Elena Bonzón-Kulichenko

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.

Anaplasma phagocytophilum Inhibits Apoptosis and Promotes Cytoskeleton Rearrangement for Infection of Tick Cells

ABSTRACT Anaplasma phagocytophilum causes human granulocytic anaplasmosis. Infection with this zoonotic pathogen affects gene expression in both the vertebrate host and the tick vector, Ixodes scapularis. Here, we identified new genes, including spectrin alpha chain or alpha-fodrin (CG8) and voltage-dependent anion-selective channel or mitochondrial porin (T2), that are involved in A. phagocytophilum infection/multiplication and the tick cell response to infection. The pathogen downregulated the expression of CG8 in tick salivary glands and T2 in both the gut and salivary glands to inhibit apoptosis as a mechanism to subvert host cell defenses and increase infection. In the gut, the tick response to infection through CG8 upregulation was used by the pathogen to increase infection due to the cytoskeleton rearrangement that is required for pathogen infection. These results increase our understanding of the role of tick genes during A. phagocytophilum infection and multiplication and demonstrate that the pathogen uses similar strategies to establish infection in both vertebrate and invertebrate hosts.

Expression of heat shock and other stress response proteins in ticks and cultured tick cells in response to Anaplasma spp. infection and heat shock

Ticks are ectoparasites of animals and humans that serve as vectors of Anaplasma and other pathogens that affect humans and animals worldwide. Ticks and the pathogens that they transmit have coevolved molecular interactions involving genetic traits of both the tick and the pathogen that mediate their development and survival. In this paper, the expression of heat shock proteins (HSPs) and other stress response proteins (SRPs) was characterized in ticks and cultured tick cells by proteomics and transcriptomics analyses in response to Anaplasma spp. infection and heat shock. The results of these studies demonstrated that the stress response was activated in ticks and cultured tick cells after Anaplasma spp. infection and heat shock. However, in the natural vector-pathogen relationship, HSPs and other SRPs were not strongly activated, which likely resulted from tick-pathogen coevolution. These results also demonstrated pathogen- and tick-specific differences in the expression of HSPs and other SRPs in ticks and cultured tick cells infected with Anaplasma spp. and suggested the existence of post-transcriptional mechanisms induced by Anaplasma spp. to control tick response to infection. These results illustrated the complexity of the stress response in ticks and suggested a function for the HSPs and other SRPs during Anaplasma spp. infection.

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.

Identification and Characterization of Anaplasma phagocytophilum Proteins Involved in Infection of the Tick Vector, Ixodes scapularis

Anaplasma phagocytophilum is an emerging zoonotic pathogen transmitted by Ixodes scapularis that causes human granulocytic anaplasmosis. Here, a high throughput quantitative proteomics approach was used to characterize A. phagocytophilum proteome during rickettsial multiplication and identify proteins involved in infection of the tick vector, I. scapularis. The first step in this research was focused on tick cells infected with A. phagocytophilum and sampled at two time points containing 10–15% and 65–71% infected cells, respectively to identify key bacterial proteins over-represented in high percentage infected cells. The second step was focused on adult female tick guts and salivary glands infected with A. phagocytophilum to compare in vitro results with those occurring during bacterial infection in vivo. The results showed differences in the proteome of A. phagocytophilum in infected ticks with higher impact on protein synthesis and processing than on bacterial replication in tick salivary glands. These results correlated well with the developmental cycle of A. phagocytophilum, in which cells convert from an intracellular reticulated, replicative form to the nondividing infectious dense-core form. The analysis of A. phagocytophilum differentially represented proteins identified stress response (GroEL, HSP70) and surface (MSP4) proteins that were over-represented in high percentage infected tick cells and salivary glands when compared to low percentage infected cells and guts, respectively. The results demonstrated that MSP4, GroEL and HSP70 interact and bind to tick cells, thus playing a role in rickettsia-tick interactions. The most important finding of these studies is the increase in the level of certain bacterial stress response and surface proteins in A. phagocytophilum-infected tick cells and salivary glands with functional implication in tick-pathogen interactions. These results gave a new dimension to the role of these stress response and surface proteins during A. phagocytophilum infection in ticks. Characterization of Anaplasma proteome contributes information on host-pathogen interactions and provides targets for development of novel control strategies for pathogen infection and transmission.

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.

Loss of the proteostasis factor AIRAPL causes myeloid transformation by deregulating IGF-1 signaling

AIRAPL (arsenite-inducible RNA-associated protein-like) is an evolutionarily conserved regulator of cellular proteostasis linked to longevity in nematodes, but its biological function in mammals is unknown. We show herein that AIRAPL-deficient mice develop a fully-penetrant myeloproliferative neoplastic process. Proteomic analysis of AIRAPL-deficient mice revealed that this protein exerts its antineoplastic function through the regulation of the insulin/insulin-like growth factor 1 (IGF-1) signaling pathway. We demonstrate that AIRAPL interacts with newly synthesized insulin-related growth factor-1 receptor (IGF1R) polypeptides, promoting their ubiquitination and proteasome-mediated degradation. Accordingly, genetic and pharmacological IGF1R inhibitory strategies prevent the hematological disease found in AIRAPL-deficient mice as well as that in mice carrying the Jak2V617F mutation, thereby demonstrating the causal involvement of this pathway in the pathogenesis of myeloproliferative neoplasms. Consistent with its proposed role as a tumor suppressor of myeloid transformation, AIRAPL expression is widely abrogated in human myeloproliferative disorders. Collectively, these findings support the oncogenic relevance of proteostasis deregulation in hematopoietic cells, and they unveil novel therapeutic targets for these frequent hematological neoplasias.

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.