Pedro R. Cutillas

Angiogenesis selectively requires the p110α isoform of PI3K to control endothelial cell migration

Phosphoinositide 3-kinases (PI3Ks) signal downstream of multiple cell-surface receptor types. Class IA PI3K isoforms couple to tyrosine kinases and consist of a p110 catalytic subunit (p110α, p110β or p110δ), constitutively bound to one of five distinct p85 regulatory subunits. PI3Ks have been implicated in angiogenesis, but little is known about potential selectivity among the PI3K isoforms and their mechanism of action in endothelial cells during angiogenesis in vivo. Here we show that only p110α activity is essential for vascular development. Ubiquitous or endothelial cell-specific inactivation of p110α led to embryonic lethality at mid-gestation because of severe defects in angiogenic sprouting and vascular remodelling. p110α exerts this critical endothelial cell-autonomous function by regulating endothelial cell migration through the small GTPase RhoA. p110α activity is particularly high in endothelial cells and preferentially induced by tyrosine kinase ligands (such as vascular endothelial growth factor (VEGF)-A). In contrast, p110β in endothelial cells signals downstream of G-protein-coupled receptor (GPCR) ligands such as SDF-1α, whereas p110δ is expressed at low level and contributes only minimally to PI3K activity in endothelial cells. These results provide the first in vivo evidence for p110-isoform selectivity in endothelial PI3K signalling during angiogenesis.

Class IA phosphoinositide 3-kinases are obligate p85-p110 heterodimers

Class IA phosphoinositide 3-kinases (PI3Ks) signal downstream of tyrosine kinases and Ras and control a wide variety of biological responses. In mammals, these heterodimeric PI3Ks consist of a p110 catalytic subunit (p110α, p110β, or p110δ) bound to any of five distinct regulatory subunits (p85α, p85β, p55γ, p55α, and p50α, collectively referred to as “p85s”). The relative expression levels of p85 and p110 have been invoked to explain key features of PI3K signaling. For example, free (i.e., non-p110-bound) p85α has been proposed to negatively regulate PI3K signaling by competition with p85/p110 for recruitment to phosphotyrosine docking sites. Using affinity and ion exchange chromatography and quantitative mass spectrometry, we demonstrate that the p85 and p110 subunits are present in equimolar amounts in mammalian cell lines and tissues. No evidence for free p85 or p110 subunits could be obtained. Cell lines contain 10,000–15,000 p85/p110 complexes per cell, with p110β and p110δ being the most prevalent catalytic subunits in nonleukocytes and leukocytes, respectively. These results argue against a role of free p85 in PI3K signaling and provide insights into the nonredundant functions of the different class IA PI3K isoforms.

mTOR regulates MAPKAPK2 translation to control the senescence-associated secretory phenotype

Senescent cells secrete a combination of factors collectively known as the senescence-associated secretory phenotype (SASP). The SASP reinforces senescence and activates an immune surveillance response, but it can also show pro-tumorigenic properties and contribute to age-related pathologies. In a drug screen to find new SASP regulators, we uncovered the mTOR inhibitor rapamycin as a potent SASP suppressor. Here we report a mechanism by which mTOR controls the SASP by differentially regulating the translation of the MK2 (also known as MAPKAPK2) kinase through 4EBP1. In turn, MAPKAPK2 phosphorylates the RNA-binding protein ZFP36L1 during senescence, inhibiting its ability to degrade the transcripts of numerous SASP components. Consequently, mTOR inhibition or constitutive activation of ZFP36L1 impairs the non-cell-autonomous effects of senescent cells in both tumour-suppressive and tumour-promoting contexts. Altogether, our results place regulation of the SASP as a key mechanism by which mTOR could influence cancer, age-related diseases and immune responses.

Fumarate is an epigenetic modifier that elicits epithelial-to-mesenchymal transition

Mutations of the tricarboxylic acid cycle enzyme fumarate hydratase cause hereditary leiomyomatosis and renal cell cancer. Fumarate hydratase-deficient renal cancers are highly aggressive and metastasize even when small, leading to a very poor clinical outcome. Fumarate, a small molecule metabolite that accumulates in fumarate hydratase-deficient cells, plays a key role in cell transformation, making it a bona fide oncometabolite. Fumarate has been shown to inhibit α-ketoglutarate-dependent dioxygenases that are involved in DNA and histone demethylation. However, the link between fumarate accumulation, epigenetic changes, and tumorigenesis is unclear. Here we show that loss of fumarate hydratase and the subsequent accumulation of fumarate in mouse and human cells elicits an epithelial-to-mesenchymal-transition (EMT), a phenotypic switch associated with cancer initiation, invasion, and metastasis. We demonstrate that fumarate inhibits Tet-mediated demethylation of a regulatory region of the antimetastatic miRNA cluster mir-200ba429, leading to the expression of EMT-related transcription factors and enhanced migratory properties. These epigenetic and phenotypic changes are recapitulated by the incubation of fumarate hydratase-proficient cells with cell-permeable fumarate. Loss of fumarate hydratase is associated with suppression of miR-200 and the EMT signature in renal cancer and is associated with poor clinical outcome. These results imply that loss of fumarate hydratase and fumarate accumulation contribute to the aggressive features of fumarate hydratase-deficient tumours.

Quantitative Profile of Five Murine Core Proteomes Using Label-free Functional Proteomics

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 ∼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.

Detection and analysis of urinary peptides by on-line liquid chromatography and mass spectrometry: application to patients with renal Fanconi syndrome.

Urinary proteomics has become a topical and potentially valuable field of study in relation to normal and abnormal renal function. Filtered bioactive peptides present in high concentration in the nephron of patients with tubular proteinuria may have downstream effects on renal tubular function. In renal Fanconi syndromes, such as Dents disease, peptides implicated in altered tubular function or injury have recently been measured in urine by immunochemical methods. However, the limited availability of antibodies means that only certain peptides can be detected in this way. We have used nanoflow liquid chromatography and tandem mass spectrometry (nanoLC-MS/MS) as a complementary technique to analyse urinary peptides. Urine was desalted by solid-phase extraction (SPE) and its peptides were then separated from neutral and acidic compounds by strong cation-exchange chromatography (SCX), which was also used to fractionate the peptide mixture. Fractions from the SCX step were separated further by reversed-phase LC and analysed on-line by MS/MS. Extraction by SPE showed a good recovery of small peptides. We detected over 100 molecular species in urine samples from three individuals with Dents disease. In addition to plasma and known urinary proteins, we identified some novel proteins and potentially bioactive peptides in urine from these patients, which were not present in normal urine. These data show that nanoLC-MS/MS complements existing techniques for the identification of polypeptides in urine. This approach is a potentially powerful tool to discover new markers and/or causative factors in renal disease; in addition, its sensitivity may also make it applicable to the direct ultramicroanalysis of renal tubule fluid.

Kinase-Substrate Enrichment Analysis Provides Insights into the Heterogeneity of Signaling Pathway Activation in Leukemia Cells

Computational analysis of phosphoproteomics data predicts the sensitivity of leukemia cells to kinase inhibitors. Therapeutic Targeting with Phosphoproteomics Because oncogenic mutations frequently occur in genes encoding kinases, kinase inhibitors are often used as cancer therapeutics. However, because of the complexity of kinase signaling networks and the heterogeneous nature of oncogenic mutations, it is difficult to predict how cancer cells will respond to a given kinase inhibitor. Casado et al. performed phosphoproteomic analysis of human acute myeloid leukemia (AML) cell lines and used a computational approach of “signal averaging” to reduce the noise in the phosphoproteomics data, thus monitoring global kinase activation patterns as well as the effects of select inhibitors. In addition to identifying kinase networks associated with AML, this approach accurately predicted the relative sensitivities of patient-derived AML cells to inhibitors. Such profiling of kinase networks could be applied to stratify cancers based on their predicted responses to kinase inhibition. Kinases determine the phenotypes of many cancer cells, but the frequency with which individual kinases are activated in primary tumors remains largely unknown. We used a computational approach, termed kinase-substrate enrichment analysis (KSEA), to systematically infer the activation of given kinase pathways from mass spectrometry–based phosphoproteomic analysis of acute myeloid leukemia (AML) cells. Experiments conducted in cell lines validated the approach and, furthermore, revealed that DNA-dependent protein kinase (DNA-PK) was activated as a result of inhibiting the phosphoinositide 3-kinase (PI3K)–mammalian target of rapamycin (mTOR) signaling pathway. Application of KSEA to primary AML cells identified PI3K, casein kinases (CKs), cyclin-dependent kinases (CDKs), and p21-activated kinases (PAKs) as the kinase substrate groups most frequently enriched in this cancer type. Substrates phosphorylated by extracellular signal–regulated kinase (ERK) and cell division cycle 7 (CDC7) were enriched in primary AML cells that were resistant to inhibition of PI3K-mTOR signaling, whereas substrates of the kinases Abl, Lck, Src, and CDK1 were increased in abundance in inhibitor-sensitive cells. Modeling based on the abundances of these substrate groups accurately predicted sensitivity to a dual PI3K and mTOR inhibitor in two independent sets of primary AML cells isolated from patients. Thus, our study demonstrates KSEA as an untargeted method for the systematic profiling of kinase pathway activities and for increasing our understanding of diseases caused by the dysregulation of signaling pathways.

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

The advent of quantitative proteomics opens new opportunities in biomedical and clinical research. Although quantitative proteomics methods based on stable isotope labeling are in general preferred for biomolecular research, biomarker discovery is a case example of a biomedical problem that may be better addressed by using label-free MS techniques. As a proof of concept of this paradigm, we report the use of label-free quantitative LC-MS to profile the urinary peptidome of kidney chronic allograft dysfunction (CAD). The aim was to identify predictive biomarkers that could be used to personalize immunosuppressive therapies for kidney transplant patients. We detected (by LC-M/MS) and quantified (by LC-MS) 6000 polypeptide ions in undigested urine specimens across 39 CAD patients and 32 control individuals. Although unsupervised hierarchical clustering differentiated between the groups when including all the identified peptides, specific peptides derived from uromodulin and kininogen were found to be significantly more abundant in control than in CAD patients and correctly identified the two groups. These peptides are therefore potential biomarkers that might be used for the diagnosis of CAD. In addition, ions at m/z 645.59 and m/z 642.61 were able to differentiate between patients with different forms of CAD with specificities and sensitivities of 90% in a training set and, significantly, of ∼70% in an independent validation set of samples. Interestingly low expression of uromodulin at m/z 638.03 coupled with high expression of m/z 642.61 diagnosed CAD in virtually all cases. Multiple reaction monitoring experiments further validated the results, illustrating the power of our label-free quantitative LC-MS approach for obtaining quantitative profiles of urinary polypeptides in a rapid, comprehensive, and precise fashion and for biomarker discovery.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

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.