeptides. We recommend that the users of phosphopeptide enrichment strategies avoid using urea as a denaturant and that careful consideration is given to chromatographic conditions and the types of variable modifications used in database searches. Thus, the capacity of IMAC and TiO(2) to enrich phosphopeptides bearing modifications other than phosphorylation is a previously unappreciated property of these chromatographies with practical implications for the field of phosphoproteomics., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Phosphorylation is a protein post-translational modification with key roles in the regulation of cell biochemistry and signaling. In-depth analysis of phosphorylation using mass spectrometry is permitting the investigation of processes controlled by phosphorylation at the system level. A critical step of these phosphoproteomics methods involves the isolation of phosphorylated peptides from the more abundant unmodified peptides produced by the digestion of cell lysates. Although different techniques to enrich for phosphopeptides have been reported, there are limited data on their suitability for direct quantitative analysis by MS. Here we report a TiO(2) based enrichment method compatible with large-scale and label-free quantitative analysis by LC-MS/MS. Starting with just 500 μg of protein, the technique reproducibly isolated hundreds of peptides, >85% of which were phosphorylated. These results were obtained by using relatively short LC-MS/MS gradient runs (45 min) and without any previous separation step. In order to characterize the performance of the method for quantitative analyses, we employed label-free LC-MS/MS using extracted ion chromatograms as the quantitative readout. After normalization, phosphopeptides were quantified with good precision (coefficient of variation was 20% on average, n=900 phosphopeptides), linearity (correlation coefficients >0.98) and accuracy (deviations <20%). Thus, phosphopeptide ion signals correlated with the concentration of the respective phosphopeptide in samples, making the approach suitable for in-depth relative quantification of phosphorylation by label-free LC-MS/MS., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Protein kinase pathways play pivotal roles in cell signaling and biology. The phosphoproteome is a reflection of protein kinase pathway activation and therefore there is considerable interest in its quantification as a means to assess the wiring of signaling networks. Although different approaches for quantitative phosphoproteomics have been described, there is no data on how accurate these are for each quantified phosphorylated site. We report a liquid chromatography-MS approach to objectively assess data quality in high-content comparison of phosphoproteomes in which samples to be compared are mixed at different proportions. The experimental data is then used to derive a linear regression function that allows calculating correlation values, linearity, and accuracy. We applied the technique to investigate phosphorylation in P31/Fuj and Kasumi-1, two leukemia cells lines showing strikingly different sensitivities to scr and PI3K inhibitors. We found that phosphopeptides quantified with accuracy were not always quantified with precision because of low ion statistics contributing to variability. Thus our approach was complementary to standard methods for calculating the precision of replicate measurements based on the coefficient of variation and provided additional information on data quality for each quantified phosphopeptide. We quantified > 2250 phosphorylation sites across cell lines with different levels of sensitivity to kinase inhibitors, of which 1847 showed an accuracy variation of < 30% (with an overall mean of 22%). Hundreds of phosphorylation sites on proteins with diverse function (including kinases, transcription, and translation factors) showed significantly distinct intensities across sensitive and resistant cells lines, indicating that kinase pathways are differentially regulated in cancer cells of distinct sensitivity to signaling inhibitors.

LC-MS is a useful technique for protein and peptide quantification. In addition, as a powerful tool for systems biology research, LC-MS can also be used to quantify post-translational modifications and metabolites that reflect biochemical pathway activity. This review discusses the different analytical techniques that use LC-MS for the quantification of proteins, their modifications and activities in a multiplex manner.

LC-MS is a powerful technique in biomolecular research. In addition to its uses as a tool for protein and peptide quantization, LC-MS can also be used to quantify the activity of signalling and metabolic pathways in a multiplex and comprehensive manner, i.e. as an 'activitomic' tool. Taking cancer research as an illustrative example of application, this review discusses the concepts of biochemical pathway analysis using LC-MS-based proteomic and activitomic techniques.

Large-scale phosphoproteomics studies are of great interest due to their potential for the dissection of signaling pathways controlled by protein kinases. Recent advances in mass spectrometry (MS)-based phosphoproteomic techniques offer new opportunities to profile protein kinase activities in a comprehensive manner. However, this increasingly used approach still poses many analytical challenges. On one hand, high stringency criteria for phosphopeptide identification based on MS/MS data are needed in order to avoid false positives; however, on the other hand, these stringent criteria also result in the introduction of many false negatives. In the current report, we employ different mass spectrometric techniques for large-scale phosphoproteomics in order to reduce the presence of false negatives and enhance data confidence. A LTQ-Orbitrap LC-MS/MS platform identified approximately 3 times more phosphopeptides than Q-TOF LC-MS/MS instrumentation (4308 versus 1485 identifications, respectively). In both cases, collision induced dissociation (CID) was used to fragment peptides. Interestingly, the two platforms produced complementary data as many of the low scoring phosphopeptide ions identified by LTQ-Orbitrap MS/MS gave rise to high score identifications by Q-TOF MS/MS analysis, and vice versa. In fact, approximately 450 phosphopeptides identified by the Q-TOF instrument were not identified by the LTQ-Orbitrap. Further data comparison revealed the extent of the problem: in one experiment, the estimated number of false negatives (1066) was close to the number of identified phosphopeptides (1485). This work demonstrates that by using standard procedures for phosphopeptide identification the number of false negatives can be even greater than the number of false positives. We propose using historical phosphoproteomic data and spectral matching algorithms in order to efficiently minimize false negative rates.

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 approximately 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.

Analysis of primary animal and human tissues is key in biological and biomedical research. Comparative proteomics analysis of primary biological material would benefit from uncomplicated experimental work flows capable of evaluating an unlimited number of samples. In this report we describe the application of label-free proteomics to the quantitative analysis of five mouse core proteomes. We developed a computer program and normalization procedures that allow exploitation of the quantitative data inherent in LC-MS/MS experiments for relative and absolute quantification of proteins in complex mixtures. Important features of this approach include (i) its ability to compare an unlimited number of samples, (ii) its applicability to primary tissues and cultured cells, (iii) its straightforward work flow without chemical reaction steps, and (iv) its usefulness not only for relative quantification but also for estimation of absolute protein abundance. We applied this approach to quantitatively characterize the most abundant proteins in murine brain, heart, kidney, liver, and lung. We matched 8,800 MS/MS peptide spectra to 1,500 proteins and generated 44,000 independent data points to profile the approximately 1,000 most abundant proteins in mouse tissues. This dataset provides a quantitative profile of the fundamental proteome of a mouse, identifies the major similarities and differences between organ-specific proteomes, and serves as a paradigm of how label-free quantitative MS can be used to characterize the phenotype of mammalian primary tissues at the molecular level.

The renal Fanconi syndrome is a defect of proximal tubular function causing aminoaciduria and low-molecular-weight proteinuria. Dent's disease and Lowe syndrome are defined X-linked forms of Fanconi syndrome; there is also an autosomal dominant idiopathic form (ADIF), phenotypically similar to Dent's disease though its gene defect is still unknown. To assess whether their respective gene products are ultimately involved in a common reabsorptive pathway for proteins and low-molecular-mass endogenous metabolites, we compared renal Fanconi urinary proteomes and metabonomes with normal (control) urine using mass spectrometry and (1)H-NMR spectroscopy, respectively. Urine from patients with low-molecular-weight proteinuria secondary to ifosfamide treatment (tubular proteinuria; TP) was also analyzed for comparison. All four of the disorders studied had characteristic proteomic and metabonomic profiles. Uromodulin was the most abundant protein in normal urine, whereas Fanconi urine was dominated by albumin. (1)H-NMR spectroscopic data showed differences in the metabolic profiles of Fanconi urine vs. normal urine, due mainly to aminoaciduria. There were differences in the urinary metabolite and protein compositions between the three genetic forms of Fanconi syndrome: cluster analysis grouped the Lowe and Dent's urinary proteomes and metabonomes together, whereas ADIF and TP clustered together separately. Our findings demonstrate a distinctive "polypeptide and metabolite fingerprint" that can characterize the renal Fanconi syndrome; they also suggest that more subtle and cause-specific differences may exist between the different forms of Fanconi syndrome that might provide novel insights into the underlying mechanisms and cellular pathways affected.

Background: Quantitative data on protein and polypeptide excretion in normal urine are lacking. In Fanconi syndrome, failure of proximal tubular protein reabsorption leads to 'tubular' proteinuria, but little is known about peptide excretion., Methods: Urine from normal (N=5) and Fanconi patients (Dent's disease, N=2; Lowe syndrome, N=3) was fractionated by size-exclusion chromatography into proteins (>10 kD) and smaller polypeptides. Each fraction was subjected to amino acid analysis after acid hydrolysis. In complementary proteomic approaches, urinary polypeptides were each subjected to reversed-phase high-performance liquid chromatography (HPLC) followed by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) and nano-flow liquid chromatography directly coupled to electrospray ionization/tandem mass spectrometry (NanoLC-ESI-MS/MS) before and after tryptic digestion., Results: Based on amino acid composition, normal human urine, excluding Tamm-Horsfall protein, contains 33.7 +/- 10.7 mg protein per 24 hr (mean +/- SEM) protein defined as polypeptide >10 kD; peptide content in range 750 Da to 10 kD is 22.0 +/- 9.6 mg. Fanconi patients excrete greatly increased amounts of protein, 1740 +/- 660 mg/24 hr, and peptide, 446 +/- 145 mg/24 hr. Peptides 2 to 5 kD were present in 12.9- +/- 3.9-fold excess in Fanconi compared with normal urine. In contrast, free amino acid excretion in Fanconi was elevated only 2.14- +/- 0.73-fold. Mass spectrometric techniques determined that the major form of albumin in both normal and Fanconi urine was the full-length protein, and did not detect significant peptides of nonrenal origin., Conclusion: There is only very low excretion of polypeptides >750 Da in normal human urine. In Fanconi syndrome, excretion of unknown peptides of mass 2 to 5 kD, possibly relevant to the development of renal failure, is greatly increased.

Polypeptides present in the glomerular filtrate are almost completely reabsorbed in the first segment of the proximal tubule by receptor-mediated endocytosis; in renal Fanconi syndrome (FS), there is failure to reabsorb many of these polypeptides. We have compared the urinary proteomes in patients with Dent's disease (due to a CLC5 mutation), a form of FS, with normal subjects using three different proteomic methods. No differences in the levels of several plasma proteins were detected when standardized to total protein amounts. In contrast, several vitamin and prosthetic group carrier proteins were found in higher amounts in Dent's urine (with respect to total protein). Similarly, complement components, apolipoproteins, and some cytokines represented a larger proportion of the Dent's urinary proteome, suggesting that such proteins are reabsorbed more efficiently than other classes of proteins. Conversely, proteins of renal origin were found in proportionately higher amounts in normal urine. Thus the uptake of filtered vitamins, which are normally bound to their respective carrier proteins to prevent urinary losses, seems a key function of the proximal tubule; in addition, this nephron segment may also play a critical role in reabsorbing potentially cytotoxic polypeptides of plasma origin, preventing them from acting at more distal nephron sites.

Phosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and cell growth. However, deriving knowledge of signalling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites as markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed. Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signalling pathways. Phosphoproteomics data from acute myeloid leukaemia (AML) cells treated separately with PI3K, AKT, MEK and ERK inhibitors were used to reconstruct individual kinase networks. We used modularity maximisation to detect communities in each network, and selected the community containing the main target of the inhibitor used to treat cells. These analyses returned communities that contained known canonical signalling components. Interestingly, in addition to canonical PI3K/AKT/mTOR members, the community assignments returned TTK (also known as MPS1) as a likely component of PI3K/AKT/mTOR signalling. We drew similar insights from an external phosphoproteomics dataset from breast cancer cells treated with rapamycin and oestrogen. We confirmed this observation with wet-lab laboratory experiments showing that TTK phosphorylation was decreased in AML cells treated with AKT and MTOR inhibitors. This study illustrates the application of community detection algorithms to the analysis of empirical kinase networks to uncover new members linked to canonical signalling pathways. Author summary: Kinases are key enzymes that regulate the transduction of extracellular signals from cell surface receptors to changes in gene expression via a set of kinase-kinase interactions and signalling cascades. Inhibiting hyperactive kinases is a viable therapeutic strategy to treat different cancer types. Unfortunately for cancer therapy, kinase signalling networks are robust to external perturbations, thus allowing tumour cells to orchestrate mechanisms that compensate for inhibition of specific kinases. Therefore, there is a need to better understand kinase network structure to identify new therapeutic targets. Here, we reconstructed kinase networks from phosphoproteomics data, and compared the activity of its kinase interactions in acute myeloid leukaemia (AML) cells. We then tested community detection algorithms to identify kinase components associated with PI3K/AKT/mTOR signalling, a paradigmatic oncogenic signalling cascade. We found that TTK was usually grouped with networks derived for PI3K, AKT and mTOR kinases. Wet-lab experiments confirmed that TTK is likely to act downstream of AKT and mTOR. We thus showed that our methods can be used to identify potential new members of canonical kinase signalling cascades. [ABSTRACT FROM AUTHOR]

We studied the extent and nature of renal involvement in a cohort of 117 adult patients with mitochondrial disease, by measuring urinary retinol-binding protein (RBP) and albumin; established markers of tubular and glomerular dysfunction, respectively. Seventy-five patients had the m.3243A>G mutation and the most frequent phenotypes within the entire cohort were 14 with MELAS, 33 with MIDD, and 17 with MERRF. Urinary RBP was increased in 29 of 75 of m.3243A>G patients, whereas albumin was increased in 23 of the 75. The corresponding numbers were 16 and 14, respectively, in the 42 non-m.3243A>G patients. RBP and albumin were higher in diabetic m.3243A>G patients than in nondiabetics, but there were no significant differences across the three major clinical phenotypes. The urine proteome (mass spectrometry) and metabonome (nuclear magnetic resonance) in a subset of the m.3243A>G patients were markedly different from controls, with the most significant alterations occurring in lysosomal proteins, calcium-binding proteins, and antioxidant defenses. Differences were also found between asymptomatic m.3243A>G carriers and controls. No patients had an elevated serum creatinine level, but 14% had hyponatremia, 10% had hypophosphatemia, and 14% had hypomagnesemia. Thus, abnormalities in kidney function are common in adults with mitochondrial disease, exist in the absence of elevated serum creatinine, and are not solely explained by diabetes. [ABSTRACT FROM AUTHOR]

Abstract: Protein kinases have important functions in the control of cell biology and are implicated in several diseases including cancer. Here we describe a technique to quantify protein kinase activity in a global fashion and without preconception of the kinases that may be active in the cell or tissue under investigation. In Global Kinase Activity Profiling (GKAP), protein kinases present in experimental cell lysates phosphorylate endogenous substrates, also present in the lysate, under defined conditions. Reaction products are then quantified using standard phosphoproteomic techniques based on LC–MS/MS. The technique thus allows measuring the combined activities of kinases targeting common substrates, which are detected as phosphopeptides by LC–MS/MS. Almost four hundred kinase reactions could be quantified in a human epithelial cell line, 177 of which increased in response to EGF treatment while others decreased in cells exposed to the kinase inhibitors LY294002 or U0126. GKAP also detected marked differences in the patterns of kinase activities in human leukemia cell lines with different sensitivities to kinase inhibitors. These results reveal that GKAP detects and quantifies hundreds of kinase activities modulated by growth factors or pharmacological inhibitors, and that these activities correlate with the phenotypes of cancer cells and their responses to kinase inhibitors. [Copyright &y& Elsevier]

Abstract: Phosphorylation is a protein post-translational modification with key roles in the regulation of cell biochemistry and signaling. In-depth analysis of phosphorylation using mass spectrometry is permitting the investigation of processes controlled by phosphorylation at the system level. A critical step of these phosphoproteomics methods involves the isolation of phosphorylated peptides from the more abundant unmodified peptides produced by the digestion of cell lysates. Although different techniques to enrich for phosphopeptides have been reported, there are limited data on their suitability for direct quantitative analysis by MS. Here we report a TiO2 based enrichment method compatible with large-scale and label-free quantitative analysis by LC–MS/MS. Starting with just 500μg of protein, the technique reproducibly isolated hundreds of peptides, >85% of which were phosphorylated. These results were obtained by using relatively short LC–MS/MS gradient runs (45min) and without any previous separation step. In order to characterize the performance of the method for quantitative analyses, we employed label-free LC–MS/MS using extracted ion chromatograms as the quantitative readout. After normalization, phosphopeptides were quantified with good precision (coefficient of variation was 20% on average, n =900 phosphopeptides), linearity (correlation coefficients >0.98) and accuracy (deviations <20%). Thus, phosphopeptide ion signals correlated with the concentration of the respective phosphopeptide in samples, making the approach suitable for in-depth relative quantification of phosphorylation by label-free LC–MS/MS. [Copyright &y& Elsevier]