Marco Trevisan-Herraz

A robust method for quantitative high-throughput analysis of proteomes by 18O labeling

MS-based quantitative proteomics plays an increasingly important role in biological and medical research and the development of these techniques remains one of the most important challenges in mass spectrometry. Numerous stable isotope labeling approaches have been proposed. However, and particularly in the case of 18O-labeling, a standard protocol of general applicability is still lacking, and statistical issues associated to these methods remain to be investigated. In this work we present an improved high-throughput quantitative proteomics method based on whole proteome concentration by SDS-PAGE, optimized in-gel digestion, peptide 18O-labeling, and separation by off-gel isoelectric focusing followed by liquid chromatography-LIT-MS. We demonstrate that the off-gel technique is fully compatible with 18O peptide labeling in any pH range. A recently developed statistical model indicated that partial digestions and methionine oxidation do not alter protein quantification and that variances at the scan, peptide, and protein levels are stable and reproducible in a variety of proteomes of different origin. We have also analyzed the dynamic range of quantification and demonstrated the practical utility of the method by detecting expression changes in a model of activation of Jurkat T-cells. Our protocol provides a general approach to perform quantitative proteomics by 18O-labeling in high-throughput studies, with the added value that it has a validated statistical model for the null hypothesis. To the best of our knowledge, this is the first report where a general protocol for stable isotope labeling is tested in practice using a collection of samples and analyzed at this degree of statistical detail.

General statistical framework for quantitative proteomics by stable isotope labeling

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including (18)O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H₂O₂ concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.

The human HDL proteome displays high inter-individual variability and is altered dynamically in response to angioplasty-induced atheroma plaque rupture

Recent findings support potential roles for HDL in cardiovascular pathophysiology not related to lipid metabolism. We address whether HDL proteome is dynamically altered in atheroma plaque rupture. We used immunoaffinity purification of HDL samples from coronary artery disease patients before and after percutaneous transluminal coronary angioplasty (PTCA), a model of atheroma plaque disruption. Samples were analyzed by quantitative proteomics using stable isotope labeling and results were subjected to statistical analysis of protein variance using a novel algorithm. We observed high protein variability in HDL composition between individuals, indicating that HDL protein composition is highly patient-specific. However, intra-individual protein variances remained at low levels, confirming the reproducibility of the method used for HDL isolation and protein quantification. A systems biology analysis of HDL protein alterations induced by PTCA revealed an increase in two protein clusters that included several apolipoproteins, fibrinogen-like protein 1 and other intracellular proteins, and a decrease in antithrombin-III, annexin A1 and several immunoglobulins. Our results support the concept of HDL as dynamic platforms that donate and receive a variety of molecules and provide an improved methodology to use HDL proteome for the systematic analysis of differences among individuals and the search for cardiovascular biomarkers. Biological significance The HDL proteome is an interesting model of clinical relevance and has been previously described to be dynamically altered in response to pathophysiological conditions and cardiovascular diseases. Our study suggests that interindividual variability of HDL proteome is higher than previously thought and provided the detection of a set of proteins that changed their abundance in response to plaque rupture, supporting the concept of HDL as dynamic platforms that donate and receive a variety of molecules.

A Novel Systems-Biology Algorithm for the Analysis of Coordinated Protein Responses Using Quantitative Proteomics

The coordinated behavior of proteins is central to systems biology. However, the underlying mechanisms are poorly known and methods to analyze coordination by conventional quantitative proteomics are still lacking. We present the Systems Biology Triangle (SBT), a new algorithm that allows the study of protein coordination by pairwise quantitative proteomics. The Systems Biology Triangle detected statistically significant coordination in diverse biological models of very different nature and subjected to different kinds of perturbations. The Systems Biology Triangle also revealed with unprecedented molecular detail an array of coordinated, early protein responses in vascular smooth muscle cells treated at different times with angiotensin-II. These responses included activation of protein synthesis, folding, turnover, and muscle contraction – consistent with a differentiated phenotype—as well as the induction of migration and the repression of cell proliferation and secretion. Remarkably, the majority of the altered functional categories were protein complexes, interaction networks, or metabolic pathways. These changes could not be detected by other algorithms widely used by the proteomics community, and the vast majority of proteins involved have not been described before to be regulated by AngII. The unique capabilities of The Systems Biology Triangle to detect functional protein alterations produced by the coordinated action of proteins in pairwise quantitative proteomics experiments make this algorithm an attractive choice for the biological interpretation of results on a routine basis.

Quantitative in-depth analysis of the dynamic secretome of activated Jurkat T-cells.

Proteins secreted by cells are of the highest biomedical relevance since they play a significant role in the progression of numerous diseases. However, characterization of the proteins specifically secreted in response to precise stimuli is challenging, since these proteins are contaminated by cellular byproducts. Here we present a method to characterize a dynamic secretome and demonstrate its utility by performing the deepest quantitative analysis to date of proteins secreted by lymphoid Jurkat T-cells upon activation. Cell-free supernatant proteins were analyzed by using an optimized protocol for differential (18)O/(16)O-labeling and LC-MS/MS, followed by statistical analysis using a random-effects model. More than 4000 unique peptides belonging to 1288 proteins were identified and a large proportion could be quantified. To determine the proteins whose secretion was up-regulated upon T-cell activation, protein variance of the null hypothesis was estimated after protein classification in terms of secretion and ontology using bioinformatic tools. 62 proteins showed a statistically significant change in abundance upon cell activation and most of them (49 proteins) were up-regulated. These proteins were functionally involved mainly in inflammatory response, signal transduction, cell growth and differentiation and cell redox homeostasis. Our approach provides a promising technology for the high-throughput quantitative study of dynamic secretomes.

Revisiting Peptide Identification by High-Accuracy Mass Spectrometry: Problems Associated with the Use of Narrow Mass Precursor Windows

Peptide identification is increasingly achieved through database searches in which mass precursor tolerance is set in the ppm range. This trend is driven by the high resolution and accuracy of modern mass spectrometers and the belief that the quality of peptide identification is fully controlled by estimating the false discovery rate (FDR) using the decoy-target approach. However, narrowing mass tolerance decreases the number of sequence candidates, and several authors have raised concerns that these search conditions can introduce inaccuracies. Here, we demonstrate that when scores that only depend on one sequence candidate are used, decoy-based estimates of the number of false positive identifications are accurate even with an average number of candidates of just 200, to the point that remarkably accurate FDR predictions can be made in completely different search conditions. However, when scores that are constructed taking information from additional sequence candidates are used together with low precursor mass tolerances, the proportion of peptides incorrectly identified may become significantly higher than the FDR estimated by the target-decoy approach. Our results suggest that with this kind of score the high mass accuracy of modern mass spectrometers should be exploited by using wide mass windows followed by postscoring mass filtering algorithms.

Quantitative HDL Proteomics Identifies Peroxiredoxin-6 as a Biomarker of Human Abdominal Aortic Aneurysm

High-density lipoproteins (HDLs) are complex protein and lipid assemblies whose composition is known to change in diverse pathological situations. Analysis of the HDL proteome can thus provide insight into the main mechanisms underlying abdominal aortic aneurysm (AAA) and potentially detect novel systemic biomarkers. We performed a multiplexed quantitative proteomics analysis of HDLs isolated from plasma of AAA patients (N = 14) and control study participants (N = 7). Validation was performed by western-blot (HDL), immunohistochemistry (tissue), and ELISA (plasma). HDL from AAA patients showed elevated expression of peroxiredoxin-6 (PRDX6), HLA class I histocompatibility antigen (HLA-I), retinol-binding protein 4, and paraoxonase/arylesterase 1 (PON1), whereas α-2 macroglobulin and C4b-binding protein were decreased. The main pathways associated with HDL alterations in AAA were oxidative stress and immune-inflammatory responses. In AAA tissue, PRDX6 colocalized with neutrophils, vascular smooth muscle cells, and lipid oxidation. Moreover, plasma PRDX6 was higher in AAA (N = 47) than in controls (N = 27), reflecting increased systemic oxidative stress. Finally, a positive correlation was recorded between PRDX6 and AAA diameter. The analysis of the HDL proteome demonstrates that redox imbalance is a major mechanism in AAA, identifying the antioxidant PRDX6 as a novel systemic biomarker of AAA.

Quantitative proteomics reveals Piccolo as a candidate serological correlate of recovery from Guillain-Barré syndrome

Guillain-Barré syndrome (GBS) is an autoimmune-mediated peripheral neuropathy of unknown cause. However, about a quarter of GBS patients have suffered a recent bacterial or viral infection, and axonal forms of the disease are especially common in these patients. Proteomics is a good methodological approach for the discovery of disease biomarkers. Until recently, most proteomics studies of GBS and other neurodegenerative diseases have focused on the analysis of the cerebrospinal fluid (CSF). However, serum represents an attractive alternative to CSF because it is easier to sample and has potential for biomarker discovery. The goal of this research was the identification of serum biomarkers associated with recovery from GBS. To address this objective, a quantitative proteomics approach was used to characterize differences in the serum proteome between a GBS patient and her healthy identical twin in order to lessen variations due to differences in genetic background, and with additional serum samples collected from unrelated GBS (N = 3) and Spinal Cord Injury (SCI) (N = 3) patients with similar medications. Proteomics results were then validated by ELISA using sera from additional GBS patients (N = 5) and healthy individuals (N = 3). All GBS and SCI patients were recovering from the acute phase of the disease. The results showed that Piccolo, a protein that is essential in the maintenance of active zone structure, constitutes a potential serological correlate of recovery from GBS. These results provided the first evidence for the Piccolos putative role in GBS, suggesting a candidate target for developing a serological marker of disease recovery.

Comprehensive Quantification of the Modified Proteome Reveals Oxidative Heart Damage in Mitochondrial Heteroplasmy

Post-translational modifications hugely increase the functional diversity of proteomes. Recent algorithms based on ultratolerant database searching are forging a path to unbiased analysis of peptide modifications by shotgun mass spectrometry. However, these approaches identify only one-half of the modified forms potentially detectable and do not map the modified residue. Moreover, tools for the quantitative analysis of peptide modifications are currently lacking. Here, we present a suite of algorithms that allows comprehensive identification of detectable modifications, pinpoints the modified residues, and enables their quantitative analysis through an integrated statistical model. These developments were used to characterize the impact of mitochondrial heteroplasmy on the proteome and on the modified peptidome in several tissues from 12-week-old mice. Our results reveal that heteroplasmy mainly affects cardiac tissue, inducing oxidative damage to proteins of the oxidative phosphorylation system, and provide a molecular mechanism explaining the structural and functional alterations produced in heart mitochondria.

SanXoT: a modular and versatile package for the quantitative analysis of high-throughput proteomics experiments

Abstract Summary Mass spectrometry-based proteomics has had a formidable development in recent years, increasing the amount of data handled and the complexity of the statistical resources needed. Here we present SanXoT, an open-source, standalone software package for the statistical analysis of high-throughput, quantitative proteomics experiments. SanXoT is based on our previously developed weighted spectrum, peptide and protein statistical model and has been specifically designed to be modular, scalable and user-configurable. SanXoT allows limitless workflows that adapt to most experimental setups, including quantitative protein analysis in multiple experiments, systems biology, quantification of post-translational modifications and comparison and merging of experimental data from technical or biological replicates. Availability and implementation Download links for the SanXoT Software Package, source code and documentation are available at https://wikis.cnic.es/proteomica/index.php/SSP. Contact jvazquez@cnic.es or ebonzon@cnic.es Supplementary information Supplementary information is available at Bioinformatics online.