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1.
Radioproteomics in patients with ovarian cancer.
McCague, C, Beer, L
The British journal of radiology. 2021;(1125):20201331
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Abstract
Radioproteomics is the integration of proteomics, the systematic study of the protein expression of an organism, with radiomics, the extraction and analysis of large numbers of quantitative features from medical images. This article examines this developing field, and it's application in high grade serous ovarian carcinoma. Seminal proteomic studies in the area of ovarian cancer, such as the PROVAR and CPTA studies are discussed, along side recent research, such as that highlighting the central role of methyltransferase nicotinamide N-methyltransferase as the metabolic regulation of cancer progression in the tumour stroma. Finally, this article considers a novel, hypothesis generating approach to integrate CT-based qualitative and radiomic features with proteomic analysis, and the future direction of the field. Combined advances in radiomic, proteomic and genomic analysis has the potential to signal the age of true precision medicine, where treatment is centered specifically on the molecular profile of the tumour, rather than based on empirical knowledge, thus altering the course of a disease that has the highest mortality of all cancers of the female reproductive system.
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Organic mercury solid phase chemoselective capture for proteomic identification of S-nitrosated proteins and peptides.
Doulias, PT, Tenopoulou, M, Zakopoulos, I, Ischiropoulos, H
Nitric oxide : biology and chemistry. 2021;:1-6
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Abstract
Cysteine S-nitrosation mediates NO signaling and protein function under pathophysiological conditions. Herein, we provide a detailed protocol regarding the organic mercury chemoselective enrichment of S-nitrosated proteins and peptides. We discuss key aspects of the enrichment strategy and provide technical tips for the best performance of the experimental protocol.
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Proximity labeling approaches to study protein complexes during virus infection.
Zapatero-Belinchón, FJ, Carriquí-Madroñal, B, Gerold, G
Advances in virus research. 2021;:63-104
Abstract
Cellular compartmentalization of proteins and protein complex formation allow cells to tightly control biological processes. Therefore, understanding the subcellular localization and interactions of a specific protein is crucial to uncover its biological function. The advent of proximity labeling (PL) has reshaped cellular proteomics in infection biology. PL utilizes a genetically modified enzyme that generates a "labeling cloud" by covalently labeling proteins in close proximity to the enzyme. Fusion of a PL enzyme to a specific antibody or a "bait" protein of interest in combination with affinity enrichment mass spectrometry (AE-MS) enables the isolation and identification of the cellular proximity proteome, or proxisome. This powerful methodology has been paramount for the mapping of membrane or membraneless organelles as well as for the understanding of hard-to-purify protein complexes, such as those of transmembrane proteins. Unsurprisingly, more and more infection biology research groups have recognized the potential of PL for the identification of host-pathogen interactions. In this chapter, we introduce the enzymes commonly used for PL labeling as well as recent promising advancements and summarize the major achievements in organelle mapping and nucleic acid PL. Moreover, we comprehensively describe the research on host-pathogen interactions using PL, giving special attention to studies in the field of virology.
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Critical role of mass spectrometry proteomics in tear biomarker discovery for multifactorial ocular diseases (Review).
Ma, JYW, Sze, YH, Bian, JF, Lam, TC
International journal of molecular medicine. 2021;(5)
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Abstract
The tear film is a layer of body fluid that maintains the homeostasis of the ocular surface. The superior accessibility of tears and the presence of a high concentration of functional proteins make tears a potential medium for the discovery of non‑invasive biomarkers in ocular diseases. Recent advances in mass spectrometry (MS) have enabled determination of an in‑depth proteome profile, improved sensitivity, faster acquisition speed, proven variety of acquisition methods, and identification of disease biomarkers previously lacking in the field of ophthalmology. The use of MS allows efficient discovery of tear proteins, generation of reproducible results, and, more importantly, determines changes of protein quantity and post‑translation modifications in microliter samples. The present review compared techniques for tear collection, sample preparation, and acquisition applied for the discovery of tear protein markers in normal subjects and multifactorial conditions, including dry eye syndrome, diabetic retinopathy, thyroid eye disease and primary open‑angle glaucoma, which require an early diagnosis for treatment. It also summarized the contribution of MS to early discovery by means of disease‑related protein markers in tear fluid and the potential for transformation of the tear MS‑based proteome to antibody‑based assay for future clinical application.
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Glycoproteomics identifies HOMER3 as a potentially targetable biomarker triggered by hypoxia and glucose deprivation in bladder cancer.
Peixoto, A, Ferreira, D, Azevedo, R, Freitas, R, Fernandes, E, Relvas-Santos, M, Gaiteiro, C, Soares, J, Cotton, S, Teixeira, B, et al
Journal of experimental & clinical cancer research : CR. 2021;(1):191
Abstract
BACKGROUND Muscle invasive bladder cancer (MIBC) remains amongst the deadliest genitourinary malignancies due to treatment failure and extensive molecular heterogeneity, delaying effective targeted therapeutics. Hypoxia and nutrient deprivation, oversialylation and O-glycans shortening are salient features of aggressive tumours, creating cell surface glycoproteome fingerprints with theranostics potential. METHODS A glycomics guided glycoproteomics workflow was employed to identify potentially targetable biomarkers using invasive bladder cancer cell models. The 5637 and T24 cells O-glycome was characterized by mass spectrometry (MS), and the obtained information was used to guide glycoproteomics experiments, combining sialidase, lectin affinity and bottom-up protein identification by nanoLC-ESI-MS/MS. Data was curated by a bioinformatics approach developed in-house, sorting clinically relevant molecular signatures based on Human Protein Atlas insights. Top-ranked targets and glycoforms were validated in cell models, bladder tumours and metastases by MS and immunoassays. Cells grown under hypoxia and glucose deprivation disclosed the contribution of tumour microenvironment to the expression of relevant biomarkers. Cancer-specificity was validated in healthy tissues by immunohistochemistry and MS in 20 types of tissues/cells of different individuals. RESULTS Sialylated T (ST) antigens were found to be the most abundant glycans in cell lines and over 900 glycoproteins were identified potentially carrying these glycans. HOMER3, typically a cytosolic protein, emerged as a top-ranked targetable glycoprotein at the cell surface carrying short-chain O-glycans. Plasma membrane HOMER3 was observed in more aggressive primary tumours and distant metastases, being an independent predictor of worst prognosis. This phenotype was triggered by nutrient deprivation and concomitant to increased cellular invasion. T24 HOMER3 knockdown significantly decreased proliferation and, to some extent, invasion in normoxia and hypoxia; whereas HOMER3 knock-in increased its membrane expression, which was more pronounced under glucose deprivation. HOMER3 overexpression was associated with increased cell proliferation in normoxia and potentiated invasion under hypoxia. Finally, the mapping of HOMER3-glycosites by EThcD-MS/MS in bladder tumours revealed potentially targetable domains not detected in healthy tissues. CONCLUSION HOMER3-glycoforms allow the identification of patients' subsets facing worst prognosis, holding potential to address more aggressive hypoxic cells with limited off-target effects. The molecular rationale for identifying novel bladder cancer molecular targets has been established.
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Tandem mass tag-based quantitative proteomic profiling of the serum of patients with abnormal uterine bleeding associated with copper intrauterine device.
Liu, J, Jiang, L, Liu, X, Xu, L, Xu, J, Zhu, W, Shen, Y, Zhang, Z, Mao, Y, Yan, X, et al
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics. 2021;(1):169-178
Abstract
OBJECTIVE To investigate changes in the level of protein in serum and uncover the underlying pathogenesis of abnormal uterine bleeding (AUB) associated with copper intrauterine devices (Cu IUD). METHODS Protein profiles were investigated via tandem mass tag (TMT)-based quantitative proteomics and bioinformatics technology. Quantification and characterization of candidate proteins were further performed in 33 controls and 45 cases by Luminex assay and enzyme-linked immunosorbent assay. RESULTS In total, 842 proteins were identified via TMT coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) in the serum of individuals with IUDs. Among them, 25 differentially expressed proteins (p < 0.05) were observed, including eight upregulated proteins and 17 downregulated proteins. Ten proteins were verified, and Alpha-1-Antitrypsin (a1AT) had a significantly elevated expression in women with AUB associated with the Cu IUD compared with healthy controls (p = 0.026) and a high area under the curve value (0.656), as well as sensitivity (64.9%) and specificity (71.9%). CONCLUSION This is the first study to explore changes in serum protein and the underlying mechanisms of AUB associated with the Cu IUD via TMT technology. a1AT with biomarker potential was validated. These findings might provide an experimental basis for the early diagnosis or treatment of AUB associated with the Cu IUD.
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Identifying DNA-binding proteins based on multi-features and LASSO feature selection.
Zhang, S, Zhu, F, Yu, Q, Zhu, X
Biopolymers. 2021;(2):e23419
Abstract
DNA-binding proteins perform an indispensable function in the maintenance and processing of genetic information and are inefficiently identified by traditional experimental methods due to their huge quantities. On the contrary, machine learning methods as an emerging technique demonstrate satisfactory speed and accuracy when used to study these molecules. This work focuses on extracting four different features from primary and secondary sequence features: Reduced sequence and index-vectors (RS), Pseudo-amino acid components (PseAACS), Position-specific scoring matrix-Auto Cross Covariance Transform (PSSM-ACCT), and Position-specific scoring matrix-Discrete Wavelet Transform (PSSM-DWT). Using the LASSO dimension reduction method, we experiment on the combination of feature submodels to obtain the optimized number of top rank features. These features are respectively input into the training Ensemble subspace discriminant, Ensemble bagged tree and KNN to predict the DNA-binding proteins. Three different datasets, PDB594, PDB1075, and PDB186, are adopted to evaluate the performance of the as-proposed approach in this work. The PDB1075 and PDB594 datasets are adopted for the five-fold cross-validation, and the PDB186 is used for the independent experiment. In the five-fold cross-validation, both the PDB1075 and PDB594 show extremely high accuracy, reaching 86.98% and 88.9% by Ensemble subspace discriminant, respectively. The accuracy of independent experiment by multi-classifiers voting is 83.33%, which suggests that the methodology proposed in this work is capable of predicting DNA-binding proteins effectively.
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Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review.
Calabrese, FM, Porrelli, A, Vacca, M, Comte, B, Nimptsch, K, Pinart, M, Pischon, T, Pujos-Guillot, E, De Angelis, M
Nutrients. 2021;(1)
Abstract
Low-grade inflammatory diseases revealed metabolic perturbations that have been linked to various phenotypes, including gut microbiota dysbiosis. In the last decade, metaproteomics has been used to investigate protein composition profiles at specific steps and in specific healthy/pathologic conditions. We applied a rigorous protocol that relied on PRISMA guidelines and filtering criteria to obtain an exhaustive study selection that finally resulted in a group of 10 studies, based on metaproteomics and that aim at investigating obesity and diabetes. This batch of studies was used to discuss specific microbial and human metaproteome alterations and metabolic patterns in subjects affected by diabetes (T1D and T2D) and obesity. We provided the main up- and down-regulated protein patterns in the inspected pathologies. Despite the available results, the evident paucity of metaproteomic data is to be considered as a limiting factor in drawing objective considerations. To date, ad hoc prepared metaproteomic databases collecting pathologic data and related metadata, together with standardized analysis protocols, are required to increase our knowledge on these widespread pathologies.
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A Combined Proteomics and Mendelian Randomization Approach to Investigate the Effects of Aspirin-Targeted Proteins on Colorectal Cancer.
Nounu, A, Greenhough, A, Heesom, KJ, Richmond, RC, Zheng, J, Weinstein, SJ, Albanes, D, Baron, JA, Hopper, JL, Figueiredo, JC, et al
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2021;(3):564-575
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Abstract
BACKGROUND Evidence for aspirin's chemopreventative properties on colorectal cancer (CRC) is substantial, but its mechanism of action is not well-understood. We combined a proteomic approach with Mendelian randomization (MR) to identify possible new aspirin targets that decrease CRC risk. METHODS Human colorectal adenoma cells (RG/C2) were treated with aspirin (24 hours) and a stable isotope labeling with amino acids in cell culture (SILAC) based proteomics approach identified altered protein expression. Protein quantitative trait loci (pQTLs) from INTERVAL (N = 3,301) and expression QTLs (eQTLs) from the eQTLGen Consortium (N = 31,684) were used as genetic proxies for protein and mRNA expression levels. Two-sample MR of mRNA/protein expression on CRC risk was performed using eQTL/pQTL data combined with CRC genetic summary data from the Colon Cancer Family Registry (CCFR), Colorectal Transdisciplinary (CORECT), Genetics and Epidemiology of Colorectal Cancer (GECCO) consortia and UK Biobank (55,168 cases and 65,160 controls). RESULTS Altered expression was detected for 125/5886 proteins. Of these, aspirin decreased MCM6, RRM2, and ARFIP2 expression, and MR analysis showed that a standard deviation increase in mRNA/protein expression was associated with increased CRC risk (OR: 1.08, 95% CI, 1.03-1.13; OR: 3.33, 95% CI, 2.46-4.50; and OR: 1.15, 95% CI, 1.02-1.29, respectively). CONCLUSIONS MCM6 and RRM2 are involved in DNA repair whereby reduced expression may lead to increased DNA aberrations and ultimately cancer cell death, whereas ARFIP2 is involved in actin cytoskeletal regulation, indicating a possible role in aspirin's reduction of metastasis. IMPACT Our approach has shown how laboratory experiments and population-based approaches can combine to identify aspirin-targeted proteins possibly affecting CRC risk.
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Precise regulation of the relative rates of surface area and volume synthesis in bacterial cells growing in dynamic environments.
Shi, H, Hu, Y, Odermatt, PD, Gonzalez, CG, Zhang, L, Elias, JE, Chang, F, Huang, KC
Nature communications. 2021;(1):1975
Abstract
The steady-state size of bacterial cells correlates with nutrient-determined growth rate. Here, we explore how rod-shaped bacterial cells regulate their morphology during rapid environmental changes. We quantify cellular dimensions throughout passage cycles of stationary-phase cells diluted into fresh medium and grown back to saturation. We find that cells exhibit characteristic dynamics in surface area to volume ratio (SA/V), which are conserved across genetic and chemical perturbations as well as across species and growth temperatures. A mathematical model with a single fitting parameter (the time delay between surface and volume synthesis) is quantitatively consistent with our SA/V experimental observations. The model supports that this time delay is due to differential expression of volume and surface-related genes, and that the first division after dilution occurs at a tightly controlled SA/V. Our minimal model thus provides insight into the connections between bacterial growth rate and cell shape in dynamic environments.