Protein signatures of seminal plasma from bulls with contrasting frozen-thawed sperm viability.

The present study investigated the seminal plasma proteome of Holstein bulls with low (LF; n = 6) and high (HF; n = 8) sperm freezability. The percentage of viable frozen-thawed sperm (%ViableSperm) determined by flow cytometry varied from -2.2 in LF to + 7.8 in HF bulls, as compared to the average %ViableSperm (54.7%) measured in an 860-sire population. Seminal proteins were analyzed by label free mass spectrometry, with the support of statistical and bioinformatics analyses. This approach identified 1,445 proteins, associated with protein folding, cell-cell adhesion, NADH dehydrogenase activity, ATP-binding, proteasome complex, among other processes. There were 338 seminal proteins differentially expressed (p < 0.05) in LF and HF bulls. Based on multivariate analysis, BSP5 and seminal ribonuclease defined the HF phenotype, while spermadhesin-1, gelsolin, tubulins, glyceraldehyde-3-phosphate dehydrogenase, calmodulin, ATP synthase, sperm equatorial segment protein 1, peroxiredoxin-5, secretoglobin family 1D and glucose-6-phosphate isomerase characterized the LF phenotype. Regression models indicated that %ViableSperm of bulls was related to seminal plasma peroxiredoxin-5, spermadhesin-1 and the spermadhesin-1 × BSP5 interaction (R2 = 0.84 and 0.79; p < 0.05). This report is the largest dataset of bovine seminal plasma proteins. Specific proteins of the non-cellular microenvironment of semen are potential markers of sperm cryotolerance.

Impact of the Identification Strategy on the Reproducibility of the DDA and DIA Results.

Data-independent acquisition (DIA) is a promising technique for the proteomic analysis of complex protein samples. A number of studies have claimed that DIA experiments are more reproducible than data-dependent acquisition (DDA), but these claims are unsubstantiated since different data analysis methods are used in the two methods. Data analysis in most DIA workflows depends on spectral library searches, whereas DDA typically employs sequence database searches. In this study, we examined the reproducibility of the DIA and DDA results using both sequence database and spectral library search. The comparison was first performed using a cell lysate and then extended to an interactome study. Protein overlap among the technical replicates in both DDA and DIA experiments was 30% higher with library-based identifications than with sequence database identifications. The reproducibility of quantification was also improved with library search compared to database search, with the mean of the coefficient of variation decreasing more than 30% and a reduction in the number of missing values of more than 35%. Our results show that regardless of the acquisition method, higher identification and quantification reproducibility is observed when library search was used.

Improving Proteomics Data Reproducibility with a Dual-Search Strategy.

Mass spectrometry-based proteomics is an invaluable tool for addressing important biological questions. Data-dependent acquisition methods effectuate stochastic acquisition of data in complex mixtures, which results in missing identifications across replicates. We developed a search approach that improves the reproducibility of data acquired from any mass spectrometer. In our approach, a spectral library is built from the identification results from a database search, and then, the library is used to research the same data files to obtain the final result. We showed that higher identification and quantification reproducibility is achieved with the dual-search approach than with a typical database search. Four datasets with different complexity were compared: (1) data from a cell lysate study performed in our lab, (2) data from an interactome study performed in our lab, (3) a publicly available extracellular vesicles dataset, and (4) a publicly available phosphoproteomics dataset. Our results show that the dual-search approach can be widely and easily used to improve data quality in proteomics data.

Label-Free Proteomic Analysis Reveals Parasite-Specific Protein Alterations in Macrophages Following Leishmania amazonensis, Leishmania major, or Leishmania infantum Infection.

Leishmania is an obligate intracellular parasite known to modulate the host cell to survive and proliferate. However, the complexity of host-parasite interactions remains unclear. Also, the outcome of the disease has been recognized to be species-specific and dependent on the host's immune responses. Proteomics has emerged as a powerful tool to investigate the host-pathogen interface, allowing us to deepen our knowledge about infectious diseases. Quantification of the relative amount of proteins in a sample can be achieved using label-free proteomics, and for the first time, we have used it to quantify Leishmania-specific protein alterations in macrophages. Protein extracts were obtained and digested, and peptides were identified and quantified using nano-LC coupled with tandem mass spectrometry analyses. Protein expression was validated by Western blot analysis. Integrated Proteomics Pipeline was used for peptide/protein identification and for quantification and data processing. Ingenuity Pathway Analysis was used for network analysis. In this work, we investigated how this intracellular parasite modulates protein expression on a host macrophage by comparing three different Leishmania species- L. amazonensis, one of the causative agents of cutaneous disease in the Amazon region; L. major, another causative agent of cutaneous leishmaniasis in Africa, the Middle East, China, and India; L. infantum, the causative agent of visceral leishmaniasis affecting humans and dogs in Latin America-and lipopolysaccharide stimulated macrophages as an in vitro inflammation model. Our results revealed that Leishmania infection downregulates apoptosis pathways while upregulating the activation of phagocytes/leukocytes and lipid accumulation.

PACOM: A Versatile Tool for Integrating, Filtering, Visualizing, and Comparing Multiple Large Mass Spectrometry Proteomics Data Sets.

Mass-spectrometry-based proteomics has evolved into a high-throughput technology in which numerous large-scale data sets are generated from diverse analytical platforms. Furthermore, several scientific journals and funding agencies have emphasized the storage of proteomics data in public repositories to facilitate its evaluation, inspection, and reanalysis. (1) As a consequence, public proteomics data repositories are growing rapidly. However, tools are needed to integrate multiple proteomics data sets to compare different experimental features or to perform quality control analysis. Here, we present a new Java stand-alone tool, Proteomics Assay COMparator (PACOM), that is able to import, combine, and simultaneously compare numerous proteomics experiments to check the integrity of the proteomic data as well as verify data quality. With PACOM, the user can detect source of errors that may have been introduced in any step of a proteomics workflow and that influence the final results. Data sets can be easily compared and integrated, and data quality and reproducibility can be visually assessed through a rich set of graphical representations of proteomics data features as well as a wide variety of data filters. Its flexibility and easy-to-use interface make PACOM a unique tool for daily use in a proteomics laboratory. PACOM is available at https://github.com/smdb21/pacom .

A pharmacoproteomic study confirms the synergistic effect of chondroitin sulfate and glucosamine.

Osteoarthritis (OA) is the most common age-related rheumatic disease. Chondrocytes play a primary role in mediating cartilage destruction and extracellular matrix (ECM) breakdown, which are main features of the OA joint. Quantitative proteomics technologies are demonstrating a very interesting power for studying the molecular effects of some drugs currently used to treat OA patients, such as chondroitin sulfate (CS) and glucosamine (GlcN). In this work, we employed the iTRAQ (isobaric tags for relative and absolute quantitation) technique to assess the effect of CS and GlcN, both alone and in combination, in modifying cartilage ECM metabolism by the analysis of OA chondrocytes secretome. 186 different proteins secreted by the treated OA chondrocytes were identified. 36 of them presented statistically significant differences (p ≤ 0.05) between untreated and treated samples: 32 were increased and 4 decreased. The synergistic chondroprotective effect of CS and GlcN, firstly reported by our group at the intracellular level, is now demonstrated also at the extracellular level.

Sequential depletion coupled to C18 sequential extraction as a rapid tool for human serum multiple profiling.

Sequential chemical depletion of serum coupled to C18 sequential extraction of peptides as a rapid tool for human serum multiple profiling is herein presented. The methodology comprises depletion with DTT and then with ACN; the extract thus obtained is then summited to fast protein digestion using ultrasonic energy. The pool of peptides is subsequently concentrated using C18-based Zip-tips and the peptides are sequentially extracted using different concentrations of ACN. Each extract is mass-spectrometry profiled with MALDI. The different spectra thus obtained are then successfully used for classification purposes. A total of 40 people, comprising 20 healthy and 20 non-healthy donors, were successfully classified using this method, with an excellent q-value<0.05. The proposed method is cheap as it entails few chemicals, DTT and ACN, simple in terms of handling, and fast. In addition, the methodology is of broad application as it can be used for any study applied to serum samples or other complex biological fluids.

Sequential depletion of human serum for the search of osteoarthritis biomarkers.

BACKGROUND: The field of biomarker discovery, development and application has been the subject of intense interest and activity, especially with the recent emergence of new technologies, such as proteomics-based approaches. In proteomics, search for biomarkers in biological fluids such as human serum is a challenging issue, mainly due to the high dynamic range of proteins present in these types of samples. Methods for reducing the content of most highly abundant proteins have been developed, including immunodepletion or protein equalization. In this work, we report for the first time the combination of a chemical sequential depletion method based in two protein precipitations with acetonitrile and DTT, with a subsequent two-dimensional difference in-gel electrophoresis (2D-DIGE) analysis for the search of osteoarthritis (OA) biomarkers in human serum. The depletion method proposed is non-expensive, of easy implementation and allows fast sample throughput. RESULTS: Following this workflow, we have compared sample pools of human serum obtained from 20 OA patients and 20 healthy controls. The DIGE study led to the identification of 16 protein forms (corresponding to 14 different proteins) that were significantly (p < 0.05) altered in OA when compared to controls (8 increased and 7 decreased). Immunoblot analyses confirmed for the first time the increase of an isoform of Haptoglobin beta chain (HPT) in sera from OA patients. CONCLUSIONS: Altogether, these data demonstrate the utility of the proposed chemical sequential depletion for the analysis of sera in protein biomarker discovery approaches, exhibit the usefulness of quantitative 2D gel-based strategies for the characterization of disease-specific patterns of protein modifications, and also provide a list of OA biomarker candidates for validation.

Identification of a panel of novel serum osteoarthritis biomarkers.

Osteoarthritis (OA) is the most common rheumatic pathology. Because currently available diagnostic methods are limited and lack sensitivity, the identification of new specific biological markers for OA has become a focus. The purpose of this study was to identify novel protein biomarkers for moderate and severe OA in serum. Sera were obtained from 50 moderate OA patients, 50 severe OA patients, and 50 nonsymptomatic controls. Serum protein levels were analyzed using isobaric tags for relative and absolute quantitation (iTRAQ) and matrix-assisted laser desorption/ionization (MALDI)-TOF/TOF mass spectrometry. We identified 349 different proteins in the sera, 262 of which could be quantified by calculation of their iTRAQ ratios. Three sets of proteins were significantly (p < 0.05) changed in OA samples compared to controls. Of these, 6 were modulated only in moderate OA, 13 only in severe OA and 7 in both degrees. Although some of these proteins, such as cartilage oligomeric matrix protein, have a previously reported putative biomarker value for OA, most are novel biomarker candidates for the disease. These include some complement components, lipoproteins, von Willebrand factor, tetranectin, and lumican. The specificity and selectivity of these candidates need to be validated before new molecular diagnostic or prognostic tests for OA can be developed.