Expert Review of Proteomics最新文献

Introduction: Every year about 800,000 complete suicide events occur. The identification of biologic markers to identify subjects at risk would be helpful in targeting specific support treatments.

Area covered: A narrative review defines the meta-analytic level of current evidence about the biologic markers of suicide behavior (SB). The meta-analytic evidence gathered so far indicates that the hypothesis-driven research largely failed to identify the biologic markers of suicide. The most consistent and replicated result was reported for: 1) 5-HTR2A T102C, associated with SB in patients with schizophrenia (OR = 1.73 (1.11-2.69)) and 2) BDNF Val66Met (rs6265), with the Met-Val + Val-Val carriers found to be at risk for suicide in the Caucasian population (OR: 1.96 (1.58-2.43)), while Val-Val vs. Val-Met + Met carriers found to be at risk for suicide in the Asian populations (OR: 1.36 (1.04-1.78)). GWAS-based meta-analyses indicate some positive replicated findings regarding the DRD2, Neuroligin gene, estrogen-related genes, and genes involved in gene expression.

Expert opinion: Most consistent results were obtained when analyzing sub-samples of patients. Some promising results come from the implementation of the polygenic risk score. There is no current consensus about an implementable biomarker for SB.

Introduction: Cell-surface proteins are extremely important for many cellular events, such as regulating cell-cell communication and cell-matrix interactions. Aberrant alterations in surface protein expression, modification (especially glycosylation), and interactions are directly related to human diseases. Systematic investigation of surface proteins advances our understanding of protein functions, cellular activities, and disease mechanisms, which will lead to identifying surface proteins as disease biomarkers and drug targets.

Areas covered: In this review, we summarize mass spectrometry (MS)-based proteomics methods for global analysis of cell-surface proteins. Then, investigations of the dynamics of surface proteins are discussed. Furthermore, we summarize the studies for the surfaceome interaction networks. Additionally, biological applications of MS-based surfaceome analysis are included, particularly highlighting the significance in biomarker identification, drug development, and immunotherapies.

Expert opinion: Modern MS-based proteomics provides an opportunity to systematically characterize proteins. However, due to the complexity of cell-surface proteins, the labor-intensive workflow, and the limit of clinical samples, comprehensive characterization of the surfaceome remains extraordinarily challenging, especially in clinical studies. Developing and optimizing surfaceome enrichment methods and utilizing automated sample preparation workflow can expand the applications of surfaceome analysis and deepen our understanding of the functions of cell-surface proteins.

Introduction: Breast cancer is one of the most prevalent cancers among women in the United States. Current research regarding breast milk has been focused on the composition and its role in infant growth and development. There is little information about the proteins, immune cells, and epithelial cells present in breast milk which can be indicative of the emergence of BC cells and tumors.

Areas covered: We summarize all breast milk studies previously done in our group using proteomics. These studies include 1D-PAGE and 2D-PAGE analysis of breast milk samples, which include within woman and across woman comparisons to identify dysregulated proteins in breast milk and the roles of these proteins in both the development of BC and its diagnosis. Our projected outlook for the use of milk for cancer detection is also discussed.

Expert opinion: Analyzing the samples by multiple methods allows one to interrogate a set of samples with various biochemical methods that complement each other, thus providing a more comprehensive proteome. Complementing methods like 1D-PAGE, 2D-PAGE, in-solution digestion and proteomics analysis with PTM-omics, peptidomics, degradomics, or interactomics will provide a better understanding of the dysregulated proteins, but also the modifications or interactions between these proteins.

Introduction: Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

Areas covered: In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

Expert opinion: Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

Introduction: Hepatocellular carcinoma (HCC) represents a significant burden globally, which ranks sixth among the most frequently diagnosed cancers and stands as the third leading cause of cancer-related mortality. Glycoproteomics, as an important branch of proteomics, has already made significant achievements in the field of HCC research. Aberrant protein glycosylation has shown to promote the malignant transformation of hepatocytes by modulating a wide range of tumor-promoting signaling pathways. The glycoproteome provides valuable information for understanding cancer progression, tumor immunity, and clinical outcome, which could serve as potential diagnostic, prognostic, and therapeutic tools in HCC.

Areas covered: In this review, recent advances of glycoproteomics contribute to clinical applications (diagnosis and prognosis) and molecular mechanisms (hepatocarcinogenesis, progression, stemness and recurrence, and drug resistance) of HCC are summarized.

Expert opinion: Glycoproteomics shows promise in HCC, enhancing early detection, risk stratification, and personalized treatments. Challenges include sample heterogeneity, diverse glycans structures, sensitivity issues, complex workflows, limited databases, and incomplete understanding of immune cell glycosylation. Addressing these limitations requires collaborative efforts, technological advancements, standardization, and validation studies. Future research should focus on targeting abnormal protein glycosylation therapeutically. Advancements in glycobiomarkers and glycosylation-targeted therapies will greatly impact HCC diagnosis, prognosis, and treatment.

Introduction: Cancer is a disease of (altered) biological pathways, often driven by somatic mutations and with several implications. Therefore, the identification of potential markers of disease is challenging. Given the large amount of biological data generated with omics approaches, oncology has experienced significant contributions. Proteomics mapping of protein fragments, derived from proteolytic processing events during oncogenesis, may shed light on (i) the role of active proteases and (ii) the functional implications of processed substrates in biological signaling circuits. Both outcomes have the potential for predicting diagnosis/prognosis in diseases like cancer. Therefore, understanding proteolytic processing events and their downstream implications may contribute to advances in the understanding of tumor biology and targeted therapies in precision medicine.

Areas covered: Proteolytic events associated with some hallmarks of cancer (cell migration and proliferation, angiogenesis, metastasis, as well as extracellular matrix degradation) will be discussed. Moreover, biomarker discovery and the use of proteomics approaches to uncover proteolytic signaling events will also be covered.

Expert opinion: Proteolytic processing is an irreversible protein post-translational modification and the deconvolution of biological data resulting from the study of proteolytic signaling events may be used in both patient diagnosis/prognosis and targeted therapies in cancer.