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Cancer is a class of diseases characterized by abnormal cell growth and one of the major reasons for human deaths. Proteins are involved in the molecular mechanisms leading to cancer, furthermore they are affected by anti-cancer drugs, and protein biomarkers can be used to diagnose certain cancer types. Therefore, it is important to explore the proteomics background of cancer. In this report, we developed the Cancer Proteomics database to re-interrogate published proteome studies investigating cancer. The database is divided in three sections related to cancer processes, cancer types, and anti-cancer drugs. Currently, the Cancer Proteomics database contains 9778 entries of 4118 proteins extracted from 143 scientific articles covering all three sections: cell death (cancer process), prostate cancer (cancer type) and platinum-based anti-cancer drugs including carboplatin, cisplatin, and oxaliplatin (anti-cancer drugs). The detailed information extracted from the literature includes basic information about the articles (e.g., PubMed ID, authors, journal name, publication year), information about the samples (type, study/reference, prognosis factor), and the proteomics workflow (Subcellular fractionation, protein, and peptide separation, mass spectrometry, quantification). Useful annotations such as hyperlinks to UniProt and PubMed were included. In addition, many filtering options were established as well as export functions. The database is freely available at http://cancerproteomics.uio.no.
Complete enzymatic digestion of proteins for bottom-up proteomics is substantially improved by use of detergents for denaturation and solubilization. Detergents however, are incompatible with many proteases and highly detrimental to LC-MS/MS. Recently; filter-based methods have seen wide use due to their capacity to remove detergents and harmful reagents prior to digestion and mass spectrometric analysis. We hypothesized that non-specific protein binding to negatively charged silica-based filters would be enhanced by addition of lyotropic salts, similar to DNA purification. We sought to exploit these interactions and investigate if low-cost DNA purification spin-filters, 'Minipreps,' efficiently and reproducibly bind proteins for digestion and LC-MS/MS analysis. We propose a new method, Miniprep Assisted Proteomics (MAP), for sample preparation. We demonstrate binding capacity, performance, recovery and identification rates for proteins and whole-cell lysates using MAP. MAP recovered equivalent or greater protein yields from 0.5-50 μg analyses benchmarked against commercial trapping preparations. Nano UHPLC-MS/MS proteome profiling of lysates of Escherichia coli had 99.3% overlap vs. existing approaches and reproducibility of replicate minipreps was 98.8% at the 1% FDR protein level. Label Free Quantitative proteomics was performed and 91.2% of quantified proteins had a %CV <20% (2044/2241). Miniprep Assisted Proteomics can be performed in minutes, shows low variability, high recovery and proteome depth. This suggests a significant role for adventitious binding in developing new proteomics sample preparation techniques. MAP represents an efficient, ultra-low-cost alternative for sample preparation in a commercially obtainable device that costs ∼$0.50 (USD) per miniprep.
Top-down proteomics, the analysis of intact proteins in their endogenous form, preserves valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down liquid chromatography-tandem MS (LC-MS/MS) data sets is rapidly increasing on account of advances in instrumentation and sample-processing protocols. However, top-down mass spectra are substantially more complex than conventional bottom-up data. New algorithms and software tools for confident proteoform identification and quantification are needed. Here we present Informed-Proteomics, an open-source software suite for top-down proteomics analysis that consists of an LC-MS feature-finding algorithm, a database search algorithm, and an interactive results viewer. We compare our tool with several other popular tools using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.
The Proteomics Identifications Database (PRIDE, www.ebi.ac.uk/pride) is one of the main repositories of MS derived proteomics data. Here, we point out the main functionalities of PRIDE both as a submission repository and as a source for proteomics data. We describe the main features for data retrieval and visualization available through the PRIDE web and BioMart interfaces. We also highlight the mechanism by which tailored queries in the BioMart can join PRIDE to other resources such as Reactome, Ensembl or UniProt to execute extremely powerful across-domain queries. We then present the latest improvements in the PRIDE submission process, using the new easy-to-use, platform-independent graphical user interface submission tool PRIDE Converter. Finally, we speak about future plans and the role of PRIDE in the ProteomExchange consortium.
EDEM2 (Endoplasmic reticulum Degradation-Enhancing alpha-Mannosidase-like protein 2) is one of the key-proteins suggested to be involved in the selection and degradation of misfolded proteins from the endoplasmic reticulum. The datasets discussed in this article are related to experiments covering affinity proteomics, label-free quantitative proteomics, deglycoproteomics and SILAC (Stable Isotope Labeling by Amino Acids in Cell Culture) proteomics data of A375 melanoma cells with modified expression of EDEM2. Our first aim was to affinity-enrich EDEM2 alongside its potential interaction partners and analyse the obtained samples by nanoLC-MS/MS to identify novel EDEM2 associated proteins. The dataset was substantiated by SDF (Sucrose Density Fractionation)-nanoLC-MS/MS experiments, in an integrated workflow to validate EDEM2 identified partners and corroborate these with previous data. Our second aim was to delineate novel EDEM2 substrate candidates using a two-step strategy. The first one refers to the deglycoproteomics dataset, which covers nanoLC-MS/MS analysis of Concanavalin A enriched glycopeptides released by endoglycosidase digestion from A375 melanoma cell lysates. This allowed us to map the fraction of glycoproteins with non-matured N-glycans from A375 melanoma cells and find or validate N-glycosylation sites of proteins from the secretory pathway. The same dataset was also used to define glycoproteins altered by the down-regulation of endogenous EDEM2, which should contain its candidate-substrates. In a second step we delineate the degradation kinetics of some of these proteins using a pulse SILAC strategy (pSILAC) thus complementing our initial findings with a fourth dataset. Beside nanoLC-MS/MS analysis our findings were also validated by various biochemical experiments. All the data described are associated with a research article published in Molecular and Cellular Proteomics [1].
Traditional proteomics analysis is plagued by the use of arbitrary thresholds resulting in large loss of information. We propose here a novel method in proteomics that utilizes all detected proteins. We demonstrate its efficacy in a proteomics screen of 5 and 7 liver cancer patients in the moderate and late stage, respectively. Utilizing biological complexes as a cluster vector, and augmenting it with submodules obtained from partitioning an integrated and cleaned protein-protein interaction network, we calculate a Proteomics Signature Profile (PSP) for each patient based on the hit rates of their reported proteins, in the absence of fold change thresholds, against the cluster vector. Using this, we demonstrated that moderate- and late-stage patients segregate with high confidence. We also discovered a moderate-stage patient who displayed a proteomics profile similar to other poor-stage patients. We identified significant clusters using a modified version of the SNet approach. Comparing our results against the Proteomics Expansion Pipeline (PEP) on which the same patient data was analyzed, we found good correlation. Building on this finding, we report significantly more clusters (176 clusters here compared to 70 in PEP), demonstrating the sensitivity of this approach. Gene Ontology (GO) terms analysis also reveals that the significant clusters are functionally congruent with the liver cancer phenotype. PSP is a powerful and sensitive method for analyzing proteomics profiles even when sample sizes are small. It does not rely on the ratio scores but, rather, whether a protein is detected or not. Although consistency of individual proteins between patients is low, we found the reported proteins tend to hit clusters in a meaningful and informative manner. By extracting this information in the form of a Proteomics Signature Profile, we confirm that this information is conserved and can be used for (1) clustering of patient samples, (2) identification of significant clusters based on real biological complexes, and (3) overcoming consistency and coverage issues prevalent in proteomics data sets.
The concept of personalized medicine is predominantly been pursued through genomic and transcriptomic technologies, leading to the identification of multiple mutations in a large variety of cancers. However, it has proven challenging to distinguish driver and passenger mutations and to deal with tumor heterogeneity and resistant clonal populations. More generally, these heterogeneous mutation patterns do not in themselves predict the tumor phenotype. Analysis of the expressed proteins in a tumor and their modification states reveals if and how these mutations are translated to the functional level. It is already known that proteomic changes including posttranslational modifications are crucial drivers of oncogenesis, but proteomics technology has only recently become comparable in depth and accuracy to RNAseq. These advances also allow the rapid and highly sensitive analysis of formalin-fixed and paraffin-embedded biobank tissues, on both the proteome and phosphoproteome levels. In this perspective, pioneering mass spectrometry-based proteomic studies are highlighted that pave the way toward clinical implementation. It is argued that proteomics and phosphoproteomics could provide the missing link to make omics analysis actionable in the clinic.
Proteomics is acquiring a pivotal role in the comprehensive understanding of human biology. Biochemical processes involved in complex diseases, such as neurodegenerative diseases, diabetes and cancer, can be identified by combining proteomics analysis and bioinformatics tools. In the last ten years, the main output of differential proteomics investigations evolved from long lists of proteins to the generation of new hypotheses and their functional verification. The Journal of Proteomics participated to this progress, reporting more and more biologically-oriented papers with functional interpretation of proteomics data. This change in the field was due to both technological development and novel strategies in exploiting the deep characterization of proteomes. In this review, we explore several approaches that allow proteomics to turn functional. In particular, systems biology tools for data analysis are now routinely used to interpret results, thus defining the biological meaning of differentially abundant proteins. Moreover, by considering the importance of protein-protein interactions and the composition of macromolecular complexes, interactomics is complementing the information given by differential quantitative proteomics. Eventually, terminomics is unveiling new functions for cleaved proteoforms, by analyzing the effect of proteolysis globally. SIGNIFICANCE: Proteomics is rapidly evolving not only technologically but also strategically. The correct interpretation of proteomics data can reveal new functions of proteins in several biological backgrounds. Systems biology tools allow researchers to formulate new hypotheses to be further functionally tested. Interactomics is shedding new light on protein complexes truly involved in biochemical pathways and how their alteration can lead to dysfunctionality (in disease pathogenesis, for example). Terminomics is revealing the function of new discovered proteoforms and attributing a novel role to proteolysis. This review would provide the biologist important insights into current applications of several proteomic approaches that could offer new strategies to investigate biological systems.
The analytical depth of investigation of the peroxisomal proteome of the model plant Arabidopsis thaliana has not yet reached that of other major cellular organelles such as chloroplasts or mitochondria. This is primarily due to the difficulties associated with isolating and obtaining purified samples of peroxisomes from Arabidopsis. So far only a handful of research groups have been successful in obtaining such fractions. To make things worse, enriched peroxisome fractions frequently suffer from significant organellar contamination, lowering confidence in localization assignment of the identified proteins. As with other cellular compartments, identification of peroxisomal proteins forms the basis for investigations of the dynamics of the peroxisomal proteome. It is therefore not surprising that, in terms of functional analyses by proteomic means, peroxisomes are lagging considerably behind chloroplasts or mitochondria. Alternative strategies are needed to overcome the obstacle of hard-to-obtain organellar fractions. This will help to close the knowledge gap between peroxisomes and other organelles and provide a full picture of the physiological pathways shared between organelles. In this review, we briefly summarize the status quo and discuss some of the methodological alternatives to classic organelle proteomic approaches.
The human blood proteome is frequently assessed by protein abundance profiling using a combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS). In traditional sequence database search, many good-quality MS/MS data remain unassigned. Here we uncover the hidden part of the blood proteome via novel SpotLight approach. This method combines de novo MS/MS sequencing of enriched antibodies and co-extracted proteins with subsequent label-free quantification of new and known peptides in both enriched and unfractionated samples. In a pilot study on differentiating early stages of Alzheimer's disease (AD) from Dementia with Lewy Bodies (DLB), on peptide level the hidden proteome contributed almost as much information to patient stratification as the apparent proteome. Intriguingly, many of the new peptide sequences are attributable to antibody variable regions, and are potentially indicative of disease etiology. When the hidden and apparent proteomes are combined, the accuracy of differentiating AD (n = 97) and DLB (n = 47) increased from ≈85% to ≈95%. The low added burden of SpotLight proteome analysis makes it attractive for use in clinical settings.
Acute pancreatitis (AP) is a common acute abdominalgia of the digestive tract. When the disease progresses to severe acute pancreatitis (SAP), the complications and mortality rate greatly increase. Determining the key factors and pathways underlying AP and SAP will help elucidate the pathological processes involved in disease progression and will be beneficial for identifying potential therapeutic targets. We conducted an integrative proteomics, phosphoproteomics and acetylation proteomics analysis of pancreas samples collected from normal, AP and SAP rat models. We identified 9582 proteins, 3130 phosphorylated modified proteins, and 1677 acetylated modified proteins across all samples. The differentiated expression proteins and KEGG pathway analysis suggested the pronounced enrichment of key pathways based on the following group comparisons: AP versus normal, SAP versus normal, and SAP versus AP. Integrative proteomics and phosphoproteomics analyses revealed 985 jointly detected proteins in the comparison of AP and normal samples, 911 proteins in the comparison of SAP and normal samples, and 910 proteins in the comparison of SAP and AP samples. Based on proteomics and acetylation proteomics analyses, we found that 984 proteins were jointly detected in the comparison of AP and normal samples, 990 proteins in SAP and normal samples, and 728 proteins in SAP and AP samples. Thus, our study offers a valuable resource to understand the proteomic and protein modification atlas in AP.
Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.
Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.
The proteome is the complete set of proteins in an organism. It is considerably larger and more complex than the genome--the collection of genes that encodes these proteins. Proteomics deals with the qualitative and quantitative study of the proteome under physiological and pathological conditions (e.g., after exposure to alcohol, which causes major changes in numerous proteins of different cell types). To map large proteomes such as the human proteome, proteins from discrete tissues, cells, cell components, or biological fluids are first separated by high-resolution two-dimensional electrophoresis and multidimensional liquid chromatography. Then, individual proteins are identified by mass spectrometry. The huge amount of data acquired using these techniques is analyzed and assembled by fast computers and bioinformatics tools. Using these methods, as well as other technological advances, alcohol researchers can gain a better understanding of how alcohol globally influences protein structure and function, protein-protein interactions, and protein networks. This knowledge ultimately will assist in the early diagnosis and prognosis of alcoholism and the discovery of new drug targets and medications for treatment.
Lipid metabolism is highly compartmentalized between cellular organelles that dynamically adapt their compositions and interactions in response to metabolic challenges. Here, we investigate how diet-induced hepatic lipid accumulation, observed in non-alcoholic fatty liver disease (NAFLD), affects protein localization, organelle organization, and protein phosphorylation in vivo. We develop a mass spectrometric workflow for protein and phosphopeptide correlation profiling to monitor levels and cellular distributions of ∼6,000 liver proteins and ∼16,000 phosphopeptides during development of steatosis. Several organelle contact site proteins are targeted to lipid droplets (LDs) in steatotic liver, tethering organelles orchestrating lipid metabolism. Proteins of the secretory pathway dramatically redistribute, including the mis-localization of the COPI complex and sequestration of the Golgi apparatus at LDs. This correlates with reduced hepatic protein secretion. Our systematic in vivo analysis of subcellular rearrangements and organelle-specific phosphorylation reveals how nutrient overload leads to organellar reorganization and cellular dysfunction.
With the growing amount of experimental data produced in proteomics experiments and the requirements/recommendations of journals in the proteomics field to publicly make available data described in papers, a need for long-term storage of proteomics data in public repositories arises. For such an upload one needs proteomics data in a standardized format. Therefore, it is desirable, that the proprietary vendor's software will integrate in the future such an export functionality using the standard formats for proteomics results defined by the HUPO-PSI group. Currently not all search engines and analysis tools support these standard formats. In the meantime there is a need to provide user-friendly free-to-use conversion tools that can convert the data into such standard formats in order to support wet-lab scientists in creating proteomics data files ready for upload into the public repositories. ProCon is such a conversion tool written in Java for conversion of proteomics identification data into standard formats mzIdentML and Pride XML. It allows the conversion of Sequest™/Comet .out files, of search results from the popular and often used ProteomeDiscoverer® 1.x (x=versions 1.1 to1.4) software and search results stored in the LIMS systems ProteinScape® 1.3 and 2.1 into mzIdentML and PRIDE XML. This article is part of a Special Issue entitled: Computational Proteomics.
Milk is one of the most important nutrients for humans during lifetime. Farm animal milk in all its products like cheese and other fermentation and transformation products is a widespread nutrient for the entire life of humans. Proteins are key molecules of the milk functional component repertoire and their investigation represents a major challenge. Proteins in milk, such as caseins, contribute to the formation of micelles that are different from species to species in dimension and casein-type composition; they are an integral part of the MFGM (Milk Fat Globule Membrane) that has being exhaustively studied in recent years. Milk proteins can act as enzymes or have an antimicrobial activity; they could act as hormones and, last but not least, they have a latent physiological activity encoded in their primary structure that turns active when the protein is cleaved by fermentation or digestion processes. In this review we report the last progress in proteomics, peptidomics and bioinformatics. These new approaches allow us to better characterize the milk proteome of farm animal species, to highlight specific PTMs, the peptidomic profile and even to predict the potential nutraceutical properties of the analyzed proteins.
The objective of this presentation is to review the major proteomic technologies available to developmental toxicologists and, when possible, to provide examples of how various proteomic technologies have been used in developmental toxicology or toxicology in general. The field of proteomics is too broad for us to go into great depth about each technology, so we have attempted to provide brief overviews supplemented with many references that cover the subjects in more detail. Proteomics tools produce a global view of complex biological systems by examining complex protein mixtures using large-scale, high-throughput technologies. These technologies speed up the process of protein separation, quantification, and identification. As an important complement to genomics, proteomics allows for the examination of the entire complement of proteins in an organism, tissue, or cell-type. Current proteomics technologies not only identify protein expression, but also post-translational modifications and protein interactions. The field of proteomics is expanding rapidly to provide greater volume and quality of protein information to help understand the multifaceted nature of biological systems.
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