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New Advances in Proteomics in Disease
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New Advances in Proteomics in Disease

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pathology, Diagnostics, and Therapeutics".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 7528

Special Issue Editor

Dr. Valeria Magnelli

E-Mail Website
Guest Editor
Department of Science and Technological Innovation, Universita' degli Studi del Piemonte Orientale "Amedeo Avogadro", Alessandria, Italy
Interests: patch-clamp on neuroblastoma cell line with PFAS agents on GABA_A receptors; morphological and physiological alterations on rat testis and pancreas upon exposure to hypergravity; proteomics and metabolomics of monocytes exposed to inflammatory cytokines

Special Issue Information

Dear Colleagues,

During the last thirty years, proteomics has proved to be a powerful tool to define the metabolic pathways and cellular mechanisms involved in the pathogenesis and development of human diseases. Besides the investigation of potential causes, the current interest in proteomics aims to identify new protein molecules as potential biomarkers, making proteomics a powerful prognostic and early diagnostic tool. Proteomics analysis enable the characterization of disease-associated proteins, their possible modifications, and reciprocal interactions contributing to understanding of human illnesses, including cancer, infectious, and autoimmune diseases. By comparing different protein profiles between healthy and sick individuals, it is possible to identify differentially expressed proteins which can change in pathological conditions, not only at single cell or tissue level, but also in intracellular organelles and body fluids. The different expression level can allow the fast detection of a disease, monitor its temporal course, and speed up the development of therapeutical agents, revealing altered cell signaling pathways and gaining insights into altered molecular interactions behind disease pathogenesis. Together with the discovery of new therapeutical agents, proteomics can lead to a personalized medicine tracing the patient profile, their probability of getting sick, and specific treatment strategies for specific patient populations. This Special Issue aims to focus on the molecular mechanisms of pathogenesis in order to identify specific pathways involved in diseases and proteins useful for fast and precise disease detection.

Dr. Valeria Magnelli
Guest Editor

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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
  • protein
  • profile
  • molecular mechanism
  • biomarker
  • therapeutical agent

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Published Papers (4 papers)

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Research

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21 pages, 9787 KiB  
Article
Pathological Changes in Extracellular Matrix Composition Orchestrate the Fibrotic Feedback Loop Through Macrophage Activation in Dupuytren’s Contracture
by Elizabeth Heinmäe, Kristina Mäemets-Allas, Katre Maasalu, Darja Vastšjonok and Mariliis Klaas
Int. J. Mol. Sci. 2025, 26(7), 3146; https://doi.org/10.3390/ijms26073146 - 28 Mar 2025
Viewed by 1216
Abstract
Dupuytren’s contracture belongs to a group of fibrotic diseases that have similar mechanisms but lack effective treatment and prevention options. The excessive accumulation of connective tissue in Dupuytren’s disease leads to palmar fibrosis that results in contracture deformities. The present study aimed to [...] Read more.
Dupuytren’s contracture belongs to a group of fibrotic diseases that have similar mechanisms but lack effective treatment and prevention options. The excessive accumulation of connective tissue in Dupuytren’s disease leads to palmar fibrosis that results in contracture deformities. The present study aimed to investigate how the tissue microenvironment in Dupuytren’s contracture affects the phenotypic differentiation of macrophages, which leads to an inflammatory response and the development of chronicity in fibrotic disease. We utilized a decellularization-based method combined with proteomic analysis to identify shifts in extracellular matrix composition and the surrounding tissue microenvironment. We found that the expression of several matricellular proteins, such as MFAP4, EFEMP1 (fibulin-3), and ANGPTL2, was elevated in Dupuytren’s tissue. We show that, in response to the changes in the extracellular matrix of Dupuytren’s contracture, macrophages regulate the fibrotic process by cytokine production, promote myofibroblast differentiation, and increase the fibroblast migration rate. Moreover, we found that the extracellular matrix of Dupuytren’s contracture directly supports the macrophage-to-myofibroblast transition, which could be another contributor to Dupuytren’s disease pathogenesis. Our results suggest that interactions between macrophages and the extracellular matrix should be considered as targets for novel fibrotic disease treatment and prevention strategies in the future. Full article
(This article belongs to the Special Issue New Advances in Proteomics in Disease)
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23 pages, 4309 KiB  
Article
Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers
by Kesheng Wang, Donald A. Adjeroh, Wei Fang, Suzy M. Walter, Danqing Xiao, Ubolrat Piamjariyakul and Chun Xu
Int. J. Mol. Sci. 2025, 26(6), 2428; https://doi.org/10.3390/ijms26062428 - 8 Mar 2025
Viewed by 990
Abstract
Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer’s disease (AD). This study aimed to compare deep learning (DL) and [...] Read more.
Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer’s disease (AD). This study aimed to compare deep learning (DL) and traditional machine learning (ML) methods in predicting MCI using plasma proteomic biomarkers. A total of 239 adults were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort along with a pool of 146 plasma proteomic biomarkers. We evaluated seven traditional ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor (KNN), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) and six variations of a deep neural network (DNN) model—the DL model in the H2O package. Least Absolute Shrinkage and Selection Operator (LASSO) selected 35 proteomic biomarkers from the pool. Based on grid search, the DNN model with an activation function of “Rectifier With Dropout” with 2 layers and 32 of 35 selected proteomic biomarkers revealed the best model with the highest accuracy of 0.995 and an F1 Score of 0.996, while among seven traditional ML methods, XGBoost was the best with an accuracy of 0.986 and an F1 Score of 0.985. Several biomarkers were correlated with the APOE-ε4 genotype, polygenic hazard score (PHS), and three clinical cerebrospinal fluid biomarkers (Aβ42, tTau, and pTau). Bioinformatics analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed several molecular functions and pathways associated with the selected biomarkers, including cytokine-cytokine receptor interaction, cholesterol metabolism, and regulation of lipid localization. The results showed that the DL model may represent a promising tool in the prediction of MCI. These plasma proteomic biomarkers may help with early diagnosis, prognostic risk stratification, and early treatment interventions for individuals at risk for MCI. Full article
(This article belongs to the Special Issue New Advances in Proteomics in Disease)
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17 pages, 3111 KiB  
Article
Proteomic Analysis Highlights the Impact of the Sphingolipid Metabolizing Enzyme β-Galactosylceramidase on Mitochondrial Plasticity in Human Melanoma
by Davide Capoferri, Luca Mignani, Marcello Manfredi and Marco Presta
Int. J. Mol. Sci. 2024, 25(5), 3062; https://doi.org/10.3390/ijms25053062 - 6 Mar 2024
Cited by 1 | Viewed by 1594
Abstract
Mitochondrial plasticity, marked by a dynamism between glycolysis and oxidative phosphorylation due to adaptation to genetic and microenvironmental alterations, represents a characteristic feature of melanoma progression. Sphingolipids play a significant role in various aspects of cancer cell biology, including metabolic reprogramming. Previous observations [...] Read more.
Mitochondrial plasticity, marked by a dynamism between glycolysis and oxidative phosphorylation due to adaptation to genetic and microenvironmental alterations, represents a characteristic feature of melanoma progression. Sphingolipids play a significant role in various aspects of cancer cell biology, including metabolic reprogramming. Previous observations have shown that the lysosomal sphingolipid-metabolizing enzyme β-galactosylceramidase (GALC) exerts pro-oncogenic functions in melanoma. Here, mining the cBioPortal for a Cancer Genomics data base identified the top 200 nuclear-encoded genes whose expression is negatively correlated with GALC expression in human melanoma. Their categorization indicated a significant enrichment in Gene Ontology terms and KEGG pathways related to mitochondrial proteins and function. In parallel, proteomic analysis by LC-MS/MS of two GALC overexpressing human melanoma cell lines identified 98 downregulated proteins when compared to control mock cells. Such downregulation was confirmed at a transcriptional level by a Gene Set Enrichment Analysis of the genome-wide expression profiling data obtained from the same cells. Among the GALC downregulated proteins, we identified a cluster of 42 proteins significantly associated with GO and KEGG categorizations related to mitochondrion and energetic metabolism. Overall, our data indicate that changes in GALC expression may exert a significant impact on mitochondrial plasticity in human melanoma cells. Full article
(This article belongs to the Special Issue New Advances in Proteomics in Disease)
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Review

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25 pages, 909 KiB  
Review
Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges
by Diletta Piana, Federica Iavarone, Elisa De Paolis, Gennaro Daniele, Federico Parisella, Angelo Minucci, Viviana Greco and Andrea Urbani
Int. J. Mol. Sci. 2024, 25(16), 8830; https://doi.org/10.3390/ijms25168830 - 14 Aug 2024
Cited by 3 | Viewed by 2570
Abstract
Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses [...] Read more.
Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization. Full article
(This article belongs to the Special Issue New Advances in Proteomics in Disease)
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