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Proteomes, Volume 6, Issue 1 (March 2018)

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Open AccessFeature PaperReview Subcellular Proteomics: Application to Elucidation of Flooding-Response Mechanisms in Soybean
Received: 10 January 2018 / Revised: 13 February 2018 / Accepted: 23 February 2018 / Published: 27 February 2018
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Abstract
Soybean, which is rich in protein and oil, is cultivated in several climatic zones; however, its growth is markedly decreased by flooding. Proteomics is a useful tool for understanding the flooding-response mechanism in soybean. Subcellular proteomics has the potential to elucidate localized cellular
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Soybean, which is rich in protein and oil, is cultivated in several climatic zones; however, its growth is markedly decreased by flooding. Proteomics is a useful tool for understanding the flooding-response mechanism in soybean. Subcellular proteomics has the potential to elucidate localized cellular responses and investigate communications among subcellular components during plant growth and during stress. Under flooding, proteins related to signaling, stress and the antioxidative system are increased in the plasma membrane; scavenging enzymes for reactive-oxygen species are suppressed in the cell wall; protein translation is suppressed through inhibition of proteins related to preribosome biogenesis and mRNA processing in the nucleus; levels of proteins involved in the electron transport chain are reduced in the mitochondrion; and levels of proteins related to protein folding are decreased in the endoplasmic reticulum. This review discusses the advantages of a gel-free/label-free proteomic technique and methods of plant subcellular purification. It also summarizes cellular events in soybean under flooding and discusses future prospects for generation of flooding-tolerant soybean. Full article
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Open AccessArticle The Gene-Centric Content Management System and Its Application for Cognitive Proteomics
Received: 31 December 2017 / Revised: 7 February 2018 / Accepted: 20 February 2018 / Published: 23 February 2018
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Abstract
The Human Proteome Project is moving into the next phase of creating and/or reconsidering the functional annotations of proteins using the chromosome-centric paradigm. This challenge cannot be solved exclusively using automated means, but rather requires human intelligence for interpreting the combined data. To
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The Human Proteome Project is moving into the next phase of creating and/or reconsidering the functional annotations of proteins using the chromosome-centric paradigm. This challenge cannot be solved exclusively using automated means, but rather requires human intelligence for interpreting the combined data. To foster the integration between human cognition and post-genome array a number of specific tools were recently developed, among them CAPER, GenomewidePDB, and The Proteome Browser (TPB). For the purpose of tackling the task of protein functional annotating the Gene-Centric Content Management System (GenoCMS) was expanded with new features. The goal was to enable bioinformaticans to develop self-made applications and to position these applets within the generalized informational canvas supported by GenoCMS. We report the results of GenoCMS-enabled integration of the concordant informational flows in the chromosome-centric framework of the human chromosome 18 project. The workflow described in the article can be scaled to other human chromosomes, and also supplemented with new tracks created by the user. The GenoCMS is an example of a project-oriented informational system, which are important for public data sharing. Full article
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Open AccessFeature PaperArticle Pilot Study on Mass Spectrometry–Based Analysis of the Proteome of CD34+CD123+ Progenitor Cells for the Identification of Potential Targets for Immunotherapy in Acute Myeloid Leukemia
Received: 17 December 2017 / Revised: 3 February 2018 / Accepted: 8 February 2018 / Published: 12 February 2018
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Abstract
Targeting of leukemic stem cells with specific immunotherapy would be an ideal approach for the treatment of myeloid malignancies, but suitable epitopes are unknown. The comparative proteome-level characterization of hematopoietic stem and progenitor cells from healthy stem cell donors and patients with acute
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Targeting of leukemic stem cells with specific immunotherapy would be an ideal approach for the treatment of myeloid malignancies, but suitable epitopes are unknown. The comparative proteome-level characterization of hematopoietic stem and progenitor cells from healthy stem cell donors and patients with acute myeloid leukemia has the potential to reveal differentially expressed proteins which can be used as surface-markers or as proxies for affected molecular pathways. We employed mass spectrometry methods to analyze the proteome of the cytosolic and the membrane fraction of CD34 and CD123 co-expressing FACS-sorted leukemic progenitors from five patients with acute myeloid leukemia. As a reference, CD34+CD123+ normal hematopoietic progenitor cells from five healthy, granulocyte-colony stimulating factor (G-CSF) mobilized stem cell donors were analyzed. In this Tandem Mass Tag (TMT) 10-plex labelling–based approach, 2070 proteins were identified with 171 proteins differentially abundant in one or both cellular compartments. This proof-of-principle-study demonstrates the potential of mass spectrometry to detect differentially expressed proteins in two compartment fractions of the entire proteome of leukemic stem cells, compared to their non-malignant counterparts. This may contribute to future immunotherapeutic target discoveries and individualized AML patient characterization. Full article
(This article belongs to the Special Issue The Proteome in Stem Cell Transplantation)
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Open AccessArticle Revealing Subtle Functional Subgroups in Class A Scavenger Receptors by Pattern Discovery and Disentanglement of Aligned Pattern Clusters
Received: 24 November 2017 / Revised: 1 February 2018 / Accepted: 1 February 2018 / Published: 8 February 2018
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Abstract
A protein family has similar and diverse functions locally conserved as aligned sequence segments. Further discovering their association patterns could reveal subtle family subgroup characteristics. Since aligned residues associations (ARAs) in Aligned Pattern Clusters (APCs) are complex and intertwined due to entangled function,
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A protein family has similar and diverse functions locally conserved as aligned sequence segments. Further discovering their association patterns could reveal subtle family subgroup characteristics. Since aligned residues associations (ARAs) in Aligned Pattern Clusters (APCs) are complex and intertwined due to entangled function, factors, and variance in the source environment, we have recently developed a novel method: Aligned Residue Association Discovery and Disentanglement (ARADD) to solve this problem. ARADD first obtains from an APC an ARA Frequency Matrix and converts it to an adjusted statistical residual vector space (SRV). It then disentangles the SRV into Principal Components (PCs) and Re-projects their vectors to a SRV to reveal succinct orthogonal AR groups. In this study, we applied ARADD to class A scavenger receptors (SR-A), a subclass of a diverse protein family binding to modified lipoproteins with diverse biological functionalities not explicitly known. Our experimental results demonstrated that ARADD can unveil subtle subgroups in sequence segments with diverse functionality and highly variable sequence lengths. We also demonstrated that the ARAs captured in a Position Weight Matrix or an APC were entangled in biological function and domain location but disentangled by ARADD to reveal different subclasses without knowing their actual occurrence positions. Full article
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Open AccessFeature PaperArticle Proteome Profiling of Diabetic Mellitus Patient Urine for Discovery of Biomarkers by Comprehensive MS-Based Proteomics
Received: 27 December 2017 / Revised: 19 January 2018 / Accepted: 2 February 2018 / Published: 6 February 2018
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Abstract
Diabetic mellitus (DM) is a disease that affects glucose homeostasis and causes complications, such as diabetic nephropathy (DN). For early diagnosis of DN, microalbuminuria is currently one of the most frequently used biomarkers. However, more early diagnostic biomarkers are desired in addition to
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Diabetic mellitus (DM) is a disease that affects glucose homeostasis and causes complications, such as diabetic nephropathy (DN). For early diagnosis of DN, microalbuminuria is currently one of the most frequently used biomarkers. However, more early diagnostic biomarkers are desired in addition to microalbuminuria. In this study, we performed comprehensive proteomics analysis of urine proteomes of diabetic mellitus patients without microalbuminuria and healthy volunteers to compare the protein profiles by mass spectrometry. With high confidence criteria, 942 proteins in healthy volunteer urine and 645 proteins in the DM patient urine were identified with label-free semi-quantitation, respectively. Gene ontology and pathway analysis were performed with the proteins, which were up- or down-regulated in the DM patient urine to elucidate significant changes in pathways. The discovery of a useful biomarker for early DN discovery is expected. Full article
(This article belongs to the Special Issue Proteomic Biomarkers in Human Diseases)
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Open AccessArticle In-Depth Proteomic Characterization of Classical and Non-Classical Monocyte Subsets
Received: 7 December 2017 / Revised: 24 January 2018 / Accepted: 1 February 2018 / Published: 5 February 2018
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Abstract
Monocytes are bone marrow-derived leukocytes that are part of the innate immune system. Monocytes are divided into three subsets: classical, intermediate and non-classical, which can be differentiated by their expression of some surface antigens, mainly CD14 and CD16. These cells are key players
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Monocytes are bone marrow-derived leukocytes that are part of the innate immune system. Monocytes are divided into three subsets: classical, intermediate and non-classical, which can be differentiated by their expression of some surface antigens, mainly CD14 and CD16. These cells are key players in the inflammation process underlying the mechanism of many diseases. Thus, the molecular characterization of these cells may provide very useful information for understanding their biology in health and disease. We performed a multicentric proteomic study with pure classical and non-classical populations derived from 12 healthy donors. The robust workflow used provided reproducible results among the five participating laboratories. Over 5000 proteins were identified, and about half of them were quantified using a spectral counting approach. The results represent the protein abundance catalogue of pure classical and enriched non-classical blood peripheral monocytes, and could serve as a reference dataset of the healthy population. The functional analysis of the differences between cell subsets supports the consensus roles assigned to human monocytes. Full article
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Open AccessArticle Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework
Received: 11 December 2017 / Revised: 26 January 2018 / Accepted: 26 January 2018 / Published: 31 January 2018
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Abstract
The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms.
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The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics “Contribution Fest“ undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software. Full article
(This article belongs to the Special Issue Metaproteomics)
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Open AccessFeature PaperArticle Evaluation of Optimized Tube-Gel Methods of Sample Preparation for Large-Scale Plant Proteomics
Received: 7 November 2017 / Revised: 24 January 2018 / Accepted: 29 January 2018 / Published: 30 January 2018
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Abstract
The so-called tube-gel method is a sample preparation protocol allowing for management of SDS for protein solubilization through in-gel protein trapping. Because of its simplicity, we assumed that once miniaturized, this method could become a standard for large scale experiments. We evaluated the
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The so-called tube-gel method is a sample preparation protocol allowing for management of SDS for protein solubilization through in-gel protein trapping. Because of its simplicity, we assumed that once miniaturized, this method could become a standard for large scale experiments. We evaluated the performances of two variants of the miniaturized version of the tube-gel method based on different solubilization buffers (Tris-SDS or urea-SDS). To this end, we compared them to two other digestion methods: (i) liquid digestion after protein solubilization in the absence of SDS (liquid method) and (ii) filter-aided sample preparation (FASP). As large-scale experiments may require long term gel storage, we also examined to which extent gel aging affected the results of the proteomics analysis. We showed that both tube-gel and FASP methods extracted membrane proteins better than the liquid method, while the latter allowed the identification and quantification of a greater number of proteins. All methods were equivalent regarding quantitative stability. However, important differences were observed regarding post-translational modifications. In particular, methionine oxidation was higher with the tube-gel method than with the other methods. Based on these results, and considering time, simplicity, and cost aspects, we conclude that the miniaturized tube-gel method is suitable for sample preparation in the context of large-scale experiments. Full article
(This article belongs to the Special Issue Plant Proteomics 2017)
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Open AccessFeature PaperReview Exploring the Psoriatic Arthritis Proteome in Search of Novel Biomarkers
Received: 18 December 2017 / Revised: 16 January 2018 / Accepted: 21 January 2018 / Published: 24 January 2018
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Abstract
Psoriatic arthritis (PsA) is an inflammatory arthritis which develops in up to one-third of patients suffering from the cutaneous disorder, psoriasis. The complex and heterogeneous nature of PsA renders it difficult to diagnose, leading to poor outcomes and, therefore, warrants an examination into
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Psoriatic arthritis (PsA) is an inflammatory arthritis which develops in up to one-third of patients suffering from the cutaneous disorder, psoriasis. The complex and heterogeneous nature of PsA renders it difficult to diagnose, leading to poor outcomes and, therefore, warrants an examination into soluble biomarkers, which may facilitate early detection of the disease. Protein biomarkers are a dynamic resource of pathophysiological information able to provide an immediate reflection of pathological changes caused by disease. Investigations of the serum and synovial fluid of PsA patients has provided new insights into the molecular basis of this disease and led to the identification of sensitive diagnostic and prognostic biomarkers. The collection of novel PsA biomarkers identified through proteomic studies has been reviewed below. Full article
(This article belongs to the Special Issue Proteomic Biomarkers in Human Diseases)
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Open AccessEditorial Acknowledgement to Reviewers of Proteomes in 2017
Received: 23 January 2018 / Revised: 23 January 2018 / Accepted: 23 January 2018 / Published: 23 January 2018
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Abstract
Peer review is an essential part in the publication process, ensuring that Proteomes maintains high quality standards for its published papers.[...] Full article
Open AccessArticle Enrichment and Identification of the Most Abundant Zinc Binding Proteins in Developing Barley Grains by Zinc-IMAC Capture and Nano LC-MS/MS
Received: 4 December 2017 / Revised: 7 January 2018 / Accepted: 11 January 2018 / Published: 17 January 2018
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Abstract
Background: Zinc accumulates in the embryo, aleurone, and subaleurone layers at different amounts in cereal grains. Our hypothesis is that zinc could be stored bound, not only to low MW metabolites/proteins, but also to high MW proteins as well. Methods: In
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Background: Zinc accumulates in the embryo, aleurone, and subaleurone layers at different amounts in cereal grains. Our hypothesis is that zinc could be stored bound, not only to low MW metabolites/proteins, but also to high MW proteins as well. Methods: In order to identify the most abundant zinc binding proteins in different grain tissues, we microdissected barley grains into (1) seed coats; (2) aleurone/subaleurone; (3) embryo; and (4) endosperm. Initial screening for putative zinc binding proteins from the different tissue types was performed by fractionating proteins according to solubility (Osborne fractionation), and resolving those via Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) followed by polyvinylidene fluoride (PVDF) membrane blotting and dithizone staining. Selected protein fractions were subjected to Zn2+-immobilized metal ion affinity chromatography, and the captured proteins were identified using nanoscale liquid chromatography coupled to tandem mass spectrometry (nanoLC-MS/MS). Results: In the endosperm, the most abundant zinc binding proteins were the storage protein B-hordeins, gamma-, and D-hordeins, while in the embryo, 7S globulins storage proteins exhibited zinc binding. In the aleurone/subaleurone, zinc affinity captured proteins were late abundant embryogenesis proteins, dehydrins, many isoforms of non-specific lipid transfer proteins, and alpha amylase trypsin inhibitor. Conclusions: We have shown evidence that abundant barley grain proteins have been captured by Zn-IMAC, and their zinc binding properties in relationship to the possibility of zinc storage is discussed. Full article
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Open AccessArticle Comparative Proteomics Analysis of Urine Reveals Down-Regulation of Acute Phase Response Signaling and LXR/RXR Activation Pathways in Prostate Cancer
Received: 15 November 2017 / Revised: 22 December 2017 / Accepted: 25 December 2017 / Published: 29 December 2017
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Abstract
Detecting prostate cancer (PCa) using non-invasive diagnostic markers still remains a challenge. The aim of this study was the identification of urine proteins that are sufficiently sensitive and specific to detect PCa in the early stages. Comparative proteomics profiling of urine from patients
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Detecting prostate cancer (PCa) using non-invasive diagnostic markers still remains a challenge. The aim of this study was the identification of urine proteins that are sufficiently sensitive and specific to detect PCa in the early stages. Comparative proteomics profiling of urine from patients with PCa, benign prostate hyperplasia, bladder cancer, and renal cancer, coupled with bioinformatics analysis, were performed. Statistically significant difference in abundance showed 20 and 85 proteins in the 2-D DIGE/MS and label-free LC-MS/MS experiments, respectively. In silico analysis indicated activation, binding, and cell movement of subset of immune cells as the top affected cellular functions in PCa, together with the down-regulation of Acute Phase Response Signaling and Liver X Receptor/ Retinoid X Receptor (LXR/RXR) activation pathways. The most promising biomarkers were 35, altered in PCa when compared to more than one group. Half of these have confirmed localization in normal or PCa tissues. Twenty proteins (CD14, AHSG, ENO1, ANXA1, CLU, COL6A1, C3, FGA, FGG, HPX, PTGDS, S100A9, LMAN2, ITIH4, ACTA2, GRN, HBB, PEBP1, CTSB, SPP1) are oncogenes, tumor suppressors, and multifunctional proteins with highly confirmed involvement in PCa, while 9 (AZU1, IGHG1, RNASE2, PZP, REG1A, AMY1A, AMY2A, ACTG2, COL18A1) have been associated with different cancers, but not with PCa so far, and may represent novel findings. LC-MS/MS data are available via ProteomeXchange with identifier PXD008407. Full article
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Open AccessArticle MetaGOmics: A Web-Based Tool for Peptide-Centric Functional and Taxonomic Analysis of Metaproteomics Data
Received: 21 November 2017 / Revised: 19 December 2017 / Accepted: 21 December 2017 / Published: 27 December 2017
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Abstract
Metaproteomics is the characterization of all proteins being expressed by a community of organisms in a complex biological sample at a single point in time. Applications of metaproteomics range from the comparative analysis of environmental samples (such as ocean water and soil) to
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Metaproteomics is the characterization of all proteins being expressed by a community of organisms in a complex biological sample at a single point in time. Applications of metaproteomics range from the comparative analysis of environmental samples (such as ocean water and soil) to microbiome data from multicellular organisms (such as the human gut). Metaproteomics research is often focused on the quantitative functional makeup of the metaproteome and which organisms are making those proteins. That is: What are the functions of the currently expressed proteins? How much of the metaproteome is associated with those functions? And, which microorganisms are expressing the proteins that perform those functions? However, traditional protein-centric functional analysis is greatly complicated by the large size, redundancy, and lack of biological annotations for the protein sequences in the database used to search the data. To help address these issues, we have developed an algorithm and web application (dubbed “MetaGOmics”) that automates the quantitative functional (using Gene Ontology) and taxonomic analysis of metaproteomics data and subsequent visualization of the results. MetaGOmics is designed to overcome the shortcomings of traditional proteomics analysis when used with metaproteomics data. It is easy to use, requires minimal input, and fully automates most steps of the analysis—including comparing the functional makeup between samples. MetaGOmics is freely available at https://www.yeastrc.org/metagomics/. Full article
(This article belongs to the Special Issue Metaproteomics)
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