Increased confidence in large-scale phosphoproteomics data by complementary mass spectrometric techniques and matching of phosphopeptide data sets

An integrated approach identifies the molecular underpinnings of murine anterior visceral endoderm migration

The anterior visceral endoderm (AVE) differs from the surrounding visceral endoderm (VE) in its migratory behavior and ability to restrict primitive streak formation to the opposite side of the mouse embryo. To characterize the molecular bases for the unique properties of the AVE, we combined single-cell RNA sequencing of the VE prior to and during AVE migration with phosphoproteomics, high-resolution live-imaging, and short-term lineage labeling and intervention. This identified the transient nature of the AVE with attenuation of "anteriorizing" gene expression as cells migrate and the emergence of heterogeneities in transcriptional states relative to the AVE's position. Using cell communication analysis, we identified the requirement of semaphorin signaling for normal AVE migration. Lattice light-sheet microscopy showed that Sema6D mutants have abnormalities in basal projections and migration speed. These findings point to a tight coupling between transcriptional state and position of the AVE and identify molecular controllers of AVE migration.

Host-directed kinase inhibitors act as novel therapies against intracellular Staphylococcus aureus

Host-directed therapeutics are a promising anti-infective strategy against intracellular bacterial pathogens. Repurposing host-targeted drugs approved by the FDA in the US, the MHRA in the UK and/or regulatory equivalents in other countries, is particularly interesting because these drugs are commercially available, safe doses are documented and they have been already approved for other clinical purposes. In this study, we aimed to identify novel therapies against intracellular Staphylococcus aureus, an opportunistic pathogen that is able to exploit host molecular and metabolic pathways to support its own intracellular survival. We screened 133 host-targeting drugs and found three host-directed tyrosine kinase inhibitors (Ibrutinib, Dasatinib and Crizotinib) that substantially impaired intracellular bacterial survival. We found that Ibrutinib significantly increased host cell viability after S. aureus infection via inhibition of cell invasion and intracellular bacterial proliferation. Using phosphoproteomics data, we propose a putative mechanism of action of Ibrutinib involving several host factors, including EPHA2, C-JUN and NWASP. We confirmed the importance of EPHA2 for staphylococcal infection in an EPHA2-knock-out cell line. Our study serves as an important example of feasibility for identifying host-directed therapeutics as candidates for repurposing.

Targeted In-Depth Quantification of Signaling Using Label-Free Mass Spectrometry

Protein phosphorylation encodes information on the activity of kinase-driven signaling pathways that regulate cell biology. This chapter discusses an approach, named TIQUAS (targeted in-depth quantification of signaling), to quantify cell signaling comprehensively and without bias. The workflow-based on mass spectrometry (MS) and computational science-consists of targeting the analysis of phosphopeptides previously identified by shotgun liquid chromatography tandem MS (LC-MS/MS) across the samples that are being compared. TIQUAS therefore takes advantage of concepts derived from both targeted (data-independent) and data-dependent acquisition methods; phosphorylation sites are quantified in all experimental samples regardless of whether or not these phosphopeptides were identified by MS/MS in all runs. As a result, datasets are obtained containing quantitative information on several thousand phosphorylation sites in as many samples and replicates as required in the experimental design, and these rich datasets are devoid of a significant number of missing data points. This chapter discussed the biochemical, analytical, and computational procedures required to apply the approach and for obtaining a biological interpretation of the data in the context of our understanding of cell signaling regulation and kinase-substrate relationships.

Quantitative measurement of phosphoproteome response to osmotic stress in arabidopsis based on Library-Assisted eXtracted Ion Chromatogram (LAXIC)

Global phosphorylation changes in plants in response to environmental stress have been relatively poorly characterized to date. Here we introduce a novel mass spectrometry-based label-free quantitation method that facilitates systematic profiling plant phosphoproteome changes with high efficiency and accuracy. This method employs synthetic peptide libraries tailored specifically as internal standards for complex phosphopeptide samples and accordingly, a local normalization algorithm, LAXIC, which calculates phosphopeptide abundance normalized locally with co-eluting library peptides. Normalization was achieved in a small time frame centered to each phosphopeptide to compensate for the diverse ion suppression effect across retention time. The label-free LAXIC method was further treated with a linear regression function to accurately measure phosphoproteome responses to osmotic stress in Arabidopsis. Among 2027 unique phosphopeptides identified and 1850 quantified phosphopeptides in Arabidopsis samples, 468 regulated phosphopeptides representing 497 phosphosites have shown significant changes. Several known and novel components in the abiotic stress pathway were identified, illustrating the capability of this method to identify critical signaling events among dynamic and complex phosphorylation. Further assessment of those regulated proteins may help shed light on phosphorylation response to osmotic stress in plants.

Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors

Background

Tumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.

Results

Mass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple myeloma cell lines in six biological replicates. The intensities of the phosphorylation sites grouped these cancer cell lines according to their tumor type. In addition, a phosphoproteomic analysis of seven acute myeloid leukemia cell lines revealed a battery of phosphorylation sites whose combined intensities correlated with the growth-inhibitory responses to three kinase inhibitors with remarkable correlation coefficients and fold changes (> 100 between the most resistant and sensitive cells). Modeling based on regression analysis indicated that a subset of phosphorylation sites could be used to predict response to the tested drugs. Quantitative analysis of phosphorylation motifs indicated that resistant and sensitive cells differed in their patterns of kinase activities, but, interestingly, phosphorylations correlating with responses were not on members of the pathway being targeted; instead, these mainly were on parallel kinase pathways.

Conclusion

This study reveals that the information on kinase activation encoded in phosphoproteomics data correlates remarkably well with the phenotypic responses of cancer cells to compounds that target kinase signaling and could be useful for the identification of novel markers of resistance or sensitivity to drugs that target the signaling network.

Advances in phosphopeptide enrichment techniques for phosphoproteomics

Phosphoproteomics is increasingly used to address a wide range of biological questions. However, despite some success, techniques for phosphoproteomics are not without challenges. Phosphoproteins are present in cells in low abundance relative to their unphosphorylated counterparts; therefore phosphorylated proteins (or phosphopeptides after protein digestion) are rarely detected in standard shotgun proteomics experiments. Thus, extraction of phosphorylated polypeptides from complex mixtures is a critical step in the success of phosphoproteomics experiments. Intense research over the last decade has resulted in the development of powerful techniques for phosphopeptide enrichment prior to analysis by mass spectrometry. Here, we review how the development of IMAC, MOAC, chemical derivatization and antibody affinity purification and chromatography is contributing to the evolution of phosphoproteomics techniques. Although further developments are needed for the technology to reach maturity, current state-of-the-art techniques can already be used as powerful tools for biological research.

Optimizing targeted cancer therapy: towards clinical application of systems biology approaches

In cancer, genetic and epigenetic alterations ultimately culminate in discordant activation of signal transduction pathways driving the malignant process. Pharmacological or biological inhibition of such pathways holds significant promise with respect to devising rational therapy for cancer. Thus, technical concepts pursuing robust characterization of kinase activity in tissue samples from cancer patients have been subject of investigation. In the present review we provide a comprehensive overview of these techniques and discuss their advantages and disadvantages for systems biology approaches to identify kinase targets in oncological disease. Recent advances in the development and application of array-based peptide-substrate kinase activity screens show great promise in overcoming the discrepancy between the evaluation of aberrant cell signaling in specific malignancies or even individual patients and the currently available ensemble of highly specific targeted treatment strategies. These developments have the potential to result in a more effective selection of kinase inhibitors and thus optimize mechanism-based patient-specific therapeutic strategies. Given the results from current research on the tumor kinome, generating network views on aberrant tumor cell signaling is critical to meet this challenge.

Characterization of a TiO₂ enrichment method for label-free quantitative phosphoproteomics

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.

Post-translation modification of proteins; methodologies and applications in plant sciences

Proteins have the potential to undergo a variety of post-translational modifications and the different methods available to study these cellular processes has advanced rapidly with the continuing development of proteomic technologies. In this review we aim to detail five major post-translational modifications (phosphorylation, glycosylaion, lipid modification, ubiquitination and redox-related modifications), elaborate on the techniques that have been developed for their analysis and briefly discuss the study of these modifications in selected areas of plant science.

Phosphoproteomic profiling of in vivo signaling in liver by the mammalian target of rapamycin complex 1 (mTORC1)

BACKGROUND

Our understanding of signal transduction networks in the physiological context of an organism remains limited, partly due to the technical challenge of identifying serine/threonine phosphorylated peptides from complex tissue samples. In the present study, we focused on signaling through the mammalian target of rapamycin (mTOR) complex 1 (mTORC1), which is at the center of a nutrient- and growth factor-responsive cell signaling network. Though studied extensively, the mechanisms involved in many mTORC1 biological functions remain poorly understood.

METHODOLOGY/PRINCIPAL FINDINGS

We developed a phosphoproteomic strategy to purify, enrich and identify phosphopeptides from rat liver homogenates. Using the anticancer drug rapamycin, the only known target of which is mTORC1, we characterized signaling in liver from rats in which the complex was maximally activated by refeeding following 48 hr of starvation. Using protein and peptide fractionation methods, TiO(2) affinity purification of phosphopeptides and mass spectrometry, we reproducibly identified and quantified over four thousand phosphopeptides. Along with 5 known rapamycin-sensitive phosphorylation events, we identified 62 new rapamycin-responsive candidate phosphorylation sites. Among these were PRAS40, gephyrin, and AMP kinase 2. We observed similar proportions of increased and reduced phosphorylation in response to rapamycin. Gene ontology analysis revealed over-representation of mTOR pathway components among rapamycin-sensitive phosphopeptide candidates.

CONCLUSIONS/SIGNIFICANCE

In addition to identifying potential new mTORC1-mediated phosphorylation events, and providing information relevant to the biology of this signaling network, our experimental and analytical approaches indicate the feasibility of large-scale phosphoproteomic profiling of tissue samples to study physiological signaling events in vivo.

Biological signalling activity measurements using mass spectrometry

MS (mass spectrometry) techniques are rapidly evolving to high levels of performance and robustness. This is allowing the application of these methods to the interrogation of signalling networks with unprecedented depth and accuracy. In the present review we discuss how MS-based multiplex quantification of kinase activities and phosphoproteomics provide complementary means to assess biological signalling activity. In addition, we discuss how a wider application of these analytical concepts to quantify kinase signalling will result in a more comprehensive understanding of normal and disease biology at the system level.

Proteomic analysis of formalin-fixed, paraffin-embedded lung neuroendocrine tumor samples from hospital archives

Hospital tissue repositories host an invaluable supply of diseased samples with matched retrospective clinical information. In this work, a recently optimized method for extracting full-length proteins from formalin-fixed, paraffin-embedded (FFPE) tissues was evaluated on lung neuroendocrine tumor (LNET) samples collected from hospital repositories. LNETs comprise a heterogeneous spectrum of diseases, for which subtype-specific diagnostic markers are lacking. Six archival samples diagnosed as typical carcinoid (TC) or small cell lung carcinoma (SCLC) were subjected to a full-length protein extraction followed by a GeLC-MS/MS analysis, enabling the identification of over 300 distinct proteins per tumor subtype. All identified proteins were categorized through DAVID software, revealing a differential distribution of functional classes, such as those involved in RNA processing, response to oxidative stress and ion homeostasis. Moreover, using spectral counting for protein abundance estimation and beta-binomial test as statistical filter, a list of 28 differentially expressed proteins was generated and submitted to pathway analysis by means of Ingenuity Pathway Analysis software. Differential expression of chromogranin-A (more expressed in TCs) and stathmin (more expressed in SCLCs) was consistently confirmed by immunohistochemistry. Therefore, FFPE hospital archival samples can be successfully subjected to proteomic investigations aimed to biomarker discovery following a GeLC-MS/MS label-free approach.

A self-validating quantitative mass spectrometry method for assessing the accuracy of high-content phosphoproteomic experiments

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.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.

Plasma membrane proteomics and its application in clinical cancer biomarker discovery

Plasma membrane proteins that are exposed on the cell surface have important biological functions, such as signaling into and out of the cells, ion transport, and cell-cell and cell-matrix interactions. The expression level of many of the plasma membrane proteins involved in these key functions is altered on cancer cells, and these proteins may also be subject to post-translational modification, such as altered phosphorylation and glycosylation. Additional protein alterations on cancer cells confer metastatic capacities, and some of these cell surface proteins have already been successfully targeted by protein drugs, such as human antibodies, that have enhanced survival of several groups of cancer patients. The combination of novel analytical approaches and subcellular fractionation procedures has made it possible to study the plasma membrane proteome in more detail, which will elucidate cancer biology, particularly metastasis, and guide future development of novel drug targets. The technical advances in plasma membrane proteomics and the consequent biological revelations will be discussed herein. Many of the advances have been made using cancer cell lines, but because the main goal of this research is to improve individualized treatment and increase cancer patient survival, further development is crucial to direct analysis of clinically relevant patient samples. These efforts include optimized specimen handling and preparation as well as improved proteomics platforms. Identification of potentially useful proteomics-based biomarkers must be validated in larger, well defined retrospective and prospective clinical studies, and these combined efforts should result in identification of biomarkers that will greatly improve early detection, prognosis, and prediction of treatment response.