Proteomics reveals the importance of the dynamic redistribution of the subcellular location of proteins in breast cancer cells

Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

Protein Glycosylation Investigated by Mass Spectrometry: An Overview

The protein glycosylation is a post-translational modification of crucial importance for its involvement in molecular recognition, protein trafficking, regulation, and inflammation. Indeed, abnormalities in protein glycosylation are correlated with several disease states such as cancer, inflammatory diseases, and congenial disorders. The understanding of cellular mechanisms through the elucidation of glycan composition encourages researchers to find analytical solutions for their detection. Actually, the multiplicity and diversity of glycan structures bond to the proteins, the variations in polarity of the individual saccharide residues, and the poor ionization efficiencies make their detection much trickier than other kinds of biopolymers. An overview of the most prominent techniques based on mass spectrometry (MS) for protein glycosylation (glycoproteomics) studies is here presented. The tricks and pre-treatments of samples are discussed as a crucial step prodromal to the MS analysis to improve the glycan ionization efficiency. Therefore, the different instrumental MS mode is also explored for the qualitative and quantitative analysis of glycopeptides and the glycans structural composition, thus contributing to the elucidation of biological mechanisms.

Moonlighting Proteins and Cardiopathy in the Spatial Response of MCF-7 Breast Cancer Cells to Tamoxifen

BACKGROUND

The purpose of this study is to apply quantitative high-throughput proteomics methods to investigate dynamic aspects of protein changes in nucleocytoplasmic distribution of proteins and of total protein abundance for MCF-7 cells exposed to tamoxifen (Tam) in order to reveal the agonistic and antagonistic roles of the drug.

EXPERIMENTAL DESIGN

The MS-based global quantitative proteomics with the analysis of fractions enriched in target subcellular locations is applied to measure the changes in total abundance and in the compartmental abundance/distribution between the nucleus and cytoplasm for several thousand proteins differentially expressed in MCF-7 cells in response to Tam stimulation.

RESULTS

The response of MCF-7 cells to the Tam treatment shows significant changes in subcellular abundance rather than in their total abundance. The bioinformatics study reveals the relevance of moonlighting proteins and numerous pathways involved in Tam response of MCF-7 including some of which may explain the agonistic and antagonistic roles of the drug.

CONCLUSIONS

The results indicate possible protective role of Tam against cardiovascular diseases as well as its involvement in G-protein coupled receptors pathways that enhance breast tissue proliferation.

Moonlighting Proteins and Cardiopathy in the Spatial Response of MCF-7 Breast Cancer Cells to Tamoxifen

BACKGROUND

The purpose of this study is to apply quantitative high-throughput proteomics methods to investigate dynamic aspects of protein changes in nucleocytoplasmic distribution of proteins and of total protein abundance for MCF-7 cells exposed to tamoxifen (Tam) in order to reveal the agonistic and antagonistic roles of the drug.

EXPERIMENTAL DESIGN

The MS-based global quantitative proteomics with the analysis of fractions enriched in target subcellular locations is applied to measure the changes in total abundance and in the compartmental abundance/distribution between the nucleus and cytoplasm for several thousand proteins differentially expressed in MCF-7 cells in response to Tam stimulation.

RESULTS

The response of MCF-7 cells to the Tam treatment shows significant changes in subcellular abundance rather than in their total abundance. The bioinformatics study reveals the relevance of moonlighting proteins and numerous pathways involved in Tam response of MCF-7 including some of which may explain the agonistic and antagonistic roles of the drug.

CONCLUSIONS

The results indicate possible protective role of Tam against cardiovascular diseases as well as its involvement in G-protein coupled receptors pathways that enhance breast tissue proliferation.

Epigenetic assays for chemical biology and drug discovery

The implication of epigenetic abnormalities in many diseases and the approval of a number of compounds that modulate specific epigenetic targets in a therapeutically relevant manner in cancer specifically confirms that some of these targets are druggable by small molecules. Furthermore, a number of compounds are currently in clinical trials for other diseases including cardiovascular, neurological and metabolic disorders. Despite these advances, the approved treatments for cancer only extend progression-free survival for a relatively short time and being associated with significant side effects. The current clinical trials involving the next generation of epigenetic drugs may address the disadvantages of the currently approved epigenetic drugs.

The identification of chemical starting points of many drugs often makes use of screening in vitro assays against libraries of synthetic or natural products. These assays can be biochemical (using purified protein) or cell-based (using for example, genetically modified, cancer cell lines or primary cells) and performed in microtiter plates, thus enabling a large number of samples to be tested. A considerable number of such assays are available to monitor epigenetic target activity, and this review provides an overview of drug discovery and chemical biology and describes assays that monitor activities of histone deacetylase, lysine-specific demethylase, histone methyltransferase, histone acetyltransferase and bromodomain. It is of critical importance that an appropriate assay is developed and comprehensively validated for a given drug target prior to screening in order to improve the probability of the compound progressing in the drug discovery value chain.

Spatial Cross-Talk between Oxidative Stress and DNA Replication in Human Fibroblasts

MS-based proteomics has been applied to a differential network analysis of the nuclear-cytoplasmic subcellular distribution of proteins between cell-cycle arrest: (a) at the origin activation checkpoint for DNA replication, or (b) in response to oxidative stress. Significant changes were identified for 401 proteins. Cellular response combines changes in trafficking and in total abundance to vary the local compartmental abundances that are the basis of cellular response. Appreciable changes for both perturbations were observed for 245 proteins, but cross-talk between oxidative stress and DNA replication is dominated by 49 proteins that show strong changes for both. Many nuclear processes are influenced by a spatial switch involving the proteins {KPNA2, KPNB1, PCNA, PTMA, SET} and heme/iron proteins HMOX1 and FTH1. Dynamic spatial distribution data are presented for proteins involved in caveolae, extracellular matrix remodelling, TGFβ signaling, IGF pathways, emerin complexes, mitochondrial protein import complexes, spliceosomes, proteasomes, and so on. The data indicate that for spatially heterogeneous cells cross-compartmental communication is integral to their system biology, that coordinated spatial redistribution for crucial protein networks underlies many functional changes, and that information on dynamic spatial redistribution of proteins is essential to obtain comprehensive pictures of cellular function. We describe how spatial data of the type presented here can provide priorities for further investigation of crucial features of high-level spatial coordination across cells. We suggest that the present data are related to increasing indications that much of subcellular protein transport is constitutive and that perturbation of these constitutive transport processes may be related to cancer and other diseases. A quantitative, spatially resolved nucleus-cytoplasm interaction network is provided for further investigations.

Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction

Protein sorting is an important mechanism for transporting proteins to their target subcellular locations after their synthesis. Mutations on genes may disrupt the well regulated protein sorting process, leading to a variety of mislocation related diseases. This paper proposes a methodology to discover such disease genes based on gene expression data and computational protein localization prediction. A kernel logistic regression based algorithm is used to successfully identify several candidate cancer genes which may cause cancers due to their mislocation within the cell. Our results also showed that compared to the gene co-expression network defined on Pearson correlation coefficients, the nonlinear Maximum Correlation Coefficients (MIC) based co-expression network give better results for subcellular localization prediction.