Proteomic Analysis of a Rat Streptozotocin Model Shows Dysregulated Biological Pathways Implicated in Alzheimer's Disease.

Alzheimer's Disease (AD) is an age-related neurodegenerative disorder characterized by progressive memory loss and cognitive impairment, affecting 35 million individuals worldwide. Intracerebroventricular (ICV) injection of low to moderate doses of streptozotocin (STZ) in adult male Wistar rats can reproduce classical physiopathological hallmarks of AD. This biological model is known as ICV-STZ. Most studies are focused on the description of behavioral and morphological aspects of the ICV-STZ model. However, knowledge regarding the molecular aspects of the ICV-STZ model is still incipient. Therefore, this work is a first attempt to provide a wide proteome description of the ICV-STZ model based on mass spectrometry (MS). To achieve that, samples from the pre-frontal cortex (PFC) and hippocampus (HPC) of the ICV-STZ model and control (wild-type) were used. Differential protein abundance, pathway, and network analysis were performed based on the protein identification and quantification of the samples. Our analysis revealed dysregulated biological pathways implicated in the early stages of late-onset Alzheimer's disease (LOAD), based on differentially abundant proteins (DAPs). Some of these DAPs had their mRNA expression further investigated through qRT-PCR. Our results shed light on the AD onset and demonstrate the ICV-STZ as a valid model for LOAD proteome description.

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V.

Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org .

Proteomics reveals that quinoa bioester promotes replenishing effects in epidermal tissue.

The continuous search for natural products that attenuate age-related losses has increasingly gained notice; among them, those applicable for skin care have drawn significant attention. The bioester generated from the Chenopodium quinoa's oil is a natural-origin ingredient described to produce replenishing skin effects. With this as motivation, we used shotgun proteomics to study the effects of quinoa bioester on human reconstructed epidermis tridimensional cell cultures after 0, 3, 6, 12, 24, and 48 h of exposure. Our experimental setup employed reversed-phase nano-chromatography coupled online with an Orbitrap-XL and PatternLab for proteomics as the data analysis tool. Extracted ion chromatograms were obtained as surrogates for relative peptide quantitation. Our findings spotlight proteins with increased abundance, as compared to the untreated cell culture counterparts at the same timepoints, that were related to preventing premature aging, homeostasis, tissue regeneration, protection against ultraviolet radiation and oxidative damage.

Mixed-Data Acquisition: Next-Generation Quantitative Proteomics Data Acquisition.

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.

Mixed-data acquisition: next-generation quantitative proteomics data acquisition

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.

A quantitation module for isotope-labeled peptides integrated into PatternLab for proteomics.

We present a new module integrated into the widely adopted PatternLab for proteomics to enable analysis of isotope-labeled peptides produced using dimethyl or SILAC. The accurate quantitation of proteins lies within the heart of proteomics; dimethylation has shown to be reliable, inexpensive, and applicable to any sample type. We validate our algorithm using an M. tuberculosis dataset obtained from two biological conditions; we used three dimethyl labels, one serving as an internal control for labeling a mixture of samples from both biological conditions. This internal control certified the proper functioning of our software. Availability: http://patternlabforproteomics.org, freely available for academic use.

Characterization of homodimer interfaces with cross-linking mass spectrometry and isotopically labeled proteins.

Cross-linking coupled with mass spectrometry (XL-MS) has emerged as a powerful strategy for the identification of protein-protein interactions, characterization of interaction regions, and obtainment of structural information on proteins and protein complexes. In XL-MS, proteins or complexes are covalently stabilized with cross-linkers and digested, followed by identification of the cross-linked peptides by tandem mass spectrometry (MS/MS). This provides spatial constraints that enable modeling of protein (complex) structures and regions of interaction. However, most XL-MS approaches are not capable of differentiating intramolecular from intermolecular links in multimeric complexes, and therefore they cannot be used to study homodimer interfaces. We have recently developed an approach that overcomes this limitation by stable isotope-labeling of one of the two monomers, thereby creating a homodimer with one 'light' and one 'heavy' monomer. Here, we describe a step-by-step protocol for stable isotope-labeling, followed by controlled denaturation and refolding in the presence of the wild-type protein. The resulting light-heavy dimers are cross-linked, digested, and analyzed by mass spectrometry. We show how to quantitatively analyze the corresponding data with SIM-XL, an XL-MS software with a module tailored toward the MS/MS data from homodimers. In addition, we provide a video tutorial of the data analysis with this protocol. This protocol can be performed in ∼14 d, and requires basic biochemical and mass spectrometry skills.

A multi-protease, multi-dissociation, bottom-up-to-top-down proteomic view of the Loxosceles intermedia venom.

Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment of the venom of Loxosceles intermedia, the so-called brown spider. Venom was extracted from 200 spiders and fractioned into two aliquots relative to a 10 kDa cutoff mass. Each of these was further fractioned and digested with trypsin (4 h), trypsin (18 h), pepsin (18 h), and chymotrypsin (18 h), then analyzed by MudPIT on an LTQ-Orbitrap XL ETD mass spectrometer fragmenting precursors by CID, HCD, and ETD. Aliquots of undigested samples were also analyzed. Our experimental design allowed us to apply spectral networks, thus enabling us to obtain meta-contig assemblies, and consequently de novo sequencing of practically complete proteins, culminating in a deep proteome assessment of the venom. Data are available via ProteomeXchange, with identifier PXD005523.

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0.

PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for the analysis of shotgun proteomic data. The contained modules allow for formatting of sequence databases, peptide spectrum matching, statistical filtering and data organization, extracting quantitative information from label-free and chemically labeled data, and analyzing statistics for differential proteomics. PatternLab also has modules to perform similarity-driven studies with de novo sequencing data, to evaluate time-course experiments and to highlight the biological significance of data with regard to the Gene Ontology database. The PatternLab for proteomics 4.0 package brings together all of these modules in a self-contained software environment, which allows for complete proteomic data analysis and the display of results in a variety of graphical formats. All updates to PatternLab, including new features, have been previously tested on millions of mass spectra. PatternLab is easy to install, and it is freely available from http://patternlabforproteomics.org.

PatternLab: from mass spectra to label-free differential shotgun proteomics.

PatternLab for proteomics is a self-contained computational environment for analyzing shotgun proteomic data. Recent improvements incorporate modules to facilitate the computational analysis, such as FastaDBXtractor for sequence database preparation and ProLuCID runner for simplifying and managing the protein identification search engine; modules for pushing the limits on proteomics standards, such as SEPro, which relies on a semi-labeled decoy approach for increasing confidence in filtering and organizing peptide spectrum matches; and modules with novel features, such as SEProQ for enabling label-free quantitation by extracted ion chromatograms according to a distributed normalized ion abundance factor approach (dNIAF). Existing modules were also improved, such as the TFold module for pinpointing differentially expressed proteins. These new modules are integrated into the previously described arsenal of tools for further data analysis. Here we provide detailed instructions for operating and understanding them.

Are gastric cancer resection margin proteomic profiles more similar to those from controls or tumors?

A strategy for treating cancer is to surgically remove the tumor together with a portion of apparently healthy tissue surrounding it, the so-called "resection margin", to minimize recurrence. Here, we investigate whether the proteomic profiles from biopsies of gastric cancer resection margins are indeed more similar to those from healthy tissue than from cancer biopsies. To this end, we analyzed biopsies using an offline MudPIT shotgun proteomic approach and performed label-free quantitation through a distributed normalized spectral abundance factor approach adapted for extracted ion chromatograms (XICs). A multidimensional scaling analysis revealed that each of those tissue-types is very distinct from each other. The resection margin presented several proteins previously correlated with cancer, but also other overexpressed proteins that may be related to tumor nourishment and metastasis, such as collagen alpha-1, ceruloplasmin, calpastatin, and E-cadherin. We argue that the resection margin plays a key role in Paget's "soil to seed" hypothesis, that is, that cancer cells require a special microenvironment to nourish and that understanding it could ultimately lead to more effective treatments.

Search engine processor: Filtering and organizing peptide spectrum matches.

The search engine processor (SEPro) is a tool for filtering, organizing, sharing, and displaying peptide spectrum matches. It employs a novel three-tier Bayesian approach that uses layers of spectrum, peptide, and protein logic to lead the data to converge to a single list of reliable protein identifications. SEPro is integrated into the PatternLab for proteomics environment, where an arsenal of tools for analyzing shotgun proteomic data is provided. By using the semi-labeled decoy approach for benchmarking, we show that SEPro significantly outperforms a commercially available competitor.

Analyzing marginal cases in differential shotgun proteomics.

SUMMARY: We present an approach to statistically pinpoint differentially expressed proteins that have quantitation values near the quantitation threshold and are not identified in all replicates (marginal cases). Our method uses a Bayesian strategy to combine parametric statistics with an empirical distribution built from the reproducibility quality of the technical replicates. AVAILABILITY: The software is freely available for academic use at http://pcarvalho.com/patternlab.

PatternLab for proteomics: a tool for differential shotgun proteomics.

BACKGROUND: A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu et al. demonstrated a method to perform semi-relative protein quantitation in shotgun proteomics data by correlating the number of tandem mass spectra obtained for each protein, or "spectral count", with its abundance in a mixture; however, two issues have remained open: how to normalize spectral counting data and how to efficiently pinpoint differences between profiles. Moreover, Chen et al. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MS-compatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired. RESULTS: To address the open issues above, we present a program termed PatternLab for proteomics. This program implements existing strategies and adds two new methods to pinpoint differences in protein profiles. The first method, ACFold, addresses experiments with less than three replicates from each state or having assays acquired by different protocols as described by Chen et al. ACFold uses a combined criterion based on expression fold changes, the AC test, and the false-discovery rate, and can supply a "bird's-eye view" of differentially expressed proteins. The other method addresses experimental designs having multiple readings from each state and is referred to as nSVM (natural support vector machine) because of its roots in evolutionary computing and in statistical learning theory. Our observations suggest that nSVM's niche comprises projects that select a minimum set of proteins for classification purposes; for example, the development of an early detection kit for a given pathology. We demonstrate the effectiveness of each method on experimental data and confront them with existing strategies. CONCLUSION: PatternLab offers an easy and unified access to a variety of feature selection and normalization strategies, each having its own niche. Additionally, graphing tools are available to aid in the analysis of high throughput experimental data. PatternLab is available at http://pcarvalho.com/patternlab.