Special Issue "Insights and Trends into Proteome Science"

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A special issue of Proteomes (ISSN 2227-7382).

Deadline for manuscript submissions: closed (20 April 2013)

Special Issue Editor

Guest Editor
Dr. Jochen Metzger

Mosaiques Diagnostics GmbH, Mellendorfer Street 7-9, 30625 Hannover, Germany
E-Mail
Interests: clinical proteomics; bioassay development; biosensorics; mass spectrometry; capillary electrophoresis; cancer proteomics; transplantation proteomics

Special Issue Information

Dear Colleagues,

Starting in the 70’s with in-gel electrophoretic separation, the analysis of proteins has developed to become an invaluable tool for global analysis of expression patterns and dynamics of proteins in a multitude of structural compartments and organisms. Nowadays, proteomics has a strong impact on multiple disciplines in the life sciences including pathogen, plant, food, veterinary, biomedical and pharmaceutical research. The first special issue of Proteomes will encompass a wide range of relevant topics in the field of proteome science. These range from basic research and technological developments in new methodologies, to recent findings and applications that have a high socioeconomic and public health impact. There is no restriction on the kind of investigated proteome or applied proteome technology. In this respect, the special issue of Proteomes is open to all relevant aspects of proteomic research to give scientists interested in the growing field of proteome research and applications a comprehensive and detailed overview from a range of perspectives.

Dr. Jochen Metzger
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Proteomes is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Keywords

  • proteomes
  • proteomic technology
  • proteome profiling
  • clinical
  • proteomics
  • protein networking
  • plant proteomics
  • animal proteomics
  • separation technologies
  • (bio)marker search
  • body fluid proteomics
  • tissue proteomics
  • pathway analysis

Published Papers (4 papers)

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Research

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Open AccessArticle Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration
Proteomes 2013, 1(1), 3-24; doi:10.3390/proteomes1010003
Received: 18 April 2013 / Revised: 16 May 2013 / Accepted: 21 May 2013 / Published: 31 May 2013
PDF Full-text (733 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and in silico approaches allow the screening of protein interactors in a large-scale manner. Therefore, the bulk of information on protein interactions deposited in databases
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Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and in silico approaches allow the screening of protein interactors in a large-scale manner. Therefore, the bulk of information on protein interactions deposited in databases and peer-reviewed published literature is constantly growing. Multiple databases interfaced from user-friendly web tools recently emerged to facilitate the task of protein interaction data retrieval and data integration. Nevertheless, as we evidence in this report, despite the current efforts towards data integration, the quality of the information on protein interactions retrieved by in silico approaches is frequently incomplete and may even list false interactions. Here we point to some obstacles precluding confident data integration, with special emphasis on protein interactions, which include gene acronym redundancies and protein synonyms. Three human proteins (choline kinase, PPIase and uromodulin) and three different web-based data search engines focused on protein interaction data retrieval (PSICQUIC, DASMI and BIPS) were used to explain the potential occurrence of undesired errors that should be considered by researchers in the field. We demonstrate that, despite the recent initiatives towards data standardization, manual curation of protein interaction networks based on literature searches are still required to remove potential false positives. A three-step workflow consisting of: (i) data retrieval from multiple databases, (ii) peer-reviewed literature searches, and (iii) data curation and integration, is proposed as the best strategy to gather updated information on protein interactions. Finally, this strategy was applied to compile bona fide information on human DREAM protein interactome, which constitutes liable training datasets that can be used to improve computational predictions. Full article
(This article belongs to the Special Issue Insights and Trends into Proteome Science)

Review

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Open AccessReview Comparative and Quantitative Global Proteomics Approaches: An Overview
Proteomes 2013, 1(3), 180-218; doi:10.3390/proteomes1030180
Received: 16 September 2013 / Revised: 8 October 2013 / Accepted: 8 October 2013 / Published: 11 October 2013
Cited by 11 | PDF Full-text (878 KB) | HTML Full-text | XML Full-text
Abstract
Proteomics became a key tool for the study of biological systems. The comparison between two different physiological states allows unravelling the cellular and molecular mechanisms involved in a biological process. Proteomics can confirm the presence of proteins suggested by their mRNA content and
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Proteomics became a key tool for the study of biological systems. The comparison between two different physiological states allows unravelling the cellular and molecular mechanisms involved in a biological process. Proteomics can confirm the presence of proteins suggested by their mRNA content and provides a direct measure of the quantity present in a cell. Global and targeted proteomics strategies can be applied. Targeted proteomics strategies limit the number of features that will be monitored and then optimise the methods to obtain the highest sensitivity and throughput for a huge amount of samples. The advantage of global proteomics strategies is that no hypothesis is required, other than a measurable difference in one or more protein species between the samples. Global proteomics methods attempt to separate quantify and identify all the proteins from a given sample. This review highlights only the different techniques of separation and quantification of proteins and peptides, in view of a comparative and quantitative global proteomics analysis. The in-gel and off-gel quantification of proteins will be discussed as well as the corresponding mass spectrometry technology. The overview is focused on the widespread techniques while keeping in mind that each approach is modular and often recovers the other. Full article
(This article belongs to the Special Issue Insights and Trends into Proteome Science)
Open AccessReview High-Throughput Proteomic Approaches to the Elucidation of Potential Biomarkers of Chronic Allograft Injury (CAI)
Proteomes 2013, 1(2), 159-179; doi:10.3390/proteomes1020159
Received: 19 July 2013 / Revised: 6 September 2013 / Accepted: 9 September 2013 / Published: 23 September 2013
Cited by 1 | PDF Full-text (944 KB) | HTML Full-text | XML Full-text
Abstract
This review focuses on the role of OMICs technologies, concentrating in particular on proteomics, in biomarker discovery in chronic allograft injury (CAI). CAI is the second most prevalent cause of allograft dysfunction and loss in the first decade post-transplantation, after death with functioning
[...] Read more.
This review focuses on the role of OMICs technologies, concentrating in particular on proteomics, in biomarker discovery in chronic allograft injury (CAI). CAI is the second most prevalent cause of allograft dysfunction and loss in the first decade post-transplantation, after death with functioning graft (DWFG). The term CAI, sometimes referred to as chronic allograft nephropathy (CAN), describes the deterioration of renal allograft function and structure as a result of immunological processes (chronic antibody-mediated rejection), and other non-immunological factors such as calcineurin inhibitor (CNI) induced nephrotoxicity, hypertension and infection. Current methods for assessing allograft function are costly, insensitive and invasive; traditional kidney function measurements such as serum creatinine and glomerular filtration rate (GFR) display poor predictive abilities, while the current “gold-standard” involving histological diagnosis with a renal biopsy presents its own inherent risks to the overall health of the allograft. As early as two years post-transplantation, protocol biopsies have shown more than 50% of allograft recipients have mild CAN; ten years post-transplantation more than 50% of the allograft recipients have progressed to severe CAN which is associated with diminishing graft function. Thus, there is a growing medical requirement for minimally invasive biomarkers capable of identifying the early stages of the disease which would allow for timely intervention. Proteomics involves the study of the expression, localization, function and interaction of the proteome. Proteomic technologies may be powerful tools used to identify novel biomarkers which would predict CAI in susceptible individuals. In this paper we will review the use of proteomics in the elucidation of novel predictive biomarkers of CAI in clinical, animal and in vitro studies. Full article
(This article belongs to the Special Issue Insights and Trends into Proteome Science)
Open AccessReview Proteomic Workflows for Biomarker Identification Using Mass Spectrometry — Technical and Statistical Considerations during Initial Discovery
Proteomes 2013, 1(2), 109-127; doi:10.3390/proteomes1020109
Received: 27 June 2013 / Revised: 22 August 2013 / Accepted: 22 August 2013 / Published: 27 August 2013
Cited by 1 | PDF Full-text (641 KB) | HTML Full-text | XML Full-text
Abstract
Identification of biomarkers capable of differentiating between pathophysiological states of an individual is a laudable goal in the field of proteomics. Protein biomarker discovery generally employs high throughput sample characterization by mass spectrometry (MS), being capable of identifying and quantifying thousands of proteins
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Identification of biomarkers capable of differentiating between pathophysiological states of an individual is a laudable goal in the field of proteomics. Protein biomarker discovery generally employs high throughput sample characterization by mass spectrometry (MS), being capable of identifying and quantifying thousands of proteins per sample. While MS-based technologies have rapidly matured, the identification of truly informative biomarkers remains elusive, with only a handful of clinically applicable tests stemming from proteomic workflows. This underlying lack of progress is attributed in large part to erroneous experimental design, biased sample handling, as well as improper statistical analysis of the resulting data. This review will discuss in detail the importance of experimental design and provide some insight into the overall workflow required for biomarker identification experiments. Proper balance between the degree of biological vs. technical replication is required for confident biomarker identification. Full article
(This article belongs to the Special Issue Insights and Trends into Proteome Science)

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