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Mass Spectrometric Proteomics III
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Mass Spectrometric Proteomics III

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 14523

Special Issue Editors

Prof. Dr. Pierluigi Luigi Mauri

E-Mail Website
Guest Editor
Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche (ITB-CNR), 20090 Segrate (MI), Italy
Interests: mass spectrometry coupled to mono- and two-dimensional liquid chromatography; MudPIT; clinical proteomics; systems biology; personalized medicine; computational tools for proteomics
Special Issues, Collections and Topics in MDPI journals
Dr. Diana Canetti

E-Mail Website
Guest Editor
Centre for Amyloidosis & Acute Phase Proteins, Division of Medicine, University College London, Rowland Hill Street, London NW3 2PF, UK
Interests: proteomics; mass spectrometry; maldi; lc-ms; amyloid characterization; clinical proteomics; mass spectrometry-based quantification; targeted proteomics; post-translational modifications; protein–protein interaction; protein biochemistry; biomarkers discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last two decades, the investigation of genome, including transcriptome, has been widely applied and has permitted us to improve our understanding of it; however, at the same time, it has increased the demand to characterize other -omes sectors, such as proteome and metabolome, in order to find a complete description at molecular level of the biological mechanisms.

In addition, the improvements of both proteomics applications and related hyphenated techniques, such as mono- and two-dimensional chromatography coupled to tandem mass spectrometry, have permitted us to develop the so-called mass spectrometry-based proteomics. Today, the MS-based approach has become the gold standard for proteomics study, as it allows the identification of thousands proteins for each analysis and the investigation of a wide range of samples, without limits concerning molecular weight, isoelectric point or hydrophobicity, including protein aggregates.

This Special Issue on “Mass Spectrometric Proteomics III” will cover several topics, including but not limited to:

  • Proteome analysis;
  • Study of protein–protein interactions;
  • Clinical proteomics;
  • Proteomics for biomarker discovery;
  • Proteomics for amyloid investigations;
  • Quantitative proteomics by label-free or label-based approaches;
  • Mass spectrometry analysis for the identification and the characterization of post-translational modifications (PTMs);
  • Development of new analytical MS-based methods.

We warmly invite our colleagues to submit their original contributions to this Special Issue in order to provide recent updates regarding analytical MS-based methods for proteomics that will be of interest to our readers, including on-line separation techniques; targeted proteome analysis, protein–protein interactions and single cell analysis; and integration with dedicated computational tools.

We would be delighted if you could respond to confirm your contribution and the proposed title by 30 November 2021 to assist in planning the whole project.

Prof. Dr. Pierluigi Luigi Mauri
Dr. Diana Canetti
Guest Editors

Manuscript Submission Information

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. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short 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 thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly 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 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  •  proteomics
  •  mass spectrometry
  •  aggregated proteins/amyloid
  •  native/structural analysis
  •  emergeting DIA and single-cell approaches
  •  targeted proteomics
  •  imaging
  •  top down
  •  protein–protein interactions
  •  quantitation
  •  biomarkers discovery
  •  post-translational modifications (PTMs)

Published Papers (6 papers)

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Research

14 pages, 388 KiB  
Article
Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification
by Kai He, Yan Wang, Xuping Xie and Dan Shao
Molecules 2023, 28(8), 3617; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28083617 - 21 Apr 2023
Cited by 1 | Viewed by 1204
Abstract
Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. In this paper, we propose a [...] Read more.
Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. In this paper, we propose a novel method to predict proteins in CSF based on protein features. A two-stage feature-selection method is employed to remove irrelevant features and redundant features. The deep neural network and bagging method are used to construct the model for the prediction of CSF proteins. The experiment results on the independent testing dataset demonstrate that our method performs better than other methods in the prediction of CSF proteins. Furthermore, our method is also applied to the identification of glioma biomarkers. A differentially expressed gene analysis is performed on the glioma data. After combining the analysis results with the prediction results of our model, the biomarkers of glioma are identified successfully. Full article
(This article belongs to the Special Issue Mass Spectrometric Proteomics III)
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17 pages, 2957 KiB  
Article
Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies
by Alexey S. Kononikhin, Alexander G. Brzhozovskiy, Anna E. Bugrova, Natalia V. Chebotareva, Natalia V. Zakharova, Savva Semenov, Anatoliy Vinogradov, Maria I. Indeykina, Sergey Moiseev, Irina M. Larina and Evgeny N. Nikolaev
Molecules 2023, 28(8), 3323; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28083323 - 09 Apr 2023
Cited by 2 | Viewed by 2294
Abstract
Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD [...] Read more.
Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG). Full article
(This article belongs to the Special Issue Mass Spectrometric Proteomics III)
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19 pages, 7253 KiB  
Article
Quantitative Proteomic Analysis of Tibetan Pig Livers at Different Altitudes
by Xuedong Gu, Xinping Chang, Lin Yang, Yangzom Chamba and Fang Geng
Molecules 2023, 28(4), 1694; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules28041694 - 10 Feb 2023
Cited by 4 | Viewed by 1386
Abstract
In this study, the differences in protein profiles between the livers of Shannan Tibetan pigs (SNT), Linzhi Tibetan pigs (LZT) and Jiuzhaigou Tibetan pigs (JZT) were comparatively analyzed by tandem mass spectrometry-labeling quantitative proteomics. A total of 6804 proteins were identified: 6471 were [...] Read more.
In this study, the differences in protein profiles between the livers of Shannan Tibetan pigs (SNT), Linzhi Tibetan pigs (LZT) and Jiuzhaigou Tibetan pigs (JZT) were comparatively analyzed by tandem mass spectrometry-labeling quantitative proteomics. A total of 6804 proteins were identified: 6471 were quantified and 1095 were screened as differentially expressed proteins (DEPs). Bioinformatics analysis results show that, compared with JZT livers, up-regulated DEPs in SNT and LZT livers mainly promoted hepatic detoxification through steroid hormone biosynthesis and participated in lipid metabolism to maintain body energy homeostasis, immune response and immune regulation, while down-regulated DEPs were mainly involved in lipid metabolism and immune regulation. Three proteases closely related to hepatic fatty acid oxidation were down-regulated in enzymatic activity, indicating higher levels of lipid oxidation in SNT and LZT livers than in JZT livers. Down-regulation of the expression of ten immunoglobulins suggests that JZT are more susceptible to autoimmune diseases. It is highly likely that these differences in lipid metabolism and immune-related proteins are in response to the ecological environment at different altitudes, and the findings contribute to the understanding of the potential molecular link between Tibetan pig livers and the environment. Full article
(This article belongs to the Special Issue Mass Spectrometric Proteomics III)
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14 pages, 2093 KiB  
Article
MALDI Mass Spectrometry Imaging for the Distinction of Adenocarcinomas of the Pancreas and Biliary Tree
by Christine Bollwein, Juliana Pereira Lopes Gonҫalves, Kirsten Utpatel, Wilko Weichert and Kristina Schwamborn
Molecules 2022, 27(11), 3464; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27113464 - 27 May 2022
Cited by 7 | Viewed by 2115
Abstract
Pancreatic ductal adenocarcinoma and cholangiocarcinoma constitute two aggressive tumor types that originate from the epithelial lining of the excretory ducts of the pancreatobiliary tract. Given their close histomorphological resemblance, a correct diagnosis can be challenging and almost impossible without clinical information. In this [...] Read more.
Pancreatic ductal adenocarcinoma and cholangiocarcinoma constitute two aggressive tumor types that originate from the epithelial lining of the excretory ducts of the pancreatobiliary tract. Given their close histomorphological resemblance, a correct diagnosis can be challenging and almost impossible without clinical information. In this study, we investigated whether mass spectrometric peptide features could be employed to distinguish pancreatic ductal adenocarcinoma from cholangiocarcinoma. Three tissue microarrays of formalin-fixed and paraffin-embedded material (FFPE) comprising 41 cases of pancreatic ductal adenocarcinoma and 41 cases of cholangiocarcinoma were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). The derived peptide features and respective intensities were used to build different supervised classification algorithms: gradient boosting (GB), support vector machine (SVM), and k-nearest neighbors (KNN). On a pixel-by-pixel level, a classification accuracy of up to 95% could be achieved. The tentative identification of discriminative tryptic peptide signatures revealed proteins that are involved in the epigenetic regulation of the genome and tumor microenvironment. Despite their histomorphological similarities, mass spectrometry imaging represents an efficient and reliable approach for the distinction of PDAC from CC, offering a promising complementary or alternative approach to the existing tools used in diagnostics such as immunohistochemistry. Full article
(This article belongs to the Special Issue Mass Spectrometric Proteomics III)
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20 pages, 3929 KiB  
Article
Discovery of Proteins Responsible for Resistance to Three Chemotherapy Drugs in Breast Cancer Cells Using Proteomics and Bioinformatics Analysis
by Hyo Kyeong Cha, Seongmin Cheon, Hyeyoon Kim, Kyung-Min Lee, Han Suk Ryu and Dohyun Han
Molecules 2022, 27(6), 1762; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27061762 - 08 Mar 2022
Cited by 7 | Viewed by 3759
Abstract
Chemoresistance is a daunting obstacle to the effective treatment of breast cancer patients receiving chemotherapy. Although the mechanism of chemotherapy drug resistance has been explored broadly, the precise mechanism at the proteome level remains unclear. Especially, comparative studies between widely used anticancer drugs [...] Read more.
Chemoresistance is a daunting obstacle to the effective treatment of breast cancer patients receiving chemotherapy. Although the mechanism of chemotherapy drug resistance has been explored broadly, the precise mechanism at the proteome level remains unclear. Especially, comparative studies between widely used anticancer drugs in breast cancer are very limited. In this study, we employed proteomics and bioinformatics approaches on chemoresistant breast cancer cell lines to understand the underlying resistance mechanisms that resulted from doxorubicin (DR), paclitaxel (PR), and tamoxifen (TAR). In total, 10,385 proteins were identified and quantified from three TMT 6-plex and one TMT 10-plex experiments. Bioinformatics analysis showed that Notch signaling, immune response, and protein re-localization processes were uniquely associated with DR, PR, and TAR resistance, respectively. In addition, proteomic signatures related to drug resistance were identified as potential targets of many FDA-approved drugs. Furthermore, we identified potential prognostic proteins with significant effects on overall survival. Representatively, PLXNB2 expression was associated with a highly significant increase in risk, and downregulation of ACOX3 was correlated with a worse overall survival rate. Consequently, our study provides new insights into the proteomic aspects of the distinct mechanisms underlying chemoresistance in breast cancer. Full article
(This article belongs to the Special Issue Mass Spectrometric Proteomics III)
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18 pages, 3613 KiB  
Article
Trial Proteomic Qualitative and Quantitative Analysis of the Protein Matrix of Submandibular Sialoliths
by Paulina Czaplewska, Aleksandra E. Bogucka, Natalia Musiał, Dmitry Tretiakow, Andrzej Skorek and Dominik Stodulski
Molecules 2021, 26(21), 6725; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26216725 - 06 Nov 2021
Cited by 4 | Viewed by 2386
Abstract
Our studies aimed to explore the protein components of the matrix of human submandibular gland sialoliths. A qualitative analysis was carried out based on the filter aided sample preparation (FASP) methodology. In the protein extraction process, we evaluated the applicability of the standard [...] Read more.
Our studies aimed to explore the protein components of the matrix of human submandibular gland sialoliths. A qualitative analysis was carried out based on the filter aided sample preparation (FASP) methodology. In the protein extraction process, we evaluated the applicability of the standard demineralization step and the use of a lysis buffer containing sodium dodecyl sulfate (SDS) and dithiothreitol (DTT). The analysis of fragmentation spectra based on the human database allowed for the identification of 254 human proteins present in the deposits. In addition, the use of multi-round search in the PEAKS Studio program against the bacterial base allowed for the identification of 393 proteins of bacterial origin present in the extract obtained from sialolith, which so far has not been carried out for this biological material. Furthermore, we successfully applied the SWATH methodology, allowing for a relative quantitative analysis of human proteins present in deposits. The obtained results correlate with the classification of sialoliths proposed by Tretiakow. The performed functional analysis allowed for the first time the selection of proteins, the levels of which differ between the tested samples, which may suggest the role of these proteins in the calcification process in different types of sialoliths. These are preliminary studies, and drawing specific conclusions requires research on a larger group, but it provides us the basis for the continuation of the work that has already begun. Full article
(This article belongs to the Special Issue Mass Spectrometric Proteomics III)
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